Abstract Real-time optimization of sucker rod pump operation is essential for maximizing production and reducing operational costs in oilfields. Frequent manual speed adjustments are impractical for fields with thousands of producing wells. This paper addresses these challenges by proposing a semi-closed loop system for intelligent automation of Variable Frequency Drive (VFD) operated sucker rod pumps using machine learning based fillbase prediction. The main goal is to enhance operational efficiency while mitigating risks, by incorporating human oversight for improved safety and reliability in remote oilfield operations. The proposed system integrates VFD-based speed control with ML-driven fillbase prediction (XGBoost), using dynamometer card analysis to determine pump fillage and further adjusts pump speed automatically to achieve the target fillage. A recommendation workflow monitors card quality, speed fluctuations, and alerts operators to anomalies such as persistent bad/no pump cards or significant speed changes. This ensures timely human intervention for safe operations. Various mechanisms like operator-set speed thresholds, snoozing of false recommendations, and feedback capture lead to continuous system refinement. A field trial validates this machine learning based approach and demonstrates its effectiveness in real-world conditions while maintaining operational safety and efficiency. The field trial conducted on 264 wells demonstrated significant improvements across multiple operational metrics. Production analysis revealed that 78 wells (approximately 30% of the test group) achieved an average 35% production uplift through automated speed increases, while 165 wells (62% of the test group) realized 30% power savings from optimized speed reductions. The system's ability to maintain operations around the ideal fillage or "sweet spot" not only enhanced production per unit power consumption, but also improved rod load management, thus reducing mechanical stress and extending equipment lifespan. From an operational standpoint, the solution achieved 90% reduction in manual surveillance requirements, effectively saving one full-time equivalent (FTE) workload while maintaining operational reliability. Notably, the human-in-the-loop feedback mechanism proved particularly useful, with operators intervening in only 12% of the cases where the system flagged potential anomalies or speed constrained operations. These interventions prevented potential issues while allowing the automated system to handle routine optimization. The trial also revealed that the wells operating at speed limits for extended period benefited from targeted recommendations, enabling operators to make informed adjustments. Overall, the results validate that this semi-automated approach successfully balances the benefits of AI-driven optimization with the critical need for operator oversight in oilfield operations. This hybrid approach enhances production, reduces power consumption, and mitigates operational risks, thus enabling intelligent oilfield operations. By balancing automation with human validation, the system ensures robust operations and high operational efficiency, thus marking a significant advancement in AI-augmented operational excellence in the area of oilfield automation.
Sarma et al. (Mon,) studied this question.
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