Back Pressure Valve Control for Autonomous ESP Optimization
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Solution Overview
Problem
Existing ESP systems face challenges with power consumption, control precision, and adaptability to changing downhole conditions, leading to increased operational costs and suboptimal hydrocarbon recovery rates, with manual intervention and traditional physics-based approaches limiting autonomous operation.
Innovation Solution
A back pressure control valve system with a programmable logic controller and machine learning model continuously adjusts the actuator setting to minimize error around a set point, integrating telemetry data from various sensors to optimize fluid flow and pump frequency, enabling semi-autonomous or autonomous operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional physics-based and rule-based approaches to ESP optimization are used, then system performance can be optimized with manual intervention, but the process is time-consuming and requires significant human experience and oversight
Solution Approach 1:
The system enables self-service through autonomous operation where the ESP control system automatically adjusts pump frequency and valve positioning based on real-time sensor data and machine learning models, eliminating the need for continuous manual intervention and expert oversight while maintaining optimization performance
Solution Approach 2:
The system implements continuous feedback loops using sensors that monitor downhole conditions, pump performance, and flow rates, feeding this data back to the control system which automatically adjusts operations in real-time, replacing manual monitoring and adjustment cycles
2Measurement precision
If conventional PID controllers with infrequent valve setting adjustment are used, then system stability is maintained, but control precision and adaptability to changing downhole conditions are limited
Solution Approach 1:
The control system transitions from static, infrequent adjustments to dynamic, continuous adjustment of valve positioning and pump frequency based on real-time downhole conditions, allowing the system to adapt rapidly to changing parameters while maintaining stability through advanced control algorithms
Solution Approach 2:
The system continuously varies operational parameters including pump frequency, valve opening percentage, and pressure setpoints based on real-time sensor data and machine learning predictions, enabling precise control adaptation without requiring complex manual reconfiguration
3Adaptability or versatility
If machine learning and AI models are implemented for real-time optimization, then adaptability and prediction accuracy improve, but data processing complexity and computational requirements increase
Solution Approach 1:
The system performs preliminary actions by using machine learning models to predict optimal operational parameters and potential downhole conditions before they occur, allowing the control system to proactively adjust operations rather than merely reacting to changes, thereby improving adaptability while managing computational load through pre-computed models
Data Source
AI summary
A back pressure control valve positioned between a wellhead of an oil well and a downstream production flowline comprises a valve combination coupled with a controller providing for continuous valve actuator setting adjustment to tightly control back pressure around an externally provided set point. The valve may be network-connected, e.g. to the Internet, and pump and well data, and the external tubing pressure set point may be communicated through the network.