A data-driven algorithm to optimize the placement of continuous monitoring sensors on oil and gas sites
Date:
Abstract: Methane emissions from the oil and gas sector exhibit high temporal variability, and infrequent, short-lived super emitter events represent a large portion of overall emissions based on current understanding. Therefore, continuous monitoring systems (CMS) will likely play an increasing role in emissions monitoring, as they provide the near real-time measurements that are necessary for capturing both highly variable emissions and short-lived events. The placement of CMS on a given facility is a critical consideration. The most commonly used approach is fence line placement, where a set of sensors are positioned around the perimeter of a site to ideally encompass all potential emissions within. However, this intuitive approach may not be the most efficient, as it does not account for the actual emission patterns and wind distributions specific to a site. In this study, we propose a data-driven algorithm to optimize sensor placement based on emissions and wind distribution which we demonstrate for a specific site. The sensor placement optimization algorithm comprises three steps: wind and emission distribution modeling, concentration simulation and sensor detection, and sensor placement optimization. In the first step, neural networks are employed to approximate the wind distribution on the site using historical wind records, considering temporal correlation. Additionally, an emission event distribution is developed based on empirical emission logs. We then sample many emission scenarios from the modeled wind and emission distributions. In the second step, methane concentrations at all candidate sensor locations are simulated using the Gaussian puff model for all sampled emission scenarios. The sensor detection status is determined by comparing the simulated concentrations at candidate sensors with a detection threshold. In the last step, given the number of available sensors (N), an objective function is defined to maximize the number of detected emission scenarios by at least one sensor from the N-sensor placement. Efficient numerical optimization algorithms can be employed to achieve this task. Alternatively, the algorithm can be adapted to find the minimum number of sensors and their corresponding placement solution that satisfies a given detection probability, and we will show practical results for these different objectives.
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