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.
© 2010. Society of Petroleum Engineers
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History
- Original manuscript received:
29 July 2009
- Meeting paper published:
4 October 2009
- Manuscript approved:
8 October 2009
- Published online:
1 February 2010
- Version of record:
1 March 2010