Hierarchical Surrogate-Assisted Evolutionary Algorithm for Integrated Multi-Objective Optimization of Well Placement and Hydraulic Fracture Parameters in Unconventional Shale Gas Reservoir

Abstract

Integrated optimization of well placement and hydraulic fracture parameters in naturally fractured shale gas reservoirs is of significance to enhance unconventional hydrocarbon energy resources in the oil and gas industry. However, the optimization task usually presents intensive computation-cost due to numerous high-fidelity model simulations, particularly for field-scale application. We present an efficient multi-objective optimization framework supported by a novel hierarchical surrogate-assisted evolutionary algorithm and multi-fidelity modeling technology. In the proposed framework, both the net present value (NPV) and cumulative gas production (CGP) are regarded as the bi-objective functions that need to be optimized. The hierarchical surrogate-assisted evolutionary algorithm employs a novel multi-fidelity particle-swarm optimization of a global–local hybridization searching strategy where the low-fidelity surrogate model is capable of exploring the populations globally, while the high-fidelity models update the current populations and thus generate the next generations locally. The multi-layer perception is chosen as a surrogate model in this study. The performance of our proposed hierarchical surrogate-assisted global optimization approach is verified to optimize the well placement and hydraulic fracture parameters on a hydraulically fractured shale gas reservoir. The proposed surrogate model can obtain both the NPV and CPG with satisfactory accuracy with only 500 training samples. The surrogate model significantly contributes to the convergent performance of multi-objective optimization algorithm.

Keywords

• shale gas reservoir
• horizontal well optimization
• machine-learning surrogate
• evolutionary algorithm