首页|A novel framework for predicting non-stationary production time series of shale gas based on BiLSTM-RF-MPA deep fusion model

A novel framework for predicting non-stationary production time series of shale gas based on BiLSTM-RF-MPA deep fusion model

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Shale gas,as an environmentally friendly fossil energy resource,has gained significant commercial development and shows immense potential.However,accurately predicting shale gas production faces substantial challenges due to the complex law of decline,nonlinear and non-stationary features in production data,which greatly repair the robustness of current models in predicting shale gas pro-duction time series.To address these challenges and improve accuracy in production forecasting,this paper introduces a novel and innovative approach:a hybrid proxy model that combines the bi-directional long short-term memory(BiLSTM)neural network and random forest(RF)through deep learning.The BiLSTM neural network is adept at capturing long-term dependencies,making it suitable for understanding the intricate relationships between input and output variables in shale gas production.On the other hand,RF serves a dual purpose:reducing model variance and addressing the concept drift problem that arises in non-stationary time series predictions made by BiLSTM.By integrating these two models,the hybrid approach effectively captures the inherent dependencies present in long and nonstationary production time series,thereby reducing model uncertainty.Furthermore,the combina-tion of BiLSTM and RF is optimized using the recently-proposed marine predators algorithm(MPA)to fine-tune hyperparameters and enhance the overall performance of the proxy model.The results demonstrate that the proposed BiLSTM-RF-MPA model achieves higher prediction accuracy and dem-onstrates stronger generalization capabilities by effectively handling the complex nonlinear and non-stationary characteristics of shale gas production time series.Compared to other models such as LSTM,BiLSTM,and RF,the proposed model exhibits superior fitting and prediction performance,with an average improvement in performance indicators exceeding 20％.This innovative framework provides valuable insights for forecasting the complex production performance of unconventional oil and gas reservoirs,which sheds light on the development of data-driven proxy models in the field of subsurface energy utilization.

Production forecastingShale gasBiLSTM-RF-MPA modelNonstationary production time seriesDeep learning

Bin Liang、Jiang Liu、Li-Xia Kang、Ke Jiang、Jun-Yu You、Hoonyoung Jeong、Zhan Meng

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State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation,Southwest Petroleum University,Chengdu,610500,Sichuan,China

Xinjiang Oilfield Corporation,PetroChina,Karamay 834000,Xinjiang,China

Research Institute of Petroleum Exploration & Development,Beijing,100086,China

Chongqing University of Science and Technology,Chongqing 401331,China

Department of Energy Resources Engineering,Seoul National University,1,Gwanak-ro,Gwanak-gu,Seoul,08826,Republic of Korea

Institute of Engineering Research,Seoul National University,Seoul,08826,Republic of Korea

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2024

10.1016/j.petsci.2024.05.012

石油科学(英文版)
中国石油大学（北京）

石油科学(英文版)

EI
影响因子：0.88
ISSN：1672-5107
年,卷(期)：2024.21(5)