Gas well production optimization: Classifying liquid loading severity in shale gas wells using semi-supervised learning

Abstract

The liquid loading of shale gas wells presents significant challenges, including a reduction in production capacity, an increase in production costs, and environmental degradation. It is of paramount importance to provide accurate and timely warnings of liquid loading in order to ensure the efficient production operations. Existing physical models often rely on assumptions regarding material properties and fluid behaviors. However, the establishment of precise physical models in complex real-world scenarios represents a significant challenge. Most existing data-driven methods rely on either supervised or unsupervised learning strategies. The former causes a considerable investment of time and human resources to generate labels, while the latter demands exceptionally high standards for data quality. These are challenging to achieve in actual production. Most studies on liquid loading adopt a dichotomous approach, whereby the presence or absence of liquid loading is determined. A paucity of studies has attempted to classify the severity of liquid loading. In this paper, a semi-supervised learning-based liquid loading severity classification model is proposed. The model integrates the benefits of supervised and unsupervised learning, enabling the utilization of the entire dataset with a minimal number of labels. The paper also presents a Dynamic Time Relationship Self-Attention (DTRSA) module, which enhances the model’s anti-interference ability against other anomalies and improves the accuracy of liquid loading severity classification. In order to assess the effectiveness of the proposed model, experiments are conducted on a dataset of 219 shale gas wells in the southwestern region of Sichuan Province, China, within an oil and gas field. The model exhibits an overall classification accuracy of 98.46% in detecting liquid loading, with an earlier warning time than existing physical models.