Subsurface Energy Research Articles

Solutions and case studies for thermally driven reactive transport and porosity evolution in geothermal systems (reactive Lauwerier problem)

Abstract. Subsurface non-isothermal fluid injection is a ubiquitous scenario in energy and water resource applications, which can lead to geochemical disequilibrium and thermally driven solubility changes and reactions. Depending on the nature of the solubility of a mineral, the thermal change can lead to either mineral dissolution or precipitation (due to undersaturation or supersaturation conditions). Here, by considering this thermo-hydro-chemical (THC) scenario and by calculating the temperature-dependent solubility using a non-isothermal solution (the so-called Lauwerier solution), thermally driven reactive transport solutions are derived for a confined aquifer. The coupled solutions, hereafter termed the “reactive Lauwerier problem”, are developed for axisymmetric and Cartesian symmetries and additionally provide the porosity evolution in the aquifer. The solutions are then used to study two common cases: (I) hot CO2-rich water injection into a carbonate aquifer and (II) hot silica-rich water injection into a sandstone aquifer, leading to mineral dissolution and precipitation, respectively. We discuss the timescales of such fluid–rock interactions and the changes in hydraulic system properties. The solutions and findings contribute to the understanding and management of subsurface energy and water resources, such as aquifer thermal energy storage, aquifer storage and recovery and reinjection of used geothermal water. The solutions are also useful for developing and benchmarking complex coupled numerical codes.

How beliefs about tampering with nature influence support for enhanced geothermal systems: A cross-national study.

Many believe that enhanced geothermal systems (EGS) can greatly increase the extraction of geothermal energy worldwide, helping to decarbonize heat and electricity production. Effective communication is key to realizing the potential of EGS, yet we currently know little about how the public perceives this emerging technology. This exploratory study contributes to the literature with a cross-national survey in the United States (n=1003) and Switzerland (n=1028), two countries with active EGS projects. Specifically, we explore how EGS support relates to beliefs about the deep underground and perceptions of EGS as tampering with nature. The results show that respondents tend to perceive the deep underground as part of nature, dangerous, and unpredictable. The majority are positive about using the deep underground as a resource, although there were variations regarding specific underground activities. In both countries, EGS support is greater for respondents who perceive the underground as something for human use, perceive more benefits than risks from EGS, and support their country's transition to renewable energy. In Switzerland, EGS support is positively related to trust in industry developers and negatively related to perceptions that EGS is tampering with nature. The results offer novel theoretical insights into perceptions of the deep underground in relation to energy development. From a practical standpoint, the results suggest that those seeking to develop EGS may want to consider how to familiarize individuals with current subsurface energy activities, including efforts to protect the underground from unwanted consequences of "tampering," alongside engaging in discussions about the risks and benefits of EGS.

Mechanochemically responsive polymer enables shockwave visualization

Understanding the physical and chemical response of materials to impulsive deformation is crucial for applications ranging from soft robotic locomotion to space exploration to seismology. However, investigating material properties at extreme strain rates remains challenging due to temporal and spatial resolution limitations. Combining high-strain-rate testing with mechanochemistry encodes the molecular-level deformation within the material itself, thus enabling the direct quantification of the material response. Here, we demonstrate a mechanophore-functionalized block copolymer that self-reports energy dissipation mechanisms, such as bond rupture and acoustic wave dissipation, in response to high-strain-rate impacts. A microprojectile accelerated towards the polymer permanently deforms the material at a shallow depth. At intersonic velocities, the polymer reports significant subsurface energy absorption due to shockwave attenuation, a mechanism traditionally considered negligible compared to plasticity and not well explored in polymers. The acoustic wave velocity of the material is directly recovered from the mechanochemically-activated subsurface volume recorded in the material, which is validated by simulations, theory, and acoustic measurements. This integration of mechanochemistry with microballistic testing enables characterization of high-strain-rate mechanical properties and elucidates important insights applicable to nanomaterials, particle-reinforced composites, and biocompatible polymers.

Coupling Upscaled Discrete Fracture Matrix and Apparent Permeability Modelling in DFNWORKS for Shale Reservoir Simulation

Modelling non-Darcy flow behaviour in shale rocks, composed of nanometer-sized pores and multi-scale fracture networks, is crucial for various subsurface energy applications. However, incorporating multiple physical mechanisms across numerous scales is not trivial. This work proposes an improved and practical upscaling workflow for coupling an Upscaled Discrete Fracture Matrix (UDFM) model and a pressure-dependent apparent permeability (Kapp) model to capture the effects of non-Darcy flow in multi-scale fractured shale reservoirs.First, a 3D DFN is upscaled into octree-refined continuum meshes, where equivalent rock parameters and rock-fluid functions are defined using the UDFM approach. Then, the flow simulation is coupled with a pressure-dependent Kapp updating scheme using an existing Kapp model and a multiple-restart technique. The effects of non-Darcy flow mechanisms (e.g., slip flow, transitional flow, Knudsen diffusion) are captured. The constructed models are then used to study the impacts of fracture network connectivity and pressure interference on production. The results of this new approach are compared against those obtained from another commercial package while preserving the advantages of DFNWORKS. Neglecting non-Darcy flow behaviours could significantly underestimate gas production and water recovery. It is illustrated that the nanoscale flow mechanisms help to enhance matrix-matrix and matrix-fracture flow. The constructed models are also utilized to study the effects of disconnected or isolated fractures, pressure interference, water retention, and shut-in durations on well performance. The proposed flexible strategies can be adopted in other commercial/open-source fractured-porous-media subsurface-flow simulation frameworks.

In-situ X-ray micro-CT quantitative analysis and modelling the damage evolution in granite rock

Micro-cracking of rock is important mechanism in underground engineering and subsurface energy extraction. Understanding and modeling microcrack evolution and coalescence, and the eventual macroscopic fracture propagation is still a challenge. A micro-mechanically motivated fracture model is developed based on observation and quantitative analysis of in-situ X-CT uniaxial compression test on brittle intact rock with the resolution of 11.98 μm. We performed uniaxial compression test on a micro-granite and acquired real-time X-CT images of crack development under different loadings to investigate cracking pattern, evolution of the crack network and geometric characteristics of the cracks. The experimental results show that the nucleation of isolated microcracks is the main damage pattern before failure. The evolution of damage is subtle under moderate loading but grows stably in density until the unstable coalescence of microcracks, followed by macro-scale failure. Moreover, the damage process is initially dominated by the development of inclined cracks; when failure is approached, the development of relatively more parallel cracks gradually become predominant, indicating the damage transition nature from shear mechanism to tensile mechanism. Based on the X-CT analysis, an improved physics-informed micro-mechanical sliding model is proposed. Compared with previous sliding models, the crack-crack interaction is improved so that this model can well characterize the abrupt failure process. The total surface area of cracks is used to quantify the damage evolution and a relative calibration method is presented. The simulation results show the stress–strain curve and damage evolution are consistent with the experimental results.

A Review of Coupled Geochemical–Geomechanical Impacts in Subsurface CO2, H2, and Air Storage Systems

Increased demand for decarbonization and renewable energy has led to increasing interest in engineered subsurface storage systems for large-scale carbon reduction and energy storage. In these applications, a working fluid (CO2, H2, air, etc.) is injected into a deep formation for permanent sequestration or seasonal energy storage. The heterogeneous nature of the porous formation and the fluid–rock interactions introduce complexity and uncertainty in the fate of the injected component and host formations in these applications. Interactions between the working gas, native brine, and formation mineralogy must be adequately assessed to evaluate the efficiency, risk, and viability of a particular storage site and operational regime. This study reviews the current state of knowledge about coupled geochemical–geomechanical impacts in geologic carbon sequestration (GCS), underground hydrogen storage (UHS), and compressed air energy storage (CAES) systems involving the injection of CO2, H2, and air. Specific review topics include (1) existing injection induced geochemical reactions in these systems; (2) the impact of these reactions on the porosity and permeability of host formation; (3) the impact of these reactions on the mechanical properties of host formation; and (4) the investigation of geochemical-geomechanical process in pilot scale GCS. This study helps to facilitate an understanding of the potential geochemical–geomechanical risks involved in different subsurface energy storage systems and highlights future research needs.

A mechanism of enhanced subsurface near-inertial kinetic energy in the East China Sea associated with successive typhoons

Near-inertial waves (NIWs) play an important role in diapycnal processes and energy dissipation. A mooring observation deployed on the continental shelf in the East China Sea captured anomalously intensified subsurface near-inertial kinetic energy (NIKE) during the passage of Typhoon Danas (2013). An early study has investigated the role of Parametric Subharmonic Instability (PSI) induced by internal tides in the intensification of the subsurface intensified near-inertial velocity. However, results based on regional numerical simulations reveal that strong subsurface near-inertial velocity persists even in the absence of tidal effects, implying the existence of additional sources of NIWs. Our analyses showed that after excluding the effect of PSI, approximately 30% of the remaining subsurface NIKE can be attributed to another Typhoon Fitow (2013), which occurred a week prior to Typhoon Danas. Constrained by the Kuroshio current and the continental shelf, the NIKE generated by Typhoon Fitow propagates northward and reaches the mooring location, leading to the intensified subsurface NIW signal. Our simulation, together with the observations, suggests complicated NIW dynamics in continental shelf regions, involving interactions between successive typhoons, topography and background current, and differing from the open ocean. These interactions will further influence vertical mixing on the continental shelf along the pathway of NIW.

Advances in Subsurface Energy Exploitation and Storage

Advances in Subsurface Energy Exploitation and Storage

A critical review of physics-informed machine learning applications in subsurface energy systems

Machine learning has emerged as a powerful tool in various fields, including computer vision, natural language processing, and speech recognition. It can unravel hidden patterns within large data sets and reveal unparalleled insights, revolutionizing many industries and disciplines. However, machine and deep learning models lack interpretability and limited domain-specific knowledge, especially in applications such as physics and engineering. Alternatively, physics-informed machine learning (PIML) techniques integrate physics principles into data-driven models. By combining deep learning with domain knowledge, PIML improves the generalization of the model, abidance by the governing physical laws, and interpretability. This paper comprehensively reviews PIML applications related to subsurface energy systems, mainly within the context of the oil and gas industry. The review highlights the successful utilization of PIML for tasks such as seismic applications, reservoir and fluid flow simulation, and hydrocarbons production forecasting. Additionally, it demonstrates PIML’s capabilities to revolutionize the oil and gas industry and other emerging areas of interest, such as carbon and hydrogen storage, by providing more accurate and reliable predictions for resource management and operational efficiency.

A novel framework for predicting non-stationary production time series of shale gas based on BiLSTM-RF-MPA deep fusion model

Shale gas, as an environmentally friendly fossil energy resource, has gained significant commercial development and shows immense potential. However, accurately predicting shale gas production faces substantial challenges due to the complex law of decline, nonlinear and non-stationary features in production data, which greatly repair the robustness of current models in predicting shale gas production time series. To address these challenges and improve accuracy in production forecasting, this paper introduces a novel and innovative approach: a hybrid proxy model that combines the bi-directional long short-term memory (BiLSTM) neural network and random forest (RF) through deep learning. The BiLSTM neural network is adept at capturing long-term dependencies, making it suitable for understanding the intricate relationships between input and output variables in shale gas production. On the other hand, RF serves a dual purpose: reducing model variance and addressing the concept drift problem that arises in non-stationary time series predictions made by BiLSTM. By integrating these two models, the hybrid approach effectively captures the inherent dependencies present in long and nonstationary production time series, thereby reducing model uncertainty. Furthermore, the combination of BiLSTM and RF is optimized using the recently-proposed marine predators algorithm (MPA) to fine-tune hyperparameters and enhance the overall performance of the proxy model. The results demonstrate that the proposed BiLSTM-RF-MPA model achieves higher prediction accuracy and demonstrates stronger generalization capabilities by effectively handling the complex nonlinear and non-stationary characteristics of shale gas production time series. Compared to other models such as LSTM, BiLSTM, and RF, the proposed model exhibits superior fitting and prediction performance, with an average improvement in performance indicators exceeding 20%. This innovative framework provides valuable insights for forecasting the complex production performance of unconventional oil and gas reservoirs, which sheds light on the development of data-driven proxy models in the field of subsurface energy utilization.

CO2-induced micro-mechanical alterations of mudstone Caprock: Examples from the Mercia Mudstone Group, East Irish Sea Basin

Mechanical properties of caprock are important for subsurface energy sequestration as they determine the rock stability under the influence of external forces. Despite some advantages, there is a lack of knowledge regarding the interplay between rock texture and mechanical properties and their impact on the caprock stability within a short-term fluid-rock reaction period. Typical caprock samples from the Mercia Mudstone Group in the East Irish Sea Basin are studied in this work. X-ray diffraction (XRD), scanning electron microscopy (SEM) with energy-dispersive X-ray spectroscopy (EDX) were used to identify minerology and local heterogeneity characteristics. Nanoindentation tests were conducted before and after CO2-brine-rock reaction to investigate the changes of micro-mechanical properties. After CO2-brine treatment, significant dolomite dissolution was observed in all samples (although dolomite content was only minor in some samples). In contrast, the micro-texture of other minerals showed no significant changes indicating minimal to no dissolution at the current resolution. The changes of mechanical properties are lower than expected, which are probably related to the impaired cementation induced by the retained water in pore throats and relatively rough surface after reaction. For long-term carbon sequestration, the layered depositional characteristics of different phases may not only form a barrier to prevent fluids leaking upward, but also complement each other in mechanical properties to maintain stability. This study improves the understanding of the effect of microscale structural and micro-mechanical changes on caprock stability in carbon sequestration related applications.

Modelling physical controls on mine water heat storage systems

The use of abandoned mines as a heat source and store has been receiving increased attention as a renewable heat source and storage solution in the transition away from traditional gas heating. The hydraulic, thermal and geomechanical processes governing heat storage and extraction are complex and understanding these processes is critical to safe heat extraction and injection into mine water systems. This paper outlines the development of a fully coupled thermo-hydraulic-mechanical (THM) 2D model to understand the mechanical stability of room and pillar workings during heat injection and extraction. It was found that the cyclical injection and extraction of heat does have an impact on both the modelled displacements and mechanical stability of the system. The impact risk reduces with temperature and the operational processes (e.g. injection temperature and water level) have more of an impact than the underlying geological conditions. These results are significant and could be included in a regulatory system to reduce the likelihood of stability impacts in mine water heating and cooling schemes. Thematic collection: This article is part of The Earth as a thermal battery: future directions in subsurface thermal energy storage systems collection available at: https://www.lyellcollection.org/topic/collections/thermal-energy

Two-phase flow thermo-hydro-mechanical modeling for a water flooding field case

Simulation of subsurface energy system involves multi-physical processes such as thermal, hydraulical, and mechanical (THM) processes, and requires a so-called THM coupled modeling approach. THM coupled modeling is commonly performed in geothermal energy production. However, for hydrocarbon extraction, we need to consider multiphase flow additionally. In this paper, we describe a three-dimensional numerical model of non-isothermal two-phase flow in the deformable porous medium by integrating governing equations of two-phase mixture in the porous media flow in the reservoir. To account for inter-woven impacts in subsurface conditions, we introduced a temperature-dependent fluid viscosity and a fluid density along with a strain-dependent reservoir permeability. Subsequently, we performed numerical experiments of a ten-year water flooding process employing the open-source parallelized code, OpenGeoSys. We considered different well patterns with colder water injection in realistic scenarios. Our results demonstrate that our model can simulate complex interactions of temperature, pore pressure, subsurface stress and water saturation simultaneously to evaluate the recovery performance. High temperature can promote fluid flow while cold water injection under non-isothermal conditions causes the normal stress reduction by significant thermal stress. Under different well patterns the displacement efficiency will be changed by the relative location between injection and production wells. This finding has provided the important reference for fluid flow and induced stress evolution during hydrocarbon exploitation under the environment of large reservoir depth and high temperature.

Seismic monitoring of EGS fracture stimulations at Utah FORGE (Part 1): Time-lapse variations of b-values and Shear-wave Splitting Rates of induced microearthquakes

Characterizing geologic formations near geothermal wells is crucial for efficient extraction of subsurface geothermal energy resources. To monitor effective fracture stimulation of enhanced geothermal systems (EGS), we analyze a catalog of induced microearthquakes to characterize seismicity induced by Phase 2C fracture stimulations of the Utah FORGE project and derive the b-values for 424 induced microearthquakes in three stages of fracture stimulation. We find the time-lapse variations of these b-values. We apply eigenvector rotation and waveform correlation to 3C geophone data to separate fast S1 and slow S2 waves, determine their directions, measure their lag time, and calculate the S-wave splitting rate (SSR) for six induced microearthquakes in Stage 1 and 21 in Stage 3. Both SSR values and b-values increase with time. The increase in these values may be related to heavily fractured zones created by fracture stimulations during Stage 2 injections. Large and highly variable SSR values in Stage 3 may reflect heterogeneities in the fracture zones. Time-lapse variations of SSR values and b-values may be one of the most useful approaches to monitoring and characterizing the effectiveness and efficiency of various types of fracture stimulations.

Dynamic analysis of geomaterials using microwave sensing

Precise characterization of geomaterials improves subsurface energy extraction and storage. Understanding geomaterial property, and the complexities between petrophysics and geomechanics, plays a key role in maintaining energy security and the transition to a net zero global carbon economy. Multiple sectors demand accurate and rapid characterization of geomaterial conditions, requiring the extraction of core plugs in the field for full-field characterization and analysis in the laboratory. We present a novel technique for the non-invasive characterization of geomaterials by using Frequency Modulated Continuous Wave (FMCW) radar in the K-band, representing a new application of microwave radar. We collect data through the delivery of FMCW wave interactions with geomaterials under static and dynamic conditions and show that FMCW can detect fluid presence, differentiate fluid type, indicate the presence of metallic inclusions and detect imminent failure in loaded sandstones by up to 15 s, allowing for greater control in loading up to a failure event. Such precursors have the potential to significantly enhance our understanding of, and ability to model, geomaterial dynamics. This low-cost sensing method is easily deployable, provides quicker and more accessible data than many state-of-the-art systems, and new insights into geomaterial behavior under dynamic conditions.

Peridynamic modeling of seepage in multiscale fractured rigid porous media

AbstractThe numerical simulation of seepage in fractured porous media holds significant relevance for subsurface energy development. The presence of fractures at various scales profoundly influences the hydraulic properties of porous media during seepage. A peridynamic (PD)‐based frame is proposed for the seepage problem analysis of multiscale fractured porous media, in which micro‐fractures are implicitly represented like the porous media, macro‐fractures are explicitly represented. For fluid flow modeling, the peridynamic bonds are divided into three types: pore seepage bond, fracture seepage bond, and fluid exchange bond. The micro‐permeabilities of different bonds are derived by Darcy's law, Poiseuille's law, and fluid exchange model, respectively. The proposed peridynamic model can naturally simulate the seepage problem of porous rock with multiscale fractures in a unified framework. Then, four cases of pore seepage and fracture seepage are analyzed using the proposed model, fractured porous media seepage with micro‐fractures and macro‐fractures are respectively considered. The predicted results are compared with the available theoretical solutions, the close agreement shows that the wide validity and applicability of the proposed model in multiscale seepage problems.

A Connectivity Metrics-Based Approach for the Prediction of Stress-Dependent Fracture Permeability

Rapid and accurate assessment of fracture permeability is critical for subsurface resource and energy development as well as rock engineering stability. Fracture permeability deviates from the classical cubic law under the effect of roughness, geological stress, as well as mining-induced stress. Conventional laboratory tests and numerical simulations are commonly costly and time-consuming, whereas the use of a connectivity metric based on percolation theory can quickly predict fracture permeability, but with relatively low accuracy. For this reason, we selected two static connectivity metrics with the highest and lowest prediction accuracy in previous studies, respectively, and proposed to revise and use them for fracture permeability estimation, considering the effect of isolated large-aperture regions within the fractures under increasing normal stress. Several hundred fractures with different fractal dimensions and mismatch lengths were numerically generated and deformed, and their permeability was calculated by the local cubic law (LCL). Based on the dataset, the connectivity metrics were counted using the revised approach, and the results show that, regardless of the connectivity metrics, the new model greatly improves the accuracy of permeability prediction compared to the pre-improved model, by at least 8% for different cutoff aperture thresholds.

Ultraviolet laser-etched Norland optical adhesive 81 micromodel for studying two-phase flow experiments

As the global energy demand grows, maximizing oil extraction from known reserves has become critical. The study of microfluidic flow and transport in porous media has become a key direction for future subsurface energy technologies. However, the high requirements of fabrication techniques and materials have constrained the progress of micro-scale experiments. In this study, we have innovatively proposed a microfluidic chip fabrication method based on ultraviolet laser, and a set of visualized microdrive platforms is developed to allow direct observation of two-phase flow processes at the pore scale. In this study, two pore structures—one with low porosity and high connectivity and the other with high porosity but low connectivity—were constructed to investigate the effect of pore structure on recovery. Two micromodels with different pore structures were fabricated, and water and surfactant drive experiments were conducted at different drive rates, respectively. The results show that increasing the replacement rate and introducing surfactant can somewhat improve the final recovery. Using surfactant is more effective in enhancing the recovery rate than increasing the replacement rate. The complexity of pore structure is one of the main factors affecting the formation of residual oil. The microfluidic experimental setup proposed in this study reduces the time and cost of conventional practical methods. It permits visualization of the oil drive process, demonstrating that the Norland Optical Adhesive 81 (NOA81) micromodel is a valuable tool in two-phase flow studies and its applications.

2D Phase-Field Modelling of Hydraulic Fracturing Affected by Cemented Natural Fractures Embedded in Saturated Poroelastic Rocks

AbstractThe interaction between a propagating hydraulic fracture (HF) and a pre-existing natural fracture (NF) embedded in saturated poroelastic rock formations is studied numerically in 2D plane–strain configurations. In this study, the phase-field method is further developed to be employed for modelling the HF propagation and the evolution of tensile and shear failure in geo-materials as gradient-type diffusive damaged zones. The shear slippage and dilation mechanisms inside the cemented NF are modelled using a Mohr–Coulomb–Griffith failure criterion that fitted in the framework of the phase-field fracture using appropriate energy functionals. The most important factors controlling the HF–NF interaction outcome are the approaching angle, differential in-situ stress, and hydro-mechanical characteristics of the NF. It is found out that higher tensile and shear strengths of the cemented NF are in favour of the crossing outcome when the differential stress is high enough to mobilise the resisting shear stresses against the slippage. Small hydraulic aperture (low hydraulic conductivity) for the NF is also in favour of the crossing outcome which helps to restrict the pressurised region local to the HF tip, lowering the possibility of shear slippage in the NF and the HF’s diversion. It is also concluded that the injection rate and the viscosity of fracturing fluid are operative factors to be adjusted for increasing the chance of crossing, a critical element for successful operation of hydraulic fracturing for effective use of subsurface energy resources.

Investigating the Frontiers of Deep Learning and Machine Learning: A Comprehensive Overview of Key Challenges in Missing Well Log Estimation

Well logging is a significant method of geological formation description and resource assessment in the exploration and development of oil, natural gas, minerals, groundwater, and sub-surface thermal energy, as well as geotechnical engineering and environmental research. However, the challenging problem of estimating well logging data always exists because well logs can only be measured through a drilling process involving costly and time-consuming field trials. This study provides a brief overview of Deep Learning (DL) models and examines the well-logging issues in estimating missing well-log data. In addition, it discusses a literature review focusing on the application of DL models for well-log estimation. The outcome of this exploratory work necessitates suitable requirements for the design and implementation.