Sucker Rod Pump Optimization via Edge Computing
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Solution Overview
Problem
Rod pump operations in the oil and gas industry often rely on traditional, legacy 'rule of thumb' practices, leading to reduced production, increased shutdown events, and requiring user expertise, which is difficult to scale.
Innovation Solution
The implementation of an edge-based computing solution that autonomously optimizes sucker rod pump (SRP) production by dynamically evaluating performance indicators and recommending optimal frequency setpoints to minimize shutdowns and maximize production.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional rule of thumb practices are used for rod pump operation, then ease of operation is maintained, but productivity is reduced and reliability deteriorates
Solution Approach 1:
The system performs self-optimization by automatically evaluating performance indicators and adjusting operational parameters without requiring expert intervention. The algorithm continuously monitors pump performance and makes autonomous decisions to optimize production, eliminating the need for manual expertise while improving productivity.
Solution Approach 2:
The system implements closed-loop feedback by continuously monitoring performance indicators (such as pump fillage, strokes per minute, and shutdown events) and using this information to dynamically adjust operational parameters. This feedback mechanism enables the system to learn from actual performance and automatically optimize production while maintaining ease of operation.
2Ease of operation
If traditional rule of thumb practices are used for rod pump operation, then ease of operation is maintained, but reliability deteriorates due to increased shutdown events
Solution Approach 1:
The system monitors performance indicators including shutdown events and pump fillage in real-time, using this feedback to dynamically adjust operational parameters. By continuously adapting to actual pump performance and preventing conditions that lead to shutdowns, the system improves reliability while maintaining ease of operation through automated control.
Solution Approach 2:
The system performs preliminary optimization by proactively adjusting operational parameters before shutdown events occur. By predicting potential failures and preemptively modifying pump operations to prevent shutdown conditions, the system improves reliability without requiring manual intervention during critical events.
3Device complexity
If traditional rule of thumb practices are used, then device complexity is low, but productivity is reduced and scalability is poor
Solution Approach 1:
The system replaces manual expert judgment and traditional mechanical adjustment methods with an automated computational algorithm. By substituting human expertise with an electronic optimization system that processes performance indicators and automatically adjusts parameters, the system achieves high productivity while managing complexity through software-based control rather than mechanical complexity.
Solution Approach 2:
The system optimizes productivity by dynamically changing operational parameters such as strokes per minute and pump fillage thresholds based on real-time performance data. Instead of using fixed rule-of-thumb values, the system adapts parameters to actual well conditions, maximizing production while keeping the control logic manageable through algorithmic automation.
4Ease of operation
If traditional rule of thumb practices are used, then ease of operation is maintained, but scalability deteriorates due to requirement for user expertise
Solution Approach 1:
The system enables scalability by performing self-optimization across multiple wells without requiring expert intervention for each site. The automated algorithm can be deployed to manage numerous wells independently, allowing the system to scale operations to hundreds of wells while maintaining ease of operation through autonomous decision-making rather than requiring manual expertise at each location.
Solution Approach 2:
The system achieves universality by using a single standardized optimization algorithm that can be applied across different wells and field conditions. Instead of requiring custom expert configurations for each well, the system uses universal performance indicators and optimization logic that adapt to various conditions, enabling easy scalability to multiple wells while maintaining ease of operation.
Data Source
AI summary
A device includes a memory configured to store first executable code and a processor coupled to the memory. The processor is configured to calculate performance indicators for a sucker rod pump (SRP) based on performance data of the SRP, determine an operational frequency corresponding to operation of the SRP based on one performance indicator selected from the performance indicators, and initiate transmission of a control signal corresponding to the operational frequency to alter operation of the SRP to correspond to the operational frequency.