Deploying an Integrated AI-Driven Portfolio Optimization and Opportunity Maturation System to Unlock Value in a Mature Onshore Assets

N. Reddicharla, Amr Mohamed Tawheed, K. Alnaqbi, M. Taha, A. Sinha, S. Ramatullayev, M. Salim, Z. Al Kindi, K. Vadivel

Abstract

Managing large well inventories in mature assets requires operators to balance technical rigor, execution speed, and economic discipline. Traditional intervention management workflows rely heavily on manual screening, fragmented data sources, and expert-driven reviews, resulting in long cycle times, inconsistent outcomes, and limited portfolio-level visibility. This paper presents the design and deployment of an AI-enabled Well Portfolio Optimization (WPO) solution applied across Onshore assets to systematically identify, mature, and prioritize well intervention opportunities at scale. The solution integrates machine learning–based diagnostics, production analytics, and enterprise workflow orchestration to automate opportunity detection and enable closed-loop tracking from identification through execution and post-job evaluation. Key workflows include inactive string prioritization, well underperformance detection, high-water-cut management, behind-casing opportunity identification, artificial lift conversion screening, and well retirement advisory. A standardized gain estimation and ranking framework ensures transparent value assessment and consistent decision-making across assets. The solution was implemented in 3 major onshore assets (covering over 3000+ wells). Field deployment demonstrated significant reductions in screening and analysis time, improved consistency in candidate selection, enhanced visibility of opportunity inventories across disciplines, and field trials with successful interventions. The WPO framework provides a scalable and repeatable approach for portfolio-level production optimization and represents a critical step toward autonomous well management in upstream operations.

Source: semanticscholar

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