Real Time Diagnostics of Gas Lift Systems Using Intelligent Agents: A Case Study
- Gregory Brock Stephenson (Occidental Petroleum Corp.) | Roman V. Molotkov (Weatherford) | Neil de Guzman (Intelligent Agent Corp) | Larry G. Lafferty (Intelligent Agent Corp)
- Document ID
- Society of Petroleum Engineers
- SPE Annual Technical Conference and Exhibition, 4-7 October, New Orleans, Louisiana
- Publication Date
- Document Type
- Conference Paper
- 2009. Society of Petroleum Engineers
- 3.1 Artificial Lift Systems, 4.2 Pipelines, Flowlines and Risers, 5.4.2 Gas Injection Methods, 4.3 Flow Assurance, 3.1.2 Electric Submersible Pumps, 5.1.2 Faults and Fracture Characterisation, 2.2.2 Perforating, 5.6.4 Drillstem/Well Testing, 2.3.4 Real-time Optimization, 3.1.7 Progressing Cavity Pumps, 6.1.5 Human Resources, Competence and Training, 3.1.6 Gas Lift, 4.1.2 Separation and Treating, 4.1.5 Processing Equipment, 5.6.5 Tracers, 7.6.6 Artificial Intelligence, 1.10.1 Drill string components and drilling tools (tubulars, jars, subs, stabilisers, reamers, etc)
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This paper describes a new method to continuously monitor and diagnose the condition of wells producing via 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 therein. 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 this data; recommending actions for correcting problems and responding to threats; and explaining their recommendations.
The performance of the system has met initial expectations and provided additional unforeseen benefits. This paper sites specific cases which 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 be used to effectively perform functions which were historically performed by a handful of experts. The paper will discuss key system design features which enable this level of functionality as well as other potential areas where the technology can be extended in the future.
One of the current challenges facing the upstream E&P industry is the growing scarcity of specialist domain expertise and trained personnel needed to efficiently operate oil and gas assets. In cases where these resources are limited or unavailable, automation technology has often been touted as a solution. While the introduction of such technology has delivered numerous improvements in operational efficiency, it has also introduced new challenges. One such challenge involves the introduction of vast quantities of data that results in minimal actionable information1,2. Operators are faced not only with the information technology task of managing this data, but also with the business challenge of leveraging the data to improve their profitability. In response to this new challenge, a growing number of projects are being initiated to help close this gap between data and information. This paper discusses one such effort.
In this project, new technology has been developed to assist production engineers in the well-by-well optimization of gas lift systems. Well-by-well optimization has long been recognized as having value3, but has often proven impractical to carry out on a routine basis due to the labor-intensive nature of the work and the limited number of individuals with the required level of expertise to perform it. This project sought to solve this problem by developing a system of intelligent agents which leverage both real time data and gas lift domain knowledge to assist engineers in these well-by-well optimization tasks.
|File Size||1 MB||Number of Pages||19|