Robust Optimization of Unconventional Reservoirs under Uncertainties

Abstract

Summary Shale oilgains much attention as an unconventional resource, which has a potential of significant oil production. Moreover, the technology of horizontal drilling and hydraulic fracturing has encouraged the exploration and development of shale oil reservoirs. An optimal hydraulic fracturing design is essential to achieve a high oil production rate. However, industry is still faced with many challenging issues in hydraulic fracturing optimization, especially under geological and economic uncertainties. These uncertainties can result in a significant increase in capital costs and negative effects on project revenue. This paper presents innovative hydraulic fracturing robust optimization fordevelopment of unconventional reservoirs to reduce the current challenges and minimize the future investment expenses. We first address a sensitivity analysis for creatingmultiple scenerios of a simulation model, consideringuncertainties of geology and reservoir properties. Most of the previous hydraulic fracturing optimization workflows often ignoredthese uncertainty parameters; therefore, the conventional optimal solution obtained based on a single realization may deviate severely from the actual optimality. To capture geological and reservoir uncertainties, a large number of geological reservoir realizations must be generated honoring geological constraints. Hydraulic fracturing parameters (e.g., fracture half-length, fracture spacing, and fracture height), injection composition and operating conditions are optimized simultaneously based on these multiple geological realizations. The sensitivity analysis results indicate that the four parameters that strongly affect the shale oil production include matrix permeability, fracture half-length, fracture spacing and rock compressibility. To maximize oil recovery, a series of physics-based optimizations have been performed for a hydraulic fracturing design by applying the Partical Swarm Optimization algorithm in a robust optimizer. Both the conventional optimization, based on a single geological realization and robust optimization, based on multiple geological realizations have been conducted. After the conventional optimization process, the ultimate oil recovery yields about 18.63% OOIP. Then the robust optimization procedure is applied with 210 realizations. Due to the high computational cost of shale oil simulations, it is impractical to use all generated models in the robust optimization workflow. Thus we propose a method for the robust optimization of hydraulic fracturing (HF) and gas injection composition based on a small set of geological realizations that represent the overall uncertainty of a reservoir. This is achieved by ranking all realizations according to the performance of each realization in terms of cumulative oil production. The comparison shows that the robust optimization increases all the NPV percentile values. This indicates the increased robustness of the optimal solution under uncertainties and thus an adoption of the robust optimization procedure can significantly reduce the project risk. This research is to present an innovative optimization approach which is fast and robust and can be used to efficiently capture the uncertainties of the key factors including geology, reservoir properties, and economic variation.