Work

Multi-Objective Optimization Inverse Analysis for Characterization of Petroleum Geomechanical Properties During Hydraulic Fracturing

Year: 2025

Type: article

Abstract: To address the difficulty in the characterization of the geomechanical properties of reservoirs in petroleum engineering using the traditional formula, due to the complexity of the reservoir, this study proposes a framework of inverse analysis to characterize the geomechanical properties of reservoirs formed through hydraulic fracturing by combining the XGBoost, multi-objective particle swarm optimization (MOPSO), and numerical models. XGBoost was used to generate a surrogate model to approximate the physical model, and the numerical model was used to generate a dataset for XGBoost. MOPSO is regarded as an optimal technology to deal with the conflict between multi-objective functions in inverse analysis. On comparing the results between the actual geomechanical properties and those obtained by using traditional inverse analysis, the proposed framework accurately characterizes the geomechanical parameters of reservoirs obtained through hydraulic fracturing. This provides a feasible, scientific, and promising way to characterize reservoir formation in petroleum engineering, as well as a reference for other fields of engineering.

Source: Processes

DOI: 10.3390/pr13082587

Authors: Shike Zhang, Zhongliang Ru, Lihong Zhao, Bangxiang Li, Hongbo Zhao, Xianglong Wang

Institutions: Anyang Normal University, Taiyuan University of Science and Technology, Shandong University of Technology

Language: English

Topic: Hydraulic Fracturing and Reservoir Analysis

Subfield: Mechanical Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funders: National Natural Science Foundation of China, Natural Science Foundation of Shandong Province, Henan Provincial Science and Technology Research Project