Abstract

Methane emissions from plugged and unplugged abandoned wells dilute rapidly with air, causing conventional detection methods to fall short of detecting methane leaks from these sources. Commonly used technologies for methane emission detection often fall short of the detection limit required for low-rate emissions from abandoned wells, and are either dependent on environmental conditions or costly and highly energy consuming, barring them from becoming a scalable solution to this problem. We introduce here a new system for outdoor testing and show how the flux chamber detection limit is progressively reduced from 700 g/h to 2 g/h and ultimately to 1 g/h, meeting the US Department of the Interior (DOI) standard for monitoring equipment used on abandoned wells. Field deployment on an actual abandoned well also revealed intermittent emissions, which may serve as an indicator of deteriorating well integrity over time when monitored periodically. To forecast the emission event timing and intensity, a Liquid Time-Constant (LTC) and a gated recurrent neural network were trained on methane concentration time series collected during field deployment. As available well sites for physical testing are limited and atmospheric conditions are not controllable, a computational fluid dynamics (CFD) simulation framework integrated with machine learning (ML) was developed to optimize wellhead chamber geometry and size for both detectability and safety. This chamber is designed to be used for testing well sites in mass number that helps well abandonment ranking systems for both operators and regulatory bodies.

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