Injection-Induced Seismic Risk Management Using Machine Learning Methodology – A Perspective Study

Abstract

Effective identification of induced seismicity and real-time management of seismic risks are hot topics due to increasing induced seismicity in areas related to energy exploitation. Existing decision-making tool for managing seismic risks, known as the traffic light system, is not robust enough. To meet the increasing needs for safe mining of energy at production sites, finding an advanced and efficient method to improve the traffic light system is essential. In recent years, machine learning, an advanced inductive and analytical method, has been widely used in seismology. In this context, research gaps associated with the identification and management of induced seismicity, as well as the current achievements of machine learning in addressing induced seismicity problems, are reviewed. A basic framework of using machine learning method to optimize the traffic light system in the industrial production process is first proposed. Then, its feasibility and rationality are demonstrated by similar cases. This framework may provide a reference for the development of a risk-based adaptive traffic light management system.

Machine Learning Tools for Fossil and Geothermal Energy Production and Carbon Geo-sequestration—a Step Towards Energy Digitization and Geoscientific Digitalization

“…For example, microseismic signatures can be used as a potential indicator of fault reactivation. As a result, risk systems can be implemented to deploy effective mitigation strategies under seismic hazard [32].…”

Association Between Injection and Microseismicity in Geothermal Fields With Multiple Wells: Data‐Driven Modeling of Rotokawa, New Zealand, and Húsmúli, Iceland

“…For instance, earthquake generating fractures are not uniformly distributed or oriented, and nor are their resolved stress conditions homogeneous. This makes it hard to separate out the randomness and to extract useful information using conventional analytical or numerical methods (He et al., 2020).…”

Data analytics to investigate the cohort of injection wells with earthquakes in Oklahoma

“…There have been several studies regarding the induced seismicity throughout the world (Davies et al, 2013;Dieterich et al, 2015;Frohlich, 2012;Frohlich et al, 2016;Gibowicz and Lasocki, 2001;Gupta, 2002;He et al, 2020;Hincks et al, 2018;Keranen et al, 2013Keranen et al, , 2014Li et al, 2007;Magnani et al, 2017;Norbeck and Horne, 2018;Pei et al, 2018;Keranen et al (2014); Snee and Zoback, 2018), and in the states of Oklahoma and Texas, but almost all of them make prior assumptions such as temporal and spatial correlation before analyzing the dataset. Dieterich et al (2015) coupled a reservoir model to the RSQSim simulator, which is an earthquake simulator, to investigate the effects of several important factors such as initial stress, injected volume, and shut-in time on the induced seismicity triggered by the increases in the pore pressure.…”

“…There have been several studies regarding the induced seismicity throughout the world (Davies et al, 2013; Dieterich et al, 2015; Frohlich, 2012; Frohlich et al, 2016; Gibowicz and Lasocki, 2001; Gupta, 2002; He et al, 2020; Hincks et al, 2018; Keranen et al, 2013, 2014; Li et al, 2007; Magnani et al, 2017; Norbeck and Horne, 2018; Pei et al, 2018; Keranen et al (2014); Snee and Zoback, 2018), and in the states of Oklahoma and Texas, but almost all of them make prior assumptions such as temporal and spatial correlation before analyzing the dataset.…”