石油科学通报2025，Vol.10Issue(5)：941-953,13.DOI:10.3969/j.issn.2096-1693.2025.03.022
基于PU学习与递减模型的页岩油产能预警方法
Shale oil production warning method based on PU Learning and decline model
李宏宏 1郑力会 1左学谦 2齐涛 2李思琦 2赵鑫祎 1郑马嘉3
作者信息
- 1. 中国石油大学(北京)人工智能学院,北京 102249
- 2. 昆仑数智科技有限责任公司,北京 102206
- 3. 中国石油勘探开发研究院,北京 100083||中国石油西南油气田公司开发事业部,成都 610051
- 折叠
摘要
Abstract
Shale oil wells commonly experience rapid production decline throughout their productive life,requiring frequent well interventions to maintain stable production.However,accurately identifying"abnormal decline"poses significant challenges in field management.On one hand,shale oil production is highly sensitive to reservoir conditions and operational fluctuations,which makes prediction difficult.On the other hand,although well interventions are frequently implemented,the corresponding records are often incomplete or missing,which hinders effective modeling and analysis.To address these challenges,this paper proposes an early warning method for abnormal production decline by integrating Positive-Unlabeled(PU)Learning with classical decline curve models.First,an LSTM-based PU Learning model is constructed to identify potential intervention points during the productive life.The model is trained on limited labeled intervention data and a large volume of unlabeled data.The production curve of each well is then segmented into multiple"natural decline intervals."Next,a double exponential decline model is employed to fit the production data within each interval,from which key decline parameters are extracted.The historical distribution of these decline parameters serves as the baseline for comparison.Based on this,an anomaly detection mechanism is developed using percentile thresholds of decline parameters to issue early warnings for segments exhibiting abnormally rapid decline.An empirical study was conducted using historical production data from over 600 wells in a typical shale oil block in China from 2021 to 2024.The results demonstrate that the proposed PU-LSTM model effectively identifies intervention points,even with incomplete data labeling.The decline model exhibits high fitting accuracy and robustness,and the overall warning system shows strong practical applicability and potential for broad engineering application in well performance monitoring and optimizing intervention timing.关键词
页岩油/产能预警/PU学习/产量递减模型/LSTMKey words
shale oil/production early warning/positive-unlabeled(PU)Learning/production decline model/LSTM分类
能源科技引用本文复制引用
李宏宏,郑力会,左学谦,齐涛,李思琦,赵鑫祎,郑马嘉..基于PU学习与递减模型的页岩油产能预警方法[J].石油科学通报,2025,10(5):941-953,13.
