The AI-Ready Downhole Microseismic Benchmark Database (AMBER)

ABSTRACT

Microseismic monitoring is often used at subsurface industrial sites, such as hydraulic fracturing for shale gas, stimulation of engineered geothermal systems, mature hydrocarbon reservoirs, and carbon capture and storage. Microseismic observations can be used to map growing fractures in the subsurface, to understand the geomechanical response of subsurface reservoirs to industrial operations, and to identify and characterize nascent induced seismicity. Downhole microseismic datasets are acquired using multistation geophone strings placed in deep boreholes at reservoir depths, with sampling rates at 1 kHz or higher. Artificial intelligence (AI) tools have driven a step-change in the processing of surface-based seismological data. However, similar improvements are yet to be realized for downhole microseismic processing tasks. To date, a major barrier in the development of AI tools for downhole microseismic monitoring has been the lack of publicly available data with which to train models. We seek to eliminate this barrier by curating an AI-ready downhole microseismic benchmark database (AMBER) consisting of waveforms and labels (arrival time picks) drawn from 10 downhole microseismic datasets. AMBER contains over 4,000 events with over 150,000 individual P and S wave picks. All picks have been manually verified. The database also contains over 10,000 examples of signal-free noise, which have also been manually verified. The database is provided in an AI-ready format, with Python functions for data ingestion and data augmentation. It is hoped that AMBER will stimulate and support the development of AI tools for downhole microseismic processing.