A Comprehensive Study on the Application of Production Analytics Solution Across Indonesia's Mature Fields

Abstract

The oil & gas industry is currently moving from traditional workflows to the implementation of analytics and artificial intelligence in accelerating and enhancing repetitive tasks. All is driven by the need to automate low-cognitive tasks enabling engineers to spend more time on high-cognitive components of the existing workflows, thus leading up to smarter decisions. This has been made possible by the recent developments and adoption of various analytics and machine learning tools. We are now changing the game in Indonesian mature field's production optimization strategy using Analytics.

Well Portfolio Optimization (WPO) is one of the implemented production analytics solutions for Workover Candidate Selection, which intends to keep asset teams permanently aware of underperforming wells in need of a workover and is particularly suited to rapidly screen and rank hundreds or thousands of wells. The solution enables the proactive management of existing wells by identifying and prioritizing workover candidates through a hybrid and automated decision-support system. ‘Hybrid’ refers to the integration of traditional petroleum-engineering analysis methods and best practices with advanced algorithms for autonomous well performance signature identification, as well as client business logic.

This solution has been implemented in one field in Indonesia. Given the result of the first deployment, this paper will describe the effect of the expansion of similar project across all mature field in this country. Comprehensive topic starting from the readiness of our data infrastructure, until the potential values of the full-scale practice, will be outlined, looking from the nation's perspective.