Digital oilfield

Well-Candidate-Recognition Solution Offers Time Savings, Production Enhancement

This paper addresses the challenges of integrating huge amounts of data and developing model frameworks and systematic workflows to identify opportunities for production enhancement by choosing the best candidate wells.

Inputs for the WCR system.
Fig. 1—Inputs for the WCR system.

A well-candidate-recognition (WCR) data-analytics solution was developed to expedite the process of identifying unhealthy wells that may require rig or rigless interventions based on data integration, automation, and advanced data-driven models. The solution expedites the well-performance-review process to pinpoint candidates for stimulation, nitrogen lift, gas lift conversion, and water or gas shutoff, providing a flexible visualization platform to highlight hidden well-performance insight.


Mature oil fields will face challenges in terms of increasing water cut, lack of pressure support, and the requirement of artificial lift. The critical concern is prioritization of remedial actions that are economically attractive with high returns and low risk. In the past, the asset was prioritizing the rig and rigless intervention candidates on a manual basis with no technical framework, mostly relying on engineers’ backgrounds and experience.

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