Automatic Optimization of Multi-Well Multi-Stage Fracturing Treatments Combining Geomechanical Simulation, Reservoir Simulation and Intelligent Algorithm

Abstract

Shale reserves have become an ever-increasing component of the world’s energy map. The optimal design of multi-well multi-stage fracturing (MMF) treatments is essential to the economic development of such resources. However, optimizing MMF treatments is a complex process. It requires geomechanical simulation, reservoir simulation, and automatic optimization. In this work, an integrated workflow is proposed to optimize MMF treatments in an unconventional reservoir, and the net present value (NPV) of reserves was treated as the objective function. The forward model consists of two submodels: a hydraulic fracturing model and a reservoir simulation model. The stochastic simplex approximation gradient (StoSAG) is used with the steepest ascent algorithm to maximize the NPV function. The computational results show that optimizing the fracture design can achieve a 20% higher NPV than that obtained with the field reference case. The drainage area of the optimal design is larger than that of the initial design. The maximum gas production rate increases from 23.75 MMSCF/day to 34.43 MMSCF/day and the maximum oil production rate increases from 497 STB/day to 692 STB/day. Therefore, new optimization paths can accelerate fracture design and help increase well production. This paper innovatively proposes a coupled workflow that can reduce the waste of manpower and improve the optimization results.