ANALYSIS OF THE SUCKER-ROD PUMP SYSTEM FOR IDENTIFYING OPERATING MODES IN OIL PRODUCTION UNDER VARIABLE CONDITIONS BASED ON DYNAMOMETRIC DATA AND NEURAL NETWORKS

Abstract

Deep-sea pumping rod systems for offshore oil production must operate properly. It is crucial to understand the modes of operation of these systems for them to function well. The use of a deep water pumping rod installation called a sucker-rod pump (SRP) achieves this. However, deep-water conditions continually change, reducing traditional SRP systems' effectiveness. Operations may deteriorate, and environmental conditions vary greatly. Purpose of study: This paper highlights why it is important to adapt SRP systems to changes in harsh deepwater regions. It also considers issues arising from this, present methodologies, or even likely future approaches. Also, optimizing efficiency, safety, and sustainability in offshore oil operations involves analyzing SRP. Other challenges include multi-path interferences that cause signal deterioration and ionospheric disturbances that result in data fusion and machine learning complexities. Also, there are additional problems occasioned by dynamic environments and computational complexities during data fusion as well as machine-learning phases. Deep-water monitoring and control requires SRP adaptation. Methodology: The method of the review article involves a wide-ranging literature review, to identify research that relates to the subject of SRP rod application for oil production. The articles are critically analyzed and distilled to obtain the main results of the study using dynamometric data collection and NN algorithms. Scientific novelty: Accurate identification of operational modes is crucial for deep-water rod systems used in offshore oil production. However, the constantly changing environments in deep waters make it difficult for typical SRP systems to perform well. The operation can be degraded, and environmental conditions may vary much. This paper aims to examine why adapting SRP systems to changing deep-water conditions is important. It also examines the challenges faced, current approaches used, and prospects that are being analyzed. Conclusion: Analysis of SRP is necessary for achieving efficiency, safety, and sustainability in offshore oil operations. Challenges comprise multipath interference-based signal degradation and ionospheric disturbances of variables. There are additional complexities due to dynamic environments as well as computational issues in data fusion and machine learning processes. Nonetheless, effective deep-water monitoring and control require SRP adaptation.