Field A, located offshore East Malaysia, has been a productive conventional oil field for more than three decades. Being a mature or brown oil field, it encounters several surface and subsurface including sand production, increasing water-cut and reservoir pressure depletion. Consequently, field production declines over the years. These issues have compelled the team to pursue for options to optimize the existing Field A's producing and idle wells in an effort to slow the production decline rates and keep up with field deliverability.

Failure to recognize and manage well behaviors may cause producers unable to meet production targets and experience other serious problems, such as well integrity. Hence, Production Enhancement Candidate Generation and Screening (PECGS), a Machine Learning (ML) tool, have been utilized in Field A to identify potential candidates for Production Enhancement (PE) from both active and inactive wells. The system has been able to establish a workflow that is capable of guiding and navigating the engineer through the process of identifying candidates for PE, such as zone changes (ZOC), Behind Casing Opportunities (BCO), add perforations and well stimulations that can be tracked all the way up to the execution stage.

This paper presents the processes of identifying production enhancement opportunities, as well as the methodology utilizes to identify underperforming candidates and analyze well integrity issues. It describes how data science and engineering analyses are integrated, and stresses how collective insights are gained from quantitative and qualitative outcomes, such as production increases, cost savings, operational efficiencies, and overall impact on field performance.

The implementation of well-engineered workflows has significantly aided and enabled users in identifying low-hanging fruit opportunities. This solution has created an efficient working environment, particularly for the team in Field A, empowering them to identify and capitalize on PE and Idle Well Reactivation (IWR) potential opportunities. By emphasizing the overall process flow from identification to results, this paper aims to provide a comprehensive understanding of the PE journey in Field A, showcasing the effectiveness of the approach and its contribution to the field's performance improvement.

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