ICV and Choke Valve Optimization for Transient Oilfield Production

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Solution Overview

Problem

Optimizing oil and gas field production is challenging due to complex interactions between field components and transient field behavior, requiring continual adjustments of inflow control valves (ICVs) and choke valves for optimal performance, which is laborious and time-consuming.

Innovation Solution

A method using machine learning to process data from oil and gas field devices to determine optimal ICV and choke valve settings, adjusting these settings automatically or manually to enhance production efficiency and maintain optimal conditions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If manual adjustment of ICV and choke valve settings is performed to optimize production, then production optimization can be achieved, but the process becomes laborious and time-consuming

Engineering Contradiction:

Improveoil and gas productionVSAvoidtime for valve adjustment

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system automatically adjusts ICV and choke valve settings without requiring manual intervention. The machine learning model continuously monitors production data and autonomously optimizes valve configurations, allowing the system to self-optimize production parameters and eliminate time-consuming manual adjustment processes

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual mechanical adjustment of valves with an automated computer-based control system. The machine learning model processes production data and generates automated valve control commands, substituting human operators and manual mechanical adjustments with an automated digital control mechanism

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Productivity

If continual adjustments of valve settings are made to address transient field behavior, then optimal production can be maintained, but operational complexity increases

Engineering Contradiction:

Improveproduction optimizationVSAvoidcomplexity of valve adjustment process

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system continuously monitors production data from the oil and gas field and uses this feedback to dynamically adjust ICV and choke valve settings. The machine learning model processes real-time production information and automatically modifies valve configurations to adapt to transient field conditions, maintaining optimal production without increasing operational complexity

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent automatically modifies operational parameters of the valves (opening degree, flow rate control) based on changing field conditions. The machine learning model continuously adjusts valve settings as production conditions change, transforming manual parameter adjustment into an automated process that adapts to transient field behavior

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS12359530B2Method to maximize oil recovery of an oil and gas field via optimization of inflow control valve and choke valve settings using machine learning

Publication Date: 2025.07.15 SAUDI ARABIAN OIL CO

AI summary

A method to optimize an oil and gas field, including receiving oil and gas field device data from a plurality of devices disposed throughout the oil and gas field, where at least one device of the plurality of devices monitors oil and gas production. The method further includes processing, by a computer processor, the device data to determine optimal inflow control valve (ICV) and choke valve settings, adjusting the ICV and choke valve settings to the optimal ICV and choke valve settings, and validating that the optimal ICV and choke valve settings optimize oil and gas production with the at least one device that monitors oil and gas production.