Optimization of shale gas field development using direct search techniques and reduced-physics models

Abstract

Abstract The economics of oil and gas field development can be improved significantly by using computational optimization to guide operations. In this work, we present a general framework for applying optimization to the development of shale gas reservoirs. Starting with a detailed three-dimensional full-physics simulation model, which includes heterogeneous geology, highly resolved fracture networks, dual-porosity, dual-permeability regions, and gas desorption, the approach first entails the generation of a much simpler, and much more computationally efficient, reduced-physics surrogate model. This reduced-physics model is tuned using a procedure akin to history matching to provide results in close agreement with the full-physics model. The surrogate model is then used for field development optimization. During the course of the optimization, the surrogate model is periodically ‘retrained’ to maintain agreement with the full-physics representation. In the optimizations considered here, we seek to determine the optimal locations, lengths, and number of fracture stages for a set of horizontal wells. A direct search optimization procedure (generalized pattern search) is applied. In two examples, involving models with properties representative of the Barnett Shale, optimization is shown to provide field development scenarios with net present values that are considerably higher than those of base case designs. In addition, speed-ups of about a factor of 100 are achieved through the use of the surrogate modeling procedure.