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

Abstract

We propose a generic, modular framework to optimize the placement of point-in-space continuous monitoring system sensors on oil and gas sites aiming to maximize the methane emission detection efficiency. Our proposed framework substantially expands the problem scale compared to previous related studies and can be adapted for different objectives in sensor placement. This optimization framework is comprised of five steps: (1) simulate emission scenarios using site-specific wind and emission information; (2) set possible sensor locations under consideration of the site layout and any site-specific constraints; (3) simulate methane concentrations for each pair of emission scenario and possible sensor location; (4) determine emissions detection based on the site-specific simulated concentrations; and (5) select the best subset of sensor locations, under a given number of sensors to place, using genetic algorithms combined with Pareto optimization. We demonstrate the practicality and effectiveness of our framework through its application to an oil and gas emission testing facility with a large search space of possible sensor locations; a setting which is computationally infeasible to solve with commonly used mixed-integer linear programming. Additionally, a case study illustrates the successful application of our algorithm to an operating oil and gas site, showcasing its real-world applicability and effectiveness.

We developed a sensor placement optimization framework for continuous monitoring systems for point-in-space methane emissions detection on oil and gas sites.