Reservoir Characterization

Seismic Reservoir Characterization

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SEISMIC RESERVOIR CHARACTERIZATION

Prepared by \ Ramy mohammed Fahmy Abo-Alhassan

Supervisor professor \ Ahmed Sayed Abu El-Ata

Geophysics Department 4th Group II

Reservoir Characterization A model of a reservoir that incorporates all the characteristics of the reservoir those are pertinent to its ability to store hydrocarbons and also to produce them. Reservoir characterization models are used to simulate the behavior of the fluids within the reservoir under different sets of circumstances and to find the optimal production techniques that will maximize the production.

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Seismic Reservoir Characterization Seismic data, in particular 3-D seismic data, are a mainstay of the petroleum industry. These geophysical data provide subsurface images and other information that may be used by geophysicists, geologists and engineers alike to identify and effectively drain hydrocarbon reservoirs. Seismic data should not be interpreted in a stand-alone fashion. Geological, geophysical and engineering expertise and data need to be included in a “complete” interpretation. This report will emphasize the use of seismic data for reservoir evaluation. The objectives are to provide a basic tool set and workflow for interpretation.

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The primary tool for probing the entire reservoir is 3-D reflection seismic surveying. While 3-D seismic data cover the entire reservoir volume. Well logs and core measurements usually are made on the scale of 0.1-1.0 m. Although the scale of resolution is quite fine, the volume of investigation is small, confined to within a few metres of the borehole. To fill in the “missing wavelengths” between borehole data and surface seismic data, we can acquire borehole seismic data. Such data, gathered by using seismic sources and receivers to fill the wavelength gap, would include vertical seismic profiling and cross-borehole seismic surveys. The key is to characterize the reservoir’s physical properties by integrating the various data sets with different wavelengths.

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Acknowledgment First and foremost, I would like to thank ALLAH for all that I have been given. I thank my father, my mother and all my family for keeping me going through all of those stressful times with their motivation. Lot of thanks to my friends’ with a great appreciates to Mr. Emad Eid Fahmy. A special thank you with a special appreciates to Professor. Dr\ Ahmed Sayed Abu El-Ata and Professor .Dr\ Abdel Moktader.

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TABLE OF CONTENTS

SECTION

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List of figures.............................................................IX Introduction..............................................................XII 1.0 Amplitude variations with offset (AVO) 1.1 Seismic Energy partitioning at boundaries……………………1 1.2 AVO Model- Zoeppritz Equations…………………….…..….. 1.3 Mechanics of quantitative AVO analysis………………...…… 1.4 Example quantitative AVO analysis………………………….. 1.5 Fatti Methodology……………………………………….......... 1.6 Lambda/Mu-Rho……………………………………………… 1.7 AVO to Carbonate Reservoirs......................................….......10

2.0 Seismic Attributes 2.1 Seismic Attributes………………………………………….…11 2.1.1 Some about Seismic Attributes…………………………….. 2.1.2 The Classification of Attributes……………………………. 2.1.3 Some Basic Attributes and Its Characteristics………..……. 2.2 Quantitative Use of Seismic Attributes Reservoir Characterization………………………………………………15 2.2.1 Does the Data Warrant the Use of Attributes? 2.2.2 What Seismic Measures and What We Require for Reservoir Characterization…………………………………. 2.2.3 Seismic Attributes - Property Predictors or False? ................ 2.2.4 Predict porosity using seismic acoustic impedance…......….. 2.2.5 What about using Linear Regression? ………………....…… 2.3 Enhancing Fault Visibility Using Bump Mapped Seismic Attribute 2.3.1 Shaded relief of the coherency…………………………….18 2.3.2 Bump Mapping Attributes………………………………… 2.3.3Bump Mapping Coherency……………………………..….20

3.0 Reservoir Characterization 3.1 pore-pressure prediction……………………….....………….21 3.1.1 Methodology………………………………………......….21 High-resolution velocity analysis (step 1)…………….…....…. Pore-pressure and effective-stress prediction (step 2)….......…. Multi-attribute seismic inversion (step 3)……………….....….. Lithofacies classification using σeff, IP, and IS (step 4)…........ 3.2 Reservoir Characterization and Heavy Oil Production 3.2.1 Methodology…………………............................................. 3.2.2 Results………………………………..........................…...30

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4.0 Time-Lapse 4.1 Reservoir Surveillance.............................................................31 4.1.1 The concept…………………………………...,,.....………. 4.1.2 The Physical Basis………………………….....,..........…… 4.1.3 Seismic Repeatability…………………..…….............…… 4.1.4 4-D Interpretation……………………………...............….. 4.2 Detecting flow-barriers with 4D seismic 4.2.1 Fault analysis…………….........……………………...........35 4.2.2 Combining 4D seismic and reservoir simulation…….……. 4.2.3 Fault sealing analysis………………………………........… 4.2.4 (4-D) fluid saturation mapping techniques……….……40

5.0 Seismic Inversion 5.1 The Post-Stack Inversion Method……………..…………….41 5.2 The Details - Constraints, QC, Annealing, Global and Colored Inversion………………….……................………. 5.3 AVO Inversion………………………………..…………….. 5.4 Geostatistical Inversion………………………..........………. 5.3 Merging Technologies ..............................……....………….. 5.4 Stochastic Seismic Inversion……………………………......50

6.0 Application to Reservoir characterization by combining time-lapse seismic analysis with reservoir simulation 6.1 Introduction……………………….……………...........……51 6.2 Reservoir simulation………………………......…….…..…. 6.3 Synthetic seismic section………………….....................….. 6.4 Discussion……………………….......................................... 6.5 Result….................................................................................55

7.0 Case study Fractured Reservoir Characterization using AVAZ on the Pinedale Anticline,Wyoming……………………….....…56 7.1 Introduction……………………………………….......……. 7.2 AVAZ Method………………………….......................…… 7.3 Results……………………………...................................…. 7.4 Fractured Reservoir Characterization……………….............60

Conclusion..................................................................* Reference....................................................................** Appendix........................................................................................A

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LIST OF FIGURES FIGURE PAGE 1-1 Think of a seismic wavefront hitting a reflector. The physical properties are different on either side of the reflector.................................................................11 1-2 Plot of reflection amplitude versus offset showing the different classes of AVO response....................................................................................................44 1-3 the amplitudes at each time sample are analyzed linearly and two AVO attributes are extracted the intercept and slope of the best-fit straight line….....55 1-4 This velocity model was compiled from regional and gas wells from the study area. The point to note about it is that even though the seismic that will be derived from it is synthetic, the rock physics as measured in the boreholes, is real..................................................................................................6 6 1-5 The Fatti ‘P’ (normal-incidence P reflectivity) & ‘S’ displays extracted from pre-stack seismic gathers………………………………………………..7 7 1-6 The scheme of the LMR™ (Rock Property Inversion) method. Post-stack inversion of P and reflectivities……………………………………………….8 8 1-7 The LambdaRho™ and MuRho displays for our model………………………9 9 1-8 AVO responses of gas charged and brine-saturated dolomite reservoir……..1010 2-1 images illustration of the integration of geological features.............................1212 2-2 Input seismic data..............................................................................................1313 2-3 Instantaneous Phase...........................................................................................1313 2-4 Instantaneous Frequency...................................................................................1313 2-5 Instantaneous Dip Azimuth...............................................................................1414 2-6 Wavelet Apparent Polarity.................................................................................1414 2-7 Isometric display of reflection strength..............................................................1414 2-8 Reflection parallelism........................................................................................1414 2-9 Reflection divergence........................................................................................1515 2-10 Scatterplot of porosity percent and seismic acoustic impedance at 7 well locations............................................................................................................1616 2-11 Map of seismic acoustic impedance and porosity percent at 7 well.................16 16 2-12 Scatter plot of measure porosity versus predicted porosity..............................17 17 2-13 The map of porosity based on a regression relation.........................................17 17 2-14 Variable density time slice of a faulted seismic section...................................18 18 2-15 Coherency slice of the same data shown in Figure (2.14)................................18 18 2-16 Shaded relief of the seismic amplitude time slice shown in Figure (2.14).......19 19 2-17 Composite density (bump mapped) display using the variable density............19 19 2-18 Shaded relief of the coherency slice.................................................................20 20 2-19 Composite density display using the variable density display.........................20 20 2-20 Close-up coherency of the faulted region using shaded relief..........................20 20 2-21 using the bump mapped....................................................................................20 20

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3-1 Structural framework of the study area with wells (the orange surface Represents the top of salt)..................................................................................C C 3-2 (a) High-resolution seismic velocity (in ft/s). (b) Final high-resolution velocity after trend-kriging with well data (in ft/s)............................................22 22 3-3 (a) Final pore-pressure volume (in units of psi). (b) Final effective stress Volume in units of psi for the same cross section............................................23 23 3-4 Reservoir characterization workflow.................................................................24 24 3-5 Absolute IP (top) and IS (bottom) along an inline.............................................24 24 3-6 Schematic plot of IP compaction trend with respect to effective stress ............D D 3-7 Maps of the (pdf) at different effective stress intervals.................................... D D 3-8 Hydrocarbon sand probability...........................................................................26 26 3-9 Effective stress and hydrocarbon sand probability values................................26 26 3-10 Hydrocarbon sand probabilities posted on a horizon without using Effective stress (left) and with effective stress (right)................................... 27 27 3-11 a possible flow diagram for reservoir characterization...................................28 28 3-12 Difference seismogram for seismic surveys in heavy oil field.......................28 28 3-13 Difference of synthetic seismograms..............................................................29 29 3-14 Time-lapse seismic survey over cold production field...................................30 30 3-15 Difference in isochron maps for heavy oil cold production fields.................30

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4-1 Illustration of time-lapse seismic......................................................................31 31 4-2 Dependence of 4-D (COS) on detectability and repeatability..........................33 33 4-3 Time-lapse seismic data from the Jotun Field..................................................35 35 4-4 Example of a property cube (instantaneous attenuation)..................................36 36 4-5 Statistical reservoir volume analysis on the baseline survey............................38 38 4-6 Shows a seismic saturation change map...........................................................39 39 4-7 4-D interpretation from Shell’s Gannet-C study...............................................40 40 4-8 This figure shows the saturation ..................................................................... 40

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5-1 seismic section and shows a post-stack, mixed-norm inversion.......................43 43 5-2 Above is the post-stack seismic inversion of the overlain seismic reflection...43 43 5-3 The inversion in Figure (5.2) has been converted to depth...............................44 44 5-4 using the observed distribution of sandstone impedances ................................44 44 5-5 The two panels compare interpretations done from the seismic using traditional methods and from the inversion......................................................45 45 5-6 The two panels show slices of P Impedance and Vp/Vs...................................45 45 5-7 Vp/Vs from AVO Inversion is shown in perspective view...............................46 46 5-8 The logs in the left panel from a heavy oil play were used to compute the synthetic PP and PS gathers in the 2nd and 3rd panels....................................46 46 5-9 Geostatistical simulation of porosity.................................................................48 48 5-10 This is a perspective view showing the results of the simultaneous geostatistical simulation of density and lithotype..........................................48

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49 5-11 The upper panel shows the results of an AVO Inversion at Blackfoot............49 5-12 Schematic depiction of the stochastic inversion process.................................5050 5-13 Input seismic data volume...............................................................................5050 50 5-14 Recursive inversion result................................................................................50 5-15 Stochastic inversion result...............................................................................5050 6-1 Reservoir model geometry.................................................................................5252 52 6-2 Temperature distributions from the reservoir simulations ................................52 53 6-3 Pressure distributions from the reservoir simulations........................................53 53 6-4 Velocity and density model for seismic modeling.............................................53 55 6-5 the synthetic seismic difference section (bottom) and the seismic survey.........55 55 6-6 Gas saturation in the three layers versus the synthetic.......................................55 55. 6-7 Pressure in the three layers versus the synthetic seismic difference section......55 55 6-8 Temperature in the three layers versus the synthetic seismic ............................55 7-1 Map of the Lance Sand Depositional Fairway over the Pinedale Anticline.......EE 7-2 Geologic formations in the Pinedale Anticline...................................................EE 7-3 Cretaceous and Tertiary stratigraphic column....................................................EE 7-4 Time structure of the Lower-Lance horizon interpreted.....................................FF 7-5 Map of the migrated Crack Density estimates calculated using the AVAZ.......F F 58 7-6 Estimated crack density by the migrated stack & by shear-wave stack.............58 58 7-7 visualization of fracture swarms around the well...............................................58 58 7-8 View from the north end of the Pinedale Anticline showing well traces ..........58 7-9 output of SUREFrac ,new well, Riverside 4-10, is added & is the output 60 using all attributes including the A VA Z attributes..........................................60 60 7-10 AVA Z crack density results along a dip line ...................................................60 60 7-11 Dip line from 3D volume after fractured reservoir characterization.................60 2.1 Table (2.1) structure, stratigraphic targets (Sheriff, 1992).....................................A 2.2 Table 2.2 structure, stratigraphic targets (Sheriff, 1992).......................................B 2.3 Table 2.3 flow simulation model factors (Sheriff, 1992)......................................... C

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Introduction In this article, we’ll examine the basics of amplitude variations with offset (AVO) and rock properties derived from them. First we’ll look at the physical cause for AVO behavior. We’ll then discuss the quantitative methodologies for calculating attributes which describe the AVO behavior of pre-stack seismic data. At the same time, we’ll show methods of interpreting these attributes and how they can help to differentiate between regional geology and hydrocarbon reservoirs. We will then introduce some great efforts have been made to apply AVO analysis to carbonate reservoir characterization. ARTICLE Since their introduction in the early 1970’s, Complex Seismic Trace Attributes have gained considerable popularity, first as a convenient display form, and later, as they were incorporated with other seismically-derived measurements, they became a valid analytical tool for lithology prediction and reservoir characterization. The amplitude content of seismic data is the principal factor for the determination of physical parameters which useful for seismic attribute. In this article we will discuss the Complex Trace Attributes, their classification and their characteristics. Then review issues related to selecting and using seismic attributes quantitatively in reservoir characterization projects, rather than to describe or classify them, with some example to predict porosity. Finally, we introduced the technique of using two separate attributes for the shaded relief and coloring of a bump mapped time slice to enhancing fault visibility. Pore pressure and effective stress overpressured sediments are predicted by some steps, we firstly show how high-resolution seismic velocities were used for thsis purpose. Effective stress allows mapping nonstationary sedimentary compaction in space and is used as an additional attribute to allow for consistency between well and seismic data and stratigraphic layers, geostatistical mapping (trend-kriging) techniques were applied using several key horizons. The final trendkriged model is constrained by the structural framework and the geology of the basin. Finally we will introduce how these informations can utilize to allow comparison of the hydrocarbon probability. In order to optimize production from heavy oil fields, its as primary application for reservoir characterization. Effective reservoir

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characterization and production will require teamwork between geosciences and engineering. This is especially true in the development of heavy oil fields where there are dozens (possibly hundreds) of wells to be combined with 3-D seismic data sets. We focus on the combined use of engineering and geoscience, while reviewing past results and forecasting future directions. Time-lapse 3-D, or 4-D, seismic technology was first introduced in the early 1980s but has gained widespread interest and use only in the past 5 years. For some companies, 4-D seismic programs are now an essential part of their global reservoir management strategy. Reservoir surveillance during production is a key to meeting goals of reduced operating costs and maximized recovery. Differences between actual and predicted performance are typically used to update the reservoir's geological model and to revise the depletion strategy. The fluid flow depends mainly on the efficiency of the reservoir seals. The primary objective of this article is to present fault characterization at the reservoir scale combined with 4D seismic. The principle objective of seismic inversion is to transform seismic reflection data into a quantitative rock property, descriptive of the reservoir. In its most simple form, acoustic impedance logs are computed at each CMP. In other words, if we had drilled and logged wells at all CMP’s, what would the impedance logs have looked like? Compared to working with seismic amplitudes, inversion results show higher resolution and support more accurate interpretations. This, in turn facilitates better estimations of reservoir properties such as porosity and net pay. An integration time lapse seismic method with reservoir simulation is image a steam front and by-passed oil. Two seismic 2D surveys, acquired in 1991 and 2000, respectively, had been reprocessed to preserve amplitudes. The results indicate that the present reservoir model is reasonably good at describing the reservoir. finally ,It has been shown that open, fluid- (or gas-) filled fractures can be identified through the use of Amplitude Versus Angle and Azimuth (AVAZ) analysis on seismic data. This technique uses variations in the amplitudes of the long shotreceiver offsets of P-wave seismic data to determine the intensity and orientation of the fractures. It assumes that the fracturing generates a rock medium. This method can be modified to produce attributes that may be useful in the identification of fractures between wells.

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1.0 Amplitude variations with offset (AVO)

1.1 Seismic Energy partitioning at boundaries Amplitude variation with offset comes about from something called ‘energy partitioning’. When seismic waves hit a boundary, part of the energy is reflected while part is transmitted. At a boundary where the incident angle is zero (normal incidence) there is no mode conversion. For example, a downward moving P-wave only generates reflected and transmitted P-waves and a normally incident S-wave only generates reflected and transmitted S-waves. At a boundary where the incident angle is not zero (non-normal incidence) the seismic energy typically generates four waves, at the boundary by splitting (mode conversion): reflected Pwave and S-wave and transmitted P-wave and S-wave (Figure 1-1). Most reflections are a superposition of events from a series of layers and will have a more complex behavior than what is shown here.

Figure (1-1) Think of a seismic wavefront hitting a reflector. The physical properties are different on either side of the reflector. The part of the P wave striking at a particular angle-of-incidence (represented by a ray) will have its energy divided into reflected and transmitted P and S waves. Another part of the incident wave with a different angle-of-incidence (represented by the second ray) will have its energy broken up into P and S waves too. How this ‘energy partitioning’ happens depends on the contrast in properties and also on the angles-of-incidence.

The amplitudes of the reflected and transmitted energy depend on the contrast in physical properties across the boundary. For us seismic people, the important physical properties in question are compressional 1

wave velocity (Vp), shear wave velocity (Vs) and density (ρ). But, the important thing to note is that reflection amplitudes also depend on the angle-of-incidence of the original ray. So if we know how the amplitudes of a reflector from a CDP change with angle-of-incidence, we can figure out something about how the physical properties of the rocks are changing across the boundary. How amplitudes change with angle-of-incidence for elastic materials is described by the quite complicated ‘Zoeppritz equations’. But there are many different simplifications of these equations that make analysis of amplitudes with angle much easier. One thing to point out now is that ‘amplitude variation with offset’ is not always an appropriate term. For proper analysis, we need to examine ‘amplitude variation with angle’.

1.2 AVO Model- Zoeppritz Equations Zoeppritz equations can be used to determine the amplitude of reflected and refracted waves at this boundary for an incident P-wave. The original equations are valid for any incident waves but only the P-wave is presented here and used in this study. The reflection and transmission coefficients depend on the angle of incidence and the material properties of the two layers. The angles for incident, reflected, and transmitted rays at a boundary are related by Snell’s l aw. The variation of reflection and transmission coefficients with incident angle and corresponding increasing offset is referred to as offset-dependant-reflectivity and is the fundamental basis for AVO. Zoeppritz (1919) equations provide a complete solution for amplitudes of transmitted and reflected P- and S- waves for both incident P- and S- waves. Zoeppritz Equation: R(θ) = A + Bsin2(θ) Where: R (θ) = Reflection coefficient (function of θ) θ = Angle of incidence A = Zero-offset reflection coefficient (AVO intercept) B = Slope of amplitude (AVO Gradient) A is the normal incidence reflection coefficient. B describes the variation at intermediate offsets and is called the AVO gradient or slope factor which is a function of average values of Poisson’s ratio, compressional velocity and density. A and B are both highly dependant on the properties of the reservoir and the overlying formation. In general, Pwave velocity is dependant on both lithology (rock type) and fluid 2

content. S-wave velocity is dependant on lithology, but not sensitive to fluid content. S-wave velocities are not generally measured directly so the Vp/Vs ratio or Poisson’s ratio is used to determine the shear velocity from the compres-sional velocity. This commonly used form of Zoeppritz equations has the interpretation that the near-offset traces reveal the P-wave impedance, and the intermediate-offset traces image contrasts in Poisson’s ratio. Amplitude variation with offset, or more simply amplitude versusoffset (AVO), computes the seismic response of an interface between two beds from the contrast in elastic properties between the overlying and underlying formations. A normal incident, or zero offset, reflection (Ro) is readily found from the contrast in acoustic impedance.

Where: Ro = Reflection Coefficient r1 = Density of medium 1 r2 = Density of medium 2 V1 = Velocity in medium 1 V2 = Velocity in medium 2 The change in amplitude of the reflection coefficient with offset is a function of the contrast in elastic properties across the interface where the AVO response is divided into three classes, Figure 1-2: 1.) A Class I AVO response has a large positive reflection at zero offset and become smaller with increasing offset. 2.) A Class II AVO response has a small positive reflection at zero offset and becomes very small or even negative with increasing offset. 3.) A Class III AVO response has a negative reflection at zero offset and increasingly large negative reflections at increasing offsets. This is the classical AVO behavior. For example, a sand-shale interface often displays a negative reflection response that is increasingly large with offset.

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Figure (1-2) Plot of reflection amplitude versus offset showing the different classes of AVO response.

1.3 Mechanics of quantitative AVO analysis Quantitative AVO analysis are done on common-midpoint-gathers (or super-gathers, or common-offset gathers, or as they’re called in the AVO business ‘Ostrander gathers’. At each time sample amplitude values from every offset in the gather are curve-fit to a simplified, linear AVO relationship (Figure 1-3). A better way of saying this is that we fit a best fit straight line to a plot of amplitude versus some function of the angle of incidence. This yields two AVO attributes basically the slope and intercept of a straight line - which describes, in simpler terms, how the amplitude behaves with angle of incidence. There are a lot of equations that have been used over the last 15 years or so, but all of them, no matter what interpretation is given to the ‘intercept’ and ‘slope’, work in this same way. The NIP is simply an estimate of the normal incidence reflectivity while the gradient indicates whether the amplitude is increasing or decreasing with offset and how strongly.

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Figures (1-3) the amplitudes at each time sample are analyzed linearly and two AVO attributes are extracted essentially the intercept and slope of the best-fit straight line. These two attributes give us the basic description of the AVO behavior. The intercept of the best fit straight line is interpreted as the ‘normal incident P’ value describes the “normal-incidence Preflectivity” (NIP) and the slope is the gradient, or how the NIP (amplitude) changes with angle. Shuey (1985) presented another approximation to Zoeppritz equations were the AVO gradient is expressed in terms of Poisson’s ratio and it extremely useful for application.

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1.4 Example data set of quantitative AVO analysis In order to examine a couple of the methodologies available for quantitative AVO analysis, we’ll use a model that was constructed from real well logs from the western Canada basin (Figure (1-4). In this example we have Bluesky and Halfway gas sand reservoirs. There are two things to note about AVO and this data set. First, though the seismic response is synthesized, the physical properties, as measured in wellbores, are real and so then are the AVO responses. Second, even though the examples are clastic gas reservoirs, the basic concepts can be applied to many different lithologies and reservoir types including carbonates. The Bluesky gas sand is higher impedance than the encasing shales (this is a Class I anomaly in the classification scheme of Rutherford and others, 1989 as above figure (1-2). This means that on gathers, we would see a peak which dims with offset.

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Figure (1-4) This velocity model was compiled from regional and gas wells from the study area. The point to note about it is that even though the seismic that will be derived from it is synthetic, the rock physics as measured in the boreholes, is real.

The Halfway gas sand is lower impedance than the encasing evaporites and carbonates (a Class III AVO anomaly). This means that we see a trough that increases in amplitude with offset on gathers. This is the classic “bright spot” anomaly we’ve all heard of.

1.5 Fatti Methodology The Fatti methodology (Fatti and others, 1994) is a more complicated amplitude-vs.-angle relationship than is the Shuey equation. Though it does not look particularly linear, there are only two unknowns, the P and S reflectivities that we solve for. This methodology not only is more accurate to higher angles-ofincidence, but is independent of any assumption of density and allows any meaningful Vp/Vs as a constraint. Also, the two attributes that we can solve for, normal-incidence P and S impedance reflectivities (Figure 1-5) are much more intuitively accessible to interpreters. We can also easily calculate other meaningful interpretive displays from these attributes. For instance, a ‘fluid display’ combines both the P and S reflectivities to highlight areas that are anomalous with respect to the regional Vp/Vs trend (the regional ‘mudrock line’).

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Figure (1-5) The Fatti ‘P’ (normal-incidence P reflectivity) and ‘S’ (normal-incidence S reflectivity scaled to P arrival time) displays extracted from pre-stack seismic gathers. The top right hand panel is the ‘Fluid’ stack derived from the P and S values and the bottom right is a cross-plot of the P and S reflectivities showing how the different regions can be differentiated.

One thing to note is that the calculated S reflectivities have P travel times. This is due to the fact that we are dealing with S reflectivity estimates derived from P wave seismic gathers. We’d also like to point out that those who still use Shuey commonly estimate S reflectivities from the gradient (and other assumptions). It is a valid method, but not necessarily as reliable as Fatti. One of the most powerful techniques of AVO interpretation is crossplotting (Figure 1-5). Here we are taking P reflectivity values (Rp), sorting them and plotting them against the corresponding S reflectivity values (Rs). We can easily see the Bluesky regional and gas sands and Halfway regional and gas sands display different trends, making them easily distinguished. In interpretational practice, modeling and well templating help to define the type of trends an interpreter should be looking for.

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1.6 Rock Properties from AVO attributes: Lambda/Mu-Rho We can go even further to analyze the rocks and fluids within them. With the Fatti P and S impedance, we can estimate the layer impedances by post-stack impedance inversion. Inverting the P and S reflectivities gives us P and S impedances for the geological layers. Taking the common equations for Vp and Vs (which depend on Lame’s parameter, modulus of rigidity and density), we can arrive at simple equations that combine the impedances to arrive at estimates of the elastic properties of the rock. We cannot isolate the density term, but are left with attributes which characterize the incompressibility (LambdaRho™) and the rigidity (MuRho) of the rocks and the fluids in their pore spaces (Figure 1-6). Note that while the common

Figure (1-6) The scheme of the LMR™ (Rock Property Inversion) method. Post-stack inversion of P and S reflectivities gives us layer impedances (pseudo well logs) which can be combined into rock properties (LambdaRho™ and MuRho) by the Vp and Vs relationships.

LMR and LambdaRho technique is also known as ‘Rock Property Inversion’ (RPI) as well as by other commercial names. What does this all mean for interpreters? Generally, sandstones are more incompressible than shales. Water filled sandstones are more incompressible than gas filled sandstones. Shales have less rigidity than sandstones. Carbonates can have considerably different incompressibilities and rigidities depending on such things as amount and type of porosity. Changes in fluid would not affect rigidity.

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Figure (1-7) The LambdaRho™ and MuRho displays for our model. Note low values of lambda and relatively unchanged values of mu for the gas sands.

For our model example, we’ve calculated LambdaRho™ and MuRho (Figure 1-7). As we might expect, the gas sands show relatively lower values of LambdaRho™ and relatively little variation in MuRho. Here, the interpretation is straight forward, but the real world can often be more perversely difficult.

1.7 Recent Advances in Application of AVO to Carbonate Reservoirs Until recently, the seismic analysis of data from carbonate reservoirs relied mainly on interpreting zero-offset (stacked) seismic volumes. Common knowledge within the world of AVO suggests that zero-offset information is often insufficient to differentiate shale from carbonate porosity, or to discriminate gas-saturated from brine-saturated reservoirs. In the last few years, great efforts have been made to apply AVO analysis to carbonate reservoir characterization. In addition to this, a lack of carbonate rock property information is considered as one of the 9

obstacles in applying AVO to carbonate reservoir characterization. So the differences between clastic AVO and carbonate AVO need to be clarified. In contrast to clastic rocks, Vp/Vs ratio experiences an increase with increasing velocity or decreasing porosity. Also gas sands have a relatively low Vp and low Vp/Vs ratio in comparison with brine-saturated sandstone. Fluid effects in carbonates, especially the gas effect, are contentious but of great interest. Common wisdom is that fluids have little or no effects on carbonate rock properties as these rocks have very high elastic moduli. Put another way, the high velocity of the carbonate rock matrix causes seismic waves to travel primarily through the matrix with less influence from pore fluids. However, an analysis of the dolomite data indicates that gas does influence carbonate rock properties and its effect is significant. Notice that the behavior of dolomite rocks due to gas saturation is similar to that of sandstones. Namely, P-wave velocity and Vp/Vs ratio decrease, and S-wave velocity increases slightly due to decrease of density. The results of limestone are not shown. In General, sensitivity to fluid increases with increasing porosity. In general they are similar to dolomites except less sensitive to fluid. Due to the complexity of clastic reservoirs, making a one-to-one comparison between clastic AVO and carbonate AVO is rather difficult. An apparent difference is that class I AVO represents a tight reservoir in carbonates, whereas in clastics it represents the wet “mudrock” background or a reservoir with higher impedance. Another unique character is that amplitude for brine-saturated reservoirs has little variation until at large offsets. These unique characters of the AVO response in carbonate reservoirs remind us that clastics and carbonates should be treated differently. Synthetic gathers for the gas charged reservoir and its brine substituted case were then generated and shown in Figure (1-8). There is a class III AVO response at the base of the gas charged reservoir that changes to a weak class II AVO after brine substitution.

Figure (1-8). AVO responses of gas charged and brine-saturated dolomite reservoir. 10

2.0 Seismic Attributes The Oxford Dictionary defines an attribute as, “A quality ascribed to any person or thing”. Seismic attributes describe seismic data. They quantify specific data characteristics, and so represent subsets of the total information. Seismic attributes should be unique andSeismic attributes should not vary greatly in response to small changes in the data.

2.1 Seismic Attributes 2.1.1 Some about Seismic Attributes Seismic Attributes are all the information obtained from seismic data, either by direct measurements or by logical or experience-based reasoning. In effect, attribute computations decompose seismic data into constituent attributes. This decomposition is informal in that there are no rules governing how attributes are computed or even what they can be. The attributes discussed here are the outcome of the work relating to the combined use of several attributes for lithology prediction and reservoir characterization. The study and interpretation of seismic attributes provide us with some qualitative information of the geometry and the physical parameters of the subsurface. It has been noted that the amplitude content of seismic data is the principal factor for the determination of physical parameters, such as the acoustic impedance, reflection coefficients, velocities, absorption etc. The phase component is the principal factor in determining the shapes of the reflectors, their geometrical configurations.

2.1.2 The Classification of Attributes Attributes can be computed from prestack or from poststack data. The procedure is the same in all of these cases. For detailed studies, prestack attributes may be incorporated. Attributes can be classified in many different ways. Here we will introduce a classification based on by their computational characteristics: Instantaneous Attributes Instantaneous attributes are computed sample by sample, and represent instantaneous variations of various parameters. Instantaneous values of attributes such as trace envelope, its derivatives, frequency and phase may be determined from complex traces.

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Wavelet Attributes This class comprises those instantaneous attributes that are computed at the peak of the trace envelope and have a direct relationship to the Fourier transform of the wavelet in the vicinity of the envelope peak. Physical Attributes Physical attributes relate to physical qualities and quantities. The magnitude of the trace envelope is proportional to the acoustic impedance contrast; frequencies relate to bed thickness, wave scattering and absorption. Instantaneous and average velocities directly relate to rock properties. Geometrical Attributes Geometrical attributes describe the spatial and temporal relationship of all other attributes. Lateral continuity measured by semblance is a good indicator of bedding similarity as well as discontinuity. Bedding dips and curvatures give depositional information. Geometrical attributes are also of use for stratigraphic interpretation since they define event characteristics and their spatial relationships. Integrating Geometrical Attributes shown in (Figure 2.1) which illustrates the integration of geological features. The image on the left represents a time slice through a 3D seismic amplitude cube. The image shows the presence of channel-like features, and their reproduces the shapes, locations, and orientations of the channel-like features in the seismic amplitude data. The center image depicts a conceptual model of the channels, in this case, a meandering channel system, with a user specified wavelength, sinuosity, etc. The right image is one of many possible pixel-based simulations. The advantage of this method is the easy implementation and conditioning to well data, regardless of the number of wells, unlike object-based modeling. Figure (2.1) these images illustrate the integration of geological features. The left image is seismic amplitude data showing channel-like features, the center image is the conceptual model, and the right image is one pixel-based realization.

2.1.3 Some Basic Attributes and Its Characteristics Instantaneous Phase. Because wave fronts are defined as lines of constant phase, the phase attribute is also a physical attribute and can be effectively used as a discriminator for geometrical shape classifications:

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(Figure 2.2) shows a 3-D section display of original seismic data. (Figure 2.3) is the corresponding instantaneous phase display.

Figure 2.2 Input seismic data

Figure 2.3 Instantaneous Phase

Instantaneous phase is: • Good indicator of lateral continuity, • Relates to the phase component of wave-propagation. • Used to compute the phase velocity, • Devoid of amplitude information, hence all events are represented, • Detailed visualization of stratigraphic elements. Instantaneous Frequency. It has been shown that the instantaneous frequency attribute relates to the centric of the power spectrum of the seismic wavelet (Figure 2.4). The instantaneous frequency attribute responds to both wave propagation effects and depositional characteristics, hence it is a physical attribute and can be used as an effective discriminator. Its uses include: • Hydrocarbon indicator by low frequency anomaly. • Fracture zone indicator, may appear as lower frequency zones. • Bed thickness indicator. Higher frequencies indicate sharp interfaces such as exhibited by thinly laminated shales, lower frequencies are indicative of more massive bedding geometries. Another piece of information we can extract from the seismic data are the locations where instantaneous frequencies jump or exhibit a negative sign. These sign reversals are caused by closely-arriving reflected wavelets.

Figure (2.4) Instantaneous Frequency

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instantaneous dip azimuth is shown in (Figure 2.5). The colors indicate various dip azimuths as measured from North. A wavelet attribute example, the apparent polarity is shown in (Figure 2.6) Red shows the positive and blue shows the negative apparent polarities.

Figure (2.5) Instantaneous Dip Azimuth

Figure (2.6) Wavelet Apparent Polarity

Reflection strength. Attribute was an amplitude measure, which he developed for bright spot analysis (Figure 2.7). Reflection strength cast seismic.

(Figure 2.7) Isometric display of reflection strength.

The excitement was reminiscent of that of bright spots, for, like amplitude, discontinuity had clear meaning and enabled interpreters to see something they couldn’t easily see before. This success breathed new life into attribute analysis. Other multi-dimensional attributes soon followed, such as parallelism and divergence (figure 2.8 & 2.9). Figure (2.8) Reflection parallelism, a seismic stratigraphic attribute. It is quantified as the local degree of variation of reflection dip from the average. Parallel reflections indicate a lower-energy depositional environment, suggestive of shales; nonparallel reflections indicate a higher-energy deposi-tional environment, suggestive of sands.

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B

A

Figure (2.9) Reflection divergence, a seismic stratigraphic attribute. It is quantified as the degree to which successive reflections diverge looking downdip. Yellow indicates divergent reflections and blue indicates convergent. (a) Vertical view; (b) 3D opacity view. The analysis window captured only small-scale divergence, such as that in the channel fill, thereby revealing the extent of the channel

2.2 Quantitative Use of Seismic Attributes Reservoir Characterization 2.2.1 Does the Seismic Data Warrant the Use of Attributes? Before dashing headlong into the computation of numerous attributes, look at quality of the data, determine the processing workflow. Too often we have seen that the data simply does not warrant use beyond a basic structural interpretation because of poor signal quality, low frequency content at the reservoir level, and improper processing. Data can be processed for structural interpretation using a minimum phase wavelet and a gain to enhance structural surfaces. Processing seismic data for stratigraphic and rock and fluid properties requires zero-phase, true amplitude, and migrated data, which is more costly and time consuming, but necessary if most attribute studies are to succeed. Perhaps geometrical attributes describing spatial and temporal continuity do not require such rigorous processing. for example, the purpose is acoustic impedance inversion, the data must be zero-phase, with true-amplitude recovery. Success depends on zero-phase, true-amplitude seismic data.

2.2.2What Seismic Measures and What We Require for Reservoir Characterization The parameters measurable from the seismic data are (1) travel time, (2) amplitude, (3) the character of events, and (4) the patterns of events. From this information we often compute a lot of structure, stratigraphic targets (Sheriff, 1992): (See APPEDIX; table 2.1 to 2.3 pages A, B&C) 15

2.2.3Seismic Attributes — Property Predictors or False Prophets? For our purposes we consider a seismic attribute as any seismically derived parameter computed from prestack or poststack data, before or after migration. Amplitude, phase, and frequency are fundamental parameters of the seismic wavelet and from these few all other attributes are derived, either singly or in combinations, and many of the new attributes duplicate each other because of the nature of the computations. One principle should be kept in mind when using attributes; the physical basis for the correlation with properties measured at the wells. Unfortunately there is a common practice of selecting attributes based solely on the strength of their observed correlations with properties measured at the wells, but with little thought given to the validity of the correlation, except that it looks good.

2.2.4 Predict porosity using seismic acoustic impedance Following example illustrates some of these principles. The seismic attribute is acoustic impedance derived from zero-phase, true amplitude, and 58-fold 3D seismic data. Inversion to acoustic impedance followed the method using sonic and density logs from three wells to calibrate the inversion. The objective of this example is to use seismic acoustic impedance to predict porosity away from the wells. (Figure 2.10) shows a scatter plot of seismic acoustic impedance and porosity measured at 7 well locations. The negative correlation coefficient is expected because acoustic impedance (AI) varies inversely with the magnitude of porosity. Even with these high correlations, we assume that the pattern displayed by the seismic AI (Fig. 2.11) is related to the true distribution of porosity and that the high correlation isn't serendipitous.

(Figure 2.10) Scatterplot of porosity percent and seismic acoustic impedance at 7 well locations

(Figure 2.11) Map of seismic acoustic impedance and porosity percent at 7 well locations. 16

(Figure 2.12) illustrates the value of integrating a seismic attribute when mapping porosity using only 7 porosity values. For this study we had the advantage of knowing the porosity values at 48 other well locations, thus we can test the accuracy of using the seismic attribute as a predictive variable. Of the 48 'new' locations, only 9 were misclassified using the criterion of finding porosity > 9 percent. The wells shown as open circles are the values for the 7 wells and are predicted perfectly, because colocated cokriging is an exact interpolator, like kriging (the red line shows the perfect line of correlation). (Figure 2.12) Scatter plot of measure porosity (Y-axis) versus predicted porosity (Xaxis). Nine wells were misclassified out of the 48 'new' wells.

The additional 48 wells were drilled in a typical west Texas 40-acre 5spot pattern. If seismic data had been available when designing the original in-fill program, we can see that many wells should not have been drilled using the porosity cutoff criteria.

2.2.5 What about using Linear Regression? Reviewing the scatterplot shown in (Figure 2.10) and recalling that the correlation coefficient is -0.95, it would seem logical to simply use linear regression to predict porosity from seismic acoustic impedance. The map can be made using the following regression equation: Y= c-bX. Porosity = 62.13 - (0.00157 * AI) Where c is the intercept and b is the slope. Figure (2.13) The map of porosity based on a regression relation.The map of porosity based on a regression relation is shown in (Figure 2.18). The fact that regression uses only one data point during the estimation is valid, because traditional regression assumes data independence. Although the results seem fine, the b term (slope of the function) imparts a spatial linear bias (trend) in the estimates during the mapping process. 17

2.3 Enhancing Fault Visibility Using Bump Mapped Seismic Attributes 2.3.1 Shaded relief of the coherency Seismic coherency is a measure of the consistency of a seismic section. Where events are continuous, coherency is high. But where events are terminated either by faulting or stratigraphy, there is a loss of continuity, which is clearly evident as dark lines on coherency displays. Coherence is calculated directly from the seismic data and thus provides an unbiased view of the faulting in a section. Consider (Figures 2.14 and 2.15), which show the time slice and coherency slice for a region around a series of prominent faults. The faults are evident on the vertical section but on the time slice of (Figure 2.14) they are difficult to follow spatially. If all we had to go on were the time slices, interpreting even the major faulting would be a time consuming task.

Fig (2.14) Variable density time slice of a faulted seismic section.

Fig (2.15) Coherency slice of the same data shown in Figure (2.14).

Notice how difficult it is to follow Notice how much clearer and more distinct the faulting is. But the faults from the vertical section onto the time slice. The faults show up very clearly, however, on the corresponding coherency slice shown in (Figure 2.15). On this display the discontinuities (faults) show up as dark lineations making their spatial interpretation a simpler task. Coherency has become one of the staples of fault interpretation. As useful as coherence displays are, they are not perfect. They show discontinuities but without the seismic amplitude data it is difficult to assign relevance to what we are seeing. To interpret a coherency slice correctly we need to make continual reference back to the amplitude data which is a time consuming and less than optimum task. 18

2.3.2 Bump Mapping Attributes For example, (Figure 2.16) is a shaded relief image of the time slice shown in (Figure 2.14) with the vertical seismic removed for simplicity. The variable density display of (Figure 2.14) shows the gross features of the time slice but it lacks any direct mechanism for identifying the actual structure. We cannot tell intuitively what is high and what is low. We can make continual reference back to the color palette, which in this case shows, red-yellow as high amplitude and blue-gray as low but we have no inbuilt mechanism for automatically equating these colors to structure. If we didn’t know the color palette we would have no idea what the time slice represented. The shaded relief display (Figure 2.16) shows how the amplitude structure of the time slice would reflect light. It is a map of reflectivity and the highs and lows are now obvious. This is because the visual cortex is proficient at interpreting these patterns of light and dark. We don’t need any other information here to see the structure – it is intuitively obvious.

From an interpretation perspective, however, (Figure 2.17) is incomplete. It shows us the structure of the data but without the variable density coloring it is still hard to interpret.

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Fig (2.17) Composite density (bump Fig (2.16). Shaded relief of the seismic mapped) display using the variable amplitude time slice shown in Figure density of Figure 2.14 and the shaded (2.14). The vertical section has been relief of Figure 2.15. Notice that the 3removed for clarity. Notice the D effect visible on Figure 2.15 persists. pronounced 3-D effect produced by the shading. Compare this now with (Figure 2.17), which is the bump mapped

2.3.3Bump Mapping Coherency Having seen the improvements in perceptibility brought about by bump mapping we can ask the question, “What would happen if we used a different attribute for the shaded relief”. (Figure 2.17) uses a shaded relief of the seismic amplitudes to modulate the colors of the variable density display (again of the seismic amplitudes). We are not restricted, 19

however, to using the same attribute for the light source that we use for the variable density component. In this section we use the shaded relief of a coherency slice to modulate the variable density seismic amplitudes.

Fig (2.18) Shaded relief of the coherency slice shown in Figure (2.15) Note how much clearer and sharper the discontinuities are than on Figure 2.15

Fig (2.19) Composite density display using the variable density display of Figure (2.14) and the shaded relief of Figure (2.18).

The discontinuities now appear as pronounced ridges whereas the bulk of the seismic amplitudes remain unaffected. Notice again how distinct the discontinuities

(Fig 2.20) Close-up coherency of the faulted region using shaded relief.

Fig (2.21) Same close-up as Figure (2.20) but using the bump mapped image of Figure (2.19).

Consider (Figure 2.18), the shaded relief image of the coherency slice shown in (Figure 2.15) Here we see that viewing the coherency slice as shaded relief has, by itself, an immediate benefit. The discontinuities, on (Figure 2.15), are now more clearly defined and easier to follow. Consider now Figures (2.19) and 2.21) where we used the shaded relief of (Figure 2.18) to bump map the seismic amplitude time slice of (Figure 2.14). The shaded relief coherency now appears as ridges on the amplitude time slice and clearly relates the discontinuities back to the amplitude data.Considering that coherency provides an unbiased view of discontinuities, (Figures 2.19 and 2.21) could be thought of almost as automatically interpreted time slices.

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3.0 Reservoir Characterization 3.1 pore-pressure prediction (A combined geomechanics-seismic inversion workflow using trendkriging techniques in a deepwater basin) To optimize drilling decisions and well planning in over pressured areas, it is essential to carry out pore-pressure predictions before drilling. Knowledge of pore pressure implies knowledge of the effective stress, which is a key input for several geomechanics applications. It is also a required input for 3D and 4D seismic reservoir characterization. (Figure 3.1- see appendix) shows the structural framework associated with the geostatistical mapping

3.1.1 Methodology The four components that comprise the joint workflow are a highresolution velocity analysis, a porepressure and effective-stress prediction, a multi-attribute seismic inversion, and a Bayesian lithofacies classification using IP, IS, and effective stress. High-resolution velocity analysis (step 1). The interval velocities in the present study were obtained using a method that maximizes the stacking power of spatially continuous events in prestack gathers. The initial interval velocity model wasn't built by inversion of it and it's regularized using a semblance-based interactive velocity analysis system. The velocity model, thus obtained, went through geostatistical mapping (trend-kriging) using the upscaled welllog velocities within several key stratigraphic layers. The kriging is done within a stratigraphic framework, to ensure consistency between the interpreted horizons and geology. (Figures 3.2a and b) show the velocity model before (Figure 3.2a) and after trend-kriging (Figure 3.2b). Note that near the wells (within the correlation length) the resolution approaches the well-log resolution and the data are influenced more by the well-log data.

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Figure (3.2) (a) High-resolution seismic velocity (in ft/s). (b) Final high-resolution velocity after trend-kriging with well data (in ft/s).

Pore-pressure and effective-stress prediction (step 2). Most methods for pore-pressure prediction are based a principle which implies that the elastic wave velocities are a function of the effective stress tensor, which is defined as the difference between the total stress tensor and the pore pressure p. In the study presented here, it is assumed that the elastic-wave velocity is a function only of the vertical effective stress σeff (weight of the rock matrix and the fluids in the pore space overlying the interval of interest). σeff = S – p S is calculated by integrating the bulk density from the surface to the specific depth: S = g ∫ ρ(Z) dZ where ρ(z) is the density at depth z below the surface and g is the acceleration due to gravity. The velocity-to-pore-pressure transform was derived from data from wells in the area of interest or offset wells. The formation pore pressure is assumed to be represented by the mud weights used for drilling these wells, because during drilling operations mud weights are increased to prevent fluid and gas influxes from the formation into the wellbore; therefore these relevant mud weights can provide a reasonably close estimate of formation pressure. By inverting to the available pore pressure data in these nearby wells in order to calibrate the velocity topore-pressure transform, the normal velocity can be accurately defined, allowing identification of possible shallow overpressures. This method contrasts with current methods that fit a trend line to velocity data as a 22

function of depth below mudline. This trend is often referred to as a “normal trend” which captures the expected velocity variation with depth when the pore pressure is hydrostatic. The calibrated velocity-to-effective-stress transform was then applied to the trend-kriged velocities. To apply the pore-pressure transform, it is necessary to determine density at all locations so that a 3D volume of total vertical stress can be calculated. To do this, a density cube was built by geostatistically mapping the available well-log data in the area, constrained by depth horizons and a 3D trend. The geostatistical mapping (trend-kriging) of the upscaled density log data is guided by a 3D density-trend volume. This density- trend cube was resampled into 3D curvilinear stratigraphic grids representing stratigraphic layers for use as a 3D trend (local mean). The upscaled density log data were then kriged in each layer assuming a geostatistical model consisting of a single spherical structure with a given vertical correlation length and an isotropic lateral correlation length (parallel to bedding). Integration of the density cube using Equation 2 thus allows the total vertical stress to be determined anywhere in the model. Note that the velocity model went through geostatistical mapping (trend-kriging) using the upscaled well-log velocities within several key stratigraphic layers, similar to the method applied in creating the density model. The use of horizons helped to maintain consistency of the well data and the geologic structure. The velocity-to-pore-pressure transform, which is established from nearby well data, is then applied to this trendkriged velocity volume. The final volumes of pore pressure and effective stress are shown in (Figures 3.3a and b).

Figure (3.3) (a) Final pore-pressure volume (in units of psi). (b) Final effective stress volume in units of psi for the same cross-section.

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Multi-attribute seismic inversion (step 3). The multi-attribute seismic inversion process has been presented previously. The workflow is schematically shown in (Figure 3.4). The processes enclosed in the light blue rectangles describe the needed modification in the workflow for incorporation of the effective stress. The background models for IP and IS were derived from the trend-kriged velocity and density volumes. This step is important, because all attributes used in the model must have a common background model and follow a consistent set of assumptions. (Figure 3.5) shows absolute IP and IS along an inline, derived from multi-attribute seismic inversion after low-frequency compensation.

Figure (3.4)- Reservoir characterization workflow. The multi-attribute seismic

inversion includes effective stress and background model from seismic velocity. SCVA stands for surface-consistent velocity analysis. VMB stands for velocity model building.

Figure (3.5). Absolute IP (top) and IS (bottom) along an inline from multiattribute

seismic inversion. Color bar is in AMO units.

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Lithofacies classification using effective stress, IP, and IS (step 4). The key to using effective stress as an attribute for reservoir characterization is to understand the underlying behavior of the elastic parameters (IP and IS) with respect to lithology and effective stress. (Figure 3.6a see Appendix) shows a schematic relationship between IP versus effective stress for shale and brine-filled sand representing sediment compaction in clastic basins. In general, IP will increase with effective stress for shales (black line) and brine sands (green line). It is clear that the presence of hydrocarbons in the pore space will reduce IP, as both VP and density will be lower than in the brine-filled case. The scatter of shale and brine sands around the compaction trend is used to derive a probability density function (pdf). It is clear that discriminating between oil and gas in this system is very difficult due to the scatter of the data. Therefore, the oil sands and gas sands are included in a single “hydrocarbon” class. The scatterplot allows the discrimination of three lithofacies classes that were used for inversion in the basin: shale, brine sand, and hydrocarbon sand. The derived 4D pdf P(IP, IS, σeff, Lithoclass) is plotted at different effective stress intervals. Note that the ability to discriminate shale, brine sand, and hydrocarbon sand changes as a function of the effective stress according to the compaction model. Furthermore, the effective stress is spatially varying, as seen clearly in (Figure 3.3). Thus, to correctly classify a lithology type, knowledge of IP and IS is necessary, and the spatially varying effective stress is needed. This allows defining the position with respect to the compaction curve The class probability values were obtained for each set of IP, IS, and effective stress for each seismic data sample and effective stress value by deriving the posterior pdf P (Lithoclass IP, IS, σeff. From (Figure 3.7) it is clear that the ability to correctly identify the pay zone is related also to the vertical effective stress, and not only to the seismic attributes. (Figure 3.8) shows the computed hydrocarbon sand probability values along an inline using the method described above. A control well shows good agreement between a saturation log and high hydrocarbon probability. (Figure 3.9) shows another inline, which allows comparison of the hydrocarbon probability and effective stress at the same location.

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Figure (3.8) Hydrocarbon sand probability along an inline derived from IP, IS, and effective stress attributes. The magenta line is a measured saturation log at a control well location.

Figure (3.9) Effective stress and hydrocarbon sand probability values along another inline (see Figure 3.8).

In (Figure 3.10), hydrocarbon sand probability values are posted on a horizon with and without effective stress as an attribute. As is evident from (Figure 3.7), without effective stress the results are not as clear as 26

when effective stress is used, because the probability functions are “smeared.” Furthermore, if effective stress is not taken into account, only the shallower events are identified as potential hydrocarbon sands. This can be seen in the left of (Figure 3.10), where potential hydrocarbons are visible only in the structurally shallower part of the horizon. If effective stress is included as an attribute (right of Figure 3.10), however, the hydrocarbon probabilities in the deeper, more compacted areas, can be mapped as well. The good match between the well and seismic (shown in Figure 3.8) is possible because the effective stress “riding” on IP and IS efficiently captures the spatial variation in the rock model (both vertically and laterally) for lithofacies class discrimination.

Figure (3.10) Hydrocarbon sand probabilities posted on a horizon without using effective stress (left) and with effective stress (right).

3.2 Reservoir Characterization and Heavy Oil Production 3.2.1 Methodology We will focus primarily on how geophysics will fit into the reservoir development strategy of heavy oil fields. Generally, we try to resolve the changes in the reservoir as a function of the production processes. Hence, we repeat the seismic survey, keeping the acquisition and processing parameters as constant as possible so that changes in the seismic response will reflect reservoir changes (no pun intended) during the production process. Seismic surveys must generally be acquired over producing fields during the production processes. Here the goal was to map steam injection fronts during the production of heavy oil from the Formation. The time-lapse seismic method becomes an enhanced oil recovery tool in which production processes such as infill drilling and steam injection can be adjusted according to reservoir changes.

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The role of geophysics and reservoir simulation in reservoir characterization is illustrated in (Figure 3.11). Much of our geophysical work involves the acquisition, processing and interpretation of seismic data. Generally, the seismic surveys are repeated so that we may see the changes in the reservoir. The repetition of seismic surveys results in “4D” or time-lapse seismology, where we examine differences in the seismic data over time. Hopefully, these differences exhibit reservoir changes. Eventually we wish to produce a seismic model whose response (the synthetic seismogram) matches the time-lapse seismic data. These models of seismic velocity and density can then be converted into earth models of porosity, permeability and temperature change. Ultimately, we would hope to link together the seismic data, the seismic model and the reservoir model.

Figure (3.11) a possible flow diagram for reservoir characterization.

3.2.2 Results As illustrated by (Figures 3.12 and 3.13), we see encouraging results in the area of heavy oil field reservoir characterization. (Figure 3.12) shows a seismic reflectivity difference section. This difference section from the heavy field was obtained by differencing seismic surveys completed in nine years in order to show the effects of steam injection into the heavy oil sands. A large difference is seen in the circled zone of interest.

Figure (3.12) Difference seismogram for seismic surveys in heavy oil field, illustrating that the zone of greatest change is in the steam flood zone.

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(Figure 3.13) shows the differencing of synthetic seismograms for nine years seismic models. The encouraging agreement between the model responses and the real data indicates that our seismic model is probably realistic. Following a successful modeling of the seismic data then developed a reservoir steam flow model. In essence, this is the goal of the reservoir characterization procedure shown in (Figure 3.11).

Figure (3.13) Difference of synthetic seismograms for nine years seismic models; the seismic model is created by the fluid substitution based on steam zones.

Our research in heavy oil fields has involved the use of seismic monitoring over fields that involve both hot and cold flow production. In both cases, we see a growing need for extensive use of multicomponent time-lapse seismology. In the case of monitoring steam injection, there are several methods of seismic monitoring, including reflectivity differencing, impedance differencing, and the computation of Vp/Vs ratios from multicomponent data. A case is made for the extensive use of multicomponent methods for mapping sand content and steam fronts. In cold production of heavy oil, seismic monitoring can be used for mapping both wormholes and foamy oil production then we can show the seismic anomalies. There are some geoscientists have describes the physics of cold production footprints and have laid the groundwork of reservoir characterization in cold production problems. Although much remains to be done in the integration of petrophysical and geophysical methods to map subsurface changes, the initial findings of geoscientists suggests that multicomponent seismology holds considerable promise. Some intriguing examples of the effects of cold production on seismic responses, in which reservoir production is related to seismic anomalies, are shown by geoscientists. (Figure 3.14) shows a subtle contrast between seismic sections recorded in nine years over an Upper sand channel for other field. There appears to be a time delay in the later survey as a result of the cold 29

production process. This delay is confirmed by isochron maps for the interval between the Top of two reflectors as indicated on Figure 4. If the isochron maps are differenced for the two data, we note travel time delays for the wells under production, as shown in (Figure 3.15). The time delay can be attributed to the presence of foamy oil or wormholes created by the cold production process. Both of these effects will tend to decrease the P wave velocity, as shown. In both hot and cold production, it shown that we need to integrate geophysics and reservoir simulation models for a complete description of the reservoir. Reservoir characterization is an evolving science. There is a constant need for integration and “closing the loop” between geoscience and reservoir engineering. Developments that have occurred in recent years include the following: • Integration of borehole and seismic information in the development of reservoir simulation models. • Increased use of AVO (amplitude variation with offset) methods. • Optimized use of multicomponent seismology. • Development of reservoir simulation models from geophysical and geological data.

Figure (3.14) Time-lapse seismic survey over cold production field. Figure (3.15) Difference in isochron maps for heavy oil cold production fields. Zones of greatest time delay (blue polygon) are near the best producing wells. The wells circled in black have a cumulative production of over 15,000 cubic metres.

An exciting feature of reservoir characterization is that we are continuously able to prove or disprove our models due to the dynamic flow of information from production.

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4.0 Time-Lapse The changes in reservoir fluid saturation, pressure, and temperature that occur during production also induce changes in the reservoir acoustic properties of rocks that under favorable conditions may be detected by seismic methods. The key to seismic reservoir surveillance is the concept of differential imaging using time-lapse or 4-D measurements and mapping of faults are important for the prediction of fluid flow in many reservoir types.

4.1 Reservoir Surveillance. 4.1.1 The concept Time-lapse seismic methods are usually based on differences in seismic images that minimize lithologic variations and emphasize production effects. The concept is illustrated in (Figure 4.1), where a base 3-D survey acquired before production is compared with a monitor 3-D survey acquired at a later time, dependent on the recovery process to be monitored.

Figure (4.1) Illustration of time-lapse seismic.

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The difference between the seismic surveys can then be interpreted in terms of the production-related changes in reservoir properties. Timelapse seismic data have been shown to increase reserves and recovery by: • Locating bypassed and undrained reserves. • Optimizing infill well locations and flood patterns. • Improving reservoir characterization by identifying reservoir compartmentalization and permeability pathways. Four-D also can decrease operating costs by: • Reducing initial development well counts. • Optimizing phased developments using early field-wide surveillance data. • Reducing reservoir model uncertainty. • Reducing dry holes and targeting optimal completions. As a result of these benefits, many oil companies are aggressively pursuing the application of time-lapse seismic data.

4.1.2 The Physical Basis Seismic velocity and density changes in a producing reservoir depend on rock type, fluid properties, and the depletion mechanism. Time-lapse seismic responses may be caused by: • Changes in reservoir saturation. Displacement of oil by gas cap expansion, gas injection or gas exsolution resulting from pressure decline below bubble point; these decrease velocity and density. Water sweep of oil increases velocity and density. • Pore fluid pressure changes during fluid injection or depletion. Injection will increase fluid pressure, decreasing the effective stress acting on the rock frame and lowering seismic velocities. Compaction during depletion reduces porosity and increases velocity and density. • Temperature changes. An increase in temperature increases fluid compressibility, and as a result decreases reservoir seismic velocities and density. Reservoir factors that affect the seismic detectability of production changes can be evaluated in order to determine which geological settings and production processes are most suited for reservoir monitoring. Each field is unique, and modeling of the seismic response to production, based 32

on reservoir flow simulation, is used to evaluate the interpretability of seismic differences and to determine how early in field life a time-lapse survey can be used to monitor reservoir changes. The optimal times for repeat seismic surveys depend on detectability and the field's development and depletion plan. Planning for repeat surveys in the context of field surveillance will maximize the value of the data.

4.1.3 Seismic Repeatability The difference between two seismic surveys is not only sensitive to changes in reservoir rock properties but also to differences in acquisition and processing. As suggested in (Figure 4.2), the chance of success for a 4-D project depends on both detectability and seismic repeatability. Some of the factors that affect repeatability include: • Acquisition geometry differences such as sail line orientation and heading, source-receiver spacing, streamer feather, and coverage due to obstructions. • Near-surface conditions resulting in statics and receiver coupling variations. • Sea level, sea state and swell noise, water temperature and salinity. • Residual multiple energy. • Ambient and shot-generated noise. • Geological factors such as shallow gas and steep geological dip.

Figure (4.2)-Dependence of 4-D chance of success (COS) on detectability and repeatability.

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4.1.4 4-D Interpretation The interpretation of time-lapse seismic differences in terms of reservoir changes requires integration of the data with detailed reservoir characterization, fluid flow simulation, petrophysics, and conventional reservoir surveillance data. Many companies use a 4-D interpretation workflow, where seismic differences are compared to predicted differences based on seismic modeling of history-matched reservoir flow simulations. The interpretation process is one of comparing, contrasting, reconciling and validating these two images of the production process. This approach is used because 4-D seismic interpretations are nonunique. • A lack of change between the baseline and monitor seismic surveys can be interpreted as unswept reservoir or as an area of no reservoir. • Four-D measurements taken once every few years can be aliased in time. Rapid changes in saturations and pressures found in some recovery processes can require more rapid seismic repeat intervals. An example of 4-D interpretation is from the North Sea Jotun Field, where oil is being depleted through a strong natural water drive. Water sweep in the reservoir results in a 10-12 percent increase in the seismic impedance. (Figure 4.3) compares the results of inverting the seismic difference acquired after three years of production to obtain impedance change with the oil saturation change predicted by the reservoir flow simulation. At this location, the simulator suggests that the reservoir is fully swept but the seismic data show that only one reservoir zone has been swept and that internal shales act as barriers or baffles to flow. This results in a flank rather than bottom water drive. Infill or sidetrack opportunities are found where there is no change in the seismic data and where reservoir characterization suggests there is high net-to-gross sand. As a result of the 4-D survey at Jotun, three successful infill wells were drilled and a potential dry hole was avoided.

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Other published 4-D case studies show that seismic data can image production changes in a variety of geological settings and production scenarios, including water and gas sweep, pressure changes and compaction, and enhanced recovery. Further, 4-D interpretation is evolving toward a more quantitative analysis of the data. By incorporating time-lapse shear wave information, either from AVO analysis, elastic inversion, or PS data, it is possible to estimate saturation and pressure changes in the reservoir. These estimates can be a strong history match constraint on reservoir simulations. More predictive simulations will result in more efficient reservoir management.

Figure (4.3)-Time-lapse seismic data from the Jotun Field, Norway, compared to reservoir flowsimulation predictions.

4.2 Detecting flow-barriers with 4D seismic

4.2.1 Fault analysis Improved spatial mapping of the transmissibility of fault systems will allow a better prediction of the fluid flow pattern. The fault analysis approach consists of two steps: 1) extract all the faults from a given reservoir layer down to the resolution of the seismic and 2) apply a structural characterization. The mapping of the faults can be based on seismic attribute grids. This means that attribute-responses related to faults are extracted along key interpreted horizons in the reservoir interval. Attribute cubes (figure 4.4) and property cubes contains a large amount of information that needs to be classified and validated. To precondition the data for the fault extraction filtering of the dipping noise 35

is typical. The automatically extracted fault systems are generally validated with well log information. Logs from horizontal wells are of special value because of the lateral extent.

(Figure 4.4) Example of a property cube (instantaneous attenuation) that can be used for automatic detection of faults in 3D.

4.2.2 Combining 4D seismic and reservoir simulation The saturation distributions from the 4D seismic measurement offer an alternative to reservoir simulation. The reservoir simulated distributions rely on an accurate knowledge of the flow properties of the reservoir. This accurate knowledge is only available in the wells and hence 4D seismic results might be combined with the reservoir simulations to reduce the uncertainties in the saturation distributions. A new methodology for a composite analysis is beeing developed named Statistical Reservoir Volume Analysis. The basis for the method is to compute the total change in saturation composition per reservoir compartment accounting for the influx of water and the production of hydrocarbons in the time period between the surveys. The reservoir simulated results are history matched and the input and output fluid-flux has been included in the modelled fluiddistributions. Taking advantage of this fact the hydrocarbon volume can be thresholded for the specific saturation level gives a seismic attribute response detectable by the classification system. Together with the top reservoir interface and the fault planes the thresholded interface can be thought of as a closed geometric "container" for hydrocarbons above a certain saturation level.

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The hydrocarbon volume in such a "container" might be expressed: VHC = (I–SW ) Ф h A where SW is the average saturation of water in percentage, Ф is the average reservoir porosity, A is the height of the oil column over which the averages are made and is the area of reservoir compartment filled with hydrocarbon. If the same procedure is applied to the 4D seismic results VHC' = (I–SW ) Ф h` A` where SW the saturation and the average porosity Ф is assumed to be identical in the two situations. If the hydrocarbon volumes VHC and VHC` have been "material balanced" as explained above: VHC = VHC' implying that the volume of hydrocarbons in a given compartment computed from the 4D seismic and the reservoir simulation have to be equal. However the geometric shape of the "container" as defined by might be different. Statistical Reservoir Volume Analysis is the methodology that allows the user to assess the match between 4D results and the simulation results in a statistical framework. The probability of the match between the hydrocarbon distribution as seen by the 4D and the geometric "container" as predicted by the simulation is computed with a cost function per reservoir compartment (figure 4.5).

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Figure (4.5) Statistical reservoir volume analysis on the baseline survey. a) Top reservoir "container". b) Hydrocarbon distribution as predicted from seismic (red = HC, blue = water). c) Connected reservoir compartments colour coded with the goodness of match. The warm colours (red to light green) represent good match and the cold colours (blue) indicate a poor match. d) Normalized total reservoir volume estimate for each reservoir compartment.

4.2.3 Fault sealing analysis The understanding of how flow-units of the reservoir are connected represents crucial information for optimal depletion of the hydrocarbons. The sealing capacity of the fault network is a typical problem in this context. Basically the saturation changes during production from the 4D seismic are combined with the information of the fault network and checked if the changes have taken place across the faults. The output for such an analysis is displayed in (figure 4.6). In the context of the statistical reservoir volume analysis the fault sealing analysis might be looked at as a situation where there is a mismatch between changes of the shapes of hydrocarbon "containers" derived from the 4D seismic and from the reservoir simulator. If the 4D shows a saturation change across a fault that has been modelled as sealing it will be colour-coded as non-sealing and the reservoir flow model might be updated and the simulator rerun.

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(Figure 4.6) Shows a seismic saturation change map where red indicates large differences in the seismic response and blue indicates no difference in the seismic response while figure 2b) shows the faults characterized after their sealing capacity. The red colour indicates sealing faults, while blue indicates non-sealing faults. This result will in turn be used to update the flow units in the reservoir model.

4.2.4 4-D fluid saturation mapping techniques Quantitative 4-D fluid saturation mapping techniques which using attributes, allow calibration of 4-D seismic responses coupled with flow simulator models. As part of a major drive to improve recovery from Statoil’s Gullfaks field in the Norwegian sector of the North Sea, timelapse seismic data have been used to guide quantitative mapping of oil saturation changes. Maps were generated (Figures 4.7 & 4.8) showing the probability of an area being drained, partially drained or undrained according to various oil saturation changes. The maps reduce the uncertainty in further development because they are quantitative and provide more powerful input to simulation models.

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Figure (4.7) 4-D interpretation from Shell’s Gannet-C study gives insight into structure and production but is qualitative in nature.

Figure (4.8) This figure shows the saturation mapping results for Statoil’s Gullfaks field.1.

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5.0 Seismic Inversion

5.1 The Post-Stack Inversion Method The modern era of seismic inversion started in the early 80’s when algorithms which accounted for both wavelet amplitude and phase spectra started to appear. Previously, it had been assumed that each and every sample in a seismic trace represented a unique reflection coefficient, unrelated to any other. The trace integration method was a popular approximation. At the heart of any of the newer generation algorithms is some sort of mathematics - usually in the form of an objective function to be minimized. Let’s look at each of these terms, starting with the seismic. It says that synthetics computed from the inversion impedances should match the input seismic. This is usually (but not always) done in a least squares sense. Invoking this term also implies knowledge of the seismic wavelet. Otherwise, synthetics could not be made. At this point, life would be good except for one thing. The wavelet is band-limited and any broadband impedances that would be obtained using the seismic term only would be non-unique. Said another way, there is more than one inversion impedance solution which, when converted to reflection coefficients and convolved with the wavelet would match the seismic. In fact, there are an unlimited number of such inversions. Enter the simple term. Every algorithm has one. It could not care less about matching the seismic data, preferring instead to create an inversion impedance log with as few reflection coefficients as possible. What about the “Match the Logs” term? When turned on, it makes the inversion somewhat model-based. Sounds reasonable - the inversion impedances should agree or a least be consistent with an impedance model constructed from the well logs. The primary use of the model term should be to help control those frequencies below the seismic band. When it is used to add high frequency information above the seismic band, great care should be exercised. High frequencies from a model will be unaddressed and unchanged by the input seismic when they are above the seismic band. They can then appear in the output inversion, even though they are completely model driven. 41

5.2 The Details – Constraints, QC, Annealing, Global and Colored Inversion There are other strategies in seismic inversion which control the way in which the output impedances are obtained. It is common to define high and low limits on the output impedances. These are supposed to keep the inversions physical and consistent with known analogues and theories. Could the inversions be then critically dependent upon inaccurate horizons interpreted from seismic data? This potential problem can be addressed in two ways. In the first pass of inversion, the constraints are relaxed to allow for inaccuracies. The horizons are then reevaluated against the initial inversion, before a final pass with tighter constraints. Second, the re-evaluation can be done on an inversion without the model-based low frequencies added in at all. We call this the relative inversion and it is by definition, free from any inaccuracies in the input model. Another technique introduced recently is the so-called Colored Inversion. It trades computation speed for resolution. Phase is first assumed to be known. Then the spectrum of the reservoir impedances is assumed to be a straight line on a log frequency cross-plot. The slope of the line is determined from available logs. Then, an operator is designed which transforms the seismic spectrum to the desired log spectrum. Putting it all together, seismic inversion can play the central role in an improved understanding of the reservoir. In Figure 1, upper panel, the facies are an alternating sequence of sands and shales. Interpretation is problematic due to the close vertical positioning of contrasting layers within half of a wavelet length. The result is severe interference (tuning) and a general complication of the seismic section. The interpretation of the yellow reservoir event is particularly difficult. (Figure 5.1), lower panel, shows the inversion result. It is generally simpler and the interpretation of the yellow event is obvious. It is now interpreted as a sequence boundary which is overlain by incised valley sand. The value of the inversion process is illustrated again in (Figure 5.2). The facies of interest are sandstones as indicated. The figure shows the original seismic, the inversion in colour with smoothed P Impedance

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logs overlain. Well cross-plot analyses showed that the best sandstones should be resolvable by P Impedance alone.

(Figure 5.1) The upper panel is a seismic section from southeast Asia and represents a sequence of sands and shales. The Yellow horizon interpretation has not been completed. It is made difficult by the close proximity of events and the structure. The lower panel shows a post-stack, mixed-norm inversion. It is a much simpler section due to the attenuation of wavelet side-lobes. The completion of the Yellow horizon is now easy. The low impedance region above the Yellow, in the middle of the figure has been interpreted to be a valley fill.

Figure (5.2)-Above is the post-stack seismic inversion of the overlain seismic reflection. Smoothed well logs are also shown. Good sandstones are represented by the cyan colour. Note the shale in the region of interest at well 121-16. It is not obvious from the seismic (partially hidden by the logs in this figure). To the west, the inversion indicates different episodes of sandstone deposition.

These are revealed upon converting to depth and preparing impedance slices (Figure 5.3). The probability of sandstone deposition can also be formalized for any inversion (post-stack or AVO), as demonstrated in (Figure 5.4). In 3D probability space, is the likelihood of occurrence of single sandstone? (Figure 5.5) is a comparison of interpretations from the seismic and the inversion. There is better definition of channeling in the inversion and the authors judge that the accuracy of net pay estimates were improved by a factor of at least two.

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5.3 AVO Inversion Instead of a single full-stack, we have a set of partial offset or angle stacks, each with their own wavelets. In addition to a P Impedance model for the low frequencies, we now need two more – S Impedance and Density. We also want to include two more “keep it simple” terms for S Impedance and Density. After that, it is pretty much the same. The Zoeppritz equations dictate the range of allowable solutions. Alternate parameterizations are possible, P Impedance, Vp/Vs and Density being popular. The example in (Figure 5.6) illustrates the classic problem of separating sandstones from shales when discrimination is not possible from P Impedance alone. In (Figure 5.6) are slices of P Impedance and Vp/Vs from a Simultaneous AV O Inversion. The two reservoir properties are indeed different. Regional and valley shales dominate the P Impedance slice. The major feature of the Vp/Vs slice is the sandstone valley itself, brighter in the south due to the presence of gas. (Figure 5.7) is a 3D perspective of the Vp/Vs volume where it can be seen that the valley development is essentially defined by Vp/Vs. The LambdaRho-MuRho (LMR) technology can offer advantages to interpretation by optimally separating fluid and rock effects. LMR volumes are easily computed from any AVO Inversion.

Figure (5. 3)-The inversion in Figure (5.2) has been converted to depth and mapped using time slices. These results reinforce the ideas of different episodes of deposition.

Figure (5.4) Using the observed distribution of sandstone impedances from logs and probability analysis, a sandstone probability volume was computed. Here the 44

probability of sand -stone exceeding 88% is shown for a particular portion of the survey.

Figure (5.5) The two panels compare interpretations done from the seismic using traditional methods and from the inversion. The authors judge that the accuracy of net pay estimations was improved by a factor of two with the inversion.

The so called Joint inversions are variants of this technology. The theory readily accommodates PP-PS or any other possible combination. We have already noted the importance of accurate alignment and the correct alignment of PS modes to PP takes these challenges to a new level. Nevertheless, this technique contains the potential for density estimation at low angles, as illustrated by the heavy oil synthetic example in (Figure 5.8). The figure shows PP and PS gathers and their simultaneous inversion to P Impedance, Vp/Vs and Density. The maximum angle used to make the synthetic gathers shown was only 35 deg. The band of the PP gather was 10-60 Hz while that of the PS was restricted to 10-35 Hz. Comparing the overlain logs to the inversion results shows that density information can be extracted. Curiously, Joint Inversions find application to 4D projects. When the low frequencies below the seismic band are believed to be constant, then a Joint PP inversion of all vintages will provide the most stable baseline, against which to measure differences. The method also works for 4D AVO.

Figure 5.6. The two panels show slices of P Impedance and Vp/Vs from a Simultaneous AVO Inversion of the Blackfoot data. Note the predominance of off-s t r u c t u r e and off-valley shales in the P Impedance map. The upper valley sandstone is clearly imaged on the Vp/Vs map.

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Figure (5.7) Vp/Vs from AVO Inversion is shown in perspective view. The two episodes of sandstone deposition are clearly visible.

Figure (5.8) The logs in the left panel from a heavy oil play were used to compute the synthetic PP and PS gathers in the 2nd and 3rd panels. The background colours in the next three panels are the Joint Simultaneous AVO Inversions for P Impedance, Vp/Vs and Density. Smoothed logs are overlain in each panel. The wavelet band was 10-60 Hz for the PP data and 10-35 Hz for the PS. Despite the fact that the maximum angle was only 35 deg., density information was recovered.

5.4 Geostatistical Inversion Geostatistical simulation differs from all of the other methods in one respect. There is no objective function and hence no need for a simplicity term to stabilize it. Rather, property solutions (impedance, porosity, etc) are drawn from a probability density function (pdf) of possible outcomes. The pdf is defined at each grid point in space and time. A priori information comes from well logs and spatial statistical property and lithology distributions. As in the other model-based methods, the logs are assumed to represent the correct solution at the well 46

locations. It is useful to run a mixed-norm inversion first, to establish this. Historically, away from wells, geostatistics has had problems. It is the inversion aspect of geostatistics which has finally guaranteed its use as a modern inversion tool. The geostatistical inversion algorithm simply accepts or discards simulations at individual grid points depending upon whether they imply synthetics which agree with the input seismic. The decision to accept or reject simulations can optionally be controlled by a simulated annealing strategy. The inversion option results in a tighter set of simulations, the variation of which can be used to estimate risk or make probability maps. The simulations can be done at arbitrary sample intervals. Close to wells, resolution beyond the seismic band can reasonably be inferred. Away from wells, the absence of a simplicity term in the simulation and the statistical conditioning hold the possibility of resolution beyond that of traditional inversion methods. Important end results of 3D Geostatistical modelling are property probability volumes. A set of volume simulations of porosity, for example, can be modelled as a Normal probability density function at each grid point in time and space. From these, volumes can be constructed giving the probability that the porosity lies within a specified range. (Figure 5.9) shows an example of this for simulations of porosity. Twenty simulations were used to generate a probability volume for the occurrence of porosity above 10%. This volume was then viewed in 3D perspective and probabilities less than 80% were set to be transparent. The tops and bottoms of the viewable remainders were picked automatically. It is the thickness of one of these high-probability bodies which is mapped in (Figure 5.9). The colours represent the thickness, within which, the probability of 10% or greater porosity exceeds 80%. In this way, uncertainty can be formally measured and input directly into risk management analyses. In geostatistical modelling, property and indicator (facies) simulations can be combined to produce both property (eg impedance) and facies volumes. This is illustrated in (Figure 5.10). The green patches are sand bodies from a single simulation. Favourable locations for new wells were determined by integrating the sand volume at each CMP for a set of simulations. The results of this development programme showed a definite improvement in sand detection. Accumulated production has been up to three times the field average in some instances, more than justifying the effort and expense of the inversion.

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Figure (5.9) Geostatistical simulation of porosity was done over a Western Canadian Devonian reef. At each point in time and space, the set of simulations was modelled by probability density function (pdf). Then a volume was created, the elements of which were probabilities that the porosity was in excess of 10%. Next, the pro b a bilities themselves was viewed in 3D perspective, and values less than 80% made transparent. Finally, the remaining visible probability bodies were automatically interpreted. A small area of the final map of (probability body thickness) is shown here. The colours are the thickness (ms) of bodies which should have greater than 10% porosity, 80% of the time.

Figure (5.10) - This is a perspective view showing the results of the simultaneous geosta -tistical simulation of density and lithotype (shale, tight sand, porous sand). The green bodies are low density sands. A set of simulations were computed and integrated at each CMP to establish the probability of occurrence of sand. Wells drilled on these analyses have shown significantly higher production rates.

5.3 Merging Technologies – The New Inversions Concatenating seismic inversion with other technologies, such as neural nets, seismic attributes or pattern recognition has been a strategy employed by some explorationists over the years. The idea has been to try and extract every last bit of information from the input data sets. We are now seeing disparate technologies beginning to be combined within the same algorithm. (Figure 5.11) is such an example from Blackfoot. It brings together aspects of pattern recognition and post-stack and AVO 48

Inversion. The top is a traditional Simultaneous AVO Inversion for Vp/Vs while the bottom is the new high resolution technology. It was run in a “blind-to-the-wells” mode, so the agreement to the logs is not perfect. As resolution is pushed to its limits, we must understand that there can be no single answer, only a collection of probable answers. The new technologies recognize this and in fact, the bottom panel in (Figure 5.11) is an average of six such realizations. The variability between the realizations could have been used to compute a probability of occurrence for the low Vp/Vs sandstones. All of this sounds very geostatistical, although upon closer examination, there are differences.

Figure (5.11)-The upper panel shows the results of an AVO Inversion at Blackfoot. The lower panel shows a high resolution AVO inversion using a technologies which brings together aspects of pattern recognition and inversion within a geosta -tistical type of framework.

5.4 Stochastic Seismic Inversion Stochastic seismic inversion is one method that can provide the vertical resolution sufficient to generate detailed 3D reservoir property models. The log data (sonic and density) are used in the simulation of pseudo-logs at each trace within the seismic survey (figure 5.12). A synthetic seismogram is generated from the pseudo-impedance log and is compared with the actual seismic trace at that location. The simulation that produces the best match between the synthetic seismogram and the actual seismic trace, as defined by some quantitative measure of goodness of fit, is retained as the inversion solution at that location. The vertical resolution of the simulated log data is determined by the selection of the vertical cell size (determined by the user), not by the frequency content of the seismic data (as determined by Mother Nature).

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The enhanced resolution of the stochastic inversion process is evident from a visual examination of the seismic data cubes displayed in (figures 5.13-5.15). (Figure 5.13) displays the input seismic volume. (Figure 14) is the result of inversion. This inversion result displays a resolution similar to that of the input data. That is to be expected, as the vertical resolution is derived from the seismic data, and is therefore subject to the inherent limitations of seismic resolution. (Figure 15) depicts the stochastic inversion cube, which displays a much finer vertical resolution than is observed in the input data or the recursive inversion result (figures 13 and 15).

Fig (5 13) Input seismic data volume

Fig.(5.14) Recursive inversion result. Note that vertical resolution is similar to that of the input data.

Figure (5.12) Schematic depiction of the stochastic inversion process.

Fig.(5.15). Stochastic inversion result. Note that the vertical resolution is significantly improved.

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6.0 Application to Reservoir characterization by combining time-lapse seismic analysis with reservoir simulation 6.1 Introduction Here we introduce the integration of time lapse seismic methods with reservoir simulation to image steam front and by-passed oil heavy oil. Two seismic 2D surveys, acquired in 1991 and 2000, respectively, had been reprocessed to preserve amplitudes. A flow simulation model was constructed for the region around the seismic profile. The simulator was run from the start of production in 1981 until 2000. The porosity, saturation, pressure and temperature of the reservoir zone were extracted from the flow simulation outputs for the early production condition in 1991 and for 2000, almost 10 years later. Comparing the seismic difference results of 1991 and 2000 and reservoir simulation outputs indicated that the gas saturation changes caused the largest change in the simulated seismic response. Seismic difference sections showed that thick zones of gas saturation caused greater time delays in sub-reservoir reflections due to the lower velocity within the reservoir zone. The simulated seismic response difference section was compared to the measured seismic response difference section. The results indicate that the present reservoir model is reasonably good at describing the reservoir. Reservoir simulation is often used to help understand the changes in reservoir conditions with the stages of production. Reservoir simulation is based on models that are created from well information, seismic data and geologic maps. The results around wells are controlled by engineering data but the results between or beyond wells cannot be verified by engineering data. Seismic surveys can be used to interpolate or extrapolate reservoir information between or beyond wells. Through rock physics equations, seismic properties such as velocity and density can be estimated from the output of reservoir simulation for use in seismic modelling.

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6.2 Reservoir simulation The reservoir grid geometry, well locations, and time-lapse seismic line location are shown in (Figure 6.1). The seismic lines are in the middle of the reservoir model in the north-south direction. The grid dimension of the reservoir model is 20 x 20 m horizontally. There are three vertical layers with varying thicknesses. To date we have considered the three layers having individually uniform but distinctive properties that correspond to two upper shale and sand interbedded layers and a lower homogenous sand layer. CSS started in the southern part of the reservoir in 1983 at well 1D2-6. Average steam injection duration was 10 to 30 days followed by 5 to 10 months of soak and production. The temperature front moves about 5 m to 8 m per year. It spreads faster in the north-south direction than in the east-west direction (Figure 6.2). Pressure spreads much quicker than temperature (Figure 6.3).

Figure (6.1). Reservoir model geometry. The arrows indicate the location of the timelapse seismic line (yellow dashed line) and Well 1D2-6.

Figure (6.2). Temperature distributions from the reservoir simulations at 1981, 1991 and 2000 time steps.

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Figure (6.3) Pressure distributions from the reservoir simulations at 1981, 1991 and 2000 time steps.

6.3 Synthetic seismic section Since the time-lapse seismic surveys were acquired in February 1991 and March 2000, we did seismic modelling only for these two time steps. We constructed velocity and density models using well logs for regions above the reservoir and calculated the velocity and density within the reservoir using reservoir simulation outputs. The velocity and density model is shown in (Figure 6.4). The seismic modelling was performed using point sources and shot gathers for better simulation results.

Figure (6.4) Velocity and density model for seismic modelling.

6.4 Discussion (Figure 6.5) shows the synthetic difference section (bottom) and seismic survey difference section (top) between time steps 2000 and 1991. The overall match between the seismic survey difference section and the synthetic seismic difference section is very good. Seismic difference banding effects are apparent in both difference sections around the CSS wells. The large difference energy between well V5 and V10 around the

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Top Cambrian appears on both difference sections. The simulated gas saturation, reservoir pressure and temperature distributions are plotted in (Figures 6.6, 6.7 and 6.8), respectively, for the three reservoir layers at 1981, 1991 and 2000 time steps. Gas saturation in 1981 was zero. The gas saturation here is for a gas phase constituted of different components such as water vapor and methane. The marked wells are those within 60 m around the seismic line. According to historical data, wells 1D2-6 and 3B1-6 have been in CSS operation since 1983 and well L8 was in CSS operation fro m 1983 to 1997. Wells L8 and 1D2-6 were shut in from 1988 to 1992; therefore in 1991 the reservoir in this part was heated up somewhat but was not at a high temperature. Wells 3C1-6, T3, V5 and V10 have been in CSS since 1992, 1995, 1996 and 1997, respectively. Large seismic difference energy appears around the area of these wells because they were not active in 1991. The W wells started CSS in late 1999; not long enough to have an impact on the seismic differences. We can see that t h e r e is little temperature change between 1991 and 2000 on the left side of well 1D26. We also found gas saturation corresponding to pressure decrease even at low temperatures (CDPs 40 to 80). Difference energy is visible around 600 ms and 750 ms at C D P locations 100 to 200 but is restricted to the top of the reservoir at other CDP locations (CDPs 40 to 80).

6.5 result By comparing the saturation, temperature and pressure results from the reservoir simulation with the synthetic difference (Figures 12 to 14), we derived the following conclusions: 1. The areas with gas saturation differences between the two compared time steps show seismic differences because the presence of gas reduces the bulk modulus and bulk density of the saturated rock. 2. Thicker gas zones correspond with larger traveltime delays in the seismic section. The thin gas zones only induce large reflectivity and do not have enough time delay to cause a strong seismic difference in the deeper regions below the reservoir zone (CDPs 40 to 80 on the synthetic seismic difference section). 3. High temperature regions also correlate with areas having seismic energy differences but the correlation is not as strong as the correlation with the gas saturation differences. 4. Pressure spreads very quickly and its value depends on whether the location is in the injection or production stage. The pressure dependence of the seismic data is due to the influence of pressure on gas saturation.

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Figure (6.5) the synthetic seismic difference section (bottom) and the seismic survey

(Figure 6.6) Gas saturation in the three layers versus the synthetic. seismic difference section

(Figure 6.7) Pressure in the three layers versus the synthetic seismic difference section.

(Figure 6.8) Temperature in the three layers versus the synthetic seismic difference section.

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7.0 Fractured Reservoir Characterization using AVAZ on the Pinedale Anticline,Wyoming, USA David Gray, Sean Boerner, Dragana Todorovic-Marinic and Ye Zheng, Veritas DGC, Calgary

7.1 Summary The Pinedale Anticline in Wyoming, USA, has become an area of particular interest since the recent success of several wells that have produced significant volumes of gas from its tight sandstone reservoirs. The best production rates appear to come from reservoirs that have had their permeability enhanced by natural fractures. A new seismic attribute uses azimuthal variations in the amplitudes of seismic data at long shotreceiver offsets to identify the presence of open natural fractures. The results have been incorporated into a fractured reservoir characterization run for this field.

7.2 Introduction The reservoirs currently of interest in the Pinedale Anticline are the tight sands of the Lance and Mesaverde Formations. These units were deposited during a period of rapid sedimentation in the late Cretaceous. Sediments were eroded off the western upland and carried by fluvial systems flowing to the east. As a result, the areal extent of these reservoirs tends to be limited, and individual reservoirs may not be commercial. However, when several of these sand bodies are stacked vertically, an amalgamated package can be as thick as 100 feet, and viable commercial wells may be drilled into these stacked sands. Through the entire sequence of more than 3000 feet, a vertical well may encounter up to 100 individual sandstone units. The Lance and Mesaverde were buried by up to 8000 feet of Tertiary section, which compressed these sands so that they are now tight sandstones with low permeability. The reservoir rocks have moderate porosity ranging from 8-12% and usually have low permeability unless enhanced by natural fractures. The key to economically producing from these formations in the Pinedale Anticline is to find areas where open natural fractures enhance the low permeability of the reservoir rocks. (See appendix figures. (7.1 to 7.3) for Geology background). 56

7.3 AVAZ Method It has been shown that open, fluid- (or gas-) filled fractures can be identified through the use of Amplitude Versus Angle and Azimuth (AVAZ) analysis on seismic data. This technique uses variations in the amplitudes of the long shot-receiver offsets of P-wave seismic data to determine the intensity and orientation of the fractures. It assumes that the fracturing generates a rock medium that is Horizontally Transverse Isotropic (HTI) – meaning one open set of near-vertical, fluid-filled (or gas-filled) fractures. The method used is based on the Azimuthal Amplitude versus Incident Angle equation. This method can be modified to produce attributes that may be useful in the identification of fractures between wells. The attributes we choose to output are: 1. Estimated Anisotropic Gradient 2. Estimated Azimuth of Anisotropic Gradient Minimum 3. Estimated P-wave Reflectivity 4. Estimated S-wave Reflectivity These attributes are useful because they each contain different information that may be relevant to identification of fracturing. The anisotropic gradient is closely related to seismic crack density. The azimuth of the anisotropic gradient is the strike of the fractures in a HTI medium overlain by an isotropic medium. If the reservoir meets this HTI criterion, then this azimuth is the strike of the fractures in the reservoir. The P-wave reflectivity is the response of the rock to compression by the seismic wave and includes information on the rock’s lithology and fluid content. The S-wave reflectivity is the response of the rock to shearing by the seismic wave and is comprised primarily of information about the lithology. This is also one of the first times that the envelope of the Anisotropic Gradient has been used. This attribute is similar to the seismic amplitude envelope, in that it removes the effects of the phase of the data. This should result in a more robust estimate of the crack density and better vertical definition of the extent of fractured bodies.

7.4 Results (Figure 7.4, see appendix) shows the structural time-interpretation of the Lower Lance over the study area. (Figure 7.5, see appendix) shows AVAZ results for a zone below the Lower Lance and depicts zones of high intensity fracturing along the top of the anticline. These fractures 57

correspond to an expected zone of more intense fracturing caused by increased stress near the top of the anticline. The highly fractured zones are cut at the fault, west of the crest (Figure 7.6). Interestingly, in (Figure 7.5), areas of high AVAZ crack density values can be seen extending east down the flank of the anticline in the northern part of the study area. (Figure 7.7) shows the visualization of the results of geobody detection used on zones of high seismic anisotropy that intersect a well-bore. These zones probably indicate swarms of intense fracturing. This visualization technique provides a good estimate of the drainage area of these reservoirs. The drainage area appears quite anisotropic and varies for each geobody Extending this analysis throughout the 50 square miles analyzed for AVAZ in the Pinedale 3D, it can be seen in (Figure 7.8) that the majority of these fractured sandstones exist to the west of the top of the anticline, where few wells have been drilled. (Figure7. 6b) Estimated crack density overlaid by the estimated shear-wave stack. The shearwave stack is estimated using the AVO method. (Figure 7.6a) Estimated crack density overlaid by the migrated stack. More intense fracturing occurs in zones of high stress where the rocks are bent the most for example, around the fault on the west side of the anticline, on the crest of the anticline and down the right flank of the anticline near the inflection point in the structure.

(Figure 7.8) View from the north end of the Pinedale Anticline showing well traces and geobodies of high crack density between the top of the Upper Lance formation and the top of the Mesaverde formation. (Figure 7.7) An example of the visualization of fracture swarms around the well track of a good gas well in the Pinedale field. The text along the well track indicates the top and bottom of zones of hydraulic fracture treatments performed in this well.

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7.5 Fractured Reservoir Characterization Recently, it has become possible to incorporate AVAZ attributes into reservoir characterization. This result is extremely important because it allows for better determination of fracture information between the wells and thus a better understanding of fluid flow in the reservoir. Historically, fractured reservoir characterization has been done with well information and indirect measures of fracturing between the wells, such as curvature, distance from faults and lithology. The seismic AVAZ results provide a direct measurement of the effects of fractures between the wells. However, these results have been difficult to tie to well control in the past due to the paucity of direct fracture indications at the wells. This causes difficulty in relating the seismic anisotropy measurements to the fractures because of this limited well control. Boerner et al (2003) use the SUREFrac neural network reservoir characterization to predict the fracturing in the Pinedale Anticline reservoir, incorporating the AVAZ attributes with those mentioned above. Based on the best ranked of these attributes, multiple realizations of reservoir simulations were generated by the SUREFrac neural network. Realizations were created both with and without the AVAZ attributes, with all other attributes remaining the same. Based on this fractured reservoir characterization, we predicted that Ultra Petroleum’s Riverside 4-10 well, then being drilled, would be among the best producers from the Pinedale Anticline Field. The Riverside 4-10 well had initial. Thus the production from the Riverside 4-10 well confirms our prediction. Furthermore, the reservoir characterization using AVAZ (shown on the right of Figure 7.9) suggests the high FI indicated at the Riverside 4-10 well may continue to the excellent wells, including Riverside 1-4, just outside our study area to the North. Incorporation of the production from this well shifts the major production to the west between the top of the anticline and the western fault. Through the incorporation of the AVAZ crack density attribute, which is high in the region where this well has been drilled, the neural network has been able to extrapolate the successes on the top of the structure down-flank to where this well has been drilled.

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Figure (7.9) On the left is the output of SUREFrac using all attributes except those from A VAZ. In the centre the new well, Riverside 4-10, is added. On the right is the output using all attributes including the A VA Z attributes. The successful Riverside 410 well drilled by Ultra Petroleum in July 2002 is highlighted as the large white circle in the upper left of the displays.

Figure (7.10) AVA Z crack density results along a dip line intersecting the Riverside 4-10 well location. Note the stacked A VA Z crack density anomalies (red) at the location of this successful well.

A (Figure 7.11) Dip line from 3D volume after fractured reservoir characterization showing variations of the Fracture Indicator with depth

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Conclusion Amplitude variations with offset seen on seismic data are due to contrasts in elastic rock properties. These amplitude responses can be easily described by linear AVO attribute extraction and can be used to infer changes in the rocks. Seismic attributes can be important qualitative and quantitative predictors of reservoir properties and geometries when correctly used in reservoir characterization studies. Geostatistical data methods, such as colocated cokriging and colocated cosimulations, offer attractive means to integrate seismic attribute and well information, without an estimation bias, and account for the scale (support) differences between the two data types. By using bump mapping, it clearly enhances an interpreter's ability to identify faults and other discontinuities on time slices. The discontinuities evident on the shaded relief coherency display appear as clearly defined ridges on the final bump mapped image. haracterization. The effective stress used in this case study (along with IP and IS as attributes in the lithofacies classification was derived from high-resolution seismic velocity analysis. The results were used successfully in low-frequency background model building for multiattribute seismic inversion and for a high quality lithofacies prediction using effective stress as an attribute. An exciting feature of reservoir characterization is that we are continuously able to prove or disprove our models due to the dynamic flow of information from production. 4D seismic data can image production changes in a variety of geological settings and production scenarios, including water and gas sweep, pressure changes and compaction, and enhanced recovery. The statistical reservoir volume analysis the fault sealing analysis might be looked at as a situation where there is a mismatch between changes of the shapes of hydrocarbon "containers" derived from the 4D seismic and from the reservoir simulator . Interpreters need to consider seismic inversion whenever interpretation is complicated by interference from nearby reflectors or when the end result is to be a quantitative reservoir property such as porosity. By comparing the saturation, temperature and pressure results from the reservoir simulation with the synthetic difference, we derived the areas with gas saturation differences between the two compared time steps. AVAZ analysis has been shown by many authors to provide good estimates of fracture azimuth and intensity at the locations of wells Because the results appear to be reasonable at well locations, it is inferred the AVAZ attributes can be trusted to estimate these parameters between wells.

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References Terra E.Bulloch, 1999, The Investigation of Fluid Properties and Seismic Attributes for Reservoir Characterization, Michigan Technological University (P.37-42). Michael Burianyk, Scott Pickford, Calgary, November 2000, AmplitudeVs-Offset and Seismic Rock Property Analysis: A primer, CSEG Recorder. Yongyi Li, Bill Goodway, and Jonathan Downton, March 2003, Recent Advances in Application of AVO to Carbonate Reservoirs, Core Laboratories Reservoir Technologies Division, Calgary; EnCana Corporation, Calgary, CSEG RECORDER.

M. Turhan Taner, September, 2001, Seismic Attributes, Rock Solid Images, Houston, U.S.A, CSEG RECORDER. Arthur E. Barnes, September, 2001, Seismic Attributes in Your Facies, Landmark Graphics Corp., Englewood, Colorado, U.S.A, CSEG RECORDER. Richard L. Chambers and Jeffrey M. Yarus, June 2002, Quantitative Use of Seismic Attributes Reservoir Characterization, Quantitative Geosciences, Inc. Broken Arrow, U.S.A, Houston, Texas, U.S.A, CSEG RECORDER. Steven Lynch, 2005, Enhancing Fault Visibility Using Bump Mapped Seismic Attributes, Divestco Inc and CREWES, John Townsley, Michael Dennis, Chris Gibson, Divestco Inc, CSEG National Convention.

Ran Bachrach, Sheila Noeth, Niranjan Banik, Mita Sengupta, George Bunge, Ben Flack, Randy Utech, Colin Sayers, Patrick Hooyman and Lennert Den Boer, Schlumberger, Houston, USA, Lei Leu, Bill Troyer and Jerry Moore, Nexen Petroleum USA, Dalls, MAY 2007, From pore-pressure prediction to reservoir characterization: A combined geomechanics-seismic inversion workflow using trend-kriging techniques in a deepwater basin, p (590- 595), THE LEADING EDGE

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Larry Lines, Ying Zou, and Joan Embleton,January 2005, Reservoir Characterization and Heavy Oil Production, Department of Geology and Geophysics, University of Calgary, Calgary, Canada, CSEG RECORDER.

David H. Johnston, February 9, 2005, Time-Lapse 4-D Technology: Reservoir Surveillance, Search and Discovery Article #40142 (2005), Adapted from the Geophysical Corner column in AAPG Explorer. Lars Sønneland, Claude Signer, Helene Hafslund Veire, Toril Sæter, Juergen Schlaf, Detecting flow-barriers with 4D seismic, European Union’s Thermie Research program (OG 117/94 UK) Steve Pickering works in reservoir services for WesternGeco in Gatwick, United Kingdom. John Waggoner is a senior reservoir engineer for WesternGeco in Houston, Texas, March 2003, Time-lapse has multiple impacts, RESERVOIR MANAGEMENT, Advanced 4-D tools are helping operators reap measurable rewards. Hart’s E&P

John Pendrel, January 2006, Seismic Inversion - Still the Best Tool for Reservoir Characterization, Jason Geosystems , Fugro-Jason Canada, Calgary, CSEG RECORDER. Gary Robinson, January 2001, Stochastic Seismic Inversion Applied To Reservoir Characterization, Englewood, Colorado, USA, CSEG RECORDER

Ying Zou, Laurence Bentley and Laurence Lines, January 2005, Reservoir characterization by combining time-lapse seismic analysis with reservoir simulation, Department of Geology and Geophysics, University of Calgary, Calgary, Canada, CSEG RECORDER.

David Gray, Sean Boerner, Dragana Todorovic-Marinic and Ye Zheng, June 2003, Fractured Reservoir Characterization using AVAZ on the Pinedale Anticline, Wyoming, Veritas, Calgary, Canada, CSEG RECORDER.

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From

Compute

Depth maps of important horizons traveltimes (and velocity information)

Velocity Differences in traveltime between source and receiver

Contrast in rock properties Measurements of reflection amplitude

Locations of faults and stratigraphic changes Discontinuities in reflection patterns

Dip and discontinuities Differences in traveltimes along a surface

Table (2.1) structure, stratigraphic targets (Sheriff, 1992):

A

From

Velocity

Compute

lithology, fluid content, abnormal pressure, or temperature

Lateral amplitude changes Hydrocarbon locations, changes in porosity, lithology, or thickness

Seismic data patterns Depositional environments, or faults and fractures

Changes in measurement direction Velocity anisotropy or fracture orientation

Time-lapse measurements (4D seismic) Locations of changes in hydrocarbon distribution or bypassed pay

Table 2.2 structure, stratigraphic targets (Sheriff, 1992):

B

For the flow simulation model the reservoir engineer N E E D

1 2 3 4 5 6

nature of the fluids and their Temperature Locations of thief zones (high permeability layers) saturations Pressure Permeability

7

Locations of barriers to flow (sealing and non-sealing faults stratigraphic barriers, etc.)

8

The amount and spatial (vertical and horizontal) distribution of porosity Table 2.3 flow simulation model factors (Sheriff, 1992):

(Figure 3.1) Structural framework of the study area with wells (the orange surface represents the top of salt).

C

Figure (3.6a) Schematic plot of IP compaction trend with respect to effective stress showing the seismic signature of sedimentary compaction in clastic basins. Shale trend and brine-sand trends are solid lines. The dotted line represents the fluid effect. (b) Scatterplot showing the expected behavior of lithofacies units as a function of IP, IS, and effective stress. Data points are generated using the scatter around the effective stress trends as observed in well-log data.

Figure (3.7) Maps of the probability distribution function (pdf) at different effective stress intervals.

D

(Figure 7.1) Map of the Lance Sand Depositional Fairway over the Pinedale Anticline.

(Figure 7.2) Geologic formations in the Pinedale Anticline.

(Figure 7.3) Cretaceous and Tertiary stratigraphic column.

E

Figure (7.4) Time structure of the Lower-Lance horizon interpreted from Veritas’ 3D multi-client survey. (Figure 7.5) Map of the migrated Crack Density estimates calculated using the A VA Z technique below the LowerLance horizon.

F

(2008-02-11) Ramy Mohamad Fahmy Abou-Alhassan is 4th group, geophysical student at Ain-Shams Uni. Faculty of science, Geophysical Dept. The essay about Seismic Reservoir Characterization, Which it's very simple complete essay to any geoscientist's level. May be its advanced article but I'm thanking (ALLAH) because I'm understand it. I finished from my new research with title " determination of fault throw without any contours, strikes or any elevation points" in field or on map by using easy method. It is single work. It's very useful in case of earth sciences (geology) and I'm asking (ALLAH) to publish it. My currently is study faulted plunging fold to obtain next information from the map: *depping of plunging folds **detecting types of faults *** determination the throw of the faults.