Data-Science-Informed Operation, Maintenance Practices Reduce Emissions
Authors: Carpenter Chris 
Published: December 2024

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This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper URTeC 3866061, “Reducing Methane Emissions: Implementing Data-Science-Informed Operation and Maintenance Work Practices Using Continuous Monitoring Technology,” by Kathleen Jenkins, Carol A. Brereton, and Alex MacGregor, Qube Technologies, et al. The paper has not been peer reviewed.

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Diverse methods are used by oil and gas operators for methane leak detection and repair (LDAR). Deploying continuous monitoring (CM) point-sensor technologies to an oil and gas facility allows operators to implement novel operation and maintenance work practices to respond efficiently to methane emissions. The authors examine how data-science-driven work practices can result in substantial reduction in methane emissions compared with other LDAR methods.

CM Technology
CM point sensors track changes in parts per million of methane in the air. When several fixed sensors are deployed at a site, a large area can be surveilled. Using proprietary algorithms based on a plume-dispersion model, the volume and site rate of the methane emission are calculated. The probable location of the emission is estimated and, together with the volume, is shown on a Web-based dashboard that displays the changes in site rate through time. Alerts generated by the CM system allow the operator to respond to leaks more quickly than when waiting for data from an intermittent inspection to provide emission data.
CM was deployed by a Permian Basin operator with a desire to operate cleanly, with transparency. The devices were installed with the operator beginning in March 2022 and continue to be active at the time of writing. Alerts generated by the system are delivered to operations personnel, including control-room operators, and investigated by field operators. When the source is identified, repairs are scheduled or completed on the spot and recorded for review. The operator has expanded the deployments throughout the field and uses the data to identify and repair leaks.

Theory and Methods
The CM devices were deployed around the fence line at several production facilities in the Permian Basin. The optimal number and placement of sensors was determined by analyzing publicly available wind data and sources of emissions at the facility.
The CM devices have an inlet port that allows the ambient air to enter the device and flow across a metal oxide sensor. Metal oxide sensors measure the change in conductivity as the air flows across them as a result of the methane adhering to the metal. The change in conductivity, directly correlated with the parts per million of methane in the air, is recorded. Also measured are temperature and wind data, read by temperature sensors and anemometers on each device. This data is sent through a mobile telecommunications network to cloud-based servers that apply an emission-estimation model based on a plume dispersion and a localization algorithm to the data. Cloud services facilitate the transfer of information from devices to Web-based software.
Each methane sensor is calibrated in the laboratory before installation by exposing it to known levels of methane. In this process, the temperature and humidity are systematically varied while the methane concentrations are changed and the reported conductivity is recorded. The resulting coefficients of conductivity are stored with the serial number of the device and sent to the device through the mobile network when it is deployed in the field.