Linear Cut Inversion for Methane Leak Source Quantification

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Solution Overview

Problem

Current methods for identifying and quantifying methane leaks in the environment are hindered by sensor placement variability and environmental constraints, leading to inefficiencies and high economic costs.

Innovation Solution

A method involving record generation, quality assessment, linear cut generation, and source term inversion using a Gaussian plume model and anemometer data to identify and quantify methane leaks, employing a robust inversion procedure with reduced search bounds.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If conventional fluid dynamics equations are used for methane leak identification, then the method is based on established physics, but sensor placement variability and environmental constraints hinder the ability to identify and quantify leaks to desired levels

Engineering Contradiction:

Improveleak identification precisionVSAvoidsensor placement complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent transforms the conventional fluid dynamics approach into a linear algebraic system by changing the mathematical parameters and representation. The nonlinear inverse problem is converted into a linear system of equations that can be solved more reliably despite sensor placement variability, thereby improving measurement precision without requiring perfect sensor positioning.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent replaces the conventional mechanical/fluid dynamics-based approach with a mathematical linear algebraic system. By substituting the physical fluid dynamics equations with a linear matrix formulation, the system becomes more robust to environmental constraints and sensor placement variations, improving leak quantification accuracy.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If conventional methods are used for methane leak quantification, then the approach is established, but economic costs associated with operations and apparatus are high

Engineering Contradiction:

Improveleak quantification reliabilityVSAvoideconomic cost

Core Design Contradiction:

ReliabilityVSLoss of energy

Solution Approach 1:

The patent employs a simplified linear algebraic model that can be implemented with lower-cost computational resources and simpler apparatus. The linear cut generation method and reduced search bounds enable reliable leak quantification using more economical sensor arrays and processing systems, reducing the economic cost while maintaining quantification reliability.

Inventive Principle:

Principle #27Cheap short-living objects (Disposable)

3Productivity

If conventional inversion procedures are used, then the method is standard, but the search space is large and computation is inefficient

Engineering Contradiction:

Improvecomputation efficiencyVSAvoidinversion procedure complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent segments the large search space into smaller, manageable regions through linear cut generation. By dividing the inversion problem into smaller sub-problems with reduced search bounds, the computational efficiency is improved while the overall procedure remains systematic and manageable through the structured linear algebraic approach.

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS12577871B2Linear cut generation method for sensor inversion constraint imposition

Publication Date: 2026.03.17 SCHLUMBERGER TECH CORP

AI summary

Embodiments presented provide for a method of analysis for methane leaks. The method of analysis includes performing a record generation event, performing a quality assessment of the record generation event, performing a linear cut generation procedure to create a linear cut generation data set, and performing a source term inversion using the linear cut generation data set.