Single Well Production Prediction Model of Gas Reservoir Based on CNN-BILSTM-AM

Energies, Год журнала: 2024, Номер 17(22), С. 5674 - 5674

Опубликована: Ноя. 13, 2024

In the prediction of single-well production in gas reservoirs, traditional empirical formula reservoirs generally shows poor accuracy. process machine learning training and prediction, problems small data volume dirty are often encountered. order to overcome above problems, a model based on CNN-BILSTM-AM is proposed. The built by long-term short-term memory neural networks, convolutional networks attention modules. input includes previous period its influencing factors. At same time, fitting error value reservoir introduced predict future data. loss function used evaluate deviation between predicted real data, Bayesian hyperparameter optimization algorithm optimize structure comprehensively improve generalization ability model. Three single wells Daniudi D28 well area were selected as database, was production. results show that compared with network (CNN) model, long (LSTM) bidirectional (BILSTM) test set three experimental reduced 6.2425%, 4.9522% 3.0750% average. It basis coupling meets high-precision requirements for which great significance guide efficient development oil fields ensure safety China’s energy strategy.

Язык: Английский

Assessment of resilient modulus of soil using hybrid extreme gradient boosting models

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Дек. 30, 2024

Accurate estimation of the soil resilient modulus (M

Язык: Английский