Active Learning Analytic Coupled with Edge Computing for Intermittent Shut-In Optimization and Carbon Emission Reduction in Shale Gas Reservoirs

Gas production from unconventional shale reservoirs is known for rapid declines. Intermittent shut-in production constitutes a technique typically applied to low-production wells during late life stages to maintain economic rates. This technique involves a cyclic process of shutting in the well temporarily to allow it to build up pressure and subsequently switching the well to production. Operators often manage hundreds of wells on intermittent shut-in production; these wells, however, incur different shut-in and production cycle times, thus requiring a complicated management approach. Because every well has a unique production behavior and reservoir characteristics, searching for optimum operational conditions individually is not only technically challenging, but also operationally time-consuming and labor- intensive. Our goal was to use active learning analytic, a type of machine learning deployed on an edge computing platform, to autonomously control and optimize these unconventional gas wells. The field trial results show increased production, reduced liquid loading, decreased manual intervention, and reduced carbon footprint. Our solution utilizes an edge computing platform to deploy the analytic on the wellhead without requiring a stable internet connection. A computing device at the edge connects to controllers on site, processes data, sets system control parameters, and enables automation for operations deploying an optimization algorithm. Active learning algorithms are valuable for use in the optimization of systems that are not mathematically definable. These algorithms are also proven to learn the relationship between the inputs and outputs and use prior knowledge to intelligently search for the optimum settings within the defined operating limits. The low latency of edge computing allows for high-frequency data collection in seconds and a rapid control of the wells. The edge device continuously monitors production and initiates re- optimization as needed when operational conditions change. We developed an analytic that autonomously controls the intermittent production technique where a well is shut-in based on a specified minimum gas production rate and opened when the pressure builds up to the specified target during the shut-in period. The analytic actively learns and measures the ways in which the specified parameters improve production rates. Additionally, the analytic continuously monitors production data and identifies any well liquid loading events. When liquid loading occurs in the wellbore as observed from the production pattern, the analytic automatically shuts in the well to build up pressure and minimizes additional liquid formation. In the field trial, we deployed the edge analytic to monitor gas production and the specified well shut-in and open conditions for 10 different wells in the Haynesville Shale Play. Analyzing each well in the context of approximately 30 intermittent production cycles (shut- in/open), the analytic successfully mapped the surface response, identified the optimal setting for well shut-in/open conditions, and continuously updated the surface response. Overall, the analytic improved production by 4% and reduced the liquid loading occurrences and manual well unloading events by 94%, resulting in an average reduction of approximately 600 tons of CO2 equivalent per well per year. In summary the active learning analytic was developed and deployed on an edge computing platform to 1) optimize intermittent shut-in by searching for the optimum settings that yield the most gas production; 2) automate the optimization process; and 3) monitor the liquid formation for potential loading events. In this paper, we present a use case for an algorithm adapted for the optimization of a dynamic system such as hydrocarbon production from a well.