In formations characterized by strike-slip or reverse (thrust) stress regimes, existing vertical fractures can help connect induced horizontal hydraulic fractures to a large volume of the reservoir, thus improving reservoir access. Gaining a better understanding of the mechanics behind these interactions is valuable both for developing more accurate numerical models and for guiding industry practices in the efficient development of such reservoirs. This article explores the mechanisms that govern the interaction between hydraulic fractures (HFs) and natural fractures (NFs) in Opalinus Clayshale rocks. By combining laboratory experiments with Digital Image Correlation (DIC) analysis, the study offers new perspectives on the key factors influencing the propagation of hydraulic fractures in the presence of pre-existing discontinuities.

This paper presents a novel approach to automated ground classification in Tunnel Boring Machine (TBM) operations, leveraging scatter plot analysis and machine learning. The proposed approach is transition-aware and utilizes a metrics feature engineering approach (including cluster metrics) to characterize the visual properties of TBM parameter scatter plots. By extracting features that capture spatial patterns and cluster characteristics in operational parameter relationships, our method provides real-time classification capabilities via a complementary suite of machine learning models spanning ensemble, kernel, boosting, and neural paradigms. The approach is benchmarked against a simpler baseline that uses only per-ring averages of TBM parameters (which achieves 76% accuracy) and demonstrates that our clustering-based feature extraction boosts classification accuracy to 91%. Beyond higher accuracy, our approach offers robust performance in ground condition transitions and improved insight into the TBM’s underlying operational patterns.

The contact quality between the well casing and surrounding rock is critical to prevent methane leakage in oil and gas fields, especially for plug‐and‐abandonment operations. Casing-wave magnitude in borehole acoustic measurements is widely used to assess overall casing-rock contact quality, yet wave signatures capable of resolving leakage channels at the interface are rarely reported. To fill this gap, we perform decimeter-scale laboratory experiments to examine how partial casing-rock contact influences acoustic-wave propagation along the borehole, supported by analytical solutions and numerical simulations that extend our interpretation beyond the laboratory testing conditions. We find that guided wave modes predicted by elastodynamic theory are clearly observed, but the measured casing-wave attenuation along the borehole deviates from numerical results. Borehole dispersion inferred from the experiments also differs from analytical and numerical predictions, whereas the casing mode remains a reliable indicator of casing-rock contact quality. Casing-wave amplitude and arrival time exhibit strong azimuthal dependence under partial contact, with arrival time demonstrating greater sensitivity. The Stoneley‐mode magnitude and arrival time show similarly pronounced azimuthal variations, but they may not readily serve as independent indicators due to azimuthal wave interference. Collectively, this study indicates that casing‐wave arrival time and Stoneley‐mode attributes provide additional constraints for locating channels beyond casing‐wave magnitude alone.

Fractures play a pivotal role in the hydromechanical behavior of rocks. Researchers have tried to understand the fundamental mechanisms and behavior of fractures, both theoretically and experimentally. Fractures produced in laboratory tests can be characterized using photographic images at a range of scales. Traditionally, identifying and extracting the temporal-spatial characteristics of fractures rely on meticulous manual labeling, entailing significant time and labor. Recently, Deep Learning (DL) methods have been deployed for automatic fracture extraction and characterization of fracture evolution from rock images. However, they generally necessitate a large dataset to train DL models for extracting fractures. More importantly, they cannot distinguish existing fractures or background noise and erroneously identify a continuous fracture as multiple disconnected segments, leading to significant errors in quantifying fracture evolution. This study introduces a new Deep Learning-based approach for mapping temporal sequence of images, the 'Hybrid Fracture Mapping Method' (HFMM). It incorporates a small training dataset using systematic sampling and employs an advanced denoising method. It is validated on a hydraulic fracturing image dataset from Massachusetts Institute of Technology (MIT) rock mechanics laboratory. The results demonstrate that the HFMM shows a notable improvement compared to the conventional DL methods in mapping hydraulic fracture evolution. It achieved a reduction in Mean Absolute Error (MAE) for quantifying fracture length and number of fracture branches, decreasing the MAE by 81 %-86 % and 69 %-93 %, respectively.

As pointed out in the GeoVision Geothermal Report (DOE, 2019), reservoir stimulation is often considered the most critical bottleneck in the development of Enhanced Geothermal Systems. Hydraulic fracturing (HF) has proven to be a game changer in many domains such as shale gas extraction. However, alternatives are being explored due to the intensive water consumption of HF, significant environmental effects, and challenges associated with fracture propagation control (which is critical for the connection between the injection and production wells). Electrical rock fracturing has recently drawn significant attention as an environmentally friendlier alternative. Electrical fracturing of rock is a reservoir stimulation technique in which a high-voltage electric current (pulsed or continuous) is used to break rock through mechanisms such as mechanical shock waves, thermal effects (Joule heating), or dielectric breakdown, either individually or in combination. This study reviews the existing literature and explores the application of electrical fracturing. We will describe the different mechanisms involved in the electric fracturing process as well as recent developments and discuss both current and potential future applications.

Automation of construction machines has grown rapidly in recent years as a response to the need to increase productivity, increase construction safety, decrease costs, and overcome the lack of availability of qualified labor. However, tunnel automation still lags. Automation of mechanized tunneling is essential to the future of tunneling construction, as the current practice still relies heavily on the experience and the human judgment of the machine operator to steer the TBM, which could lead to undesirable events. The primary motivation for this review paper stems from the statement: the success of tunneling automation relies on precise ground prediction. Even small inaccuracies can have significant implications, and the current reliance on human experience and judgment presents limitations and risks. With the abundance of machine data now available from TBMs, and the advancements in data analytics and machine learning (ML), many models have been proposed. These models have the potential to revolutionize tunneling by providing better decision-making support, including geology forecasts and anomaly detection. However, despite the numerous research studies and advantages of these models, they are not widely implemented in real-world scenarios. Thus, this review paper aims to address this issue by focusing on ground prediction models for TBM tunnels, providing a comprehensive overview of the current state-of-the-art, illuminating the existing practices, and highlighting the limitations that hinder these models from being the catalysts of tunneling automation. By emphasizing these challenges, the paper seeks to not just critique but also guide, providing recommendations for future research that promise to bridge the gaps and potentially usher in an era of fully automated TBMs.

If a U.S. Air Force operated airfield is attacked, the current methodology for assessing its condition is a slow manual inspection process, exposing personnel to dangerous conditions. Advances in drone technology, remote sensing, deep learning, and computer vision have sparked interest in developing autonomous remote solutions. While digital image processing techniques have matured in recent decades, a lack of application-specific training data presents significant obstacles for developing reliable solutions to detect specific objects amongst rubble, debris, variations in pavement types, changing surface features, and other variable runway conditions. Consequently, near-surface hyperspectral imaging has been proposed as an alternative to RGB digital images, due to its discriminatory power in classifying materials. Spatio-spectral data acquired by hyperspectral imagers help address common challenges presented by data scarcity and scene complexity; however, raw data acquired by hyperspectral sensors must first undergo a reflectance correction process before it can be of use. This paper presents an expedient method, tailored to airfield damage assessment, for performing autonomous reflectance correction on near-surface hyperspectral data using in-scene pavement materials with a known spectral reflectance. Unlike most reflectance correction methods, this process eliminates the need for human intervention with the sensor (or its data) pre or post flight and does not require pre-staged reference targets or an additional downwelling irradiance sensor. Positive initial results from real-world flights over pavements are presented and compared to traditional methods of reflectance correction. Three separate flight tests report mean errors between 2% and 2.5% using the new method.

The Air Force's Rapid Airfield Damage Assessment (RADA) process was conceived as a means of evaluating airfield pavement assets after attacks to inform subsequent threat mitigation and repair efforts. The classification and geolocation of small objects of interest (< 7.5cm), like unexploded ordnance, is a critical component of this assessment process. In its original form, RADA was conducted manually, exposing teams of service members to dangerous and unknown conditions for hours at a time. In an effort to both expedite and remotely automate this critical task, researchers are developing small Uncrewed Aerial Systems (sUAS) equipped with various sensor payloads to perform object detection across the compromised airfield environment. Hyperspectral imaging has been specifically targeted as a promising sensor solution due to its enhanced discriminatory power in classifying materials. This study is focused on understanding how measurements of these small objects are affected by changes in parameters that govern operation of the drone-sensor system. Radiometric precision and spatial resolution are evaluated with respect to changes in flight speed, altitude, shutter speed, gain, and frames per second, in realistic field conditions. Within the ranges evaluated for each system parameter, the drone-sensor system presented spectrally and spatially resolves objects captured by just a few pixels with sufficient accuracy and precision for the RADA application.

ABSTRACT: The interaction between Hydraulic Fractures (HF) and Preexisting Discontinuities (PD, like bedding planes and natural fractures, is affected by the coefficient of friction of the PD, the state of stress around the interacting fractures' tips, and the angle between the approaching HF and the PD. Recent formulations (Kresse et. al, 2013; Chuprakov and Prioul, 2016), also consider the effect of flow rate, fluid viscosity, and PD permeability in the crossing interaction. In this study, a series of permeability tests on cylindrical Opalinus Clayshale specimens were conducted in a triaxial pressure cell. The specimen of 1.5 in diameter and 3 in height includes a throughgoing longitudinal cut, representing the PD. Different confining pressure (CF) and flow rate (FR) conditions were tested for the same fluid (constant fluid viscosity) and temperature. The results obtained are comparable with other authors, with the Opalinus clayshale showing a higher fracture conductivity than Barnett shale (Zhang et al., 2013)), and Fayetteville shale (Jansen et al., 2015). 1. INTRODUCTION Hydraulic permeability of rock discontinuities is widely investigated in geotechnical engineering and geomechanics. This is mainly due to the influence of mechanical discontinuities, such as fractures and faults, on the permeability of rock formations. Permeability is a critical parameter in the evaluation of numerous geotechnical and environmental aspects, including slope stability, underground fluid flow, and storage, exploitation of hydrocarbon resources, etc. Preexisting discontinuities (PD) act as preferential channels for flow, and the characterization of their permeability is vital to accurately model flow. Furthermore, in the context of geological storage and carbon sequestration, the permeability of discontinuities is essential to evaluate the long-term integrity of the host formations. Many authors have studied the effect of permeability of discontinuities. For instance: Carlsson and Olsson (1993) analyzed the increase in conductivity in fractures due to shear displacement. Makurat (1996) showed that the permeability of natural fractures is affected, among other factors, by tortuosity, roughness, and wall strength.

The paper presented at the International Congress and this extended version are intended to pay homage to Professor Leopold Müller who was a leading developer and user of physical models. This will be done by first reviewing physical models of stone-built artificial structures from ancient times till now being used to investigate the flow of forces and the effect of material properties. The same issues affect rock mechanics and engineering. Consequently, physical Querymodels of fundamental material behavior, geologic mechanisms, and especially jointed rock will then be described. On this basis complex models of geologic processes and of structures on and in rock masses will be discussed. Finally, critical aspects, namely, the issues of scaling and of possible obsolescence because of powerful simulation models, will be addressed leading to the outlook where physical models can and should be used.

ABSTRACT: Most granites have preferred directions ("rift", "grain", and "hardway") that affect the ease with which the rock can be split. In this study, we present a series of uniaxial and triaxial tests on the directional-dependent properties of Sierra White Granite, a rock that has been chosen as a laboratory analog for the FORGE site granitoid. We performed triaxial tests on 1.5" by 3.5" cylindrical cores involving a test program including isotropic compression, cyclic loading, and triaxial compression in three preferred directions under low to high confining pressure. In these tests, we measured stress, displacement, axial and circumferential strain, and P-wave velocity. We found no significant degree of direction dependency in the deformation (stiffness) and strength parameters. On the other hand, the P-wave velocity results show the largest velocity along (equation) (direction perpendicular to plane C) then (equation) and (equation). More tests are needed to accurately quantify the direction-dependent behavior of the given test material. 1. INTRODUCTION The direction dependence (anisotropy) of granite is often reported in the literature, both in relation to practical engineering and scientific aspects. With respect to practical engineering, the best known are the orthogonally oriented so-called "quarry planes" rift, grain, and hardway, indicating the ease of splitting or cleavage in this sequence (i.e., rift the easiest, and hardway the hardest). In quarrying, this direction dependence is often used to split the rock by a hammer blow in the rift direction. From the geotechnical viewpoint, one wants to know what causes the direction dependence, if it affects other small- and larger-scale properties, and what these effects are. Moreover, understanding the material reaction to external influences such as stress, temperature, electrical currents, and chemical agents, is also important to better design geotechnical structures. Most researchers agree that the mechanical properties along the rift orientation depend on the orientation of microcracks and fluid intrusions – which in turn are affected by the direction dependence of quartz (see e.g. Douglass & Voight, 1969; Osborne, 1935). The reasons behind the other two directional dependencies are less clear, but most likely involve the effects of foliation, large-scale stress, and/or temperature changes (Osborne, 1935). It should be noted, however, that some granite samples used in laboratory experiments do not show preferred directions (Chen et al., 1999).

The fracturing behavior and associated mechanical characterization of rocks are important for many applications in the fields of civil, mining, geothermal, and petroleum engineering. Laboratory testing of rocks plays a major role in understanding the underlying processes that occur on the larger scale and for predicting rock behavior. Fracturing research requires well-defined and consistent boundary conditions. Consequently, the testing design and setup can greatly influence the results. In this study, a comprehensive experimental program using an artificial material was carried out to systematically evaluate the effects of different parameters in rock testing under uniaxial compression. The parameters include compression platen type, specimen centering, loading control method, boundary constraints, and flaw parameters. The results show that these testing conditions have a significant effect on the mechanical behavior of rocks. Using a fixed compression platen helped reduce bulging of the material. Centering of the specimen played a critical role to avoid buckling and unequal distribution of stress. Slower displacement rates can control the energy being released once failure occurs to prevent the specimen from exploding. Also, the frictional end effects were investigated by comparing friction-reduced and non-friction-reduced end conditions. Very importantly, the study also identified variations in crack initiation and propagation between specimens with internal flaws and specimens with throughgoing flaws. This investigation showed that wing cracks appeared in specimens with throughgoing flaws, while wing cracks with petal cracks were associated with the internal flaws. It also showed that the mechanical properties are influenced by the inclination of the flaws and established that specimens with internal flaws generally exhibit higher strength compared to specimens with throughgoing flaws. The systematic analysis presented in this work sheds light on important considerations that need to be taken into account when conducting fracture research and adds knowledge to the fundamental understanding of how fractures occur in nature.

ABSTRACT: The primary mechanisms to initiate hydraulic fractures in rock include overcoming the tensile resistance of the rock, overcoming its shear resistance, or a combination of both. The focus of this study is the solid-liquid interaction in hydraulic fractures using basic measurements and models to aid in investigating these mechanisms. The materials of study were Opalinus Shale and hydraulic oil. The basic measurements included liquid density, viscosity, surface tension, and contact angle with the rock. These measurements were used to determine the characteristic capillary length, capillary number, spreading, and imbibition parameters. Two fracture scenarios were considered for a range of apertures: A- liquid inside a pre-existing fracture was modelled as parallel plates to determine the Laplace pressure and adhesion force. B- liquid penetrating a newly created fracture was modelled as a capillary to determine the dynamic and equilibrated rise. This study provides a theoretical framework to assess solid-liquid interaction effects in fractures and introduces the capillary draw amplification coefficient. The results of this study provide insight into the lag or lack thereof between the liquid front and the fracture tip during hydraulic fracturing and how the liquid interacts with the fracture after it has been created. These, in turn, have implications on the mechanism of fracturing and potential operational considerations. 1. INTRODUCTION Several mechanisms play a role in the hydraulic fracturing process. There are two main ways to initiate hydraulic fractures in rock: 1. increasing the liquid pressure beyond the tensile resistance of the rock will result in tensile fractures. 2. decreasing the effective stress (by increasing the pore pressure) to have the Mohr circle tangent to the shear failure envelope will result in shear fractures. After initiation, the propagation of hydraulic fractures depends on many factors. One of these factors that affect hydraulic fracture behavior is the solid-liquid interaction. A higher viscosity liquid pressurizing rock may result in different behavior than a lower viscosity liquid. The motivation of this study was to investigate the solid-liquid behavior that occurs in the hydraulic fracturing, specifically in the experiments conducted by AlDajani (2022). These hydraulic fracture experiments are beyond the scope of this paper, but the materials used in the study are the same. The liquid used as a fracturing fluid was hydraulic oil and the rock being hydraulically fractured was Opalinus Shale.

When fielding near-surface hyperspectral imaging systems for computer vision applications, raw data from a sensor are often corrected to reflectance before analysis. This research presents an expedient and flexible methodology for performing spectral reflectance estimation using in situ asphalt cement concrete or Portland cement concrete pavement as a reference material. Then, to evaluate this reflectance estimation method's utility for computer vision applications, four datasets are generated to train machine learning models for material classification: (1) a raw signal dataset, (2) a normalized dataset, (3) a reflectance dataset corrected with a standard reference material (polytetrafluoroethylene), and (4) a reflectance dataset corrected with a pavement reference material. Various machine learning algorithms are trained on each of the four datasets and all converge to excellent training accuracy (>94 % ). Models trained on the raw or normalized signals, however, did not exceed 70% accuracy when tested against new data captured under different illumination conditions, while models trained using either reflectance dataset saw almost no drop between training and testing accuracy. These results quantify the importance of reflectance correction in machine learning workflows using hyperspectral data, while also confirming practical viability of the proposed reflectance correction method for computer vision applications.(c) 2023 Society of Photo-Optical Instrumentation Engineers (SPIE)

Tunnel Design Methods covers analytical, numerical, and empirical methods for the design of tunnels in soil and in rock. The material is intended for design engineers looking for detailed methods, for graduate students who are interested in tunnelling, and for researchers working on various aspects of ground-support interaction under static and seismic loading. The book is divided into seven chapters, covering fundamental concepts on ground and support behavior and on ground-excavation-support interaction and provides detailed information on analytical and numerical methods used for the design of tunnels, with applications, and on the latest developments on empirical methods. The principles and formulations included are used, throughout the book, to provide insight into the response of tunnels under both simple and complex loading conditions, thus providing the reader with fundamental understanding of tunnel behavior. Both authors have experience in tunnelling and have worked extensively in practice, designing tunnels both in the United States and abroad, and in research.

ABSTRACT High-voltage electrical current can be utilized to increase rock permeability and can be a sustainable alternative to decrease water consumption associated with hydraulic fracturing. During previous applications of this technique, ion carriers, mainly brine water, evaporated as temperature increased during electrical resistive (joule) heating of the reservoir, limiting effectiveness of the technique. Understanding how electrical conductivity evolves as a function of applied electrical current is fundamental for developing methods to overcome previous limitations of the technique. In this study, we developed a first-of-its kind triaxial cell that allows one to apply electrical current, with temperature and pore pressure data collected during the joule heating process of a Berea Sandstone rock specimen. We investigated the relationships between pore fluid composition, pore pressure, and electrical conductivity evolution. The relationships between pore fluid composition, and joule heating evolution under variable electrical energy inputs are important to understand for analyzing the long-term effectiveness of electrical rock fracturing. INTRODUCTION Waterless permeability enhancement techniques including mechanical, gaseous, and electrical based methods have been previously investigated to address the environmental concerns associated with hydraulic fracturing (e.g., Gandossi 2013, Kumar et. al, 2017, Wang et. al, 2016). However, widespread adoption of these techniques has stalled due to limitations in stimulated reservoir volume, and environmental safety concerns associated with the exposure of hazardous gases as a fracturing fluid. Alternatively, electrical resistive heating has proven effective at stimulating petroleum reservoirs to increase production (e.g., Elder et. al, 1957, Rehman and Meribout, 2012). Waterless permeability enhancement techniques including mechanical, gaseous, and electrical based methods have been previously investigated to address the environmental concerns associated with hydraulic fracturing (e.g., Gandossi 2013, Kumar et. al, 2017, Wang et. al, 2016). However, widespread adoption of these techniques has stalled due to limitations in stimulated reservoir volume, and environmental safety concerns associated with the exposure of hazardous gases as a fracturing fluid. Alternatively, electrical resistive heating has proven effective at stimulating petroleum reservoirs to increase production (e.g., Elder et. al, 1957, Rehman and Meribout, 2012).

A decades-long effort by many contributors to bring rational decision making to rock engineering and engineering geology — for instance, in dam safety, underground construction, mining, and nuclear waste management — has led to an increasingly unified approach in practice. The addition of observational techniques quantified through Bayesian thinking leads to adaptive designs characterized in a decision-making cycle of the sort illustrated in Figure 1. This way of thinking fosters a balancing of cost and safety.

In this paper we present a novel pressure‐controlled Hele‐Shaw cell to investigate different physical processes in rough fractures using 3D‐printed rock analogs. Our system can measure high‐resolution fracture aperture and tracer concentration maps under relevant field stress conditions. Using a series of hydraulic and visual measurements, combined with numerical simulations, we investigate the evolving fracture geometry characteristics, pressure‐dependent hydraulic transmissivity, flow channeling, and the nature of mass transport as a function of normal stress. Our experimental results show that as the fracture closes and deforms under increasing normal loading: (a) the contact areas grow in number and size; (b) the flow paths become more focused and tortuous; and (c) the transport dynamics of conservative tracers evolve toward a higher dispersive regime. Moreover, under the applied experimental conditions, we observed excellent agreement between the simulated‐ and the experimentally measured‐hydraulic behavior.