Optimization of Fracturing Parameters by Modified Genetic Algorithm in Shale Gas Reservoir

Abstract

Shale gas reservoirs have extremely low porosity and permeability, making them challenging to exploit. The best method for increasing recovery in shale gas reservoirs is horizontal well fracturing technology. Hence, fracturing parameter optimization is necessary to enhance shale gas horizontal fracturing well production. Traditional optimization methods, however, cannot meet the requirements for overall optimization of fracturing parameters. As for intelligent optimization algorithms, most have excellent global search capability but incur high computation costs, which limits their usefulness in real-world engineering applications. Thus, a modified genetic algorithm combined based on the Spearman correlation coefficient (SGA) is proposed to achieve the rapid optimization of fracturing parameters. SGA determines the crossover and mutation rates by calculating the Spearman correlation coefficient instead of randomly determining the rates like GA does, so that it could quickly converge to the optimal solution. Within a particular optimization time, SGA could perform better than GA. In this study, a production prediction model is established by the XGBoost algorithm based on the dataset obtained by simulating the shale gas multistage fracturing horizontal well development. The result shows that the XGBoost model performs well in predicting shale gas fracturing horizontal well production. Based on the trained XGBoost model, GA, SGA, and SGD were used to optimize the fracturing parameters with the 30-day cumulative production as the optimization objective. This process has conducted nine fracturing parameter optimization tests under different porosity and permeability conditions. The results show that, compared with GA and SGD, SGA has faster speed and higher accuracy. This study’s findings can help optimize the fracturing parameters faster, resulting in improving the production of shale gas fracturing horizontal wells.

Production prediction based on ASGA-XGBoost in shale gas reservoir

“…In other words, GA optimization accuracy is typically challenging to sustain within a certain number of iterations. Zhou and Ran (2023) proposed a modified GA based on the Spearman correlative coefficient (SGA) to improve the optimization speed and accuracy. Compared to GA, SGA modifies the crossover and mutation rates’ determination methods.…”