bluuebunny/crossref_metadata_2025 huggingface dataset:
Underground Space Design Using DFN-Based Tools: Comparisons of Static Kinematic Block Stability vs Distinct Element Modelling Assessments
Authors: Ng J. Wang , C. J. Button E , Ye J. Z.
Published: August 2024

Discrete fracture networks (DFN) are applied extensively in the design of underground spaces for both mining and civil infrastructure industries. DFN-based design tools enable discrete modelling of rock masses and kinematic stability assessments to be undertaken for design. When coupled with distinct element modelling, this approach significantly enhances the quality of designs by more precisely predicting the behavior of the ground. This combined approach can identify critical mechanisms, such as block interlocking and unravelling, which are pivotal in controlling the overall behavior of the ground.

The paper presents the design method undertaken for a large underground cavern employing DFN models through both static block kinematic assessment and DEM numerical analysis. A comparison of the two approaches highlights the importance of DEM in simulating ground behavior to ensure that critical mechanisms are incorporated into the design. This DFN-based method promotes safer and more efficient designs, leading to improved risk identification and cost savings for the project.

1. INTRODUCTION

Discrete fracture networks (DFN) are applied extensively in the design of underground spaces for both mining and civil infrastructure industries. DFN-based design tools are useful as they allow discrete modelling of a rock mass and kinematic stability assessment of individual blocks. When coupled with distinct element modelling (DEM) techniques, the design can be enhanced by more accurately predicting ground behavior, including identifying adverse failure mechanisms that may be critical for the design or interlocking mechanisms that maintain stability of the excavation.

The DFN-based design approach to underground space have been successfully applied by authors such as Haryono (2022) and Weir (2018) to predict the range of unstable block sizes for support design. This method enables a real realistic and more efficient designs that lead to better design risk identification as well as cost savings for the project.

Block kinematic assessments are a typical first step for rock excavation support design. There are, however, limitations with the method as the approach is usually focused on limit equilibrium and a static block boundary condition preventing the influence of dynamic block interactions (i.e. displacements) from being considered. Impacts of dynamic block interactions may result in interlocking and/or unravelling effects that have important controls on the overall ground behavior.
Numerical Simulation of Hydraulic Fracture Vertical Propagation in Bedded Shale by Phase-Field Method
Authors: Tian Ganghua , Wang Haizhu , Wang Bin , Shi Mingliang , Zheng Yong
Published: August 2024

Volumetric fracturing technology is an effective tool for shale oil exploration. The developed bedding planes and strong inhomogeneity of continental shale make them limited in fracture seam height propagation, which restricts the efficient extraction of shale oil. To better understand the law of hydraulic fracture propagation in the vertical direction, a coupled hydraulic-mechanic-damage model is established based on the phase field method to study the propagation of fractures in bedded shales. Based on this model, the effects of fracturing fluid viscosity and alternative injection methods were simulated. Following research results were obtained. The strong filtration loss of low-viscosity fracturing fluid leads to a shorter fracture height and tends to open the bedding planes. Increasing the viscosity can significantly enhance the penetration capacity of the hydraulic fracture and thus improve the stimulation efficiency. Additionally, the alternative injection method of injecting high-viscosity fracturing fluid before the low-viscosity one can breach the constraints of the laminae in the near-well zone and achieve sufficient reconstruction of the reservoir in the longitudinal direction. Therefore, in practical applications, reasonable fracturing fluid design and injection schemes need to be chosen according to different geological conditions and engineering needs to get better fracturing modification performance.

1. INTRODUCTION

Hydraulic fracturing is an important tool for the efficient exploitation of unconventional oil and gas resources (Yu et al., 2023). However, this also increases the risk of water contamination and geological instability. Therefore, numerical simulations are needed to accurately predict hydraulic fractures to guide fracturing scheme optimization while avoiding potential risks (Yang et al., 2022).

Current numerical methods for hydraulic fracture propagation investigations are categorized into two main types: discrete and continuous methods. The latter saves the work of dealing with displacement discontinuities of complex crack surfaces when simulating crack extension and is easier to implement numerically. Continuum methods such as the gradient damage model (Sun et al., 2021), screening Poisson method (Areias et al., 2016), peridynamic model (Nadimi et al., 2016, Ni et al., 2020, Qin et al., 2021), and phase field model (PFM) (Zhou & Zhuang, 2020, Liu et al., 2021, Liu et al., 2022, Zhuang et al., 2023) are widely used.
Lithology Identification of Deep Coal Seam Based on Machine Learning
Authors: Qi An , Li Yesong , Hai Liu , Feng Zhang , Qi Yang , Hao Liu , Ningbo Lv , Cheng Zhang , Hui Li , Zhou Fujian , Li Ben
Published: August 2024

Lithology identification is a crucial aspect of coalbed methane exploration and development. Given the intricate composition and lithology of deep coal seams, with frequent interbedding of coal seams, mudstone, and fine sandstone, accurate identification is challenging, particularly due to the similar logging responses of mudstone and fine sandstone. Conventional lithology interpretation algorithms based on crossplots often yield inaccurate results. This study focuses on the deep coal seam in Block SF on the east margin of the Ordos Basin, China. A dataset is compiled based on field logging and coring results. Through correlation analysis, seven key parameters—caliper, compensated neutron porosity, transit-time of compressional wave, gamma ray, deep resistivity log, spontaneous potential, formation bulk density—are selected from the logging data. The comparative analysis results of 16 machine learning algorithms including extra tree, random forest, and xgboost indicate that the extra tree algorithm provides superior fitting and generalization ability, with an F1 score of the classification model reaching 0.798. At the same time, it has high computational efficiency. The predictive outcomes of the model serve as a foundation and reference for determining fracturing intervals and identifying geological sweet spots in subsequent stages.

1. INTRODUCTION

Coalbed methane, also known as coal seam gas, is one of the typical clean energy sources. Its extraction holds significant importance for both coal mine safety and strategic energy reserves. China boasts abundant proven coalbed methane resources, with a gas-in-place resource volume reaching 30 trillion cubic meters (Tao et al., 2019). As exploration and development of coalbed methane continue to advance, a trend is emerging from single coal seams to coal series, from low-rank coal to high-rank coal, and from shallow to deep coalbed methane development (Guo et al., 2024). In the eastern SF block of the Ordos Basin in China, coal series formations are widely distributed, exhibiting a rich variety of lithologic combinations and distinct differences in reservoir formation modes (Cai et al., 2022). Particularly noteworthy is the development of thin interbeds of coal and sandstone-shale, posing challenges for reservoir identification and completion design for hydraulic fracturing. Therefore, conducting identification and prediction of lithology in geological formations is of vital significance for deep coalbed fracturing design and favorable area prediction for reservoir formation.
Experimental and Numerical Investigation on Fracture Propagation Sensitivity Parameters in Deep Coal Seams
Authors: Jian Wu , Libo Zhou , Yao Jiang , Yun Wang , Qi Yang , Chunjiang Qian , Cheng Zhang , Ningbo Lv , Hui Li , Zhou Fujian , Li Ben
Published: August 2024

Hydraulic fracturing is the primary method for increasing hydrocarbon production in the extraction of deep coal bed methane. Understanding the initiation and propagation mechanisms of hydraulic fractures in deep coal seams, as well as developing design principles for hydraulic fracturing operations, can improve the efficiency of such operations. This study aims to identify the key factors affecting hydraulic fracture initiation and propagation in deep coal seams through indoor experiments and numerical simulations of deep coal seam wells. The study conducted an indoor experiment on hydraulic fracturing, using CT three-dimensional reconstruction technology and a two-dimensional fractal dimension analysis method to analyze the fracture morphology of deep coal seams. The study assessed the impact of various parameters, including fracturing displacement, fracturing fluid viscosity, lithology, and well trajectory, on hydraulic fracture initiation and fracture propagation. Additionally, a numerical model of fracture propagation was established to simulate hydraulic fracture initiation and propagation in typical wells in deep coal seams. The study also employed a two-dimensional fractal dimension analysis method and weight analysis method to analyze sensitivity parameters affecting fracture complexity and identified the main controlling factors of fracture initiation and propagation in deep coal seams. This study explores the basic principles of complex fracture propagation mechanisms through small-scale indoor hydraulic fracturing experiments. It aims to identify the key operational parameters that can enhance the complexity of fracture networks in hydraulic fracturing. The study also uses numerical simulations to analyze the important factors that influence the complexity of fractures through well-scale numerical simulations of fracture propagation. This approach provides design tools and theoretical insights for optimizing hydraulic fracturing design in deep coal seams.

1. INTRODUCTION

Coalbed methane is an unconventional oil and gas resource. More than 47.6 trillion cubic meters of coalbed methane resources exist in coal reservoirs buried at a depth of more than 1500 meters (Zuo H.C, 2015). Coal seams have a unique bedding and cutting development system and usually require hydraulic fracturing methods to obtain a high yield. Deep coal seams have poor physical properties, high stress, low permeability, and strong adsorption capacity, so the effect of conventional fracturing transformation is not acceptable.
Modelling Faults as Fracture Swarms to Introduce Multi-Scale Heterogeneity
Authors: Libby S. A.
Published: August 2024

Faults modify the geomechanical and hydrogeological properties of their host rock. Consequently, proper characterization of faults is relevant to a wide range of sectors including energy, storage and sequestration, mining and geotechnical. Although faults may be conceptualized as simple surfaces when considered at their full scale, a detailed view reveals a heterogeneous structure with damage zones and anastomosing fault cores. Consequently, simulations of whole fault systems can achieve reasonable results by modelling the faults as simple surfaces, but where model applications require a higher resolution another approach is required to introduce out-of-plane heterogeneities. This paper discusses two methods that achieve this by simulating faults as swarms of fractures located around midplanes, including a novel method that can readily scale to very large networks. Crucially, both methods can use midplanes generated as large-scale discrete fracture networks (DFNs) to produce meter scale DFNs with aspects controlled by kilometer scale features. This offers two benefits: a single model of the fault system can be applied across a wider range of scales, and the intensity distribution of the DFNs better simulate naturally occurring fracture networks. Both geomechanical and hydrogeological models can benefit from this approach.

1. INTRODUCTION

The impact of faults on the geomechanical and hydrogeological properties of their host rock is a widely studied topic, with implications for a wide range of sectors including energy, storage and sequestration, mining and civil engineering (e.g. Fossen, 2010). As numerical modelling continues to become a more prevalent tool for understanding the subsurface, the numerical characterization of faults becomes increasingly important.

Although faults may be considered near planar features without a discrete thickness when conceptualized at their full scale (a concept subsequently referred to as a fault midplane), a view of a fault at a smaller scale reveals a heterogeneous internal structure, both in the local geometry of the features comprising the fault core and the geomechanical and hydrogeological properties that arise (Aaltonen et al., 2016; Caine et al., 1996; Choi et al., 2016; de Joussineau, 2023; Faulkner et al., 2008; Fossen, 2010; Gabrielsen & Braathen, 2014; Kim et al., 2004; Nordbäck & Mattila, 2018; Wang et al., 2023). Consequently, simulations of fault systems at a scale that encompasses whole faults can achieve reasonable results by modelling the faults as simple surfaces. However, if the model also needs to simulate faults at a meter scale, a single planar surface may not adequately represent observed geological features such as damage zones and anastomosing fault cores.
Field Experiences of Near-Wellbore Poroelastic Effects and Benefits of Pump-Out in Microfrac in Unconventional Reservoirs
Authors: Sen Souvik , Chakrabarti Prajit , Franquet Javier A.
Published: August 2024

This study discusses the challenges faced during the wireline straddle packer microfrac tests and the effective solutions, based on our field experiences from the recently concluded successful microfrac campaign targeting the unconventional reservoirs (tight sandstones and dolomites from Triassic-Cambrian) of north Africa. The main challenges were posed by the near wellbore poroelastic effects by mud filtrate that yields pore pressure and stress perturbations locally and artificially elevates formation breakdown pressure. We adopted the ‘pump-out’ technique as an effective solution where the removal certain volume of mud from the near wellbore region reduces the artificial strengthening effect of the mud filter cake and helps reduce the breakdown pressure. The majority of the microfrac tests exhibited the proven benefit of multiple rapid loading-unloading cycles by injection (hole pressurization) and pump-out (hole depressurization) in inducing near-wellbore fatigue to effectively weaken the formation before achieving successful breakdown. In some of the stations, increasing trend observed in fracture reopening and closure in the consecutive test cycles, contributed by the poroelastic effect. The pump-out technique was also effective in eliminating such effects. This study demonstrates the benefits of pump-out technique in achieving successful breakdown and at times conclusive closure in challenging environments.

1. INTRODUCTION

The application and importance of geomechanical modeling is well accepted and therefore extensively utilized for tackling mechanical wellbore instabilities, optimizing wellbore trajectories, hydraulic fracture designs and completion strategies, etc. (Almalikee and Sen, 2021, 2022; Farouk et al., 2022; Darjani et al., 2023). It is critically required in any fluid injection program including CO2 sequestration to ensure a long-term confinement of the injected fluid volume without compromising caprock integrity (Sen et al., 2023). Minimum horizontal stress (Shmin) is one of the most important parameters in geomechanical modeling. An over- or under-estimation of Shmin can significantly affect the inferences, therefore requires downhole calibration to reduce uncertainties. For example, lack of Shmin calibration can misinterpret the stress contrast between reservoir and caprock, thus affecting the fluid injection designs and hydraulic fracturing management etc.
A New Geomechanical Modeling Workflow for CCUS Near-Well Integrity Assessment in Layered Poroelastic Medium
Authors: Zhai Guang , Peters Elisabeth , Candela Thibault
Published: August 2024

The practice of cold CO2 injection into depleted oil/gas reservoirs addressing the increasing global emissions requires efficient and robust geomechanical assessment of near-well (caprock) damage risks. The geomechanical responses involve driving forces of small-footprint cooling zones from thermal effects, large-footprint pressure plumes from fluid diffusion, and possible continuing reservoir inelastic compaction. The workflow of coupling these processes to rock mechanics for geomechanical assessment is based on numerical or (semi-)analytical solutions. Among the latter, the recent development of MACRIS at TNO considers the fundamental point-source solution in homogeneous half-space which has been validated with FEM methods and is computationally efficient. Here, we extend the MACRIS framework and use the point-source solution in a layered, poroelastic half-space. Such improvement can provide several advantages of considering vertical heterogeneity of earth medium, solid-fluid coupling outside the reservoir, and undrained conditions in caprock. By incorporating the layered poroelastic point-source solution, we develop a workflow consisting of reservoir hydro-thermal simulation for temperature and fluid pressure distributions, calculating point-source mechanical influence functions in layered poroelastic medium, and integration for geomechanical assessment at targets. Finally, we demonstrate the workflow and show results from a case study using realistic model parameters provided by operators.

1. INTRODUCTION

Enabling large-scale CO2 capture, utilization and storage (CCUS) is crucial to ensure global emission reductions in line with the Paris agreement (Hennessy et al., 2022). Several previous large-scale projects for CO2 storage have been focused on deep aquifers, e.g., in the Salah CO2 storage site in Algeria (Ringrose et al., 2013). Recently, using depleted oil/gas reservoir for large-scale CCUS has been proposed to be promising for addressing the increasing global emissions, since they are well characterized from previous O&G activities with large pressure margins for safe storage (Wei et al., 2023).

During the practice of CCUS in the context of depleted oil/gas reservoirs, one of the major challenges is the timely and robust geomechanical assessment of near-well damage hazards, which can help mitigate potential damage-related risks and enhance the safe operations (Wei et al., 2023). The geomechanical assessment may require the consideration of both reservoir depletion (oil/gas production) and re-pressurization (CO2 injection) phases, since the reservoir deformation processes during the two phases can be physically linked. In reservoir leakage pathways. Such processes have not received much study to date (Fan et al., 2020; Liu et al., 2020).
Unsupervised Machine Learning for Delineating Stratigraphy in Subsurface Reservoirs for the Utah FORGE Geothermal Project Ayyaz Mustafa
Authors: Mustafa Ayyaz , Kelley Mark , Lu Guanyi , Bunger Andrew P.
Published: August 2024

The correlation of rock mechanical properties from one well to another across an area of interest poses a classical and ongoing problem in rock mechanics. This work illustrates identification of the mechanical layers/zones in a geothermal reservoir using unsupervised machine learning (ML) techniques. Mechanical stratigraphy was defined using well logs obtained from three wells located at the Utah FORGE geothermal site: 58-32, 16A(78)-32 and 16B(78)-32. The widely accepted unsupervised ML techniques including K-means clustering, Gaussian mixture models, and DBSCAN (density-based spatial clustering of applications with noise) were utilized to generate the rock classes based on similarities/differences in mechanical attributes. The rock mechanical classifications were performed using a combination of parameters including measured log data (compressional and shear wave interval transit times) and augmented features such as Poisson's ratio, and Young's modulus. The performance of ML clustering models were evaluated using Silhouette index (SI) and Davies-Bouldin index (DBI) criteria. The evaluation measures of predicted classification reflected the effectiveness and applicability of the proposed ML approaches to generate mechanical stratigraphy. Evaluation measures SS and DBI represent the good quality and reliability of proposed classification with higher SI, CHI, and lower DBI scores. The best performance for the proposed clustering model was exhibited by K-means algorithm with SI, DBI and CHI scores of 0.86, 0.4, and 79, respectively. The proposed mechanical units cluster models were observed to be consistent with the lithological stratigraphy of the studied wells. This approach is therefore shown to provide efficient and reliable identification of mechanical stratigraphy for FORGE with the capability for application across a wide range of subsurface reservoirs.

1. INTRODUCTION

Rocks are formed in different lithostratigraphic units that have a wide range of mechanical characteristics (Boersma et al., 2020). According to Ferrill et al. (2017) and Smart et al. (2014). The mechanical characteristics are often described in terms of stiffness and strength properties, including elastic parameters, tensile strength, and compressive strength (Roche et al., 2013).
Numerical Study of the Proppant Transportation in the Nonplanar Fracture
Authors: Chen S. Y. , Jin Y. , Wei S. M. , Xu D. , Zhou W. D.
Published: August 2024

The sand-carrying fluid flow in complex hydraulic fracture is crucial to improve the fracture conductivity. Previous research on proppant transportation simulation has mainly focused on planar fracture. This paper presents a proppant transportation model of the nonplanar fracture. Based on the computational fluid dynamics (CFD) and discrete element method (DEM), a 3D coupled model was established to simulate the transportation of proppant in the nonplanar fracture. Proppant distribution is quantitatively evaluated by the height and length as the proppant bed reaches the equilibrium state. The numerical model also investigated the effects of proppant density, proppant size and fluid velocity on the proppant transportation. The results show that the sand-carrying fluid flow in nonplanar fracture has higher flow resistance compared with the planar fracture. Proppant with large size may reduce the usage amount but tend to form sand plugging. Further analysis reveals that as the proppant density decreases, the average height of the proppant bed also increases and the distribution distance is much longer. The results in this paper can help design the parameters of proppant and fracturing fluid, which can make the nonplanar fracture maintain a high and enduring conductivity.

1. INTRODUCTION

In recent years, hydraulic fracturing technology has often been required to perform reservoir stimulation when extracting unconventional oil and gas (Barbati et al., 2016). Proppant plays an important role in supporting fracture conductivity after hydraulic fracturing. The flow of sand-carrying fluid is a complicated process (Osiptsov, 2017; Lecampion and Garagash, 2014). Therefore, studying the transportation and distribution of proppant in fractures has an important impact on evaluating the reservoir stimulation effect.

Most previous studies were based on the fact that the fractures were planar, and they conducted experiments on proppant transportation rules by building visual devices. Babcock et al. (1967) first proposed two parameters describing proppant distribution, namely equilibrium velocity and bank build-up constant, through straight fracture experiments. Based on the research, Clark et al. (1977) designed a transparent and planar fracture model to observe the suspended prop flow. Later, Roodhart (1985) built a vertical slot flow model to study the proppant setting in non-Newtonian fracturing fluids. Then, Liu and Sharma (2005) presented an experimental study to investigate the impact of fracture width and fluid rheology on proppant transportation. Fernández et al. (2015) showed the final proppant placement under different pumping rates and particle sizes by a narrow planar wedge-shaped fracture setup. Through similar experimental equipment, Peng et al. (2023) studied the effects of pumping rates, liquid viscosity and proppant type on the stimulation.
GEMINI, a Novel Software System to Improve the Penetration Rate of a Tunnel Boring Machine
Authors: Aliguer I. , Oliver I. , de Santos C. , Vara F. , Gomez J.
Published: August 2024

This technical paper presents GEMINI, a software system designed to optimize the performance of tunnel boring machines (TBMs) using machine learning-based techniques. The system key features include an advanced architecture for querying and processing data from the TBM database, big data analysis and visualization in a web browser, a ground-machine interaction model to predict the TBM advance rate and operation anomalies detection via machine learning algorithms to enhance the efficiency and reliability of tunnel excavation operations. In collaboration with ACCIONA, the system has been validated in the Mularroya (Spain) hydraulic tunnel (small diameter) in fractured rock mass with faulted and sheared zones, and implemneted in a larger diameter tunnel in Sao Paulo (Brazil) in weathered granite materials. In both cases, the ground-machine excavation model was trained with excavation data from each tunnel and the model was deployed to provide real-time predictions of the penetration rate during the TBM operation.

1. INTRODUCTION

Generally., in tunnelling operations regardless the excavation method, penetration rate and advance rate are the most relevant indicators to deliver the project on time and within budget. In this context, an appropriate methodology to predict their values during operation is a challenge that ACCIONA as a contractor is willing to tackle for mechanized excavations with TBMs in collaboration with SAALG Geomechanics. Geotechnical back-analysis to characterize ground behaviour has received attention from both the academia and the industry since the 80s (Gioda and Maier, 1980) within the context of forensic geotechnics and the Observational Method. It consists in comparing a set of ground measurements that capture the actual ground response with a conceptual model that is able to predict such response by means of some ground-related parameters. The parameters that best represent the ground behaviour are those that minimize the difference between the measurements and model results, expressed as an objective function.

SAALG Geomechanics develops and commercializes DAARWIN (Acosta et al. 2023), a cloud-based web application that implements the general workflow of geotechnical back-analysis at the same pace as construction progresses, also referred as real-time back-analysis – RTBA). Its inputs are: i) Finite Elements (FE) as a predictive model of ground behaviour, ii) data from sensors installed within the construction site and uses genetic algorithms as the optimization technique to minimize the objective function (de Santos, 2015).
Modelling the Hydraulic Injection Fracture Response in Layered Media for Megablock Experiments
Authors: Magsipoc E. , Grasselli G.
Published: August 2024

Finite-discrete element method (FDEM) models were employed to explore the fracturing response of Montney Formation outcrop samples to hydraulic fracturing. Variability envelopes were employed to characterize its behaviour under different compressive loading orientations with respect to the layering in the samples. These envelopes were used to conduct calibration using Bayesian optimization. Four calibration schemes were then used as the material parameter set for simulating the "megablock" experiments under gravity loading. In these experiments, the models demonstrated a complex pattern of fracturing around the borehole, including planar fractures that extended both vertically and horizontally. Interestingly, fractures that intersected with layering interfaces could lead to horizontal fractures, despite the influence of gravity-induced stress. An isotropic parameter set used to simulate a mortar megablock sample with embedded weak DFNs also showed fracturing preference along the DFNs and horizontally against gravity due to the availability of fluid storage in the DFN cavities and ductility.

1. INTRODUCTION

Hydraulic fracturing (HF) is often performed in layered sedimentary formations. These embedded layers with varying mineralogy may impact the strength and elastic characteristics of the overall material when subjected to failure (Guo et al., 2018). While it is difficult to investigate these impacts at reservoir depths, numerical modelling, supplemented with laboratory testing and experimentation, can provide insights into these mechanisms (Yushi et al., 2016).

Megablock hydraulic fracturing experiments are proposed to provide laboratory access to the fracture formation. Three megablocks (0.7 × 0.7 × 0.7 m3) are currently being prepared for laboratory hydraulic fracturing tests to observe surface deformation, acoustic emission, and fracture response to layering anisotropy. Two of these specimens are Montney Formation (MF) outcrop acquired from Kamenka Quarry in Canmore, AB, Canada. The last megablock is a concrete mortar with 30 cm layers made to simulate an anisotropic material and to provide pilot test material before proceeding with the rock specimens. While the pilot mortar specimen will provide learning lessons in implementing data acquisition and monitoring systems, numerical modelling can also provide insights into the expected fracture mechanisms and help make decisions in implementing the megablock testing program such as optimal sensor placement, expected weak areas, and monitoring areas for deformation.
The Perlite Usage in High Density Oil-Well Cement
Authors: Ahmed Abdulmalek , Mohamed Ahmed , Elkatatny Salaheldin
Published: August 2024

Well cementing is an essential process for well stability, involving the pumping of cement slurry into the annulus between casing and formation. This prevents fluid movement and provides support for the casing. Therefore, various additives are employed in cement design to meet downhole specifications. In this paper, experimental work has been performed to study the possibility of using perlite as an additive to improve the properties of high-density cement. Base cement sample (without perlite) and perlite cement sample (with 4% by weight of cement of perlite) were prepared under high pressure and high temperature to examine their effect on the oil-well cement properties such as compressive strength, viscosity, permeability, and particles settling. The findings demonstrated a significant enhancement in oil-well cement properties with the addition of perlite. Integrating perlite into the cement composition increased its strength, leading to an 88% improvement in compressive strength compared to the base cement. The inclusion of perlite particles resulted in a 30% reduction in the viscosity. Furthermore, the addition of perlite decreased the cement permeability by 66%. Additionally, perlite effectively minimized the particles settling along the cement column to a minimal percentage of 0.1%.

1. INTRODUCTION

The global economy is significantly influenced by the oil and gas industry, which plays a crucial role in meeting the increasing energy demands associated with a growing population. Predictions suggest a nearly 50% rise in global energy consumption by 2040 (Goraya et al., 2019). To enhance oil production, advanced technologies like deeper drilling and extended-reach multi-lateral wells are deemed necessary (Kumar et al., 2022). In the oil industry, well cementing is a crucial operation to maintain the stability of the wellbore during drilling operations or production periods. Well cementing emerges as a pivotal operation in the oil industry, ensuring wellbore stability during drilling and production. The cementing process involves placing cement slurry downhole in the annulus between the formation and casing (Ahmed et al., 2009; Nelson & Guillot, 2006; Newlove et al., 1987). Well cement serves various functions, including preventing fluid movement, sealing lost circulation zones, supporting casing, absorbing shocks during drilling, isolating water or gas production zones, and sealing abandoned wells that are no longer economically viable (Mahmoud et al., 2019).
Comparing Compression and Shear Behavior of Intact and Resedimented Mudrock to Illuminate the Rheology of Mudrocks
Authors: Bhandari A. R. , Ewy R. T. , Flemings P. B. , Germaine J. T.
Published: August 2024

We performed compression and strength measurements on intact and resedimented cores from offshore West Africa from a depth of 975 meters below the seafloor. The in-situ vertical effective stress was 7.9 MPa, and the yield stress of the intact core was ∼21 MPa. We uniaxially consolidated the intact specimen to 15 MPa and unloaded it to undo coring, sampling, and re-saturation effects. We uniaxially consolidated the resedimented specimen to ∼22 MPa effective axial stress and unloaded it to reach the in-situ void ratio. We then performed undrained shear. The intact and resedimented specimens were within the elastic zone (inside yield surface) and had similar void ratios before shearing. We observed a higher peak shear strength (at ∼lower axial strain) and significant post-peak strength softening in the intact material. Both the materials start similarly in the mean effective stress versus shear stress plot. However, after shear stress of ∼4 MPa, the resedimented material departs significantly to the right from the straight ∼vertical stress path of the intact material, indicating dilative behavior. We interpret that the bonding in the intact material delays dilative behavior until just before the peak. Overall, this work identifies a key difference between intact and resedimented behavior due to bonding caused by creep and/or diagenesis in the intact material over geological time.

1. INTRODUCTION

The fine-grained siliciclastic sediments start as weak, unconsolidated materials when initially deposited. However, over time and with burial at depth, these sediments undergo various mechanical and chemical processes that lead to develop them stronger (Maltman, 1994). A proper characterization of the geomechanical behavior of these mudrocks is essential for ensuring wellbore stability, predicting reservoir behavior, evaluating seal integrity, etc.

Intact mudrocks derived from deep coring on the world's continental margins often demonstrate yield stress much greater than the maximum past geologic stress when loaded at lab rates (Ewy et al., 2020; Finnegan, 2020; Gutierrez et al., 2008). This overconsolidated state may record 1) creep and/or 2) bonding occurring over geologic time. On the other hand, it is common to use resedimented mudrocks to study in-situ stress state and strength (e.g., Casey et al. (2016)), assuming they would successfully reproduce in-situ properties. We systematically test intact and resedimented specimens, bringing them to a similar void ratio, and explore their undrained shear behavior to illuminate the rheology of mudrocks and fully understand in-situ rock properties.
Enhancing Joint Detection and RQD Estimation in Acoustic Televiewer Imaging Through Automated Instance Segmentation and Deep Learning
Authors: Houshmand N. , Esmaeili K. , Goodfellow S.
Published: August 2024

Under certain ground conditions, obtaining adequate core specimens for logging can be challenging, leading to low recoveries and poor rock quality designation (RQD). Borehole well logging offers a cost-effective alternative to handling expensive core samples for logging purposes, particularly advantageous for projects constrained by limited resources. To construct a secure subsurface structure, it is vital to identify and detect weak planes. Borehole imaging provides a fast and efficient way to characterize fractures. Acoustic televiewer (ATV) imaging is an effective tool in fracture detection and RQD estimation. However, the conventional process of manually detecting and characterizing joints in ATV images is laborious, subjective, and inconsistent. This study introduces an automated method for joint detection and RQD estimation. For the study, 1390 meters of data from 24 boreholes across 5 different mines were collected. A deep learning algorithm called Mask R-CNN was utilized. The model's predictions yielded a mean absolute error of 1.4% for the RQD prediction. This indicates that the model is effective in estimating RQD. By automating joint detection and RQD estimation, the proposed method streamlines and enhances the process of automated subsurface structure analysis.

1. INTRODUCTION

Understanding the structural setting of rock mass is crucial for designing safe and efficient geomechanical projects. These characteristics define the rock's behavior during excavation and influence potential failure mechanisms. However, acquiring accurate geotechnical data remains a challenge, often leading to uncertainties and hindering design reliability (Manzoor et al., 2020; Medinac and Esmaeili, 2020). Drill core logging and borehole well logging are prevalent techniques for rock characterization. In core logging, the Rock Quality Designation (RQD) is traditionally assessed through direct measurement or subjective judgment (Hadjigeorgiou, 2012). These methods are time-consuming, prone to inconsistencies between loggers, and impeded by poor core recoveries or low rock quality (Usta and Esmaeili, 2023; Rodgers et al., 2020; Esmaeili, 2019; Bhuiyan and Esmaeili, 2018). In addition, handling core samples can be expensive for the scope of some projects. In these situations, borehole well logging can be a suitable alternative for rock mass characterization.
Development of a Prototype Thermal Imaging Rockfall Detection System
Authors: Potter Julia , Meyer Benjamin , Ross Brad , McNabb James , Keefner John , Williams Chad , Brown Leonard D. , Prescott Bobby , Cabrejo Albert
Published: August 2024

Rockfalls pose a critical risk to the mining industry. In collaboration with NIOSH and industry partners, the Geotechnical Center of Excellence (GCE) has previously shown that thermal video can effectively observe rockfall events. Currently, work is underway to automate the detection, tracking, and alarming of these events. To facilitate this work, a prototype thermal imaging system that uses an early version of the detection algorithm has been developed. Designed to withstand extreme environments, the system can be easily transported with a light vehicle and installed in under 20 minutes. It includes a high-resolution security-type thermal camera, a tripod, a processing unit in a weatherproof case, and a backup battery to mitigate temporary power loss. While initially developed for detecting rockfall in open pit mines, the system can be customized for specific use cases. The presentation will provide an overview of the prototype's deployments and associated research to date, document lessons learned, and outline plans for future prototype development.

1. INTRODUCTION

Rockfalls and slope failures represent a critical and escalating risk to the mining industry, posing threats to both personnel and infrastructure. Despite great progress in monitoring solutions targeting movement at rates of inches per year to inches per day, there are limited methods to detect rapid movement due to rockfall in real time (Sharon and Eberhardt, 2020). Currently, rockfall mitigation is largely limited to ‘manual’ methods such as trigger lines and human spotters. While important progress has been made to analyze rockfall sources and deposition zones with unmanned aerial vehicle (UAV) and light detection and ranging (LiDAR) systems, this requires significant post-processing time (Walton, et. al, 2023; Graber and Santi, 2023; Wang, et.al, 2021). Two Doppler radar systems are in commercial development for rockfall detection, but neither has been widely adopted in the US mining industry, and there remains a notable absence of widely adopted tools in this domain (Viviani, et. al, 2020).
Shale Anisotropy Made Simple
Authors: Holt R. M. , Bakk A. , Lozovyi S.
Published: August 2024

Shales are anisotropic, but their anisotropy is often neglected in geomechanical field applications. A major reason for this omission is the need for input data, which includes at least five different elastic parameters that are not commonly available. Recent developments have pointed to internal correlations between elastic anisotropy parameters that can be linked to textural and compositional similarities between different shales. Oriented clay minerals constitute the main source of lithological anisotropy. Utilizing the internal correlations in a pragmatic and simple manner, we demonstrate that a reasonably good anisotropic characterization can be given, even if only one or two input parameters are available. Such a default anisotropic formulation is likely to yield more accurate results for several engineering applications rather than simply assuming isotropy. Several improvements are available, and some already exist, but at the cost of requiring more input from logs or core measurements. Further refinement of our current approach is a low-hanging fruit.

1. INTRODUCTION

Shales serve as caprocks above hydrocarbon and CO2 storage as well as unconventional reservoir rocks. Shales are anisotropic, which has impact on reservoir compaction or extension, surface displacements, borehole stability during drilling, as well as depletion or injection induced stress and pore pressure changes. Still, most rock mechanical analyses assume isotropy, simplifying the computational process. Anisotropic analysis requires at least five elastic moduli, depending on symmetry, and if poroelasticity is incorporated, this number increases further.

Subsurface formations are normally not sufficiently characterized to enable determination of all the required elastic parameters. To determine all five static moduli for a transversely isotropic (TI) rock, at least three differently oriented cores would be needed. With a cylindrical core drilled with its axis aligned with the symmetry axis, an undrained triaxial test gives the undrained Young's modulus and Poisson's ratio for that specific orientation. In the field, gamma ray (to identify shale formations), density and sonic log data would be available, which may provide P-wave modulus and in most cases also shear modulus. However, three additional elastic moduli are required to fully characterize the anisotropy. Further, the discrepancy between static and dynamic moduli needs to be corrected for.
Predicting Strength Properties Applying Machine Learning Techniques for Highly Heterogeneous Microbial Limestones from Brazilian Pre-Salt Reservoirs
Authors: Ferreira F. H. , Borba A. M. , Garcia P. F. V. , Santos E. S. R. , Falcão F. O. L.
Published: August 2024

Comprehensive knowledge of rock strength and deformability properties holds paramount importance in the E&P activities. This understanding proves critical across a spectrum of designs such as wellbore stability, sanding production, compaction, subsidence, as well as facilitating intricate 3D Geomechanical modeling, and more. This study presents a comprehensive compilation of 525 triaxial and uniaxial tests conducted on samples extracted from a highly heterogeneous limestone formation from the Brazilian pre-salt region over the course of the last 12 years. By incorporating both triaxial and uniaxial test data, the research delves into the rock's response to diverse stress conditions, contributing to the understanding of its mechanical behavior. To analyze this dataset, machine learning techniques were employed to establish correlations, specifically focusing on Uniaxial Compressive Strength (UCS) in relation to porosity and shear envelopes. This approach aimed to unveil intricate patterns and dependencies within a broad spectrum of limestone porosity levels. By harnessing the power of machine learning, the study sought to derive robust correlations that encapsulate the nuanced interplay between UCS, porosity, and shear strength. Such approach not only enhances our understanding of the complex relationships within the dataset but also offers a predictive framework for estimating UCS and CCS based on varying limestone porosities.

1. INTRODUCTION

Engineering rock mechanics plays a pivotal role in various human activities, spanning civil, mining, petroleum, and environmental engineering. In the realm of rock mechanics specific to Petroleum Engineering, professionals grapple with multifaceted challenges, including reservoir compaction, surface/mudline subsidence, fault reactivation, cap rock integrity, wellbore stability during drilling phases, onset sanding production, and hydraulic fracture design and much more. Effectively addressing these structural issues hinges on a comprehensive understanding of a triad of parameters: i) in situ stress; ii) mechanical rock properties, and iii) underground geometry. This amalgamation of parameters is encapsulated in the concept known as the Mechanical Earth Model (MEM), which has been instrumental since the early 90s.