Deployment of an Artificial Intelligence-Based Well Integrity Monitoring Solution
Authors: Aditama P. , Eita M. , Koziol T. , Zhukova K. , Dillen M. , Sanchez F. P. A. , Schemmer M. , Nitsche M. , Tomina M. , Kimari T. 
Published: June 2024

This paper provides comprehensive insights into deploying and operationalizing an Artificial Intelligence (AI)-based well integrity monitoring solution. The paper's central objective is to present the successful implementation of the advanced monitoring solution, focusing on the insights collected from employing Machine Learning Operations (MLOps) principles within the oil and gas industry. Following a successful Proof of Concept (PoC) phase, the project progressed to developing the Minimum Viable Product (MVP), involving the deployment of selected AI models through stages consisting of live data connection, pre-processing, inferencing, post-processing, suppression rules, and email notification, complemented by the development of a user interface to facilitate visualization and validation of notifications, with user feedback stored in a database for continuous model enhancement. Drawing from the conclusions derived from the project, several key insights are provided. Firstly, the strategic architecture of the MVP emerges as a pivotal factor for transitioning from the PoC phase to the final product. The MVP must be carefully crafted to facilitate rapid learning through iterative user feedback while ensuring technical feasibility and economic viability. Secondly, introducing the "AI factory" concept is identified as a fundamental enabler of efficiency in both model development and deployment process, particularly relevant as project teams expand. Thirdly, incorporating MLOps principles and best practices provides invaluable guidance in constructing a scalable AI solution without pre-existing architecture. Fourthly, integrating the "Human-In-The-Loop" concept underlines the importance of continuous feedback in refining and maintaining the continuous operation of AI solutions. Fifthly, central to user adoption is the emphasis on user experience, with specialized suppression rules and an intuitive user interface pivotal in facilitating user validation of detections. Sixthly, explainable AI methodologies play a crucial role in fostering trust in the tool's performance by explaining the rationale behind AI model decisions. Seventhly, the deployed tool performs robustly detecting annulus leakage incidents with high accuracy and minimal false alarms, significantly reducing engineers’ response time. Eighthly, challenges encountered during the scaling process across different assets, such as data availability and quality variations, were addressed through tailored data pre-processing methods and feature engineering techniques. Ninthly, leveraging expertise in real-time data analytics, the tool was extended to address additional use cases, including automatic leak rate test analysis and liquid loading detection and prediction. Finally, counterfactual explanations for LSTM (Long Short-Term Memory) autoencoder models are introduced. It represents a significant technical advancement and is qualitatively evaluated for its impact on user experience. This paper contributes valuable insights into deploying and operationalizing AI-based well integrity monitoring solutions, with practical implications for industry practitioners.