Machine Learning Gas-Lift Optimization for Stable Well Production

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Solution Overview

Problem

Gas-lifting operations in hydrocarbon wells, particularly in offshore fields, face challenges with high operational costs and instability due to transient conditions, leading to reduced production rates and equipment damage.

Innovation Solution

A method and system utilizing a machine learning model and optimizer to predict and adjust gas-lift parameters automatically, optimizing gas injection to maintain stable and efficient hydrocarbon production.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If gas-lift operations are performed manually or with traditional control methods, then operational flexibility is maintained, but production efficiency is reduced and operational costs increase

Engineering Contradiction:

Improveproduction efficiencyVSAvoidcontrol system complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The gas-lift system performs self-optimization by automatically adjusting gas injection parameters based on real-time well performance data. The system uses machine learning models to predict optimal settings and executes adjustments without human intervention, enabling the system to serve itself and eliminate the need for complex manual control procedures

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

Traditional manual control mechanisms are replaced with an automated electronic control system that uses sensors, data transmission, machine learning algorithms, and actuators. This substitution of mechanical/manual operations with electronic automation and intelligence increases production efficiency while managing complexity through software-based solutions

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Stability of the object's composition

If gas injection parameters are adjusted frequently to respond to transient conditions, then production stability is improved, but equipment wear increases and operational costs rise

Engineering Contradiction:

Improvegas-lift stabilityVSAvoidequipment wear

Core Design Contradiction:

Stability of the object's compositionVSObject-generated harmful factors

Solution Approach 1:

The system predicts future well performance and optimal gas injection parameters in advance using machine learning models trained on historical and real-time data. By determining optimal settings before conditions change significantly, the system can make proactive adjustments that maintain stability while minimizing the frequency and magnitude of parameter changes, thereby reducing equipment wear

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The control system continuously monitors well performance parameters and uses this feedback to adjust gas injection settings. The feedback loop enables the system to respond to transient conditions only when necessary, maintaining stability through targeted adjustments rather than frequent changes, thus reducing equipment wear and operational costs

Inventive Principle:

Principle #23Feedback

3Productivity

If traditional gas-lift control methods are used, then system simplicity is maintained, but production rates decrease and operational costs increase

Engineering Contradiction:

Improveproduction rateVSAvoidoperational cost

Core Design Contradiction:

ProductivityVSLoss of energy

Solution Approach 1:

The system optimizes production by dynamically changing gas injection parameters including injection rate, pressure, and timing based on real-time well conditions and predictive analytics. These parameter adjustments maximize hydrocarbon recovery rates while minimizing the energy required for gas injection, thereby increasing production rates and reducing operational costs

Inventive Principle:

Principle #35Parameter changes

Applied Scientific Principles

This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.

Function Achieved in This Case

Enhances production efficiency, reduces operational costs, and improves safety by maintaining optimal gas-lift stability and minimizing equipment wear through real-time parameter adjustments.

Implementation Method 1

Gas-lift uses a source of high-pressure gas to lower the bulk density of a fluid mixture with hydrocarbons and 'lift' the mixture to the surface

Methodology Applied

Scientific EffectGas lift: Gas Lift

Implementation Method 2

The gas mixes with the hydrocarbon fluids in the production tubing reducing the density of the hydrocarbon fluid and gas mixture

Methodology Applied

Scientific EffectDensity reduction through gas mixing:

Data Source

PatentUS20250215772A1Methods and systems for optimizing gas-lifting operations

Publication Date: 2025.07.03 SAUDI ARABIAN OIL CO

AI summary

A method for optimal execution of a gas-lifting procedure. The method includes obtaining gas-lift data from a well site with gas-lift, the well site with gas-lift including an oil and gas well with access to a hydrocarbon reservoir and a gas-lift system including a gas pump. The method further includes obtaining a set of gas-lift parameters related to the well site with gas-lift and determining, with a machine learning (ML) model, a predicted gas-lift based on the gas-lift data and the set of gas-lift parameters, and determining, with an optimizer applied to the ML model, an optimal set of gas-lift parameters such that the predicted gas-lift is optimized. The method further includes adjusting, automatically, the set of gas-lift parameters to the optimal set of gas-lift parameters and injecting gas into the well using the gas-lift system according to the optimal set of gas-lift parameters.