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

TL;DR

This paper presents an efficient closed-loop field development framework using multipoint geostatistics and statistical performance assessment, demonstrating significant improvements in net present value through iterative optimization and data integration.

Contribution

It introduces a novel CLFD implementation for complex systems with multipoint geostatistics, combining geostatistical simulation and PCA-based model calibration.

Findings

Full CLFD improved true NPV in 96% of cases

Single-step CLFD improved true NPV in 64-80% of cases

Average NPV improvement was 37%

Abstract

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 an efficient 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 is presented to assess the performance of CLFD. 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%.