Methodology for Oil Production-Loss Control in a Digital Oilfield Implementation

摘要

Measuring daily production losses requires that operators be aware of different events that may affect well performance. Operators normally have the responsibility of closing the gap between the platform-measured production and theoretical-production potential. Common actions to close this gap include transforming events into production downtime, distributing plant process inefficiencies among all wells, and identifying and allocating losses to underperforming wells. This process presents challenges related to accurately identifying the wells manifesting production loss events and in calculating and distributing the production losses among identified wells. These challenges normally cause misinterpretation and misallocation of production losses, consequently impacting the final analysis of operations. This paper presents the methodology and technology deployed in a production-loss control module of an intelligent-asset implementation. The production-loss control module is implemented as a workflow that allows for the identification of events, loss calculation, and classification of results for analysis. Using graphical analysis of well-by-well plots of production and potential data, the operators are able to identify individual well or platform process anomalies, allowing them to clearly identify events that produce a gap between production and well potential. An algorithm to minimize effects of errors in production measurement and well potential variations is implemented. The workflow is concluded by classifying production-loss events based on the identification of the root cause and remedial actions. The technology implements a process for the proper quantification and qualification of production losses and provides a reliable platform for analysis of historical results. This process supports remedial actions to be taken to minimize the recurrence of production losses, positively impacting the operational efficiency of the asset. This solution represents a good example to the industry in the integration of people, processes, and technology for an intelligent-asset implementation.