Robust Optimization of Hydraulic Fracturing Design for Oil and Gas Scientists to Develop Shale Oil Resources

Shale plays with pre-existing natural fractures can yield significant production when operating horizontal wells with multi-stage hydraulic fracturing (HWMHF). This work proposes a general, robust, and integrated framework for estimating optimal HWMHF design parameters in an unconventional naturally fractured oil reservoir. This work considers uncertainty in both the distribution of the natural fractures and uncertainty in three geo-mechanical parameters: the internal friction factor, the cohesion coefficient, and the tensile strength. Because a maximum of five design variables is considered, it is appropriate to apply derivative-free algorithms. This work considers versions of the genetic algorithm (GA), particle swarm optimization (PSO), and general pattern search (GPS) algorithms. The forward model consists of two linked software programs: a geo-mechanical simulator and an unconventional shale oil simulator. The two simulators run sequentially during the optimization process without human intervention. The in-house geo-mechanical simulator model provides sufficient computational efficiency so that it is feasible to solve the robust optimization problem. An embedded discrete fracture model (EDFM) is implemented to model large-scale fractures. Two cases strongly verified the feasibility of the framework for the optimization of HWMHF, and the average comprehensive NPV increases by 35% and 102.4%, respectively. By comparison, the pattern search algorithm is more suitable for HWMHF optimization. In this way, oil and gas scientists are contributing to the energy industry more accurately and resolutely.