Abnormal Energy Consumption Diagnosis Method of Oilfields Based on Multi-Model Ensemble Learning

Petroleum is a significant source of global energy supply, and accurate and efficient anomaly diagnosis of energy consumption in oilfields plays a crucial role in controlling operational costs and ensuring environmental sustainability. This study utilizes daily production data from the Shengli Oilfield and applies various encoding methods to construct a dataset for diagnosing anomalies in energy consumption. We propose a method for diagnosing energy consumption anomalies based on multi-model ensemble learning, aiming to effectively reduce energy consumption and optimize oilfield management. Based on the constructed dataset, we trained an efficient and reliable anomaly diagnosis model that blurs the boundaries between classification and regression problems. The model leverages the strengths of various machine learning algorithms, including Support Vector Machines, random forest, gradient boosting, and ridge regression, while considering the analysis requirements under different computational power and real-time scenarios. Experimental results demonstrate that the mean squared error of the proposed ensemble model is 0.04, with accuracy, precision, recall, and F1 score all reaching 96%, indicating excellent performance and significantly surpassing that of individual models and benchmark algorithms. Additionally, a new iterative data cleaning model based on the multi-task random forest framework is introduced, which effectively handles missing values and anomalies, demonstrating high processing accuracy for most features. This study provides a practical framework for optimizing energy consumption management in oilfields and offers insights into broader applications in energy-intensive industries.