Real-Time Diagnostics of Gas Lift Systems Using Intelligent Agents: A Case Study

Abstract

Summary This paper describes a new method to continuously monitor and diagnose the condition of wells producing through continuous gas lift. The paper describes the application of this system in a mature onshore gas lift field in the western United States and the results obtained. A central problem related to the operation of gas lift wells is the ability to identify underperforming wells and to address the underlying issues appropriately and in a timely manner. This problem is compounded by the trend toward leaner operations and relative scarcity of application-specific domain knowledge. The purpose of this method is to address these issues by leveraging real-time data, gas lift domain expertise, and proven steady-state analysis techniques in a desktop software application. This system performs four key functions: Monitoring the wells' condition by collecting data, assessing the meaning of these data, recommending actions for correcting problems and responding to threats, and explaining recommendations. The performance of the system has met initial expectations and has provided additional unforeseen benefits. This paper cites specific cases that compare agent predictions to expert diagnoses and quantify the benefits of taking the recommended actions. What was found was that while the correct diagnoses of well performance issues was beneficial, the real benefit was in allowing production engineers to analyze a greater number of wells in far less time. To that end, the paper will discuss the role of this system as it relates to the overall production management workflow. The success of this project has demonstrated that intelligent agents can effectively perform functions historically performed by a handful of experts. The paper will discuss key system-design features that enable this level of functionality as well as other potential areas in which the technology can be extended in the future.