Field Development Optimization with Subsurface Uncertainties

Abstract

Abstract This paper outlines a framework for simultaneous optimization of a broad range of field development decisions with subsurface uncertainties. We optimize discrete and continuous decision variables such as the number of production or injection wells, their locations, perforation intervals, drilling schedules, well rates, etc. As a novel approach, we include additional categorical variables such as depletion strategy, well pattern, or facility size in the optimization process. We consider a limited number of discrete scenarios for each categorical variable (e.g., primary depletion, gas injection, or water injection as three development scenarios). Field development constraints on well locations, rig schedules, economic risks etc. are incorporated in the optimization. Hydrocarbon recovery or some economic indicator can be used as the objective function for the optimization and applied for ranking the field development options. Subsurface uncertainties are represented by incorporating multiple reservoir models in the optimization process. Ideally, all reservoir models in the ensemble should be evaluated for every considered field development option to define cumulative probability functions. However, this would make CPU demands very large in some cases. We propose two effective approaches to reduce CPU requirements: (1) one reservoir model is run to test the optimization criterion, and the remaining models are only run if the objective function is significantly improved; or (2) a novel application of a statistical proxy procedure to define a subset of the reservoir model ensemble that is run during the optimization cycle. The efficiencies gained with these techniques allow us to incorporate the additional decision variables in the full optimization process. Our results indicate that the proposed algorithms sufficiently reduce CPU requirements to effectively handle field development optimization problems with many reservoir models representing subsurface uncertainties. The algorithms have been effectively applied in many fields for simultaneous optimization of well placement, drilling schedule, well production/injection rates, perforation strategy, injection strategy, and facility modifications. They have also been successfully applied in giant oil/gas fields optimizing general field development scenarios.