AI Monitoring and Optimisation System for Single-Well Production Performance of Onshore Oilfields in Xinjiang

Onshore oilfields in Xinjiang operate large portfolios of rod-pumped wells under harsh conditions, where production losses are often driven by incipient equipment degradation, inflow variability, and delayed diagnosis across dispersed sites. This paper develops an AI-enabled monitoring and optimization system that closes the loop at the single-well level by integrating surface electrical measurements, dynamometer/dynagraph indicators, and production-operations logs into three functions: (i) operating-condition recognition and early warning, (ii) short-horizon forecasting of production and energy intensity, and (iii) constrained prescriptive optimization that recommends feasible actions under equipment and safety limits. The design combines physics-informed inference with residual learning, semi-supervised multi-source recognition to reduce reliance on extensive labels, and a constraint-aware optimizer to ensure recommendations are auditable and policy-compliant. A case study using operator-governed field data from a Xinjiang onshore asset evaluates recognition, forecasting, and operational KPIs, demonstrating the feasibility of scalable, closed-loop decision support for single-well performance management.