Video: Large Scale Field Development Optimization Using High Performance Parallel Simulation and Cloud Computing Technology

Tanaka, Shusei (Chevron Energy Technology Company) | Wang, Zhenzhen (Chevron Energy Technology Company) | Dehghani, Kaveh (Chevron Energy Technology Company) | He, Jincong (Chevron Energy Technology Company) | Velusamy, Baskar (Chevron Energy Technology Company) | Wen, Xian-Huan (Chevron Energy Technology Company)


Field development optimization for oil and gas reservoirs is typically challenging due to large number of control parameters, model complexity, as well as subsurface uncertainties. In this study, we propose a joint field development and well control optimization workflow using robust parameterization technique and demonstrate its application through a offshore oil field development.

Traditionally, using simulation models for optimization of field development plan was considered time and cost prohibitive when incorporating models to cover range of uncertainties in reservoir properties. Consequently, the problem was simplified by reducing the number of control parameters through multi-disciplinary workflows. In this paper, we aim to optimize field development strategy by simultaneously controlling topside facility, number of wells, their trajectories, drilling sequence, and completion strategy etc., considering subsurface uncertainties and constraints. To achieve this, we used our next generation reservoir simulator and commercial cloud computing to explore the possibility of achieving an optimized development scenario within reasonable time and cost constraints.

We have applied the proposed workflow to the Olympus field case, which is an optimization benchmarking problem set up by Netherland Organization for Applied Scientific Research (TNO) using a synthetic North-sea type reservoir. Our objective is to improve the net present value (NPV) after 20 years of operation by controlling the number and location of platforms, 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 were compared. Best results in terms of NPV improvement was obtained by using the mixed-integer Genetic Algorithm method. More than ten thousand simulation runs were required by the method to reach to optimal development of well location, trajectory, drilling sequence etc. This was made possible by utilizing a high performance parallel simulator and cloud computing. The estimated cost of the commercial cloud service is almost negligible compared with the improvement in the economic value of the optimized asset development plan. The developed workflow and parameterization technique are flexible in well trajectory configuration and completion design allowing application to primary depletion as well as waterflooding.