Well Portfolio Optimization: Rapid Screening of Production Enhancement Opportunities

Abstract

Abstract Objectives/Scope A system providing a standardized and automated approach to rapidly screen and rank large well count assets (hundreds or thousands of wells) in a fraction of the time and repeatably, is proposed. The solution enables the proactive management of existing wells by keeping the production enhancement opportunity pipeline full and expediting potential candidates through the Opportunity maturation process (OMP) in an integrated and collaborative framework. Methods, Procedures, Process The solution is based on a hybrid and automated decision-support system anchored in a knowledge-based framework. Hybrid refers to the integration of (1) petroleum engineering analysis methods and best practices with (2) advanced machine learning algorithms for autonomous well performance signature identification, and (3) the operator's business logic driving the economics of the field. Automated alludes to a continuous running workflow requiring minimal interaction, utilizing machine learning approaches such as neural networks, bayesian belief network models, logistic regressions, advanced optimization, etc. Results, Observations, Conclusions The application of the proposed solution led to workover and intervention candidate evaluations now routinely performed on a weekly basis (compared to the previous bi-yearly approval review and approval process across almost 200 well completions, with engineers confirming and validating the system-generated opportunities before escalating them for approval. Moreover, in its early evaluation, the new system has provided 89% in time savings in both the identification and review of intervention candidates and 88% in cost savings resulting from the elimination of manually tedious and routine work. Novel/Additive Information Asset teams are entrusted with proposing enough well production opportunities on a regular basis. The identification of potential workover candidates remains a manual, tedious and repetitive process consuming several weeks of engineers’ time in every iteration. With the addition of the proposed solution in the existing body of literature, asset teams can proactively manage opportunities in their fields.