A Combination Approach of the Numerical Simulation and Data-Driven Analysis for the Impacts of Refracturing Layout and Time on Shale Gas Production

Abstract

Refracturing can alleviate the rapid decline of shale gas production with a low drilling cost, but an appropriate fracture layout and optimal refracturing time have been unclear without a heavy computation load. This paper proposes a combination approach with a numerical simulation and data-driven analysis to quickly evaluate the impacts of the refracturing layout and refracturing time on shale gas production. Firstly, a multiphysical coupling model with the creep of natural fractures is established for the numerical simulation on shale gas production. Secondly, the effects of the refracturing layout and refracturing time on the shale gas production are investigated through a single factor sensitivity analysis, but this analysis cannot identify the fracture interaction. Thirdly, the influence of fractures interaction on shale gas production is explored through a combination of a global sensitivity analysis (GSA) and an artificial neural network (ANN). The GSA results observed that the adjacent fractures have more salient interferences, which means that a denser fracture network will not significantly increase the total gas production, or will reduce the contribution from each fracture, resulting in higher fracturing costs. The new fractures that are far from existing fractures have greater contributions to cumulative gas production. In addition, the optimal refracturing time varies with the refracturing layout and is optimally implemented within 2–3 years. A suitable refracturing scale and time should be selected, based on the remaining gas reserve. These results can provide reasonable insights for the refracturing design on the refracturing layout and optimal time. This ANN-GSA approach provides a fast evaluation for the optimization of the refracturing layout and time without enormous numerical simulations.

Volume Expansion Rate Index Reveals the Damage Process of Surrounding Rock: A Machine Learning-Based Effectiveness Evaluation

“…Moreover, underground engineering also involves energy industries, such as mining tunnels and hydroelectric power tunnels, which are crucial for the sustainable development of countries [1][2][3]. Surrounding rock stability is the critical technical target of underground engineering design and construction [4,5].…”