A New Approach to Seismic Data Integration During Reservoir Characterization Using Optimal non-Parametric Transformations

Xue, Guoping; Datta‐Gupta, Akhil

doi:10.2118/36500-ms

Cited by 10 publications

(2 citation statements)

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“…For highresolution seismic data, several geostatistical techniques such as cokriging and collocated cokriging have been proposed to estimate areal distribution of porosity. 3,4,6,7,8,9,10,11 In practice, variogram modeling with a limited data set can be difficult and strongly user dependent. 3, 4, 6, 7, 8, 9 10, 11 These include techniques such as Sequential Gaussian Simulation with Block Kriging (SGSBK) 3 and Bayesian updating of point kriging.…”

Section: Introductionmentioning

confidence: 99%

Multiscale Data Integration Using Markov Random Fields and Markov Chain Monte Carlo: A Field Application in the Middle-East

Malallah

Pérez

Datta‐Gupta

et al. 2003

Middle East Oil Show

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TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractIntegrating multi-resolution data sources into high-resolution reservoir models for accurate performance forecasting is an outstanding challenge in reservoir characterization. Well logs, cores, seismic and production data scan different length scales of heterogeneity and have different degrees of precision. Current geostatistical techniques for data integration rely on a stationarity assumption that is often not borne out by field data. Geologic processes can vary abruptly and systematically over the domain of interest. In addition, geostatistical methods require modeling and specification of variograms that can often be difficult to obtain in field situations.In this paper, we present a case study from the Middle East to demonstrate the feasibility of a hierarchical approach to spatial modeling based on Markov Random Fields (MRF) and multi-resolution algorithms in image analysis. The field is located in Saudi Arabia south of Riyadh and produces hydrocarbons from the Unayzah Formation, a late Permian siliclastic reservoir. Our proposed approach provides an efficient and powerful framework for data integration accounting for the scale and precision of different data types. Unlike their geostatistical counterparts that simultaneously specify distributions across the entire field, the MRF are based on a collection of full conditional distributions that rely on local neighborhood of each element. This critical focus on local specification provides several advantages: (a) MRFs are far more computationally tractable and are ideally suited to simulation-based computation such as MCMC (Markov Chain Monte Carlo) methods, and (b) model extensions to account for non-stationarities, discontinuity and varying spatial properties at various scales of resolution are accessible in the MRF.We construct fine scale porosity distribution from well and seismic data explicitly accounting for the varying scale and precision of the data types. First, we derive a relationship between the neutron porosity and the seismic amplitudes. Second, we integrate the seismically derived coarse-scale porosity with fine-scale well data to generate a 3-D field-wide porosity distribution using MRF. The field application demonstrates the feasibility of this emerging technology for practical reservoir characterization.

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Section: Introductionmentioning

confidence: 99%

Multiscale Data Integration Using Markov Random Fields and Markov Chain Monte Carlo: A Field Application in the Middle-East

Malallah

Pérez

Datta‐Gupta

et al. 2003

Middle East Oil Show

Self Cite

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“…An iterative procedure using alternating conditional expectations (ACE) forms the basis for this calibration. 12,13 Next, stochastic cosimulation 14,15 is carried out to generate conditional realizations of resistivity and porosity in the interwell region. Finally, water saturation distribution is deduced from the resistivity and porosity distributions through the use of Archie's law.…”

Section: Introductionmentioning

confidence: 99%

Characterizing Fluid Saturation Distribution Using Cross-Well Seismic and Well Data: A Geostatistical Study

Idrobo¹,

Malallah²,

Datta‐Gupta³

et al. 1999

Proceedings of SPE Annual Technical Conference and Exhibition

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TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractOne of the goals of reservoir characterization, particularly in mature reservoirs, is to identify unswept regions containing high oil saturation for targeted infill drilling or enhanced recovery. A common approach to the problem is to generate high resolution distribution of reservoir properties such as permeability and porosity and then conduct flow simulations to identify regions of high oil saturation. One possible alternative to flow simulations would be to generate spatial distribution of properties that are related to fluid saturations and then infer fluid saturation distribution through the use of appropriate correlations.In this paper we present a field application to infer interwell water saturation distribution by combining crosswell seismic and well data. First, we use a non-parametric transformational approach to correlate sonic velocity with resistivity and porosity at the wells. An iterative procedure using alternating conditional expectations (ACE) forms the basis for this calibration. Next, stochastic cosimulation is carried out to generate conditional realizations of resistivity and porosity in the interwell region. In this cosimulation, cross-well seismic velocity is considered as the secondary data while resistivity and porosity are treated as primary data. Finally, water saturation distribution is deduced from the resistivity and porosity distributions through the use of Archie's law.

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Bayesian Partition Models for Subsurface Characterization

Efendiev¹,

Datta‐Gupta²,

Hwang³

et al. 2010

Large‐Scale Inverse Problems and Quantification of Uncertainty

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A New Approach to Seismic Data Integration During Reservoir Characterization Using Optimal non-Parametric Transformations

Cited by 10 publications

References 0 publications

Multiscale Data Integration Using Markov Random Fields and Markov Chain Monte Carlo: A Field Application in the Middle-East

Multiscale Data Integration Using Markov Random Fields and Markov Chain Monte Carlo: A Field Application in the Middle-East

Characterizing Fluid Saturation Distribution Using Cross-Well Seismic and Well Data: A Geostatistical Study

Bayesian Partition Models for Subsurface Characterization

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