摘要：Production dynamics forecasting plays an important role in the decision-making and development scenario evaluation process throughout the entire life cycle of the unconventional tight/shale gas reservoirs. The traditional method such as decline curve analysis can't be applied prior to the wells are put into production as it heavily depends on the historical production for the estimation of parameters. In this work, a new artificial intelligence framework is proposed to predict the well behaviors by simultaneously processing the sequential and tabular data including well depth, proppant tonnage, and fracturing stages. Specifically, a time evolution network is employed first to encode the tabular features matrix into a pseudo-sequence tensor, and then an encoder-decoder architecture based on the dual-stage attention mechanism is used to extract effective information from the encoded information and capture long-term dependencies relationship. A comparison of the proposed model with the fully connected neural network (FCNN) and the long and short-term memory (LSTM) network indicates that the new framework has better generalization performance and robustness to predict well productivities, that is, the prediction errors are reduced by 65% and 50% respectively compared with LSTM and FCNN. Moreover, a bidirectional parametric rectified linear unit (BPReLU) is employed to adaptively learn the sign and magnitude of slopes. It is found that the error is further reduced by approximately 10% compared to that using PReLU. Also, four different target variables are defined, and the experimental results reveal that the average rate within the production time Vi is much easier to predict, with an average error of 19%. ? 2023 Elsevier B.V.

ISSN号：2949-8910

卷、期、页：卷223

发表日期：2023-04-01

影响因子：0.000000

收录情况：SCI（科学引文索引印刷版）,EI（工程索引）,SCIE（科学引文索引网络版）

发表期刊名称：Geoenergy Science and Engineering

参与作者：Wang, Hai,Wang, Shuhua,Chen, Shengnan

第一作者：惠钢

论文类型：期刊论文

论文概要：Wang, Hai,Wang, Shuhua,Chen, Shengnan,惠钢，Predicting long-term production dynamics in tight/shale gas reservoirs with dual-stage attention-based TEN-Seq2Seq model: A case study in Duvernay formation，Geoenergy Science and Engineering，2023，卷223

论文题目：Predicting long-term production dynamics in tight/shale gas reservoirs with dual-stage attention-based TEN-Seq2Seq model: A case study in Duvernay formation