Want to learn about the management of induced seismicity related to hydraulic fracturing operations?
Check out Sepideh Karimi's presentation on "Operational real-time induced seismicity risk management" at the 2019 UIC Conference on Monday, February 25 between 1:30 p.m. and 3:00 p.m. at the Sheraton Fort Worth Downtown Hotel.
Operational real-time induced seismicity risk management
Presenter: Sepideh Karimi
Practical management of induced seismicity risk and effective mitigation approaches are crucial to oil and gas operations. The majority of the regulatory traffic light protocols introduced to date are based on staged magnitude threshold. The operators are required to establish operational orders designed to minimize the likelihood of the occurrence of large magnitude events and are in some instances mandated to implement higher resolution seismic monitoring arrays. The ultimate goal of these seismic networks beyond simple regulatory compliance is to provide operators with a near real-time measure of the induced seismicity risk and an indication of the risk mitigation protocol effectiveness. Practical risk management procedures benefit from an accurate forecast of the largest potential magnitude event in near real-time, allowing the adjustment of operational parameters to reduce the probability of a felt or damaging event.
In this study, we present the current state of technology that provides operators with real-time feedback, allowing for the management of induced seismicity related to hydraulic fracturing operations. We present examples of a near real-time risk management application to demonstrate the performance of this tool in forecasting expected maximum magnitude and future seismicity. The seismicity predictions from different published models are validated by playing back over 30 datasets. This approach has been adopted by operators and is currently in production with the ultimate objective of continued extraction of natural resources without triggering large magnitude events. Continuous feedback from operators using this tool in a production environment allows for the improvement in the value and accuracy of predictions with time. This development is an excellent example of collaboration between industry by financing datasets, academia by developing the models, and service providers through innovation and development of a product.