bluuebunny/crossref_metadata_2025_split huggingface dataset:
Carbon Mineralization in Mafic Formations: Fracture Topology and Hydro-Chemo-Mechanical Coupling
Authors: Muñoz-Ibáñez A. , Santamarina J. C. 
Published: August 2024

ABSTRACT:
Coupled hydro-chemo-mechanical processes affect engineered geosystems, including geothermal energy recovery, long-term nuclear waste disposal, and carbon geological storage. In particular, dissolution-transport-precipitation can significantly alter the permeability field and alter the evolution of these coupled processes. Carbon mineralization in mafic rocks is a salient example. Fractures within these rocks, including column-normal and entablature fractures, serve as conduits for fluid transport and host associated chemical processes. This study investigates fracture characteristics in mafic formations, using photographic records from published studies and data collected by the authors during site visits. Different fracturing topologies – from well-developed columnar structures to irregular patterns – exhibit distinct aperture and spacing characteristics. Additionally, new experimental results reveal the intricate interplay between water absorption kinetics and induced strains, which could potentially impact fracture aperture and, subsequently, permeability. The study also highlights the importance of flow rate and residence time in influencing the evolving pore fluid chemistry, including variations in pH and dissolved species.

1. INTRODUCTION
Carbon mineralization in mafic and ultramafic rocks offers a promising solution for permanent carbon dioxide (CO2) sequestration (Snæbjörnsdóttir et al. 2020). The injection of CO2 into mafic formations such as basalts releases divalent metal cations (e.g., Ca2+, Mg2+, and Fe2+) from silicate minerals, which react with carbonate ions (CO3-) to form stable carbonate minerals such as calcite (CaCO3), magnesite (MgCO3) and siderite (FeCO3). While pilot projects in Iceland (Carbfix; Matter et al., 2016) and United States (Wallula; McGrail et al., 2017) have shown promising results, significant uncertainties remain for industrial-scale operation (Kelemen et al. 2019).
Basalts exhibit distinctive fracture networks, often in the form of vertically aligned polygonal columns, that influence permeability and the extent of hydro-chemomechanical (HCM) processes (Lore et al. 2001; Kelemen et al. 2018). Fractures provide reactive surface area and space for precipitated minerals, which can potentially clog fluid pathways (Adeoye et al. 2017). However, in some cases, a significant decrease in permeability has been reported even with minimal changes in porosity, and the underlying mechanisms remain unclear (Kelemen et al. 2020). The dissolution of basalt, influenced by factors such as mineralogy, fluid chemistry, and flow rate, introduces additional complexity (Gislason and Arnorsson 1993).
This study aims to enhance the understanding of fracture networks in mafic formations and their impact on HCM processes.
Artificial Neural Network for the Prediction of Unconfined Compressive Strength of Ultra Lightweight Cement for Oil and Gas Wells
Authors: Addo-Yobo A. D. , Hussain A. , Emadi H. , Maslowski de Moraes E. , Watson M. 
Published: August 2024

ABSTRACT:
This study demonstrates the use of Artificial Neural Network (ANN) modeling techniques to estimate the Unconfined Compressive Strength (UCS) of lightweight oil and gas well cement. At varied simulated wellbore temperatures, 172 cement samples were reinforced with variable doses of strength-enhancing ceramic cenospheres. An Isothermal water bath was used to cure the cement samples for 24 hours and the samples were crushed using a compressive load frame. Multiple samples comprising the same properties were crushed to confirm the consistency of the dataset. The model was trained with 70% of the data set, validated with 15%, and tested with the remaining 15%. The proposed ANN model has four layers: a three-neuron input layer, twenty-five neurons in the first hidden layer, five neurons in the second hidden layer, and a single-neuron output layer. The two hidden layer ANN architecture was determined to be the optimum configuration to prevent overfitting the data. After many rounds of hyperparameter tuning, optimum performance was achieved, and the model parameters were retained and utilized. With a Root Mean Squared Error (RMSE) score of 144 psi, coefficient of determination (R2) exceeding 0.9 and accuracy of approximately 90%, the ANN produced a highly accurate model and signified a good model fit when evaluated on the test set. Similarly strong results were observed on the validation set indicating that the model generalized well to unseen data. These results demonstrate that the ANN approach is adept at producing high fidelity models for UCS prediction of lightweight cement for use in Oil and Gas well cementing operations. This paper discusses a new UCS prediction method as an alternative to rigorous experimental testing for lightweight cement systems where achieving optimum strength is highly important for the proper zonal isolation of the wellbore.

1. INTRODUCTION
Global energy demand has been and is projected to increase steadily in the coming decades. To match this demand, the oil and gas industry continuously expands its capabilities by operating in extreme and challenging environments (Canbaz, et al., 2021). Since many of the easy-to-reach reservoirs are maturing, it is not uncommon to drill multi-mile wells, both vertically and horizontally, to maximize reservoir contact (Anya, 2018). Many of these new wells are drilled into high-pressure and high-temperature zones (Arbad & Teodoriu, 2020), which require differing cement properties from those traditionally used.
Development of Chilean Open Stope Empirical Stability Database Using MineRoc and their Comparison with Literature
Authors: Vallejos J. A. , Retamal F. , Barberán A. , Velásquez J. 
Published: August 2024

ABSTRACT:
The optimal dimensions of stopes for underground mining using the Sublevel Stoping method are determined to guarantee the stability of the excavation. The graphical stability method developed by Mathews, later modified by different authors, is used as an analysis tool to achieve this objective. The stability graph plots the hydraulic radius of the stope wall against its stability number N.
In this context, the MineRoc® software is used to perform the back analysis of overbreak of stopes walls exploited through Sublevel Stoping in Chile. A database of over 600 cases is developed, including Chilean geological, geotechnical, and operational conditions. This Chilean database is compared with literature databases, such as those of Mawdesley and Clark.
Based on the comparison of databases, the scope of applicability of literature curves as design guidelines for sublevel stoping in Chile is established. Possible improvements to the empirical method to better reflect the conditions of Chilean mines are discussed.

1. INTRODUCTION
The estimation of the potential instability of the rock mass around underground mine cavities is a subject of study in sublevel stoping (SLS) mining. Over the years, various tools have been developed for the design of unfilled stopes with the aim of improving these estimations.
Mathews et al. (1981), based on a study of 26 historical cases in SLS, proposed the graphical stability method, in which a qualitative stability state is defined for each stope wall based on visual assessments and/or reconciled data. The method presented by Mathews became popular after the expansion of the original database and the recalibration of stability numerical factors. According to Madenova and Suorineni (2020), to date, there are five main variations of the stability graph: Potvin's modified Mathews stability graph (1988), Mawdesley and Trueman's expanded Mathews stability graph (2003), the Equivalent Linear Overbreak Slough (ELOS) stability graph proposed by Clark & Pakalnis (1997), the stability graph for cable bolt support design proposed by Diederichs et al. (1999), and the stability graph based on dilution presented by Papaioanou & Suorineni (2015). Authors such as Nickson (1992), Suorineni (1998), Bewick & Kaiser (2009), Vallejos et al. (2016a, 2018a), Vallejos & Díaz (2020a), and Vallejos et al. (2022), among others, have also contributed by adding new historical cases to the original method, adapting some stability factors, and proposing new qualitative or quantitative boundaries. It should be noted that, in most cases, the stability graph has been adapted for other SLS operations (new site-specific conditions), narrow vein operations, and even for caving operations.
Geotechnical Implications of Large Shale Deposits on the Stability and Productivity of Iron Ore Mines: A Comparative Study
Authors: Mohanty A. , Mishra M. K. , Ghosh S. , Sahoo B. B. , Behera C. , Rostami J. , Sesay M. D. 
Published: August 2024

ABSTRACT:
This research study examines challenges of mining in the presence of shale due to its brittle nature. Shale's presence can substantially decrease the ultimate pit angle in a geological setting, reducing extraction. While earlier studies were focused on shale's impact on the slope stability of a soil structure due to its weak geotechnical properties, this paper showcases an analysis of shale's impact on production dynamics by investigating adjustments in mining strategies. The study area focuses on four iron ore mines in the Singhbhum Craton region in India. The research methodology encompassed onsite surveys, laboratory testing, and multiple analyses of iterated geological models replicating real-time situations in numerical codes, as well as a comparative analysis of handling notorious shale layers, both with and without the assistance of support systems. Findings of this study reveal a 10% decrease in yearly production of a mine, solely attributed to the presence of shale. Moreover, this paper provides insights into practiced solutions for managing issues frequently encountered in mines with shale, such as handling the material in varied seasonal conditions and mitigating the ore grade reduction by shale contamination of run-of-mine material.

1. INTRODUCTION
In today's age, on the global pedestal, mining stands as the strongest pillar in the critical structure of modern civilization, supplying essential raw materials fueling various critical industries, ranging from construction to the production of energy and technology. The pivotal role of mining in meeting the world's needs is indeed undeniable. Moreover, every aspect of an individual's daily life dramatically relies on extracting minerals and metals from resourceful ore deposits. The impacting domain ranges from the use of hematite ore used in steel production to rare earth elements like Neodymium (Nd) and europium (Eu), which are crucial for electronics manufacturing-electric vehicles and LEDs, respectively; the breadth and depth of impact of mining are inescapable. In addition, as the growth rate of the urbanizing populations accelerates, the demand for minerals continues to surge, creating unprecedented pressure on mining operations to amplify the production to meet this ever-growing need.
Experimental Investigation of Crack Propagation in High Carbonate Shale
Authors: Zhang Xudong , Wang Xiaoyang , Zhou Linbo , Chang Xin , Guo Yintong , Zhang Xinao 
Published: August 2024

ABSTRACT:
In recent times, significant advances have been made in the exploration and exploitation of Permian shale gas reservoirs within the Sichuan Basin, China, resulting in commendable gas production rates. However, challenges have emerged during field tests of Permian shale gas fracturing, particularly in relation to high fracturing pressures and early-stage fracture initiation complexities. Mineralogical assessments have revealed a pronounced elevation in carbonate content within Permian shale formations in comparison to their Ordovician-Silurian counterparts. Presently, there exists a conspicuous dearth of scholarly inquiry into the crack propagation mechanisms operative within high-carbonate shale matrices. This study endeavors to bridge this knowledge gap by undertaking a comprehensive investigation utilizing shale samples exhibiting varying carbonate concentrations through true triaxial fracturing physical model experiments. By meticulously analyzing fracture morphological characteristics and pressure profiles, we are able to discern the nuanced attributes of fracture initiation and expansion across diverse in-situ stress regimes and operational parameters, including viscosity, pumping rates, and acid pretreatment. Our findings serve to elucidate the multifaceted factors governing fracture expansion within high carbonate content shale formations, thereby enhancing the holistic understanding of shale gas extraction dynamics.

1. INTRODUCTION
The Sichuan Basin and its surrounding regions host a variety of organic-rich shale formations, including those from the Cambrian, Ordovician-Silurian, and Permian periods. These regions have emerged as pivotal locales for shale gas extraction in China (Bao et al., 2023). Notably, the Permian strata within the basin serve as crucial hydrocarbon source rocks for the Puguang and Yuanba gas fields. Recent endeavors have witnessed a notable breakthrough with the attainment of high-yield shale gas flows, marking a significant milestone in the exploration and exploitation of deep-seated Permian shale gas resources.
However, unlike their counterparts in shallower strata, Permian shale formations exhibit a substantial carbonate mineral content, averaging 33.9%. Consequently, the tensile strength and fracture toughness of the rock are significantly elevated, posing considerable challenges to the initiation and propagation of shale fractures. Currently, operational strategies primarily are derived from fracturing construction parameters that have been tailored for medium and shallow shale reservoirs. However, a transformational technology specifically addressing the unique attributes of high-carbonate shale reservoirs remains elusive.
Advances in Hot Dry Rock Engineering for Extracting Heat from Earth: From Permeability Enhancement Strategies to Field Experiences in Enhanced Geothermal Systems (EGS)
Authors: Mindygaliyeva B. 
Published: August 2024

ABSTRACT:
Geothermal energy, sourced from the Earth's internal heat, offers a renewable solution for clean electricity production. This energy is continuously replenished by heat flowing from the Earth's core to its surface and is especially accessible through hot dry rock (HDR) formations. These formations, located deep underground with high temperatures but low permeability, require innovative engineering to extract heat. The process involves circulating a fluid to transport heat to the surface, thus enabling power generation. The key to utilizing HDR lies in the generation of a conduit path for fluid circulation through methods like hydraulic fracturing, which is the most effective technique for creating extensive surface areas for heat extraction. This approach involves inducing fractures in the rock to facilitate heat flow. Such advancements have led to the development of Enhanced Geothermal Systems (EGS), which build on the HDR concept first realized at Fenton Hill in 1977. This paper reviews significant EGS projects over the last five decades, including notable examples like Fenton Hill and Soultz-sous-Forêts, among others. Our analysis reveals that as a byproduct of stimulation, micro- and macro-fractures can be created and/or some natural fractures may reopen, which would lead to fluid retention. This evaluation underscores the potential and challenges of EGS, offering insights into optimizing geothermal energy extraction from HDR formations.

1 INTRODUCTION
Geothermal energy is a pivotal aspect of renewable energy resources, characterized by its ability to provide consistent, sustainable power derived directly from the Earth's heat. Its significance lies in its vast potential to contribute to the energy mix with minimal environmental impact, offering a solution to the pressing demands for clean, reliable energy sources. Among the various technologies for harnessing geothermal energy, Hot Dry Rock (HDR) or Enhanced Geothermal Systems (EGS) stand out due to their innovative approach to extracting heat from the Earth's crust (Zhang and Zhao, 2020; Breede et al., 2013a).
HDR/EGS technology capitalizes on the Earth's abundant thermal energy stored in dry underground rocks. By creating a man-made geothermal reservoir, these systems inject water into hot rocks, fracturing them to enhance permeability, thereby allowing water to circulate, absorb heat, and be extracted from another well as hot water or steam.
This process effectively turns inaccessible heat resources into viable energy supplies, capable of generating electricity or providing direct heating (Zhang et al., 2019; Gong et al., 2020).
Optimization of Fracturing Parameters for Channelized Tight Sandstone Gas Reservoir Development with Surrogate Models
Authors: Tang Huiying , Ni Yingying , Luo Shangui , Du Cheng , Xiao Honglin , Hou Minghua , Zhang Liehui 
Published: August 2024

ABSTRACT:
The channelized tight sandstone gas reservoir in Jinqiu gas field, China, is mainly produced with hydraulic fractured horizontal wells. Currently, the fracturing parameters (e.g, cluster spacing and injection volume) are optimized with given sand body shape, which has strong limitations when applied to complex sand body geometries in practice. In this work, we try to construct a method to find the optimal parameters for arbitrarily shaped sand. First, a surrogate model for fracture geometry prediction based on numerical simulation results is built using a deep neural network (DNN). Then, the embedded discrete fracture model (EDFM) is used to characterize the fractures and evaluate the effectiveness of different fracturing designs. With the above models, we first find the optimal designs of fluid injection volume and fracture spacing for difference reservoir types and sand height/length. Optimization for horizontally and vertically curved sand bodies are then conducted to show the impact of sand geometries on fracturing parameter designs. The surrogate model proposed in this work helps to quickly predict the fracture geometries, which makes it possible to optimize fracturing parameters with optimization algorithms.

1. INTRODUCTION
Tight gas, due to its advantages such as widespread distribution and abundant reserves, has occupied a key position in the development of unconventional natural gas in China (Zeng et al., 2024). Compared with conventional gas reservoirs, tight gas reservoirs usually have characteristics such as low porosity, high water saturation, and low permeability, making the optimization of fracturing parameters more complex (Zhang et al., 2022).
The Shaximiao reservoir in the Jinqiu gas field is a typical tight gas reservoir, with porosity ranging from 8% to 16%, averaging 12.31%, and permeability ranging from 0.01 mD to 1 mD. The channel width is 200 – 4800 m and the sand thickness is 5 – 40 m (Fig. 1 and Fig. 2).
The Jinqiu gas field is a typical narrow channelized sandstone gas reservoir, which is mainly produced with hydraulic fractured horizontal wells. To further increase the production, it is necessary to optimize the fracturing parameters considering the sand body geometries.
Machine Learning Enhanced Interpretation of Wellbore Data for Underground Storage
Authors: Bondarenko N. , Ankul A. , Makhnenko R. Y. , Ganesh P. , Williams-Stroud S. , Goldberg C. 
Published: August 2024

ABSTRACT:
The Illinois Basin has an excellent history of pilot- and demonstration-scale CO2 injection projects, positioning it as a promising potential hub for carbon capture and storage. Since the presence of fractures increases risk of an induced seismic response and CO2 leakage, it is essential that potential injection sites are properly characterized before project deployment. Accurately measured distribution of seismic waves velocities is essential for prediction of fracture locations and monitoring of the injected CO2. Site investigation for the pilot-scale CO2 storage projects yielded abundant high-quality data, especially in terms of geophysical wellbore measurements. This information is utilized to create a dataset specific to the Illinois Basin aimed at assistance during the wellbore and seismic data interpretation, which (as any inverse problem) heavily relies on physical constraints to limit the range of potential solutions. The developed machine learning algorithms allow predicting P- and S-wave velocities based on mineralogical composition from ELAN log with accuracy superior to conservative effective media approaches. Three deployed algorithms include random forest, gradient boosting, and neural network – all of them yielded significantly more accurate estimations of seismic velocities compared to the sonic log. Additionally, this technique reconstructs complex patterns in variations of velocities associated with heterogeneities of geologic formations. It opens ways for further enhancement of datasets to evaluate involved hydromechanical parameters (e.g., poroelastic properties and permeability) to refine the velocity models and make identification of subsurface features and monitoring of the fluid migration processes more accurate.

1. INTRODUCTION
Successful implementation of geoenergy projects requires knowledge of the hydromechanical, stratigraphic, and lithological features of the participating geological formations. The access to the sites can be gained during the well boring (1D data), as well as via the use of geophysical methods (3D data), such as seismic, gravimetric, and magnetic surveys (Kearey et al., 2002; Manivannan, 2018). These techniques measure potential, resistivity, porosity, formation factor, temperature, fluid saturation, and sonic data obtained from various wellbore logs (Asquith et al., 2004), as well as mineralogy, lithology, and hydrocarbon content (Pelton et al., 1978). The datasets can contain thousands of records for each of the parameters as a function of depth, and combined, represent huge arrays of data to be interpreted. Machine learning (ML) techniques have been shown to serve as a powerful algorithm for enhancing the datasets and capturing intricate nonlinear relationships within them (Bressan et al., 2020; Glubokovskikh et al., 2020). The overall effort is in augmenting subsurface characterization efforts and enhancing understanding of the geological conditions.