Research on Abnormal Detection and Intelligent Diagnosis Algorithm of Reservoir Test Data

As an important basis for evaluating oilfield conditions, predicting productivity and formulating development strategies, the accuracy and reliability of reservoir test data are very important. However, the abnormal test data caused by many factors, if not detected and processed in time, will seriously affect the oilfield production decision, and even bring security risks and economic losses. Aiming at the problem of abnormal detection and intelligent diagnosis of reservoir test data, this study puts forward a solution that comprehensively uses statistics, machine learning and deep learning technology. In the design of anomaly detection algorithm, this study proposes an anomaly detection algorithm based on ensemble learning and adaptive threshold. By combining basic learners such as decision tree and support vector machine (SVM), and using Boosting integrated learning strategy and adaptive threshold mechanism, the algorithm significantly improves the accuracy and stability of anomaly detection. The experimental results show that, compared with the single decision tree and SVM algorithm, the ensemble learning algorithm in this study performs better in accuracy, recall and F1 score, reaching $0.95,0.92$ and 0.93 respectively. Further, this study designed an intelligent diagnosis algorithm, combined with abnormal detection results and other related data, such as equipment maintenance records, operation logs, etc., and built a classification model by using the Gradient Boosting Machine (GBM) to realize accurate diagnosis of abnormal causes. The intelligent diagnosis model not only provides targeted solution suggestions, but also improves the accuracy and efficiency of diagnosis through continuous optimization and feedback mechanism. This study not only improves the accuracy and reliability of reservoir test data, but also provides scientific decision support for oilfield staff, effectively improving the production efficiency and economic benefits of the oilfield.