Seismic Inversion for Reservoir Characterization

Seismic inversion for reservoir characterization and well planning in the Snorre Field DAG HELLAND-HANSEN, I NRID MAGNUS, ATLE EDVARDSEN, and EGIL HANSEN, Saga Petroleum ASA, Sandvika, Norway

This seismic inversion project was performed on data

from the Statfjord Formation in the Snorre Field of the North Sea (Figure 1). The formation consists of complex fluvial depositional system; the reservoir sands are stacked, sand-filled channels at depths of 2400-2700 m. The lower strata have highly sinuous, restricted sand channels. The general depositional energy level increased with time, resulting in low-sinuosity, continuous sand channels with higher sand/shale ratios at stratigraphically higher levels. Figure 2 shows the geologic model of the field. The impedance values of the nonreservoir lithologies significantly overlap those of the reservoir sands, causing problems in the use of impedance to predict reservoir sands. Jason Geosystem (with support from Statoil, Norsk Hydro, and Saga Petroleum) created a new inversion scheme (InverMod) to overcome this challenge. Statistical analysis of well data performed prior to inversion evaluated the relationship between impedance, velocity, and density along with their potential for pre-

dicting lithology. Impedance alone was too weak to distinguish reservoir sand facies from nonreservoir shaly facies but a combination of density with either velocity or impedance was quite effective (Figure 3). Building a 3-D geologic model. An a priori reservoir model was needed in our inversion scheme. The first step in constructing the model was generation of a structural framework from seismic interpretation and geologic information of the strata, and their relationship with adjacent layers. Petrophysical parameters, obtained from well logs, were interpolated between the wells in accordance with this structural framework. Composite velocity-density logs were generated at each well and analyzed for principal components within each geologic layer; principal component weights were then interpolated throughout the model. By splitting the principal components of the composite logs into the velocity and density parts, separate velocity and density volumes can be generated from the same weights. The quality of the final model largely depends on the quality of the a priori model. There fore, extensive testing and evaluation was undertaken to determine which wells should be included and how weights should be assigned. Time-to-depth conversion is performed to assure the model is consistent with the interpolated velocity logs and the formation tops in the wells. Depth conversion is tied to a previously depth-converted horizon. Figure 4 is a cross section of the 3-D volume that passed through three wells.

Figure 1. The location of the Snorre Field in the North Sea. The inverted area is marked with a yellow box.

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Wavelet estimation. Se v eral wavelets were estimated from different wells. The most promising were tested by inverting seismic lines which passed close to wells not used in the inversion. This allowed compar-

Figure 2. Geologic model of the Statfjord Formation.

Figure 3. Crossplot of density versus impedance. Red points are shale facies and blue represents sand facies. ison of inverted logs with actual logs for the different wavelets. The results varied in resolution and stability. The wavelet which gave the most optimal results over the whole volume was chosen for an inversion towards a basis model. However, when performing well planning, we estimate, test, and choose the wavelet with the best performance for that region and geologic significance. Model estimation. The inversion is based on the a priori interpolated weight model. In the inversion, the weights are updated at each trace location to minimize the difference between the seismic and the synthetic data. The updating is performed trace by trace by perturbing the datum, the principal components, and the trace scale factors. Trace-to-trace and global constraints are added to improve stability. The updating of the seismic parameters is constrained by the total solution space of the principal components of the composite logs and, thereby, the information in the well log data. As explained earlier, these are a combination of the velocity and density logs for each geologic layer. From the final reflectivity volume, weight maps for each geologic layer are derived and used to output separate inverted density and velocity volumes. These volumes are generated in both time and depth (the latter satisfying the updated velocity volume). Figure 5 shows the inverted density section from the line in Figure 4.

Verification of results. In order to quality control the inversion, we excluded several wells in the inversion process. We evaluated the results of the inversion by comparing transformation of the a priori model to the final model to the actual well log. Figure 6 illustrates that the inverted section adds more heterogenity to the reservoir model, and that this corresponds with the log. Close inspection of the log and the inverted section shows that the major heterogeneities have been reproduced except between the top of the Statfjord Formation (TSTAT) and a zonation top (TS3). This is pro bably due to an incorrect a priori model because of misinterpretation of a seismic reflector. Overall the blind tests showed that in general the inverted model represents the reservoir better than the apriori model, and in some instances we were able to predict sand bodies with thicknesses as small as 5-10 m. Yet some sand were not correctly mapped due to the use of incorrect horizon interpretations, varying petrophysical p roperties, lack of resolution, noise in the seismic data set, and other effects, which emphasizes the importance of the apriori model. Our total experience tells us that the InverMod technique is useful as a lithology predictor on the Snorre Field. We use the method with confidence in areas with good well control and between wells rather than extrapolating away from wells. Lithology predictions should be done with care when interpreting across major faults. In less reliable areas the technique is combined with a more seismically driven (sparse spike) inversion scheme to reduce uncertainty. Several wells, drilled after inversion, quantified that this technique could successfully predict lithology from seismic data in the Snorre Field. However, pitfalls remain; one, a well planned and drilled in the early stages of this project, is illustrated in Figure 7. In this case, the inversion results were misinterpreted in the top of the Statfjord Formation due to lack of information about the rock physics in the area. The green area at the top right of the stru ctu re is a zone of low density that was interpreted as a good, thick reservoir sand, similar to those in other wells. The logs of the subsequently drilled well did measure low density values (similar to those estimated by the inversion) but they were anomalous low density values in the overlying shales and not a reservoir sand. Deterministic modeling. The inverted density and velocity volumes were run through Geomatics Irap-RMS, a software for visualization, facies interpretation, and geologic modeling. This enhanced the interpretation and generated an output easily understood by geologists and engineers. For facies interpretation, the inverted cubes were first run through functions which detrended the geophysical parameter volume of their depth dependencies (found from well logs). This step was needed to work within a geologic volume with a large depth interval. The detrended cubes were then put through a multivariate discriminator function to filter sandy facies from nonreservoir facies. Due to the seismic resolution, the facies crevasses, levee, and barrier were not accounted for in the geomodeling. These facies were assigned to the shale class because of their high velocities.

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Figure 5. Inverted density volume in time of the line from Figure 4. (The lower strata below the target zone are not inverted and therefore not displayed.)

Figure 4. A priori model of the density parameter in depth. The a priori model is interpolated between wells. More wells are used than the three in this figure, allowing for the lateral heterogeneity one can observe. The well log parameters are averaged in the user-specified block size (6 m in this case). This discriminator function outputs a cube of a parameter which is a combination of all the other parameters. This discriminator parameter, or best-par, is statistically the best for distinguishing facies sand from shale on the basis of inverted volumes. The best-par cube was compared with log data to determine the probability distribution (through histogram analysis of the data) for the best-par to represent a sand facies. The probability function was then applied to the best-par cube to output a cube of sand probability (Figure 8). Purple represents sand-prone facies (i.e., probability of 0.5 or greater). The probability of sand cube is used both in deterministic and stochastic geomodeling. In Figure 9, the barely visible sand channel from Figure 8 is assigned a cut-off criteria of 0.5. Asand value was then assigned to all grid cells inside the sand cut-off. The sand channel belt (in red) was the average over a depth interval of 15 m. Such a cube we used for well planning as explained below. It is important to note that a reasonably good knowledge of the subsurface is needed to translate a geophysical parameter volume to a geologic parameter volume. This means that interaction between geologists and geophysicists is essential. In the Snorre Field (and probably most other fields), the process must be restricted to volumes of similar geologic significance. Therefore, it has been done only inside each geologic layer. Noise due to faults, multiples, AVO-ef fects, and processing artifacts should be treated carefully. Stochastic geomodeling. At present our flow simulation models are based on object-based stochastic generated models. The stochastic models are constrained by well log information, production data, analog studies in outcrops, and general geologic information about the field. In order to best incorporate all available information (including seismic, well, and geologic data), we are testing a new algorithm (developed by Statoil and The Norwegian Computing Centre) in the Fluvial module of the Storm software. This enables us to constrain the object-based simulation by using seismic inversion results in addition to con-

Figure 6. Typical blind test of a 1-m resolved model. Warm colors are high densities. The a priori section is on the left and the inverted section to the right. (Higher density is plotted to the right on the log.)

Figure 7. Inverted section. Green represents low density, red and blue are higher densities. Sonic log (yellow) and density log (black) are from a subsequently drilled well. straints from wells, production, and geologic knowledge. We have tested the algorithm within small volumes. Figure 10 is a typical result. Examination reveals that most of the major sand channel belts simulated with seismic constraints are positioned in areas with high seismic probability of sand. We found that adding constraints from seismic inversion often improved evaluation of net/gross sand, channel directions, and correlation of sand channel belts between wells.

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Figure 8. 3-D “best -par” cube which discriminates sand from shale. Purple indicates sand probability of 0.5 or higher.

Figure 9. Enhancement of the channel in Figure 8. Box marks area of inversion.

Figure 10. Two of many simulated sand channel systems through a small 3-D cube of the probability of sand parameter. Light red indicates probability of less than 0.5. Blue, green, and yellow represent consecutively higher probabilities.

Figure 11. 3-D cube of sand probability. Top is a lateral display and the bottom is a cross section along the well trajectory.

As a result, we plan to extend this simulation to the entire field in 1997. Current use in well planning. The 3-D volumes generated from the geomodeling (either deterministic, sto-

chastic, or a combination) are used alongside seismic on a day-to-day basis for lithology interpretation and well planning. Figure 11, a typical well plan display, shows how we can identify which seismic sand bodies the well penetrates and, consequently, where to position an optimal well path. The volume of sand intersected by the well can be cal-

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Figure 12. Inverted density section in depth along the production well. Base Cretaceous reflector is marked in red, and top and base reservoir reflectors are in blue. Density values are color-coded on the right side. The well trajectory is in light blue. The density log is, more or less, directly on the well trajectory. The gamma ray log is above and the sonic log below. (On the density log, the “up tending” amplitudes indicate lower density and the “down tending” amplitudes higher density.) culated, and the sand channel systems reached by the well can be determined. In a recent simulation and drilling program with one injection well and one producing well, the inverted seismic volumes played a key role in the decisionmaking process. One of the major questions was whether to drain the sand in the lower part of the formation. Very few of the stochastic-generated geologic models (used in reservoir management) indicated drainable sand volumes in this area were uneconomical. However, the inverted seismic showed substantially larger volumes. Figure 12 is an example of the latter. Low density values are represented by green-to-yellow on this inverted density section. These inversion results (and other information normally used in well planning) caused both injection and production wells to be drilled in order to drain the lower part of the reservoir formation. The substantially larger volumes of sand predicted by the seismic inversion have now been verified by well logs. LE Acknowledgments: We would like to thank Saga Petroleum ASA and partners in the Snorre Field for permission to publish this paper. We are grateful to Kari Nesboe, Jan-Inge Tollefsrud, and Jan Helgesen for help and good cooperation throughout the project and for applying our results into the daily well planning program. In addition, we thank Elis abeth Omholt for outstanding technical support. Corresponding author: Dag Helland-Hansen, email [email protected]; fax 47-6712-6910.

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