Parallel Simulation and Cloud Computing Can Optimize Large-Scale Field Development

Abstract

This article, written by JPT Technology Editor Judy Feder, contains highlights of paper SPE 191728, “Large-Scale Field Development Optimization Using High-Performance Parallel Simulation and Cloud Computing Technology,” by Shusei Tanaka, SPE, Zhenzhen Wang, SPE, Kaveh Dehghani, SPE, Jincong He, SPE, Baskar Velusamy, and Xian-Huan Wen, SPE, Chevron, prepared for the 2018 SPE Annual Technology Conference and Exhibition, Dallas, 24–26 September. The paper has not been peer reviewed. Field-development optimization is challenging because of the large number of control parameters, model complexity, and subsurface uncertainties. The complete paper discusses a study in which the authors propose a joint field-development and well-control-optimization work flow using high-performance parallel simulation and commercial cloud computing, and demonstrate its application through an•offshore oilfield•development. Introduction Designing a field-development plan to maximize the profitability of the asset involves accounting for many factors. Commonly, a field-development plan follows a staged process in which several alternatives are appraised and the one with the best economics is selected. The selected plan is then optimized while considering schedule and cost. Earth modeling and reservoir simulation are often used to provide probabilistic ranges of oil in place or net recovery for a given design at each phase of the process. This requires effective cross-functional teamwork to achieve quality decisions by relying on knowledge and skills spanning different disciplines. Proposed Work Flow Closed-loop or real-time reservoir management are terms often used to describe field-development optimization. These involve rapid data assimilation to define or update subsurface uncertainties and follow optimization processes to find the optimal combination of control parameters. These processes are not single-step but are executed iteratively during reservoir life. The focus of this paper is to describe the optimization process for maximizing economical values by selecting the best development plan under given ranges of subsurface uncertainties. The work flow proposed by the authors controls topside facilities, the number of wells, their trajectories, the drilling sequence, and completion strategy simultaneously, while considering subsurface uncertainties and constraints. The authors use a next-generation reservoir simulator and commercial cloud computing to explore the possibility of achieving an optimized development scenario within reasonable time and cost constraints. The combination of high-performance simulators and virtually unlimited scalability in the cloud provides opportunities for joint field-development optimization without reducing the number of control parameters, enabling the developed work flow to be applicable to various asset-development scenarios. The proposed work flow was applied to the Olympus field case, which is an optimization benchmarking problem developed by the Netherlands Organization for Applied Scientific Research using a synthetic North Sea-type reservoir. The study objective was to improve the net present value (NPV) after 20 years of operation by controlling the number and location of platforms and the number of injectors and producers as well as their trajectories and drilling sequence. The large number of control parameters and subsurface uncertainties make the optimization process challenging. Three optimization techniques—genetic algorithm (GA), particle swarm optimization (PSO), and ensemble-based optimization (EnOpt)—were tested and their performances compared. Best results, in terms of NPV improvement, were obtained by using the mixed-integer genetic algorithm method.