Petrophysics of Carbonates.pdf

Petrophysics of Carbonates

Presented by Michael Holmes, Digital Formation Presentation and graphics by: Jennifer Bartell, Digital Formation Analysis and output created in LESA, Digital Formation Petrophysical Software

Outline 

Calculating VSH □ □ □

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GR SP Porosity

Lithology Interpretation Cores vs. Wells Logs Carbonates and Seismic Reservoir Characterization

GR  

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Gamma ray – identify clean formation and shale volume Measures naturally occurring gamma ray activity in the formation from: □ Potassium: clay minerals, evaporates □ Thorium: clay minerals □ Uranium: minerals and organic matter (uranium not necessarily associated with clay) Regular (non-spectral) GR measures all activity, and is most common GR run Spectral GR separates and isolates K, Th, and U responses, can be very beneficial in distinguishing clay minerals from charging by the presence of uranium

Calculating VSH: GR For each zone, GRclean and GRshale baselines are chosen by the interpreter Two common models to calculate shale volume (VSH) using GR:

Krygowski, 2012i

Calculating VSH: SP 

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Clean baseline

Shale baseline

Krygowski, 2012

Identify intervals of clean formation and shale volume Identify permeable intervals Estimate formation water salinity, knowing mud filtrate resistivity Correlate formations from well to well As with GR, the interpreter must chose SPshale and SPclean baselines The common model used to calculate VSH from SP:

Porosity 

Sonic/Acoustic (DT) – reciprocal speed of sound in the formation, microseconds/ft (or meter)

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Density (RhoB) – bulk density of the rock, gm/cc

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Neutron (NPhi) – measures hydrogen concentration in formation, lithology specific □

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Free porosity and clay porosity

Pe – measures photo electric adsorption cross section. Used in combination with other porosity logs to determine lithology.

Porosity 

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Cross plots of density vs. neutron and sonic vs. neutron yield porosity with no requirement for matrix and fluid input VSH is also available from a density/neutron combination:

This calculation is unreliable in gas-bearing formations

Porosity  

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All porosity logs measure total porosity Of equal, probably greater importance, is effective porosity The two porosity measurements are related as follows: Total Porosity = Effective Porosity + (VSH×Shale Porosity)

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This means that effective porosity is influenced by both the choice of VSH and of shale porosity As well as any influence of changing matrix and fluid properties in the total porosity calculation

Porosity 

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A good way to verify the integrity of calculations is to compare all porosity calculations (total and effective) on a depth plot In an ideal world, all effective porosity calculations should converge Reasons for non-convergence of effective porosities include: □

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Incorrect fluid and matrix input for the single porosity log calculations The way in which porosity is distributed

Moldic Porosity •

Density/neutron porosity measures the entire pore network where as acoustic porosity is directional and does not “see” moldic porosity

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The example shows where acoustic porosity is less than density/neutron porosity, implying moldic porosity – illustrated with yellow color fill

Probable Shale

Interpretations – Porosity, Density vs. Neutron Niobrara, Colorado

Interpretations – Density vs. Pe Niobrara, Colorado

Probable Shale

Interpretations – Density vs. Sonic Niobrara, Colorado

Probable Shale

Interpretations – Sonic vs. Neutron Niobrara, Colorado

Lithology 

As with porosity calculations, the starting point for lithologic differentiation are porosity cross plots: □ □ □ □

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Density vs. neutron Acoustic vs. neutron Density vs. Pe Rho matrix vs. DT matrix Rho matrix vs. U matrix

This presentation focuses on the Density vs. Neutron and Rho matrix vs. U matrix plots since they give the most rigorous results

Lithology – Density vs. Neutron 

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The density vs. neutron is the most commonly used Wide-spread application because of abundance of measurements □

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Most wells since the 1980’s have these measurements

In addition to a good measure of cross plot porosity, the plots yield lithologic information Sandstone, limestone, dolomite, and anhydrite each have distinct grain densities – extrapolation to zero porosity

Lithology – Density vs. Neutron 

Pitfalls of using this plot in isolation: □ □

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Gas effects reduce apparent grain densities In the absence of other information, gas bearing limestones can be misinterpreted as sandstone, and gas-bearing dolomite as limestones. In the case of very high gas saturation, as sandstones. Dolomite cemented sandstones will be misinterpreted as carbonates with no silica

Lithology – Rho matrix vs. U matrix 

Rho matrix vs. U matrix is the most powerful cross plot for lithologic differentiation □

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Caveat being the Pe issue with barite drilling mud

Quantitative distinction among quartz, calcite, dolomite, and anhydrite are possible Dolomite-cemented sandstone can be unequivocally distinguished from carbonate only assemblages

Lithology Example of a calcium carbonate sequence Kansas

Mostly limestone, minor sandstone and dolomite

Courtesy of Lynn Watney, KGS

Lithology Example of a dolomitic carbonate sequence from the Niobrara of Colorado

Mostly limestone and dolomite

Lithology Example of a cherty carbonate sequence from Kansas Mostly dolomite and quartz, minor limestone

Courtesy of Lynn Watney, KGS

Archie’s Equation 

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Archie’s equation in conjunction with the Pickett Plot is used to determine water saturation and ultimately hydrocarbon saturation

Sw = water saturation Rw = resistivity of formation water n = saturation exponent, starting point 2.0 a = saturation constant, often accepted as 1.0 m= cementation exponent, starting point 2.0

Pickett Plot – Resistivity vs. Porosity

Pickett Plot – Resistivity vs. Porosity

Saturation Determination 

Effective water saturation using Dewan’s equation:

is calculated

Permeability 

Permeability is determined using a modified Timur equation:

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is the lower of log-calculated or theoretical from a Buckles equation:

Interpretation – Example

Niobrara, Colorado

Cores vs. Well Logs – Porosity 

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Most likely a measurement of connected porosity only There are no service company standards in place, as a result, procedures for measuring porosity vary Generally, core porosities equates well with petrophysically defined effective porosity

Cores vs. Well Logs – Fluid Saturation 

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Measurements are made at the surface, following decompression as the core is brought to the surface In general, one might anticipate: □ □ □

Oil saturation to be reduced due to mud filtrate invasion Gas saturation to be increased in volatile oil reservoirs, due to gas generation as pressure is reduced Water saturation to increase do to mud filtrate invasion as well

Cores vs. Well Logs – Grain Density 

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Core measurements clearly eliminate any influence of fluid saturation (gas effect) Closest comparisons should be in in clean oil or water bearing sandstones and carbonates

Cores vs. Well Logs – Permeability 

There are many different ways of measuring core permeability: □ □ □

Air, at ambient conditions Klinkenberg – extrapolation to infinite pressure. This is preferred, and will be lower than air permeability Permeability at various overburden pressure ■

Can have extreme influence, reducing permeabilities by orders of magnitude in tight gas sand

Cores vs. Well Logs – Permeability 

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Correlation with petrophysically-defined permeability is hazardous Petrophysical estimates involve empirical relations between porosity and irreducible water saturation □

This is only applicable when the reservoir is indeed at Swi

Core Shifting 

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It is imperative to try to shift the core data to agree with logs For sidewall cores, this should not be an issue For continuous coring it is essential □ □

Core depth made by the driller may have discrepancies with log depth – sometimes up to 10 feet or more! For core recoveries of less than 100%, the assumption is frequently made that loss has occurred at the base of the cores as the core barrel is brought to the surface. This might not be a valid assumption as loss could occur by “rubble-izing” incomplete levels at any location

Core vs. Log Scale 

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Core plug samples are usually about 2 cubic inches (33 cubic cm) in volume. On the other hand, log measurements sample at least a cubic foot (0.03 cubic meter) at a time. The difference in volume measurements, logs to cores is at a minimum close to 1000. For some logging tools with poor vertical resolution and deeper depths of investigation, that difference could be as high as 1,000,000!

Vertical Resolution of Wireline Logs

Courtesy of Schlumberger

Vertical Resolution of Wireline Logs 

Approximate volumes measured by wireline logs: Log

Vertical, ft.

Depth, ft.

Approximate Volume, ft3

GR

0.75

1

2.36

Density

1

0.5

0.78

Neutron

2

0.75

3.53

Acoustic

2

2

25.13

Laterolog

2

4

100.5

Induction

7

7

1077.56

Methodology of Upscaling 

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The basic methodology is to "upscale" the core data measurements to the approximate level of the log measurements, to improve the correlations between the core and log data Evaluation of correlation coefficients between the upscaled core data and the log measurements to find suggested depth shifts in a rigorous manner

Applications  

Aid in depth shifting The upscaled core data output is a continuous curve – it is much easier to compare wireline logs with upscaled core curves rather than discrete core data points

Data shift

Data shift

Example Texas Panhandle

Carbonate Unconventional Reservoirs 

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The Bakken and Niobrara are two examples of carbonate reservoirs undergoing active development Both produce from carbonate intervals with organic-rich shales in close proximity

Carbonate Unconventional Reservoirs 

Carbonate conventional reservoir model:

Matrix

Effective Porosity

Water

Oil/Gas

The Reservoir

Shale

Carbonate Unconventional Reservoirs

The Reservoir Shale

Clay Solids

Matrix

Effective TOC Porosity

Clay Water

Elements of unconventional reservoirs, both carbonate and clastic: Free Shale Porosity

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Silt

Carbonate Unconventional Reservoirs 

Digital Formation has developed a deterministic petrophysical model, which is designed to identify four porosity components: □ □ □

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Effective Porosity TOC Clay Porosity Free Shale Porosity

Both adsorbed and free hydrocarbons are also determined

Carbonate Unconventional Reservoirs – Example Output Niobrara, Colorado

Adsorbed oil in Niobrara Shale intervals

Carbonate Unconventional Reservoirs – Example Output, Adsorbed vs. Free Hydrocarbons Niobrara, Colorado

Carbonate Unconventional Reservoirs – Example Output Bakken, Montana

Adsorbed oil in Bakken Shale

Carbonate Unconventional Reservoirs – Example Output, Adsorbed vs. Free Hydrocarbons Bakken, Montana

Rock Physics Modeling 

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The Rock Physics Model Digital Formation has developed involves solution to the Gassmann and Krief geophysical models The Rock Physics Model uses density and neutron logs to mimic both compressional and shear acoustic data In the absence of shear measurements, the pseudo shear log can be reliable

Texas Panhandle

Rock Physics Modeling 

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By applying changing fluid properties to both density and neutron curves, which is particularly important in gas reservoirs, the effects of fluid substitution can be quantified This technique can also be applied to the effects of pressure reduction on pseudo log responses From the pseudo acoustic and density logs, synthetic seismograms at different saturation or pressure levels can be created, and related to measured seismic responses Texas Panhandle

Rock Physics Modeling •

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Actual data is shown in black Actual data is incomplete Interpolation using pseudo logs to create a continuous curve shown in red

Texas Panhandle

Reservoir Characterization 

In a reservoir sequence, levels at irreducible water saturation (and a singular rock type) can be distinguished from other levels: □ □

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Belonging to a different rock type Due to the presence of mobile water

Can be shown on a log Phi vs. log Sw cross plot

Texas Panhandle

Lower quality rocks

Higher quality rocks

Reservoir Characterization Basic example of log Phi vs. Log Sw plot used to determine rock quality level-by-level

Reservoir Characterization – Example Log Phi vs. Log Sw

Core Porosity vs. Core Permeability

Lower quality rocks

Higher quality rocks

Higher quality rocks

Lower quality rocks

West Texas

References 

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Bond, D.C. "Determination of residual oil saturation." Oklahoma City, Interstate Oil Compact Commission Report (1978): n. pag. Print. Buckles, R.S. "Correlating and averaging connate water saturation data." Journal of Canadian Petroleum Technology 9.1 (1965): 42-52. Print. Chilingar, George V, Robert W. Mannon, and Herman H. Rieke. Oil and Gas Production from Carbonate Rocks. New York: American Elsevier Pub. Co, 1972. Print. Dewan, John T. Essentials of Modern Open-Hole Log Interpretation. Tulsa, Okla: PennWell Books, 1983. Print. Doveton, John H. Geologic Log Analysis Using Computer Methods. Tulsa, Okla: American Association of Petroleum Geologists, 1994. Print. Ellis, Darwin V, and Julian M. Singer. Well Logging for Earth Scientists. Dordrecht: Springer, 2007. Print. Holmes, Michael, et al. "A Petrophysical Model to Estimate Relative and Effective Permeabilities in Hydrocarbon Water Systems." Oral presentation given at the SPE Annual Technical Conference and Exhibition held in San Antonio, Texas, USA, 8-10 October (2012): n. pag. Print. Holmes, Michael, et al. "A Petrophysical Model to Estimate Free hydrocarbons in Organic Shales." Poster Prestentation given at the AAPG Annual Conference and Exhibition, Houston, TX (2011): n. pag. Print.

References 

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Holmes, Michael, et al. "Relationship Between Porosity and Water Saturation: Methodology to Distinguish Mobile from Capillary Bound Water." Oral presentation given at the AAPG ACE, Denver, Colorado 7-10 June (2009): n. pag. Print. Krygowski, Dan. Basic Openhole Log Interpretation. Golden, CO: Petrolium Technology Transfer Council, 2013. Print. Morris, R.L., and W.P. Biggs. "Using log-derived values of water saturation and porosity." ransactions of the SPWLA 8th Annual Logging Symposium Paper X, 26 p (1967): n. pag. Print. Passey, Q.R., et al. "From Oil-Prone Source Rock to hydrocarbons-Producing Shale Reservoir ? Geologic and Petrophysical Characterization of Unconventional Shalehydrocarbons Reservoirs." SPE 131350 (2010): n. pag. Web. Passey, Q.R. "A Practical Model for Organic Richness from Porosity and Resistivity Logs." AAPG Bulletin 74.12 (1990): 1777-1794. Print. "Shale Petrophysics." Denver Well Logging Society 2010 Fall Workshop, Golden CO (2010): n. pag. Print. "Special Core Analysis." DWLS Spring Workshop, Golden CO (2008): n. pag. Web.

Petrophysics of Carbonates Q&A Presented by Michael Holmes, Digital Formation Presentation and graphics by: Jennifer Bartell, Digital Formation Analysis and output created in LESA, Digital Formation Petrophysical Software