An Improved Probability Conditioning Method for Constraining
Multiple-Point Statistical Facies Simulation on Nonlinear Flow Data
- W. Ma (University of Southern California) | B. Jafarpour (University of Southern California)
- Document ID
- Society of Petroleum Engineers
- SPE Western Regional Meeting, 22-26 April, Garden Grove, California, USA
- Publication Date
- Document Type
- Conference Paper
- 2018. Society of Petroleum Engineers
- 6.1 HSSE & Social Responsibility Management, 6 Health, Safety, Security, Environment and Social Responsibility, 5.1.5 Geologic Modeling, 5.5 Reservoir Simulation, 5.6.1 Open hole/cased hole log analysis, 6.1.5 Human Resources, Competence and Training, 5 Reservoir Desciption & Dynamics, 5.6 Formation Evaluation & Management
- Soft Data Conditioning, MultiplePoint Geostatistics, Tau Model, Ensemble Smoother, Facies Probability Maps
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We evaluate the use of facies probability maps for conditioning discrete multiple-point statistical (MPS) facies simulation on dynamic production data. In MPS simulation, conditional probabilities are estimated from a training image (TI), a conceptual model of geologic connectivity, and used to draw samples of facies distributions that are statistically consistent with those encoded in the TI. Whereas conditioning MPS simulation on both static hard data (e.g., well logs) and soft data (e.g., seismic) is straightforward, calibration of facies against nonlinear flow data is nontrivial. The use of facies probability maps for conditioning MPS simulation on dynamic production data presents a promising approach for calibration of complex facies models. We present an overview of MPS-based conditional simulation with probability maps and discuss some of the important properties and implementation issues of this approach. The paper presents two important contributions: (1) improvement of the original probability conditioning method (PCM) by constructing the facies probability maps based on the first and second order statistical moments of the updated permeabilities at each cell; (2) generalization of the "tau" model to include pixel-based τ values that can assign different confidence levels to the facies probabilities at different grid blocks. Results from numerical examples demonstrate that the proposed approach outperforms the original PCM by incorporating additional information from the observed dynamic data into MPS-based facies simulation.
|File Size||1 MB||Number of Pages||13|
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