Multiscale Data Integration Using Markov Random Fields and Markov Chain Monte Carlo: A Field Application in the Middle-East Adel Malallah; Adel Malallah Kuwait University Search for other works by this author on: This Site Google Scholar Hector Perez; Hector Perez Ecopetrol Search for other works by this author on: This Site Google Scholar Akhil Datta-Gupta; Akhil Datta-Gupta Texas A&M University Search for other works by this author on: This Site Google Scholar Waleed Alamoudi Waleed Alamoudi Saudi Aramco Search for other works by this author on: This Site Google Scholar Paper presented at the Middle East Oil Show, Bahrain, June 2003. Paper Number: SPE-81544-MS https://doi.org/10.2118/81544-MS Published: June 09 2003 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Malallah, Adel, Perez, Hector, Datta-Gupta, Akhil, and Waleed Alamoudi. "Multiscale Data Integration Using Markov Random Fields and Markov Chain Monte Carlo: A Field Application in the Middle-East." Paper presented at the Middle East Oil Show, Bahrain, June 2003. doi: https://doi.org/10.2118/81544-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE Middle East Oil and Gas Show and Conference Search Advanced Search 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:MRFs are far more computationally tractable and are ideally suited to simulation-based computation such as MCMC (Markov Chain Monte Carlo) methods, andmodel 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.IntroductionThe principal goal of reservoir characterization is to provide a reservoir model for accurate reservoir performance prediction. Integrating various data sources is an essential task in reservoir characterization. In general, we have hard data such as well logs and cores and soft data such as seismic traces, production history, a conceptual depositional model, and regional geological analysis. Seismic data in particular can play a major role in enhancing the geological model. It can be a block constraint when generating property distributions at a finer scale. However, integrating such information into the reservoir model is nontrivial. This is because different data sources scan different length-scales of heterogeneity and can have different degree of precision.1 It is essential that reservoir models preserve small-scale property variations observed in well logs and core measurements and capture the large-scale structure and continuity observed in global measures such as seismic and production data. Keywords: seismic data, reservoir characterization, application, different scale, fluid dynamics, data integration, artificial intelligence, spe 81544, bayesian inference, geologic modeling Subjects: Reservoir Characterization, Reservoir Fluid Dynamics, Geologic modeling, Seismic processing and interpretation, Flow in porous media This content is only available via PDF. 2003. Society of Petroleum Engineers You can access this article if you purchase or spend a download.