Closed-loop field development with multipoint geostatistics and statistical performance assessment

JOURNAL OF COMPUTATIONAL PHYSICS

卷:390

Article

Shirangi, Mehrdad G.¹ 

[1] Stanford Univ, Stanford, CA 94305 USA

关键词: Optimization under uncertainty;    Closed-loop optimization;    Massive computational experiments;    Statistical performance assessment;    Data assimilation;    Spatial statistics;   

DOI  :  10.1016/j.jcp.2019.04.003

来源: Elsevier

【 摘 要 】

Closed-loop field development (CLFD) optimization is a comprehensive framework for optimal development of subsurface resources. CLFD involves three major steps: 1) optimization of full development plan based on current set of models, 2) drilling new wells and collecting new spatial and temporal (production) data, 3) model calibration based on all data. This process is repeated until the optimal number of wells is drilled. This work introduces a new CLFD implementation for complex systems described by multipoint geostatistics (MPS). Model calibration is accomplished in two steps: conditioning to spatial data by a geostatistical simulation method, and conditioning to production data by optimization-based PCA. A statistical procedure (TruMAP) is presented to assess the performance of CLFD. For performance assessment by TruMAP, the methodology is applied to an oil reservoir example for 25 different true-model cases. Application of a single-step of CLFD, improved the true NPV in 64%-80% of cases. The full CLFD procedure (with three steps) improved the true NPV in 96% of cases, with an average improvement of 37%. These results indicate that probability of improving true NPV increases with closed-loop step. This massive computational experiment involved about 9.5 million reservoir simulation runs that took about 320,000 CPU hours. (C) 2019 Elsevier Inc. All rights reserved.

【 授权许可】

Free