In recent decades large volumes of saltwater, a byproduct of oil and gas production from unconventional reservoirs, were disposed into the subsurface resulting in increased earthquake rates. Here, we integrate seismicity observations and earthquake simulations to explore disposal-induced earthquake sequence behavior. Data recorded by dense seismic instrumentation in areas of saltwater disposal illuminate a range of spatiotemporal sequence evolutions. Disposal-induced earthquake sequences in Oklahoma and Kansas show strong variability in their magnitude distributions and clustering. Some sequences have recurrence times that are consistent with being driven by a steady stress increase, such as stresses due to long-term injection, while other sequences display more typical mainshock-aftershock clustering reflecting earthquake-earthquake triggering. We also find that clustered induced sequences tend to have more extended foreshock sequences than is typically observed for natural earthquakes. Pore-pressure modeling suggests that these behaviors correlate with pore-pressure changes from widespread injection. Seismic data can also map crustal stress orientations and reveal the types and orientations of activated faults. We find no evidence that fluid injection changes local stress orientations, but that faults that are both well and poorly oriented in the stress field can fail. Additionally, we explore the initial stress conditions and injection protocols that influence sequence behaviors using an earthquake simulator, RSQSim. We find that the maximum magnitude of the triggered events depends strongly on the pre-existing fault stress, such as initial stress level and heterogeneity. The specific injection schedule can influence sequence statistics, including the number of events and their distribution away from the injection well. Our observations and modeling provide insights into the first-order effects of how pore-pressure changes induce fault slip. By combining observation and modeling studies we can develop insights into the processes by which fluid injection may induce earthquakes, improving the potential for mitigating the resulting seismic risk.

The Illinois Basin Decatur Project (IBDP) pioneered large-scale CO2 injection into a deep saline reservoir. During the project, many microseismic events were detected in the basement. It was inferred that the pressure plume reached unknown basement faults, altering their stress conditions and triggering microseismic activity. Understanding the presence and behavior of these basement faults is essential for managing and monitoring induced seismicity risks. However, basement faults with near-vertical dips are difficult to detect using conventional seismic imaging techniques due to unfavorable incident angles. In a previous study, we designed an LSTM model to map basement faults using microseismic clouds, which exhibited significant limitations, with errors ranging from 50% to 100%. To improve the model, we generated new synthetic microseismic data using a fault-zone model, which provided better correlation between the locations of the microseismic events and the fault plane. We switched to a PointNet model, a simple yet powerful deep learning network that directly consumes point clouds. PointNet, originally designed for object classification, part segmentation, and scene semantic parsing, was modified to perform a mapping task. Instead of outputting labels for each point, the modified model outputs arrays containing the probability of fault existence and fault param-eters, including location, size, and orientation. The input is solely the microseismic point cloud. The new model demonstrated a substantial improvement over the previous study, achieving an error of around 10% for fault parameters, including location, size, and orientation. This advancement underscores the potential of ML-based methods for accurately and efficiently mapping previ-ously invisible basement faults, providing critical information to enable rapid decision-making during CO2 injection to prevent and mitigate induced seismic risks. Our work showcases a fast and robust model for mapping basement faults, highlighting the significant progress made in the application of deep learning techniques to geophysical data interpretation and the management of induced seismicity in carbon storage projects. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under contract DE-AC52-07NA27344. Release Number: LLNL-CONF-2002849

ABSTRACT As the carbon sequestration community prepares to scale up the number and size of commercial operations, the need for tools and methods to assess and mitigate risks associated with these operations becomes increasingly important. One outstanding question is whether aftershocks of induced events decay quickly after injection operations cease or if aftershock activity persists for hundreds of years before returning to background levels more akin to tectonic events (Stein and Liu, 2009). Appropriate estimates of the aftershock duration impact several operational management decisions including mitigation strategies and post-injection monitoring for seismic activity. It is hypothesized that induced earthquake rates may diminish more quickly after injection is stopped, owing to higher stressing rates from injected fluids. Alternatively, it is plausible that only the first event in the sequence is induced by increased fluid overpressures, whereas subsequent events (e.g., aftershocks) respond to the stored tectonic stresses and static and dynamic stress changes due to the mainshock (Keranen et al., 2013). If the aftershock duration can be linked to stressing rates due to injection, then it follows that operational strategies to reduce seismic hazard by reducing injection rates or volumes may be successful. However, if aftershocks of induced events are relieving stored tectonic stresses, then altering injection volumes may not alleviate ongoing seismic activity. Furthermore, knowledge of an aftershock duration could aid in the determination of an appropriate post-injection monitoring period for ongoing seismicity, which is a factor in overall operational costs. In this study, we model induced seismicity sequences in Oklahoma with a coupled Coulomb rate–state earthquake rate model (Dieterich, 1994; Kroll et al., 2017) to estimate aftershocks durations. Results for the current study indicate that elevated rates of aftershock activity following induced mainshocks return to background seismicity rates in less than five years, contrary to the tens to hundreds of years observed for tectonic aftershocks.

ABSTRACT: Rock fractures are the most important element of Enhanced Geothermal Systems (EGS) but we lack the necessary data to predict the combined effect of temperature, flow, mechanics, and chemistry on the forecasted effectiveness of these fractures for heat extraction and power production. Here, we seek to quantify the strength, deformation, and hydraulic conductivity of rock fractures before and after shear slip, at geothermal reservoir conditions. This information enables evaluation of the likelihood that hydraulically induced shear slip will improve the performance of EGS. This data can also aid risk forecasting for injection induced seismicity. Concurrent effluent analysis yields insights regarding the chemical reactivity of fresh shear fractures. Our measurements were obtained using triaxial direct-shear experiments conducted at conditions replicating well 16A(78)-32 at the Frontier Observatory for Research in Geothermal Energy (FORGE) in Milford, Utah. We also implement these measurements into reservoir models to evaluate their impact on predicted reservoir performance relative to prior estimates. 1. INTRODUCTION Geothermal energy retains promise to supply clean stable electrical power and heat (Hamm et al., 2018), as well as flexible power to accommodate the intermittency of wind and solar energy (Ricks et al., 2024). Predicting the behavior of rock fractures is key to expansion, especially for Enhanced Geothermal Systems (EGS) because of its requirement for fractures to facilitate fluid flow and heat extraction (Tester et al., 2006; Brown et al., 2012). However, the properties of fractures at hot and deep conditions are not well known, especially if the effects from stimulation, chemistry, and mechanics are to be considered. The Frontier Observatory for Research in Geothermal Energy (FORGE) is an opportunity to better understand such processes by comprehensive scientific assessment of a hot dry rock (HDR) geothermal resource (Podgorney et al., 2023), along with the performance of technologies needed to access and harvest the heat. Here, we present laboratory experiments to characterize thermal, hydraulic, mechanical, and chemical (THMC) coupled processes related to the stimulation of and flow of fluid through shear-induced fractures. Results from this work reveal insights into the potential of hydraulic shear stimulation to enhance flow and quantify key properties for fractures, including strength, frictional properties, dilation tendencies, and chemical reactivity; the details of which are included herein.

of stress and risk of reactivating existing fractures. This study relied upon the initial site characterization work performed by the Illinois State Geologic Survey (ISGS) along with analogue data collected from other carbon sequestration projects in the region.

As carbon storage technologies advance globally, methods to understand and mitigate induced earthquakes become increasingly important. Although the physical processes that relate increased subsurface pore pressure changes to induced earthquakes have long been known, reliable methods to forecast and control induced seismic sequences remain elusive. Suggested reservoir engineering scenarios for mitigating induced earthquakes typically involve modulation of the injection rate. Some operators have implemented periodic shutdowns (i.e., effective cycling of injection rates) to allow reservoir pressures to equilibrate (e.g., Paradox Valley) or shut-in wells after the occurrence of an event of concern (e.g., Basel, Switzerland). Other proposed scenarios include altering injection rates, actively managing pressures through coproduction of fluids, and preinjection brine extraction. In this work, we use 3D physics-based earthquake simulations to understand the effects of different injection scenarios on induced earthquake rates, maximum event magnitudes, and postinjection seismicity. For comparability, the modeled injection considers the same cumulative volume over the project’s operational life but varies the schedule and rates of fluid injected. Simulation results show that cyclic injection leads to more frequent and larger events than constant injection. Furthermore, with intermittent injection scenario, a significant number of events are shown to occur during pauses in injection, and the seismicity rate remains elevated for longer into the postinjection phase compared to the constant injection scenario.

numerical simulations, we conclude that the heterogeneity in the fracture properties is essential for the scattering of seismic waves to be sensitive to the permeability of a fracture.

Our goal is to develop, apply and validate a holistic thermal, hydrologic, mechanical, and chemical (THMC) workflow that includes evaluation of induced seismic slip in EGS reservoirs. We will integrate experimental and modeling approaches to reduce parameter uncertainty and better predict/mitigate seismic hazard at EGS sites. Our novel approach couples 3D physics-based earthquake simulations with THMC models (THMc+E). This capability will enable improve engineering decisions at Utah-FORGE and move EGS operations toward repeatable, robust, economically viable, and socially accepted development. For example, our THMC+E models will predict circulation scenarios and related seismic hazard for a suite of flow rates and under uncertainty, thus enabling evaluation of optimal circulation strategy. Laboratory experiments will be performed to constrain key model parameters and Bayesian techniques will provide a probabilistic evaluation of parameters used in models. THMC+E simulations will enable exploration various circumstances that may hinder EGS success and develop mitigation strategies.

SUMMARY Observational and modelling studies indicate that earthquake ruptures can jump between fault sections as large as ∼3 and ∼5 km for compressional and extensional offsets, respectively. Here, we compare characteristics of the rupture jump process on parallel but offset fault sections from traditional 3-D dynamic rupture simulations governed by slip weakening friction using the finite element code, FaultMod, to those from quasi-dynamic simulations governed by rate- and state-dependent friction (rate-state friction) using the code RSQSim. These simulations use spatially uniform initial stresses. For a variety of measures the rupture renucleation position on the offset fault, the rate-state friction and slip weakening friction models produce very similar results. The principal difference is the additional occurrence of delayed rupture jumps that arise from the time- and stress-dependent nucleation that is characteristic of rate-state friction. For immediate rupture jumps, models with slip weakening friction span greater offsets than those with rate-state friction. However the jump distances are nearly identical when delayed rupture jumps are included in the comparisons. We propose that delayed rupture jumps are the likely mechanism for adjacent large-earthquake pairs and clusters. Based on the similarity of renucleation positions with both dynamic and quasi-dynamic models, we conclude that the renucleation positions for rupture initiation on the receiver fault (separated by less than ∼3 km from the source fault) are primarily controlled by static stress changes induced by slip on the initiating fault. However, in light of the slightly greater maximum jump distances (>3 km) seen with the dynamic slip weakening friction model, dynamic stress changes from seismic waves play an increasingly important role as offset distances increase.

Earthquake sequences induced by fluid disposal into the subsurface show strong variability in their magnitude distributions and clustering behavior. We attempt to untangle the processes that control the occurrence and evolution of disposal-induced earthquake sequences by integrating detailed seismicity observations, pore pressure modeling, and 3D physics-based earthquake simulations. Observations of earthquake sequences in Oklahoma and Kansas include some sequences that have near-Poissonian distribution of interevent times and robust foreshock sequences, while other sequences display more typical mainshock-aftershock clustering behavior. Pore-pressure modeling shows that these behaviors correlate with the amplitude of the pore-pressure changes. Close to disposal wells where pore pressure changes are high, seismicity is controlled by both diffusion and earthquake stress interactions. Farther from the wells where pore pressure changes are lower, seismicity appears driven primarily by stress interactions. We further explore the stress and fault conditions that may allow for these diverse behaviors using an earthquake simulator, RSQSim. We find that the maximum magnitude of the triggered events depends strongly on the pre-existing stress on the fault. The roughness, distribution, and background stress state of pre-existing primary and secondary faults also likely influence the sequence behavior and is an area of ongoing work.

The geologic storage of carbon dioxide (CO2) is one method that can help reduce atmos-pheric CO2 by sequestering it into the subsurface. Large-scale deployment of geologic carbon storage, however, may be accompanied by induced seismicity. We present a project lifetime approach to address the induced seismicity risk at these geologic stor-age sites. This approach encompasses both technical and nontechnical stakeholder issues related to induced seismicity and spans the time period from the initial consid-eration phase to postclosure. These recommendations are envisioned to serve as gen-eral guidelines, setting expectations for operators, regulators, and the public. They contain a set of seven actionable focus areas, the purpose of which are to deal proac-tively with induced seismicity issues. Although each geologic carbon storage site will be unique and will require a custom approach, these general best practice recommenda-tions can be used as a starting point to any site-specific plan for how to systematically evaluate, communicate about, and mitigate induced seismicity at a particular reservoir.

This dataset contains previously published data on induced seismicity that has been processed to be machine learning ready. The data contains time series of the cumulative number of seismic events in certain areas and the corresponding pressures induced from injecting fluids into the ground. The natural task is to forecast future seismicity given past seismicity and pressures. These datasets aim to require as little seismology experience as necessary to prepare the data for forecasting algorithms. Data is provided for different locations. For Decatur Illinois, the seismic data was taken from Williams-Stroud et al., 2018 and the pressure data originated from Luu et al., 2022. Data aggregated over the whole region lies in the temporal_datasets/decatur_illinois/ folder. The region was further subdivided into subregions and the corresponding data stored separately (e.g. in loc1). The Kansas data originated from Cochran et al., 2018 and is further divided into subregions. The Cushing, Oklahoma is adapted from Skoumal et al., 2020. Each seismic file contains the following columns: epoch latitude longitude depth easting northing magnitude. The epoch corresponds to the number of seconds since a certain date (e.g. November 17, 2011 for Decatur). Each seismic event corresponds to one row in the file. Each pressure file contains the following columns: epoch pressure dpdt. dpdt is the derivative of pressure.

The 2005 Intergovernmental Panel on Climate Change (IPCC) Special Report on CCS raised the profile of CO2 capture and storage (CCS) as an important technology for reducing greenhouse gas (GHG) emissions. CCS is now recognized as a key component of most climate change mitigation scenarios. Since publication of that report the international research, development, and deployment (RD&D) community has advanced key technical aspects, clarified regulatory requirements, explored value chain and infrastructure solutions, and developed incentive paradigms to enable and promote large-scale deployment of CCS. These efforts have included research to better characterize geologic storage resources, to improve injection performance and storage efficiency, to assess and manage subsurface environmental risks, and to advance monitoring technologies to assure system conformance. These efforts have helped to build confidence in the viability of geologic carbon storage (GCS), but stakeholder concerns about long-term risks and liability associated with GCS remain a hurdle to broad acceptance and large-scale deployment of CCS. Since 2010, the U.S. DOE’s National Risk Assessment Partnership (NRAP) – a research collaboration between five contributing national laboratories – has worked to establish and demonstrate methods and tools to quantify and manage the subsurface environmental risks associated with GCS, amidst uncertainty. This work supports the Office of Fossil Energy and Carbon Management Carbon Transport and Storage Program’s goal of advancing safe and secure commercial-scale GCS deployment. To address the technical challenge of simulating the physical response of the GCS site to large-scale CO2 injection, NRAP has adopted an approach that relies on coupling computationally efficient reduced-order and/or data-driven proxy models of important system components (i.e., storage reservoir, sealing caprock, leakage pathways, intermediate formations, overlying groundwater aquifers, and the atmosphere) in an integrated assessment framework. That integrated model of the physical system is complemented with fit-for purpose functionality to support site characterization and risk-related decisions. The recently released NRAP Phase II toolset includes the Open-Source Integrated Assessment Model (NRAP-Open-IAM) for evaluation of trends in leakage risk and potential impact, tools to support monitoring design optimization (Designs for Risk Evaluation and Management – DREAM v3.0 and Passive Seismic Monitoring Tool - PSMT), and tools for state of stress evaluation (State-of-Stress Analysis Tool - SOSAT) and forecasting induced seismicity risk. The NRAP team has also released a pair of reports describing conceptual workflows to incorporate physics-based, quantitative risk assessment into many of the design, planning, operation, and closure decisions for GCS projects. An online catalogue highlights published studies where these tools and methods are demonstrated. In this presentation, the utility of these products to assess risks and address key stakeholder questions will be highlighted through examples, and related insights about the safety and security of geologic carbon storage in qualified storage sites will be discussed. The prospect of rapid, large-scale deployment of GCS technology to aggressively reduce anthropogenic CO2 emissions requires careful consideration of interference between multiple commercial-scale storage projects within a basin. Going forward, NRAP is expanding and adapting site-scale risk quantification tools and methods to enable assessment of risks and inform management decisions for basin-scale deployment. Increasingly, this work will leverage next-generation approaches for surrogate modelling, fast prediction, and advanced visualization enabled by machine learning and artificial intelligence to promote virtual learning, scenario evaluation, and augment risk-based decision making.