Operational Solution Framework: Leveraging Machine Learning and Natural Language Processing for Automatic Well Test Validation

Abstract

Abstract The objective of this study is to summarize a proven solution workflow to address the challenges to handle the high volume of well tests daily incorporating information from operational activities, and especially, potential delays and errors in validation impacting other dependent business processes. The proposed solution aims to reduce processing time, minimize human error, and enhance accuracy in well test analysis. Having up-to-date and reliable well test data, engineers can improve engineering workflows, and optimize production. The solution covers data consumption, data preparation, machine learning (ML) solution, cooperating with dependent business processes, deployment and retrain strategy. The ML solution learns from historical well test data with accepted and rejected flag to build a rule-based deterministic ML model to automatically validate and detect the invalid well test with probability. The solution does not only consume structure data but also textual data with natural language processing (NLP), such as well test comments provided by well testing engineers and operational activities in Daily Operational Reports (DORs). Data consumption, operational activities, dependent workflow control are customizable based on different projects. Retrain strategy is based on model prediction accuracy trend and defined during deployment. The solution triggers insights with confidence scores, suggesting acceptance/rejection or review of new well tests. Early detection of possible rejections enables timely actions, including retesting if necessary. The solution was implemented and significantly reduces well test validation time from weeks to hours, enhancing the accuracy of production analysis and optimizations. The data-driven approach offers flexibility and adaptability to meet operation needs, presenting a robust alternative to rule-based validation. By integrating ML and NLP, the solution provides a comprehensive and efficient framework for well test validation, improving decision-making and ensuring compliance with Standard Operation Procedure (SOP). This study introduces a novel approach to well test validation by leveraging ML and NLP. By considering both historical data and manual operational event inputs from engineers, the solution enhances the accuracy and efficiency of the validation process. It contributes to improved production performance analysis, diagnostics, and issue detection. The solution deployment can be customized and adaptable to different data storage and availability, to automate well test validation process in the oil and gas industry.