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Related Experiment Video

Updated: May 30, 2025

In Situ Soil Moisture Sensors in Undisturbed Soils

In Situ Soil Moisture Sensors in Undisturbed Soils

Published on: November 18, 2022

Optimizing Point-in-Space Continuous Monitoring System Sensor Placement on Oil and Gas Sites.

Meng Jia¹, Troy Robert Sorensen¹, Dorit Martina Hammerling^1,2

¹Department of Applied Mathematics and Statistics, Colorado School of Mines, Golden, Colorado 80401, United States.

ACS Sustainable Resource Management

|January 29, 2025

Summary

This summary is machine-generated.

We developed a new framework to optimize methane sensor placement for oil and gas sites, improving emission detection efficiency. This method uses advanced algorithms to find the best sensor locations, even in complex environments.

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Area of Science:

Environmental Science and Engineering
Chemical Engineering
Data Science and Optimization

Background:

Accurate methane emission detection is crucial for the oil and gas industry to mitigate environmental impact and comply with regulations.
Optimizing sensor placement is challenging due to complex site layouts, variable wind conditions, and emission sources.
Existing methods often struggle with the scale and complexity of real-world oil and gas sites.

Purpose of the Study:

To propose a generic, modular framework for optimizing the placement of continuous monitoring system sensors.
To maximize methane emission detection efficiency on oil and gas sites.
To develop a scalable solution adaptable to various objectives and site-specific constraints.

Main Methods:

A five-step optimization framework: emission scenario simulation, sensor location definition, methane concentration simulation, detection determination, and sensor subset selection.
Utilized site-specific wind and emission data for realistic scenario modeling.
Employed genetic algorithms combined with Pareto optimization for selecting optimal sensor subsets in large search spaces.

Main Results:

The framework successfully optimized sensor placement in a large-scale oil and gas emission testing facility, outperforming traditional methods.
Demonstrated computational feasibility for large search spaces where mixed-integer linear programming is infeasible.
A case study on an operating oil and gas site confirmed the framework's real-world applicability and effectiveness.

Conclusions:

The proposed framework provides an effective and scalable solution for optimizing methane sensor placement in the oil and gas sector.
The modular design allows for adaptation to different monitoring objectives and site-specific challenges.
This approach enhances methane emission detection efficiency, contributing to environmental protection efforts.