Fuzzy-PID Neural Network Choke Valve Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Offshore oil production in Brazil faces inefficiencies due to manual actuation of choke valves, leading to suboptimal production levels and instability in flow rates, as existing systems lack closed-loop control and automatic deviation correction.
Innovation Solution
A system utilizing Fuzzy-PID logic and artificial neural networks to control oil and gas flow rates based on choke valve opening percentage and wellhead pressure data, enabling continuous nominal production and automatic correction of deviations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual actuation of choke valves is used, then the system is simple to operate, but production efficiency is reduced due to delayed response to disturbances
Solution Approach 1:
The patent implements a closed-loop control system that continuously monitors production parameters (flow rate, pressure, temperature) and automatically adjusts choke valve positions based on deviations from target values. This feedback mechanism eliminates the delayed manual response while maintaining operational simplicity through automated decision-making algorithms that process real-time data and generate control commands.
Solution Approach 2:
The patent replaces the manual mechanical operation of choke valves with an automated control system that uses electronic sensors, processors, and actuators. The system substitutes human reaction and decision-making with automated algorithms that continuously optimize valve positions based on real-time production data, thereby eliminating response delays while maintaining ease of operation through centralized control.
2Device complexity
If open-loop control is used, then the control system is simple, but production operates below nominal levels due to inability to correct deviations automatically
Solution Approach 1:
The patent implements a closed-loop control system that continuously monitors production parameters (flow rate, pressure, temperature) and automatically adjusts choke valve positions based on deviations from target values. This feedback mechanism eliminates the delayed manual response while maintaining operational simplicity through automated decision-making algorithms that process real-time data and generate control commands.
Solution Approach 2:
The control system performs self-correction by automatically detecting deviations from nominal production levels and adjusting choke valve positions without external intervention. The system uses real-time data from sensors and automated algorithms to maintain optimal production levels, eliminating the need for manual monitoring and correction while operating at or above nominal capacity.
3Ease of manufacture
If test data from gravitational separator vessels is used, then production tests can be conducted, but the results differ from actual production conditions due to different dimensions and operating conditions
Solution Approach 1:
The patent addresses the discrepancy between test and actual production conditions by implementing dynamic parameter adjustment. The system continuously adapts control parameters (choke valve positions, flow rates, pressure settings) based on real-time measurements from actual production conditions rather than relying on static test data. This allows the system to maintain accurate control despite differences in vessel dimensions and operating conditions between testing and production phases.
Data Source
AI summary
The present invention relates to a system for controlling the flow rate of a platform comprising at least one controller, wherein the at least one controller uses Fuzzy-PID logic; at least one processor, wherein the at least one processor includes at least one artificial neural network model; wherein the at least one artificial neural network model has at least two inputs, wherein the at least two inputs comprise choke valve opening percentage data of the at least one well and head pressure data upstream of the choke valve of the at least one well; and wherein the at least one artificial neural network model has an output, wherein the at least one output is the gas flow rate or the oil flow rate produced by the platform; wherein the controller feeds the at least one artificial neural network model with the choke valve opening percentage data of the at least one well and head pressure data upstream of the choke valve of the at least one well.