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From Data Monitoring to Performance Monitoring

Authors: Michael Stundner; Gustavo Nunez; Frank Møller Nielsen;

From Data Monitoring to Performance Monitoring

Abstract

Abstract The availability of accurate performance information throughout the production system is fundamental to optimization of the economic potential of the reservoir. Without this understanding, a company's field development and operational decisions may not permit the maximization of economic value and may undermine the accuracy of the reserves estimates. Present digital oilfield technology gives the production engineers all the data needed to monitor process parameters and fluid production, under the assumption that deviation from any target would be detected with just monitoring the data collected. Then, any "good" decision for improvement or optimization would be taken based on this data. However, the answer to questions such as "how good is the decision?" or "how does the production engineer know that it is operating at the best performance possible?" is not easy, because a reference for comparison is needed. Performance efficiency can be defined as the ratio between theoretical result from a model or test results and the actual results achieved. A Production Performance System (PPS) based on workflows can be implemented to calculate key performance indicators comparing the actual performance versus targets, models, or base case and identifying performance deviation on time in order to avoid undesired lost production. PPS includes an events management and knowledge base system that allows not only detecting events but also identifying possible reasons for them based on historic data and artificial intelligent models. Our objective is to provide recommendations on how a PPS should be implemented to improve well productivity as well as standardize process and data management. To fulfill this objective, the paper will address the following topics: brief introduction of previous work, processes and workflows descriptions, technology available, real-time environment, automated workflow application recommendations, and potential benefits.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
4
Average
Top 10%
Average
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