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Causal mechanisms for fluid injection-induced earthquakes remain a challenge to identify. Past studies largely established spatiotemporal correlations. Here, we propose a multi-process causal mechanism for injection-induced earthquakes through a case study of the 2017 M w 5.5 induced earthquake near Pohang Enhanced Geothermal System, Korea, where detailed hydraulic stimulation and on-site seismicity monitoring data provide an unprecedented opportunity. Pore pressure modeling reveals that pore pressure changes initiate seismicity on critically stressed faults and Coulomb static stress transfer modeling reveals that earthquake interactions promote continued seismicity, leading to larger events. On the basis of these results, we propose the following causal mechanism for induced seismicity: pore pressure increase and earthquake interactions lead to fault weakening and ultimately triggering larger earthquakes later in the process. We suggest that it is prudent that pore pressure change, initial seismicity locations, and Coulomb static stress transfer from seismicity earlier in the sequence are assessed in real-time. The authors here suggest a causal mechanism for injection-induced earthquakes. They further suggest pore pressure modeling as a practical alternative to direct in-situ pore pressure observation which can then be used for stress build-up monitoring.

The permeability of a pore structure is typically described by stochastic representations of its geometrical attributes (e.g. pore-size distribution, porosity, coordination number). Database-driven numerical solvers for large model domains can only accurately predict large-scale flow behavior when they incorporate upscaled descriptions of that structure. The upscaling is particularly challenging for rocks with multimodal porosity structures such as carbonates, where several different type of structures (e.g. micro-porosity, cavities, fractures) are interacting. It is the connectivity both within and between these fundamentally different structures that ultimately controls the porosity–permeability relationship at the larger length scales. Recent advances in machine learning techniques combined with both numerical modelling and informed structural analysis have allowed us to probe the relationship between structure and permeability much more deeply. We have used this integrated approach to tackle the challenge of upscaling multimodal and multiscale porous media. We present a novel method for upscaling multimodal porosity–permeability relationships using machine learning based multivariate structural regression. A micro-CT image of Estaillades limestone was divided into small 60 3 and 120 3 sub-volumes and permeability was computed using the Darcy–Brinkman–Stokes (DBS) model. The microporosity–porosity–permeability relationship from Menke et al. (Earth Arxiv, https://doi.org/10.31223/osf.io/ubg6p , 2019) was used to assign permeability values to the cells containing microporosity. Structural attributes (porosity, phase connectivity, volume fraction, etc.) of each sub-volume were extracted using image analysis tools and then regressed against the solved DBS permeability using an Extra-Trees regression model to derive an upscaled porosity–permeability relationship. Ten test cases of 360 3 voxels were then modeled using Darcy-scale flow with this machine learning predicted upscaled porosity–permeability relationship and benchmarked against full DBS simulations, a numerically upscaled Darcy flow model, and a Kozeny–Carman model. All numerical simulations were performed using GeoChemFoam, our in-house open source pore-scale simulator based on OpenFOAM. We found good agreement between the full DBS simulations and both the numerical and machine learning upscaled models, with the machine learning model being 80 times less computationally expensive. The Kozeny–Carman model was a poor predictor of upscaled permeability in all cases.

Despite the global interest in green energy alternatives, little attention has focused on the large-scale viability of recycling the ground heat accumulated due to urbanization, industrialization and climate change. Here we show this theoretical heat potential at a multi-continental scale by first leveraging datasets of groundwater temperature and lithology to assess the distribution of subsurface thermal pollution. We then evaluate subsurface heat recycling for three scenarios: a status quo scenario representing present-day accumulated heat, a recycled scenario with ground temperatures returned to background values, and a climate change scenario representing projected warming impacts. Our analyses reveal that over 50% of sites show recyclable underground heat pollution in the status quo , 25% of locations would be feasible for long-term heat recycling for the recycled scenario, and at least 83% for the climate change scenario. Results highlight that subsurface heat recycling warrants consideration in the move to a low-carbon economy in a warmer world. Using shallow geothermal energy systems to recycle the heat accumulating in the subsurface due to climate change and urbanization is a feasible, sustainable, and opportunistic alternative to conventional space heating in the face of climate change

The rise in the frequency of anthropogenic earthquakes due to deep fluid injections is posing serious economic, societal, and legal challenges to many geo-energy and waste-disposal projects. Existing tools to assess such problems are still inherently heuristic and mostly based on expert elicitation (so-called clinical judgment). We propose, as a complementary approach, an adaptive traffic light system (ATLS) that is function of a statistical model of induced seismicity. It offers an actuarial judgement of the risk, which is based on a mapping between earthquake magnitude and risk. Using data from six underground reservoir stimulation experiments, mostly from Enhanced Geothermal Systems, we illustrate how such a data-driven adaptive forecasting system could guarantee a risk-based safety target. The proposed model, which includes a linear relationship between seismicity rate and flow rate, as well as a normal diffusion process for post-injection, is first confirmed to be representative of the data. Being integrable, the model yields a closed-form ATLS solution that is both transparent and robust. Although simulations verify that the safety target is consistently ensured when the ATLS is applied, the model from which simulations are generated is validated on a limited dataset, hence still requiring further tests in additional fluid injection environments.

The traditional model of solid dissolution in porous media consists of three dissolution regimes (uniform, compact, wormhole)—or patterns—that are established depending on the relative dominance of reaction rate, flow, and diffusion. In this work, we investigate the evolution of pore structure using numerical simulations during acid injection on two models of increasing complexity. We investigate the boundaries between dissolution regimes and characterize the existence of a fourth dissolution regime called channeling, where initially fast flow pathways are preferentially widened by dissolution. Channeling occurs in cases where the distribution in pore throat size results in orders of magnitude differences in flow rate for different flow pathways. This focusing of dissolution along only dominant flow paths induces an immediate, large change in permeability with a comparatively small change in porosity, resulting in a porosity–permeability relationship unlike any that has been previously seen. This work suggests that the traditional conceptual model of dissolution regimes must be updated to incorporate the channeling regime for reliable forecasting of dissolution in applications like geothermal energy production and geologic carbon storage.

The hypothesis that the brittle–ductile transition (BDT) drastically reduces permeability implies that potentially exploitable geothermal resources (permeability >10 −16  m 2 ) consisting of supercritical water could occur only in rocks with unusually high transition temperatures such as basalt. However, tensile fracturing is possible even in ductile rocks, and some permeability–depth relations proposed for the continental crust show no drastic permeability reduction at the BDT. Here we present experimental results suggesting that the BDT is not the first-order control on rock permeability, and that potentially exploitable resources may occur in rocks with much lower BDT temperatures, such as the granitic rocks that comprise the bulk of the continental crust. We find that permeability behaviour for fractured granite samples at 350–500 °C under effective confining stress is characterized by a transition from a weakly stress-dependent and reversible behaviour to a strongly stress-dependent and irreversible behaviour at a specific, temperature-dependent effective confining stress level. This transition is induced by onset of plastic normal deformation of the fracture surface (elastic–plastic transition) and, importantly, causes no ‘jump’ in the permeability. Empirical equations for this permeability behaviour suggest that potentially exploitable resources exceeding 450 °C may form at depths of 2–6 km even in the nominally ductile crust. The brittle–ductile transition is thought to control crustal permeability. Laboratory experiments and model simulations show that permeability is also stress dependent and ductile granitic rocks may have enough permeability to host geothermal resources.

The effect of fracture networks on EGS performance remains worth further investigation to guide the formulation of geothermal extraction strategy. We established models that account for thermo-hydraulic-mechanical (THM) coupling and that are based on the framework of discrete fracture network (DFN) to evaluate the heat extraction performance in deep-seated fractured reservoir. Our numerical results reveal that the zones of temperature, pressure, and stress perturbation diffuse asynchronously during the circulation of injection-production, and the stress perturbation always lags behind the other two. Furthermore, the effects of the fracture network characteristics including randomness, geometry, length, aperture, and injection parameters on the heat production are quantitatively investigated. Under the same number of fractures, different network geometry leads to different EGS production performance, the network with horizontal fracture set shows better thermal extraction performance but poor injection performance, which is because the fracture dip affects the thermal evolution on the horizontal plane. The effect of fracture length on EGS performance highly depends on its orientation, the excessive increase of fracture length towards injection-production wells is detrimental to heat extraction. The fracture aperture affects the working fluid transport and thus the EGS performance, the fractured reservoir with smaller fracture aperture shows the worse fluid flow performance but the better geothermal extraction performance, thus we believe that the optimal fracture aperture should be kept at a level of 0.5–1.0 mm in a self-propping fractured granitic system. The main influence of injection parameters on thermal extraction from the fractured reservoirs is the injection mass rate, because a high injection rate results in significant solid responses, including failure stress concentration, decreased safety factor, and increased permeability, which occur in those fractures that are originally connected to the injection well. These results of our research and the insights obtained have important implications for deep geothermal geoengineering activities.

Bentheim Sandstone is considered a suitable conventional georeservoir rock even at great depth because of its mineral composition, homogeneity, micro- and macrostructure, and is also used as a reference material in rock deformation tests. However, a full characterization of the permeability at representative depths has never been performed. Here we report new experimental data where the permeability of Bentheim Sandstone is measured both with a simultaneous variation and with a sequential variation of three different variables to simulate georeservoir conditions. The results indicate a decrease in permeability with simulated increasing depth until 2–3 km, followed by a partial permeability recovery until 4–5 km depth. During the exhumation path, initially, permeability is unaffected, but at shallow depths, a sharp increase in permeability is observed, likely due to microcracking. These variations are a consequence of a complex interaction between stress, pore pressure and temperature, highlighting the importance of experiments considering all three variables when studying the evolution of permeability at depth. These results will aid with the accurate estimation of permeability at different georeservoir conditions.

When drilling wells for energy explorations, it is important to regulate the formation pressures appropriately to prevent kicks, which can lead to unimaginable loss of lives and properties. This is usually done by controlling the equivalent circulating density (ECD), which responds to the dynamic conditions that occur during drilling. The conventional approach to determine ECD is via mathematical modeling or downhole measurements. However, the downhole measurement tools can be very expensive, and the mathematical models do not provide a high degree of accuracy. Some previous authors have proposed using machine learning (ML) techniques to improve the degree of accuracy of the ECD predictions. In this work, we employed an extreme gradient-boosting (XGBoost) methodology to predict ECD values. The model's accuracy was determined using correlation coefficients (R 2 ) and root mean square errors (RMSE) as their performance metrics. The results showed a strong prediction capability with an R 2 and RMSE of 1.00 and 0.0005 for the training data and an R 2 and RMSE of 0.989 and 0.023 for the testing/blind data set, respectively. The developed model outperformed those obtained using other popular machine learning techniques. Lastly, an interpretation of the model results showed that mud weight, weight on hook, and standpipe pressure contributed the most to the ECD prediction values.

Micro-structural attributes of Chumathang granite from Leh, India, were experimentally determined in the temperature range from 25 to 600 °C for enhanced geothermal systems (EGS). P-wave velocity, thermal crack generation, and pore attributes were analyzed using a combination of pulse ultrasonic velocity study, 3D X-ray tomography and low-pressure gas adsorption experiments, respectively. Results indicate that thermal crack development is driven by mineral composition and differential thermal expansion, with a significant increase in the thermal damage factor between 450 ∘ C and 600 ∘ C , accompanied by visible cracks at 600 ∘ C . Surface area and pore volume decreased up to 300 ∘ C due to mineral dissolution, then slightly increased up to 600 ∘ C due to microfracture formation. Pore size distribution showed a dominance of coarser mesopores, and fractal dimensions decreased with temperature, reflecting simpler pore geometries. These findings enhance the understanding of granite’s microstructural changes under thermal stress, informing the optimization of EGS heat extraction efficiency.