From Data to Decision: A Field-Validated AI-Assisted Production Intelligence Framework for Opportunity Identification, Artificial Lift Optimization, and Surveillance in the Indus Basin

Abstract

Abstract Objectives/Scope The increasing complexity of mature oil, gas, and condensate assets has expanded surveillance data volumes while making rapid identification of high-value production opportunities increasingly difficult. Conventional workflows rely on fragmented datasets, manual interpretation, and reactive assessments resulting in delayed intervention planning, suboptimal artificial lift performance, and inconsistent prioritization across large portfolios. This study presents a field-validated AI-assisted production intelligence framework to enhance opportunity identification, artificial lift optimization, and surveillance efficiency across a multi-well oil, gas, and condensate portfolio in the Indus Basin, Pakistan. Methods, Procedures, Process The framework integrates production, pressure, temperature, choke, gas lift injection, well testing, downtime, and intervention datasets from production databases into a centralized Python-based environment connected to business intelligence dashboards. Isolation Forest and gradient boosting-based anomaly detection algorithms were combined with petroleum engineering workflows including nodal analysis, liquid loading diagnostics using Turner and Coleman critical velocity models, gas lift performance diagnostics, and operating envelope validation to establish a physics-guided decision intelligence system. The framework screens wells across natural flow and artificial lift modes, diagnosing liquid loading, gas lift inefficiency, skin buildup, and choke instability, ranking opportunities by uplift potential and operational feasibility. Results, Observations, Conclusions Field deployment across 41 active wells including 4 gas lift producers reduced manual well review cycles from bi-weekly to daily automated anomaly alerts. Within 6 months, 29 anomaly flags were reduced to 16 physics-validated opportunities across four categories: 5 liquid loading remediation candidates drawn from 8 wells confirmed below Turner critical rate with deficits ranging from 12% to 78%, 2 gas lift optimization candidates, 4 choke and operating envelope deviations, and 5 rigless intervention candidates. Gas lift optimization contributed an incremental ~80 BOED, while aggregate uplift reached approximately 650 BOED (~3.9 MMSCFD) representing an ~18% improvement over the 12-month pre-deployment baseline. Decision cycle time was reduced by an estimated 55%, with full portfolio coverage achieved without additional engineering headcount. Novel/Additive Information This methodology demonstrates how AI-assisted surveillance integrates with petroleum engineering domain knowledge to create scalable production decision intelligence systems for mature assets. Unlike purely data-driven approaches, the framework combines machine learning with physics-based validation at every decision node preserving auditability, operational trust, and engineering accountability. This scalable, low-infrastructure model confirms that AI-assisted production intelligence is deployable without enterprise-scale investment across the Indus Basin and similar producing regions globally.