@article{LI2025, 
author = {Honghong LI and Lihui ZHENG and Xueqian ZUO and Tao QI and Siqi LI and Xinyi ZHAO and Majia ZHENG},
title = {Shale oil production warning method based on PU Learning and decline model},
year = {2025},
journal = {Petroleum Science Bulletin},
volume = {10},
number = {5},
pages = {941-953},
keywords = {shale oil, LSTM, production early warning, positive-unlabeled (PU) Learning, production decline model},
url = {https://www.sciopen.com/article/10.3969/j.issn.2096-1693.2025.03.022},
doi = {10.3969/j.issn.2096-1693.2025.03.022},
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.}
}