From Seismic to Reservoir Properties With Geostatistical Inversion

1999 ◽  
Vol 2 (04) ◽  
pp. 334-340 ◽  
Author(s):  
Philippe Lamy ◽  
P.A. Swaby ◽  
P.S. Rowbotham ◽  
Olivier Dubrule ◽  
A. Haas
Keyword(s):  
Seismic Data ◽  
Acoustic Impedance ◽  
A Priori ◽  
Three Dimensional ◽  
Well Logs ◽  
Inversion Method ◽  
Inversion Process ◽  
Reservoir Properties ◽  
Stratigraphic Model ◽  
Geostatistical Inversion

Summary The methodology presented in this paper incorporates seismic data, geological knowledge and well logs to produce models of reservoir parameters and uncertainties associated with them. A three-dimensional (3D) seismic dataset is inverted within a geological and stratigraphic model using the geostatistical inversion technique. Several reservoir-scale acoustic impedance blocks are obtained and quantification of uncertainty is determined by computing statistics on these 3D blocks. Combining these statistics with the kriging of the reservoir parameter well logs allows the transformation of impedances into reservoir parameters. This combination is similar to performing a collocated cokriging of the acoustic impedances. Introduction Our geostatistical inversion approach is used to invert seismic traces within a geological and stratigraphic model. At each seismic trace location, a large number of acoustic impedance (AI) traces are generated by conditional simulation, and a local objective function is minimized to find the trace that best fits the actual seismic trace. Several three-dimensional (3D) AI realizations are obtained, all of which are constrained by both the well logs and seismic data. Statistics are then computed in each stratigraphic cell of the 3D results to quantify the nonuniqueness of the solution and to summarize the information provided by individual realizations. Finally, AI are transformed into other reservoir parameters such as Vshale through a statistical petrophysical relationship. This transformation is used to map Vshale between wells, by combining information derived from Vshale logs with information derived from AI blocks. The final block(s) can then be mapped from the time to the depth domain and used for building the flow simulation models or for defining reservoir characterization maps (e.g., net to gross, hydrocarbon pore volume). We illustrate the geostatistical inversion method with results from an actual case study. The construction of the a-priori model in time, the inversion, and the final reservoir parameters in depth are described. These results show the benefit of a multidisciplinary approach, and illustrate how the geostatistical inversion method provides clear quantification of uncertainties affecting the modeling of reservoir properties between wells. Methodology The Geostatistical Inversion Approach. This methodology was introduced by Bortoli et al.1 and Haas and Dubrule.2 It is also discussed in Dubrule et al.3 and Rowbotham et al.4 Its application on a synthetic case is described in Dubrule et al.5 A brief review of the method will be presented here, emphasizing how seismic data and well logs are incorporated into the inversion process. The first step is to build a geological model of the reservoir in seismic time. Surfaces are derived from sets of picks defining the interpreted seismic. These surfaces are important sincethey delineate the main layers of the reservoir and, as we will see below, the statistical model associated with these layers, andthey control the 3D stratigraphic grid construction. The structure of this grid (onlap, eroded, or proportional) depends on the geological context. The maximum vertical discretization may be higher than that of the seismic, typically from 1 to 4 milliseconds. The horizontal discretization is equal to the number of seismic traces to invert in each direction (one trace per cell in map view). Raw AI logs at the wells have to be located within this stratigraphic grid since they will be used as conditioning data during the inversion process. It is essential that well logs should be properly calibrated with the seismic. This implies that a representative seismic wavelet has been matched to the wells, by comparing the convolved reflectivity well log response with the seismic response at the same location. This issue is described more fully in Rowbotham et al.4 Geostatistical parameters are determined by using both the wells and seismic data. Lateral variograms are computed from the seismic mapped into the stratigraphic grid. Well logs are used to both give an a priori model (AI mean and standard deviation) per stratum and to compute vertical variograms. The geostatistical inversion process can then be started. A random path is followed by the simulation procedure, and at each randomly drawn trace location AI trace values can be generated by sequential Gaussian simulation (SGS). A large number of AI traces are generated at the same location and the corresponding reflectivities are calculated. After convolution with the wavelet, the AI trace that leads to the best fit with the actual seismic is kept and merged with the wells and the previously simulated AI traces. The 3D block is therefore filled sequentially, trace after trace (see Fig. 1). It is possible to ignore the seismic data in the simulation process by generating only one trace at any (X, Y) location and automatically keeping it as "the best one." In this case, realizations are only constrained by the wells and the geostatistical model (a-priori parameters and variograms).

Incorporation of geology with rock physics enables subsalt poststack inversion: A case study in the Gulf of Mexico

Geophysics ◽  
2011 ◽  
Vol 76 (5) ◽  
pp. WB53-WB65 ◽  
Author(s):  
Huyen Bui ◽  
Jennifer Graham ◽  
Shantanu Kumar Singh ◽  
Fred Snyder ◽  
Martiris Smith
Keyword(s):  
Seismic Data ◽  
Acoustic Impedance ◽  
Low Frequency ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Inversion Method ◽  
Reservoir Properties ◽  
Blind Test ◽  
Frequency Model ◽  
Layer Sequence

One of the main goals of seismic inversion is to obtain high-resolution relative and absolute impedance for reservoir properties prediction. We aim to study whether the results from seismic inversion of subsalt data are sufficiently robust for reliable reservoir characterization. Approximately [Formula: see text] of poststack, wide-azimuth, anisotropic (vertical transverse isotropic) wave-equation migration seismic data from 50 Outer Continental Shelf blocks in the Green Canyon area of the Gulf of Mexico were inverted in this study. A total of four subsalt wells and four subsalt seismic interpreted horizons were used in the inversion process, and one of the wells was used for a blind test. Our poststack inversion method used an iterative discrete spike inversion method, based on the combination of space-adaptive wavelet processing to invert for relative acoustic impedance. Next, the dips were estimated from seismic data and converted to a horizon-like layer sequence field that was used as one of the inputs into the low-frequency model. The background model was generated by incorporating the well velocities, seismic velocity, seismic interpreted horizons, and the previously derived layer sequence field in the low-frequency model. Then, the relative acoustic impedance volume was scaled by adding the low-frequency model to match the calculated acoustic impedance logs from the wells for absolute acoustic impedance. Finally, the geological information and rock physics data were incorporated into the reservoir properties assessment for sand/shale prediction in two main target reservoirs in the Miocene and Wilcox formations. Overall, the poststack inversion results and the sand/shale prediction showed good ties at the well locations. This was clearly demonstrated in the blind test well. Hence, incorporating rock physics and geology enables poststack inversion in subsalt areas.

Geostatistical Inversion in Carbonate Reservoirs to Map Reservoir Quality With High Predictability – A Case Study From Onshore Abu Dhabi

2021 ◽  
Author(s):  
S Al Naqbi ◽  
J Ahmed ◽  
J Vargas Rios ◽  
Y Utami ◽  
A Elila ◽  
...  
Keyword(s):  
High Resolution ◽  
Seismic Data ◽  
Pore Volume ◽  
Rock Physics ◽  
Static Model ◽  
Reservoir Quality ◽  
Reservoir Properties ◽  
Geostatistical Inversion ◽  
High Predictability ◽  
The Impact

Abstract The Thamama group of reservoirs consist of porous carbonates laminated with tight carbonates, with pronounced lateral heterogeneities in porosity, permeability, and reservoir thickness. The main objective of our study was mapping variations and reservoir quality prediction away from well control. As the reservoirs were thin and beyond seismic resolution, it was vital that the facies and porosity be mapped in high resolution, with a high predictability, for successful placement of horizontal wells for future development of the field. We established a unified workflow of geostatistical inversion and rock physics to characterize the reservoirs. Geostatistical inversion was run in static models that were converted from depth to time domain. A robust two-way velocity model was built to map the depth grid and its zones on the time seismic data. This ensured correct placement of the predicted high-resolution elastic attributes in the depth static model. Rock physics modeling and Bayesian classification were used to convert the elastic properties into porosity and lithology (static rock-type (SRT)), which were validated in blind wells and used to rank the multiple realizations. In the geostatistical pre-stack inversion, the elastic property prediction was constrained by the seismic data and controlled by variograms, probability distributions and a guide model. The deterministic inversion was used as a guide or prior model and served as a laterally varying mean. Initially, unconstrained inversion was tested by keeping all wells as blind and the predictions were optimized by updating the input parameters. The stochastic inversion results were also frequency filtered in several frequency bands, to understand the impact of seismic data and variograms on the prediction. Finally, 30 wells were used as input, to generate 80 realizations of P-impedance, S-impedance, Vp/Vs, and density. After converting back to depth, 30 additional blind wells were used to validate the predicted porosity, with a high correlation of more than 0.8. The realizations were ranked based on the porosity predictability in blind wells combined with the pore volume histograms. Realizations with high predictability and close to the P10, P50 and P90 cases (of pore volume) were selected for further use. Based on the rock physics analysis, the predicted lithology classes were associated with the geological rock-types (SRT) for incorporation in the static model. The study presents an innovative approach to successfully integrate geostatistical inversion and rock physics with static modeling. This workflow will generate seismically constrained high-resolution reservoir properties for thin reservoirs, such as porosity and lithology, which are seamlessly mapped in the depth domain for optimized development of the field. It will also account for the uncertainties in the reservoir model through the generation of multiple equiprobable realizations or scenarios.

Novel fracture zone identifier attribute using geophysical and well log data for unconventional reservoirs

2015 ◽  
Vol 3 (3) ◽  
pp. T155-T167 ◽  
Author(s):  
Debotyam Maity ◽  
Fred Aminzadeh
Keyword(s):  
Elastic Properties ◽  
Fracture Zone ◽  
Seismic Data ◽  
Integrated Approach ◽  
Temporal Analysis ◽  
Microseismic Monitoring ◽  
Well Logs ◽  
Reservoir Properties ◽  
Velocity Models ◽  
Microseismic Data

We have characterized a promising geothermal prospect using an integrated approach involving microseismic monitoring data, well logs, and 3D surface seismic data. We have used seismic as well as microseismic data along with well logs to better predict the reservoir properties to try and analyze the reservoir for improved mapping of natural and induced fractures. We used microseismic-derived velocity models for geomechanical modeling and combined these geomechanical attributes with seismic and log-derived attributes for improved fracture characterization of an unconventional reservoir. We have developed a workflow to integrate these data to generate rock property estimates and identification of fracture zones within the reservoir. Specifically, we introduce a new “meta-attribute” that we call the hybrid-fracture zone-identifier attribute (FZI). The FZI makes use of elastic properties derived from microseismic as well as log-derived properties within an artificial neural network framework. Temporal analysis of microseismic data can help us understand the changes in the elastic properties with reservoir development. We demonstrate the value of using passive seismic data as a fracture zone identification tool despite issues with data quality.

Incorporating Seismic Data of Intermediate Vertical Resolution Into Three-Dimensional Reservoir Models: A New Method

1999 ◽  
Vol 2 (04) ◽  
pp. 325-333 ◽  
Author(s):  
R.A. Behrens ◽  
T.T. Tran
Keyword(s):  
Simulated Annealing ◽  
Seismic Data ◽  
Three Dimensional ◽  
Earth Model ◽  
Vertical Resolution ◽  
Middle Ground ◽  
Reservoir Properties ◽  
Seismic Resolution ◽  
Geostatistical Techniques ◽  
3D Volume

Summary Three-dimensional (3D) earth models are best created with a combination of well logs and seismic data. Seismic data have good lateral resolution but poor vertical resolution compared to wells. The seismic resolution depends on seismic acquisition and reservoir parameters, and is incorporated into the 3D earth model with different techniques depending on this resolution relative to that of the 3D model. Good vertical resolution of the seismic data may warrant integrating it as a continuous vertical variable informing local reservoir properties, whereas poor resolution warrants using only a single map representing vertically averaged reservoir properties. The first case best applies to thick reservoirs and/or high-frequency seismic data in soft rock and is usually handled using a cokriging-type approach. The second case represents the low end of the seismic resolution spectrum, where the seismic map can now be treated by methods such as block kriging, simulated annealing, or Bayesian techniques. We introduce a new multiple map Bayesian technique with variable weights for the important middle ground where a single seismic map cannot effectively represent the entire reservoir. This new technique extends a previous Bayesian technique by incorporating multiple seismic property maps and also allowing vertically varying weighting functions for each map. This vertical weighting flexibility is physically important because the seismic maps represent reflected wave averages from rock property contrasts such as at the top and base of the reservoir. Depending on the seismic acquisition and reservoir properties, the seismic maps are physically represented by simple but nonconstant weights in the new 3D earth modeling technique. Two field examples are shown where two seismic maps are incorporated in each 3D earth model. The benefit of using multiple maps is illustrated with the geostatistical concept of probability of exceedance. Finally, a postmortem is presented showing well path trajectories of a successful and unsuccessful horizontal well that are explained by model results based on data existing before the wells were drilled. Introduction Three-dimensional (3D) earth models are greatly improved by including seismic data because of the good lateral coverage compared with well data alone. The vertical resolution of seismic data is poor compared with well data, but it may be high or low compared with the reservoir thickness as depicted in Fig. 1. Seismic resolution is typically considered to be one-fourth of a wavelength (?/4) although zones of thinner rock property contrasts can be detected. The seismic resolution relative to the reservoir thickness constrains the applicability of different geostatistical techniques for building the 3D earth model. Fig. 1 is highly schematic and not meant to portray seismic data as a monochromatic (single-frequency) wave. The reference to wavelength here is based on the dominant frequency in the seismic data. Fig. 1 is meant to illustrate the various regimes of vertical resolution in seismic data relative to the reservoir thickness. While there are all sorts of issues, such as tuning, that must be considered in the left two cases, we need to address these cases because of their importance. Seismic data having little vertical resolution over the reservoir interval, as in the left case of Fig. 1 can use geostatistical techniques that incorporate one seismic attribute map. The single attribute can be a static combination of multiple attributes in a multivariate sense but the combination cannot vary spatially. These techniques include sequential Gaussian simulation with Block Kriging1 (SGSBK), simulated annealing,2 or sequential Gaussian simulation with Bayesian updating.3,4 Some of these methods are extendable beyond a single seismic map with modification. Seismic data having good vertical resolution over the reservoir interval, as in the right seismic trace of Fig. 1, can use geostatistical techniques that incorporate 3D volumes of seismic attributes. Techniques include simulated annealing, collocated cokriging simulation,5 a Markov-Bayes approach,6 and spectral separation. The term "3D volume" of seismic, as used here, is distinguished from the term "3D seismic data." (A geophysicist speaks of 3D seismic data when it is acquired over the surface in areal swaths or patches for the purpose of imaging a 3D volume of the earth. Two-dimensional (2D) seismic is acquired along a line on the surface for the purpose of imaging a 2D cross section of the earth.) The 3D volume distinction is made based on the vertical resolution of the seismic relative to the reservoir. To be considered a 3D volume here, we require both lateral and vertical resolution within the reservoir. Seismic data often do not have the vertical resolution within the reservoir zone to warrant using a 3D volume of seismic data. The low and high limits of vertical resolution leave out the case of intermediate vertical resolution as depicted by the middle curve of Fig. 1. Because typical seismic resolution often ranges from 10 to 40 m and many reservoirs have thicknesses one to two times this range, many reservoirs fall into this middle ground. These reservoirs have higher vertical seismic resolution than a single map captures, but not enough to warrant using a 3D volume of seismic. It is this important middle ground that is addressed by a new technique presented in this paper.

Probabilistic petrophysical-properties estimation integrating statistical rock physics with seismic inversion

Geophysics ◽  
2010 ◽  
Vol 75 (3) ◽  
pp. O21-O37 ◽  
Author(s):  
Dario Grana ◽  
Ernesto Della Rossa
Keyword(s):  
Seismic Data ◽  
Measurement Errors ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Joint Estimation ◽  
Physical Models ◽  
Inversion Method ◽  
Petrophysical Properties ◽  
Reservoir Properties ◽  
Statistical Rock Physics

A joint estimation of petrophysical properties is proposed that combines statistical rock physics and Bayesian seismic inversion. Because elastic attributes are correlated with petrophysical variables (effective porosity, clay content, and water saturation) and this physical link is associated with uncertainties, the petrophysical-properties estimation from seismic data can be seen as a Bayesian inversion problem. The purpose of this work was to develop a strategy for estimating the probability distributions of petrophysical parameters and litho-fluid classes from seismics. Estimation of reservoir properties and the associated uncertainty was performed in three steps: linearized seismic inversion to estimate the probabilities of elastic parameters, probabilistic upscaling to include the scale-changes effect, and petrophysical inversion to estimate the probabilities of petrophysical variables andlitho-fluid classes. Rock-physics equations provide the linkbetween reservoir properties and velocities, and linearized seismic modeling connects velocities and density to seismic amplitude. A full Bayesian approach was adopted to propagate uncertainty from seismics to petrophysics in an integrated framework that takes into account different sources of uncertainty: heterogeneity of the real data, approximation of physical models, measurement errors, and scale changes. The method has been tested, as a feasibility step, on real well data and synthetic seismic data to show reliable propagation of the uncertainty through the three different steps and to compare two statistical approaches: parametric and nonparametric. Application to a real reservoir study (including data from two wells and partially stacked seismic volumes) has provided as a main result the probability densities of petrophysical properties and litho-fluid classes. It demonstrated the applicability of the proposed inversion method.

Time-lapse acoustic impedance variations during CO2 injection in Weyburn oilfield, Canada

Geophysics ◽  
2019 ◽  
Vol 85 (1) ◽  
pp. M1-M13 ◽  
Author(s):  
Yichuan Wang ◽  
Igor B. Morozov
Keyword(s):  
Oil Recovery ◽  
Seismic Data ◽  
Acoustic Impedance ◽  
Time Lapse ◽  
Time Shift ◽  
Co2 Injection ◽  
Inversion Method ◽  
Data Sets ◽  
Storage Site ◽  
Data Set

For seismic monitoring injected fluids during enhanced oil recovery or geologic [Formula: see text] sequestration, it is useful to measure time-lapse (TL) variations of acoustic impedance (AI). AI gives direct connections to the mechanical and fluid-related properties of the reservoir or [Formula: see text] storage site; however, evaluation of its subtle TL variations is complicated by the low-frequency and scaling uncertainties of this attribute. We have developed three enhancements of TL AI analysis to resolve these issues. First, following waveform calibration (cross-equalization) of the monitor seismic data sets to the baseline one, the reflectivity difference was evaluated from the attributes measured during the calibration. Second, a robust approach to AI inversion was applied to the baseline data set, based on calibration of the records by using the well-log data and spatially variant stacking and interval velocities derived during seismic data processing. This inversion method is straightforward and does not require subjective selections of parameterization and regularization schemes. Unlike joint or statistical inverse approaches, this method does not require prior models and produces accurate fitting of the observed reflectivity. Third, the TL AI difference is obtained directly from the baseline AI and reflectivity difference but without the uncertainty-prone subtraction of AI volumes from different seismic vintages. The above approaches are applied to TL data sets from the Weyburn [Formula: see text] sequestration project in southern Saskatchewan, Canada. High-quality baseline and TL AI-difference volumes are obtained. TL variations within the reservoir zone are observed in the calibration time-shift, reflectivity-difference, and AI-difference images, which are interpreted as being related to the [Formula: see text] injection.

Geophysical pore type inversion in carbonate reservoir: integration of cores, well-logs, and seismic data (Yadana field, offshore Myanmar)

Geophysics ◽  
2021 ◽  
pp. 1-69
Author(s):  
Thomas Teillet ◽  
François Fournier ◽  
Luanxiao Zhao ◽  
Jean Borgomano ◽  
Fei Hong
Keyword(s):  
Seismic Data ◽  
Effective Medium Theory ◽  
Rock Physics ◽  
Carbonate Reservoir ◽  
Well Logs ◽  
Inversion Method ◽  
Gas Field ◽  
Medium Theory ◽  
Scale Thickness ◽  
Pore Types

Detection of pore types and diagenetic features from seismic data is a major challenge for the evaluation of carbonate reservoirs in the subsurface. Based on a detailed petrographical and petrophysical analysis of carbonate rock using optical and scanning electron microscopy, mercury-injection measurements, digital image analysis, and well logs, we have determined the potential of the geophysical pore type (αP) inversion a rock physics inversion scheme based on the differential effective medium theory – to quantitatively and qualitatively characterize the pore type distribution from acoustic data in the Yadana carbonate gas field (Early Miocene, offshore Myanmar). The geophysical pore type (αP) is revealed to be an upscalable parameter, whose depositional/diagenetic interpretation may be performed at well log and at seismic scales. We apply the inversion method on a 3D seismic data to map the reservoir-scale distribution and highlight the occurrence of laterally extended (100–1000 m) subseismic- to seismic-scale (thickness >5 m) geologic bodies. From this approach, two main reservoir geobodies are discriminated and interpreted in terms of depositional and diagenetic fabrics: (1) highly microporous, decameter-scale reservoir units (approximately 80% of the reservoir), mainly consisting of foraminiferal, red algae floatstone to rudstone with vuggy, moldic porosity, and characterized by moderate to high αP (0.11–0.20) and (2) thin, stratiform, cemented scleractinian floatstone/brecciated units (5–10 m; approximately 20% of the reservoir) with low microporosity and macroporosity and exhibiting low αP values (<0.11).

Delineation of reservoir channels by different seismic attributes and geobody extractions for robust volumetric estimation, Saffron Field, offshore Nile Delta, Egypt

2021 ◽  
Vol 40 (7) ◽  
pp. 484-493
Author(s):  
Doha Monier ◽  
Azza El Rawy ◽  
Abdullah Mahmoud
Keyword(s):  
Seismic Data ◽  
Nile Delta ◽  
Three Dimensional ◽  
Dynamic Test ◽  
Data Interpretation ◽  
Seismic Attributes ◽  
Reservoir Properties ◽  
Root Mean Square Amplitude ◽  
Seismic Attribute ◽  
Attribute Analysis

The Nile Delta Basin is a major gas province. Commercial gas discoveries there have been proven mainly in Pleistocene to Oligocene sediments, and most discoveries are within sandstone reservoirs. Three-dimensional seismic data acquired over the basin have helped greatly in imaging and visualization of stratigraphy and structure, leading to robust understanding of the subsurface. Channel fairways serve as potential reservoir units; hence, mapping channel surfaces and identifying and defining infill lithology is important. Predicting sand distribution and reservoir presence is one of the key tasks as well as one of the key uncertainties in exploration. Integrating state-of-the-art technologies, such as including 3D seismic reflection surveys, seismic attributes, and geobody extractions, can reduce this uncertainty through recognition and accurate mapping of channel features. In this study, seismic attribute analysis, frequency analysis through spectral decomposition (SD), geobodies, and seismic sections have been used to delineate shallow Plio-Pleistocene El Wastani Formation channel fairways within the Saffron Field, offshore Nile Delta, Egypt. This has led to providing more reliable inputs for calculation of volumetrics. Interpretation of the stacked-channels complex through different seismic attributes helped to discriminate between sand-filled and shale-filled channels and in understanding their geometries. Results include more confident delineation of four distinct low-sinuosity channelized features. Petrophysical evaluation conducted on five wells penetrating Saffron reservoirs included electric logs and modular dynamic test data interpretation. The calculated average reservoir properties were used in different volumetric calculation cases. Different approaches were applied to delineate channel geometries that were later used in performing different volumetric cases. These approaches included defining channels from root-mean-square amplitude extractions, SD color-blended frequencies, and geobodies, all calculated from prestack seismic data. The different volumetric cases performed were compared against the latest field volume estimates proven after several years of production in which an area-versus-depth input showed the closest calculated hydrocarbon volumes to the actual proven field volumes.

Stochastic inversion of prestack seismic data using fractal-based initial models

Geophysics ◽  
2010 ◽  
Vol 75 (3) ◽  
pp. R47-R59 ◽  
Author(s):  
R. P. Srivastava ◽  
M. K. Sen
Keyword(s):  
Seismic Data ◽  
High Frequency ◽  
Fractal Theory ◽  
Well Logs ◽  
Inversion Method ◽  
Frequency Variation ◽  
Well Log ◽  
Statistical Parameters ◽  
Stochastic Inversion ◽  
Frequency Variations

In general, inversion algorithms rely on good starting models to produce realistic earth models. A new method, based on a fractional Gaussian distribution derived from the statistical parameters of available well logs to generate realistic initial models, uses fractal theory to generate these models. When such fractal-based initial models estimate P- and S-impedance profiles in a prestack stochastic inversion of seismic angle gathers, very fast simulated annealing — a global optimization method — finds the minimum of an objective function that minimizes data misfit and honors the statistics derived from well logs. The new stochastic inversion method addresses frequencies missing because of band limitation of the wavelet; it combines the low- and high-frequency variation from well logs with seismic data. This method has been implemented successfully using real prestack seismic data, and results have been compared with deterministic inversion. Models derived by a deterministic inversion are devoid of high-frequency variations in the well log; however, models derived by stochastic inversion reveal high-frequency variations that are consistent with seismic and well-log data.

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