Shirish Patil

Hot dry rock (HDR) represents a highly promising sustainable clean energy resource for the future, with the fracture network induced by hydraulic fracturing serving as the cornerstone for thermal energy extraction efficiency. However, conventional numerical simulation tools are computationally expensive and fail to meet the requirements for real-time, dynamic on-site predictions. While machine learning models, such as traditional artificial neural network (ANN) and Long Short-Term Memory (LSTM), suffer from severe “black-box” limitations, they also lack the capability to capture the dynamic evolutionary characteristics of complex time series. To resolve these limitations, this study proposes a deep learning framework based on the Temporal Fusion Transformer (TFT) to dynamically predict the temporal evolution of fracture morphology. The research dataset was automatically generated in batches using GOHFER software (version 9.5.6), and feature selection was subsequently completed through parametric sensitivity analysis. A comparative analysis between the TFT model and traditional baseline models (ANN and LSTM) demonstrates that the TFT model achieves superior accuracy in capturing non-linear features that evolve dynamically over time, effectively overcoming the severe underfitting issues exhibited by conventional models under complex temporal constraints. Furthermore, leveraging the inherent attention mechanism of the TFT, this study elucidates the influence weights of injection rate, proppant concentration, and carrier fluid volume on fracture propagation across different fracturing stages, thereby enhancing the interpretability of the deep learning model. This research provides a novel decision-making tool that balances high efficiency with interpretability for the efficient and sustainable design of HDR.

The transition to low-carbon energy systems is expanding the use of subsurface resources for heat extraction, energy storage, carbon management, and infrastructure reuse. This review examines hydraulic fracturing as a context-dependent engineering intervention across enhanced geothermal systems, geothermal reuse of depleted reservoirs and wells, unconventional gas, underground hydrogen storage, CO₂-based subsurface engineering, and natural hydrogen. We synthesize how stimulation can improve permeability, connectivity, heat exchange, injectivity, and deliverability, while evaluating constraints related to water use, induced seismicity, leakage, well and caprock integrity, life-cycle emissions, and public acceptance. The central trade-off is that higher stimulation efficiency does not necessarily produce greater sustainability. Short-term gains in flow or energy delivery can increase long-term risks to containment, thermal performance, seismic safety, and environmental accountability. Evidence is strongest for enhanced geothermal systems and commercial unconventional gas, whereas field support remains limited for porous-media hydrogen storage, CO₂-based fracturing, and natural hydrogen. Responsible deployment therefore requires site-specific boundaries, real-time monitoring, multi-physics and data-driven modeling, life-cycle assessment, and adaptive governance. Hydraulic fracturing can enable selected sustainable subsurface applications, but its value depends on balancing engineering performance against long-term environmental and integrity constraints.

This study is focused on the development of a fluid dynamic model for fluid leakage dynamics through the cement sheath within the casing-liner overlap, assuming the existence of a microannulus. The model is based on computational fluid modeling using Darcy’s law under transient conditions. The effects of permeability, porosity, microannulus size, casing-liner cement overlap length, pressure, and temperature on fluid leakage were investigated. The model showed that higher permeability, larger microannuli, and shorter casing-liner cement overlap lengths significantly enhance the leakage rates, while lower porosity has an adverse effect. It was further observed that high operating pressures enhance leakage flow rates significantly, while temperature variations moderately affect the leakage rates. Additionally, the compressibility of the fluid plays a critical role by amplifying leakage rates at high pressures due to increased gas expansivity, leading to a more pronounced flow behavior under transient conditions. These results provide a strong foundation to optimize casing-liner overlap cement sheath properties and configurations to ensure good integrity under different well conditions.

Summary: Foam has been widely used to control gas mobility during subsurface oil displacement processes, mitigating challenges such as reservoir heterogeneity, gas channeling, and gravity override. Its applications extend to enhanced oil recovery (EOR) and CO2 storage, where foam improves sweep efficiency and reduces gas leakage. However, the thermodynamic instability of foam necessitates a reliable evaluation of its quality and stability before field deployment. This work introduces a novel multifrequency dielectric technique for characterizing both bulk foam and foam flow in porous media. This study presents the first application of a multifrequency dielectric technique for foam characterization, validated against conventional foam analyzer measurements of quality and stability. In the first part of the study, dielectric properties (permittivity and conductivity) were measured for bulk foams generated with sodium dodecyl sulfate (SDS) surfactant at two concentrations (0.1 wt% and 0.3 wt%) using deionized water (DW) and seawater (SW). Measurements were performed across a 1-MHz to 3-GHz frequency range using an open-ended coaxial probe connected to a Keysight impedance analyzer, and results were compared with those from a standard foam analyzer. The findings showed that conductivity increased with frequency due to ion polarization, particularly at low to intermediate frequencies. Foams generated with SDS in DW exhibited lower permittivity than bulk water, reflecting the reduced polarization caused by air bubbles and surfactant molecules interfering with water’s electric field. Conductivity increased slightly with SDS concentration, correlating with improved foam stability. In the second part, the dielectric response was measured along core samples under different conditions, including dry, brine-saturated, surfactant-saturated, and foam-filled states, to characterize foam flow in porous media. The dielectric profiles defined clear upper and lower boundaries corresponding to brine-saturated and dry-rock conditions, respectively. Stable and high-quality foams exhibited permittivity and conductivity values closer to surfactant-saturated profiles, while poor foams are closer to the dry-rock behavior. Foam flooding experiments were performed under both high- and low-pressure conditions to investigate foam stability and potential disruption during dielectric measurements. The results demonstrate that dielectric properties can be used to quantitatively assess foam distribution, texture, and gas/liquid saturation within the pore structure. The study further discusses the polarization mechanisms governing dielectric behavior at low frequencies to capture interfacial polarization related to gas/liquid and rock/fluid interactions, while high frequencies reflect dipolar polarization within the bulk foam. The observed decline in dielectric constant over time corresponds to foam drainage, gas expansion, and bubble coalescence. Overall, the multifrequency dielectric technique provides a new approach for foam evaluation, capable of quantifying foam quality, stability, and propagation in both bulk and porous systems. The proposed method enhances the understanding of foam/rock/fluid interactions and offers a foundation for real-time monitoring and optimization of foam-based EOR and carbon dioxide (CO2) storage operations.