| dc.description.abstract | This research applies advanced causal inference techniques to uncover intricate causal relationships in two domains within the oil and gas industry - microseismic events and induced seismicity. Microseismic events are small-magnitude seismic events that occur due to crack propagation and rock failure caused by hydraulic fracturing operations. In contrast, induced seismicity refers to larger magnitude earthquakes that occur later in time and over a more extensive area due to subsurface fluid injection operations such as wastewater disposal or hydraulic fracturing. Regarding microseismic events, the study utilizes data from two horizontal wells, MIP 5H and MIP 3H, situated within the Marcellus Shale Energy and Environment Laboratory (MSEEL). Through meticulous spatiotemporal sampling and the formulation of treatment, outcome and confounder variables, Double Machine Learning (DML) is implemented to estimate causal effects. The findings reveal pivotal causal mechanisms governing the characteristics of new microseismic events based on attributes of prior proximal events. High-magnitude prior events are linked to subsequent high-magnitude and delayed new events. Spatial and temporal concentration of prior events also causally impact new event proximity and timing. Appropriate confounder selection is critical, as errors may lead to over/underestimations of the Average Treatment Effect (ATE) by up to 156%.
For induced seismicity, the research explores the causal relationship between injection activities and earthquakes in Oklahoma state using well and seismic data over a 7-year period. Spatiotemporal sampling facilitates the analysis of various grid-time combinations. DML results indicate that approximately 100 water disposal wells induce about 53 earthquakes over 4400 km2 within 54 to 58 days, while 100 hydraulic fracturing wells lead to 36 earthquakes within 16 to 324 km2 over 102 to 110 days. This reveals a more rapid and expansive impact of water disposal versus hydraulic fracturing on induced seismicity. However, minimal causal association is discerned between injection activities and earthquake magnitude.
Overall, the integration of causal inference techniques enhances geoscience data analysis, unearthing genuine causal mechanisms beyond correlational approaches. It strengthens decision-making capabilities regarding extraction practices and mitigation strategies for induced seismicity. This research marks a vital advancement in leveraging causal thinking to unravel the intricate complexities that characterize geophysical systems and phenomena. | |
