Optimization Framework Improves Mariner Field Development

Abstract

This article, written by JPT Technology Editor Judy Feder, contains highlights of paper SPE 193883, “Robust Multiobjective Field Development Optimization for the Mariner Asset,” by Remus Gabriel Hanea, SPE, Ole Petter Bjorlykke, Yastoor Hasmi, and Tao Feng, Equinor, and Rahul Mark Fonseca, TNO, prepared for the 2019 SPE Reservoir Simulation Conference, Galveston, Texas, USA, 10–11 April. The paper has not been peer reviewed. The growing popularity of model-based optimization work flows has resulted in an increase in their application to field cases. In the complete paper, the authors present the challenges, results, and learnings from a 2-year robust multiobjective optimization application at the Mariner heavy-oil asset in the UK sector of the North Sea. The authors used the efficient stochastic simplex approximate gradient technique (StoSAG) to achieve optimization incorporating geological and petrophysical uncertainty. Depending on the problems, significant increases of between 5 and 20% in the expected value of the objective function were achieved. For the multiobjective optimization cases, nontrivial optimal strategies reduced gas production by 40% with less than 1% loss in the economic objective. Introduction To date, most studies of single- and multiobjective optimization have focused on a single study with a specific purpose. Very few studies have used these work flows in an operational setting (i.e., during the field-development-planning stage at an asset tackling a variety of•problems). Including uncertainty is an important step in decision making during field-development planning, especially because uncertainty is so extensive at this stage in the life cycle of a field. Considering a range of potential development scenarios that will provide the necessary tools to enable robust decisions is imperative. These scenarios should also account for the fact that different objectives often conflict with one another. Thus, there is a need for multiobjective optimization under uncertainty, which can be applied in an operational setting. Adapting model-based optimization work flows requires computational efficiency. While the adjoint-based gradient method is computationally very efficient, it is not suitably flexible to incorporate different types of control variables and requires access to the simulation source code as well as a significant implementation effort. Thus, in this paper, the authors use the StoSAG technique. They chose to use an approximate gradient method for the optimization instead of a derivative-free technique because the computational costs for derivative-free methods are usually higher when uncertainty, in terms of different model realizations, is considered. StoSAG is an approximate-gradient-based approach that has proven practical for optimization under uncertainty. A user must decide on the ratio of geological model realizations to control perturbations. The authors used a ratio of 1:1 for all experiments unless otherwise specified, making the number of control perturbations equal to the number of geological realizations. This is computationally the most-efficient approach for large-scale optimization under uncertainty when using high-fidelity, full-physics-simulation models. The objective function is the usual expression for (simple) net present value as used in life-cycle-optimization studies.