Press Releases > Advanced Machine Learning Algorithms for Geological Carbon Storage Verification

Advanced Machine Learning Algorithms for Geological Carbon Storage Verification

Press Release

Press Release

Physical Sciences Inc. (PSI) has been awarded a research program from the U.S. Department of Energy (DOE), to develop advanced innovative data processing techniques for sensors that monitor the integrity of carbon dioxide sequestration sites and pipelines.

Ensuring environmental safety and public acceptance of geologic carbon sequestration (GCS), a geoengineering means of mitigating carbon dioxide (CO2) emissions from coal-fired power plants and other industrial sources, requires cost-effective tools for monitoring, verification, and accounting to detect unintended CO2migration from storage reservoirs and injector wellbores. Identifying leaks is challenging because they are difficult to distinguish from the varying natural ambient CO2.

Collaborating with the University of Texas at Austin, PSI is automating long-term data processing to recognize signatures of slow seepage of CO2and CH4 from GCS sites. We are applying data-driven advanced machine learning algorithms to process information provided by sensors that continuously monitor the surface and subsurface. Both CO2and CH4 may be emitted in leak events, especially when the storage formation has been or is being used for oil production. However, the normal temporal (e.g. diurnal) and spatial variation of CO2and CH4 concentration present in the natural ambient environment may exceed the magnitude of local concentration increase during a leak event. In previous work, PSI developed reliable, sensitive, cost-effective laser-based gas monitoring sensors that have been installed at both carbon sequestration and natural gas facilities to continuously and autonomously monitor near-surface concentrations of CO2and CH4. Field data acquired with these sensors reveal that leak plumes create distinct anomalies in the concentration temporal statistics, yielding statistical features that are distinguishable from natural background variations. These tools will enhance safety and public acceptance while verifying that sequestration performs the intended function of reducing greenhouse gas emissions.

For more Information, contact:

Shin-Juh Chen
Group Leader, Industrial Sensors
Physical Sciences Inc.
(978) 689-0003