bluuebunny/crossref_metadata_2025_split huggingface dataset:
Characteristics of Conicl Diamond Tooth for Breaking Hard Rock Under Vibratory Percussion
Authors: Ji Ran , Shi Huaizhong , Sun Zhaowei , Chen Han , Xiong Chao , Song Hengyu 
Published: September 2023

ABSTRACT
The application of conicl PDC teeth to vibratory percussion drilling technology is expected to break deep hard rock efficiently and improve drilling efficiency. In this paper, the characteristics of conicl diamond tooth cutting and hard rock under percussion are investigated. Firstly, experiments on percussion compression of conicl diamond tooth into the broken rock were carried out to analyse the percussion energy and the characteristics of rock crushing pits. Secondly, a numerical model of the cutting process of conicl diamond tooth under the vibratory percussion was established. Three stages of rock-breaking by conical tooth cutting under high frequency number percussion are analyzed. Stress peaks with percussion of 6 times increased by 36.8% and 25.5% compared with percussion of 0 times and percussion of 3 times. There is the highest point of rock-breaking efficiency between percussion of 3 times and 4 times. Cutting force with percussion amplitude of 1.5mm increased by 42.7% compared with percussion amplitude of 0mm. The rock-breaking specific energy with percussion amplitude of 1.5 mm increased by 32.4% compared with percussion amplitude of 0 mm. This study provides a theoretical basis for the use of conical tooth in PDC drill bits under percussion.

INTRODUCTION
In the oil and gas exploration and development industry, drilling cost accounts for a large proportion of the overall development cost. The application of efficient drilling technology directly affects the final benefit(Wang, 2003)i. The exploration and development of deep and ultra-deep wells has become the focus of current attention(Meiling et al., 2014)ii and conventional drilling methods are increasingly unable to meet the needs of efficient drilling.
As the well depth increases, the hardness and plasticity of the rock increases and the drillability decreases, which also increases the difficulty of drilling in deep hard formations. How to improve ROP has become one of the key issues in drilling engineering technology research. Previous studies have shown that vibratory percussion drilling can significantly improve rock breaking efficiency in hard rock(Hustrulid, 1971;Li et al.,2020;Pavlovskaia et al.,2015)iii.
Effects of Tuning Hyperparameters in Random Forest Regression on Reservoir's Porosity Prediction. Case Study: Volve Oil Field, North Sea
Authors: Sandunil K. , Bennour Z. , Ben Mahmud H. , Giwelli A. 
Published: September 2023

ABSTRACT
Ensemble learning is a recent development in machine learning. Random forest regression (RFR) is one such widely utilized ensemble learning algorithm. However, the current literature lacks studies that primarily focus on the effects of hyperparameter tuning in RFR when predicting reservoir properties of hydrocarbon reservoirs. Thus, in this study we investigated the effects of three commonly used hyperparameters; namely, n_estimators, max_features and min_samples_leaf to predict porosity of Volve oil field in North Sea. Four parameters; depth, gamma ray logs, neutron porosity logs and resistivity logs were used as inputs, while calculated porosity was used as target outputs to develop the RFR models. The RFR models were developed through: (i) tuning each hyperparameter individually, (ii) tuning hyperparameters by coupling them into three groups and, (iii) tuning all three hyperparameters at once. Results showed that the highest performing model had an R2 value of 0.8517 with n_estimators of 100, max_features of 0.5 and min_samples_leaf of 1. Furthermore, it was observed that tuning max_features had a higher impact on improving the performance of the RFR model when predicting porosity of Volve oil field in North Sea.

INTRODUCTION
World is gradually moving towards an era of artificial intelligence (AI) where every major sector possibly be supplemented by man-like machines. Machine learning (ML) is a branch of AI which has the ability to predict or forecast outputs, decrease computational time and extract features from complex and high-dimensional data sets (Zhan and Kitchin, 2021). These properties of ML are favourable in industries where big data is utilized in several stages of the production line. Oil and gas industry is a perfect match for ML since it uses huge number of data to analyse and interpret both in upstream and downstream sectors.
There are several ML algorithms which have been utilized in the literature for reservoir characterization including prediction of petrophysical properties. Artificial neural network (ANN) is one such ML algorithm. ANN's originated from mimicking biological neural systems (Wang et al., 2019). They are widely used in predicting petrophysical properties of hydrocarbon reservoirs (Al Khalifah et al., 2020, Urang et al., 2020, Okon et al., 2021). Supportive vector machine is another commonly used ML algorithm in the literature to estimate reservoir properties (Jamalian et al., 2018, Zhong & Carr, 2019, Wu et al., 2020). Deep learning is an ML algorithm which can be defined as an advanced version of ANN's. This algorithm is also being used in reservoir characterization (Chen et al., 2020, Zhang et al., 2021, Arigbe et al., 2019). 2019).
An Automated Data-Driven Workflow for Identifying Fractured Horizontal Well Sweet Spots in Shale Reservoirs
Authors: Deng Yuxuan , Wang Wendong , Du Xianfei , Su Yuliang , Sun Shibo , Jia Feng , Jiang Zhaoqiang 
Published: September 2023

ABSTRACT
The unique characteristics of shale reservoirs, including micro-nanopores, fast lithofacies fluctuations, ultra-tight reservoirs, and complex hydrocarbon occurrence states, make sweet spot evaluation for shale different from conventional tight oil reservoirs. Fortunately, data science techniques have proven effective in managing the large volume of data collected during shale reservoir development. This study presents a comprehensive sweet spot evaluation workflow in shale plays using a machine learning approach based on two key features: fracability and mobility. Quantitative shale fracability and mobility parameters are calculated from conventional logging data using optimal assessment indicators. Additionally, the study proposes a modified comprehensive sweet spot evaluation model based on the independent weight coefficient method. A deep clustering algorithm based on autoencoder (AE) is trained to classify the partition pattern of the shale sweet spot and different classes of shale reservoir characteristics. The established models exhibit higher agreement rates and better reservoir classification results compared to traditional models. The modified ML model successfully uncovers nonlinear relationships in shale reservoir characterization and provides more reliable sweet spot evaluation results, which further facilitates automatic identification and search of shale reservoir sweet spots.

INTRODUCTION
Shale oil and gas resources have gained increasing attention in recent years due to the shift in exploration focus from conventional to unconventional resources (Ghosh et al., 2018; B. Liu et al., 2019, 2020; Luo et al., 2018, 2020). As an unconventional hydrocarbon resource, shale oil has become crucial to a diversified global energy pattern. Unlike the North American shale oil deposited in marine environments usually, China's continental shale oil possesses multiple types, wide distribution, and significant development potential, providing an essential supplement and replacement energy source for stable and increased oil production in oilfields. The shale oil reservoir is characterized by rapid lithofacies changes, large physical property differences, and strong heterogeneity, necessitating extensive volume transformation to achieve effective utilization and development benefits.
Fracture Surface Alteration Examined by Surface Analysis of Carbonate Samples Tested Under Direct Shear
Authors: Gallas J. , Duda M. , Backers T. 
Published: September 2023

ABSTRACT
Fracture surface properties are essential for rock mass stability assessment. Joint surfaces can be altered by deformation processes resulting in considerable changes of joint surface morphology often classified with joint roughness coefficients. We carry out direct shear tests on Upper Jurassic carbonate samples with photogrammetric recordings of the joint surfaces before and after shear testing. To compare the surface morphologies before and after shear testing, reference holes were drilled and have proven to enable a successful alignment of both scans. Changes in surface morphology of the specimen before and after shearing are then evaluated by highlighting areas of mechanical erosion as well as roughness statistics. This workflow permits to detect changes in JRC and identifies even small changes in surface morphology. By combining different surface evaluation methods with our reference hole approach, we present a simple, reproducible workflow for the evaluation of surface morphology in the laboratory scale.

INTRODUCTION
In the context of reservoir behavior, the surface morphology of discontinuities of various types and scales is crucial. Surface morphology has a major influence on fracture flow, particularly its anisotropy (Schmittbuhl et al., 2008; Thompson & Brown, 1991; Koyama et al., 2009). Fundamental experimental studies have described the dependency of shear strength on fracture roughness (Barton, 1973; Barton & Choubey, 1977). Furthermore, the impact of fracture roughness on shear strength and slip stability of fractures has been documented by DEM modeling (Wang et al., 2020), which showed that an increasing asperity height as well as the anisotropy of asperity wavelength are significant factors. Further experimental studies on this topic showed, that breakage and degradation of asperities are linked to normal stress, bonding strength and asperity geometry (Asadi et al., 2013). The impact of shearing on roughness and therefore, shear strength is of great importance. Detailed studies on the morpho-mechanical behavior of artificial and natural discontinuities have been carried out under varying shear test boundary conditions with different specimen types (Homand et al., 2001; Yiang et al., 2006; Ge et al., 2016, Wang et al., 2019; Tatone & Grasselli, 2015). Following these contributions, we wanted to establish an easy and quick workflow for the visual and statistic description of surface morphologies (Fig. 2). We chose to investigate the surface morphologies of direct shear test samples that are part of a current lab study.
Enhancing Breakdown Pressure Predictions in Ultra-Tight Formations Through Robust Machine Learning Techniques
Authors: Mustafa Ayyaz , Tariq Zeeshan , Gudala Manojkumar , Yan Bicheng , Sun Shuyu , Mahmoud Mohamed 
Published: September 2023

ABSTRACT
Accurately estimating rock breakdown pressure is crucial for designing effective hydraulic fracturing operations, particularly in unconventional ultra-tight reservoirs where hydrocarbon extraction is challenging. However, conducting experimental studies on hydraulic fracturing is both time-consuming and costly. To address this, we employed robust machine learning (ML) tools to estimate the breakdown pressure. The research comprised two stages: an extensive experimental phase followed by the development of ML prediction models using the obtained data. The ML models were trained using experimental factors such as injection rate, confining stress, fluid viscosity, and rock characteristics, including unconfined compressive strength, Poisson's ratio, tensile strength, porosity, permeability, and bulk density. Six machine learning techniques—K-Nearest Neighbor (KNN), Random Forest (RF), Decision Trees (DT), artificial neural networks (ANN), gradient boosting (GB), and adaptive gradient boosting (Adaboost)—were employed to construct the prediction models. With the optimal settings for the ML models, the breakdown pressure of the tight formations was accurately predicted with a 99% accuracy. The proposed ML approaches not only offer significant cost savings but also serve as a quick evaluation tool to assess the development prospects of tight rocks.

INTRODUCTION
Low porosity and permeability are typically characteristics of unconventional tight reservoirs. The most cost-effective method of extracting resources from these reservoirs is to inject high-pressure fluids into engineered channels or fissures known as fractures. Hydraulic fracturing treatment is the name of the entire industrial procedure (Agency, 2016; Teufel and Clark, 1984; van Eekelen, 1982; Wang et al., 2018). The pressure at which rock cracks during a hydraulic fracturing procedure is known as the breakdown pressure. It is the highest pressure ever logged during the injection of wellbore's fracturing fluid injection (Detournay and Carbonell, 1997; Patel et al., 2017; Song et al., 2019; Warpinski et al., 2004). In order to extend the economic life of a well or field, the fracturing procedure might be done numerous times. Several mixtures of fracturing fluids may be employed to increase the effectiveness of the fracturing process depending on the kind of formation and its properties. In order to fracture unconventional reservoirs, common fracturing fluids include polymers, water, methanol, oil, linear gel, or a mixture of methanol and water (Gomaa et al., 2014a; Wu et al., 2019). Many analytical, computational, and experimental research has been performed to predict and understand the fracture breakdown pressure (Barree et al., 2009; B. Dusseault et al., 2014; Goodarzi et al., 2015; Gutierrez and Lewis, 1998; Haifeng et al., 2013; Schmitt and Zoback, 1993; Warpinski et al., 1979).
Predicting Trapping Indices in CO2 Sequestration: A Data-Driven Machine Learning Approach for Coupled Chemo-Hydro-Mechanical Models in Deep Saline Aquifers
Authors: Tariq Zeeshan , Yan Bicheng , Sun Shuyu 
Published: September 2023

ABSTRACT
Storing carbon dioxide (CO2) in deep geological formations, such as saline aquifers and depleted oil and gas reservoirs, through Geological Carbon Sequestration (GCS) offers tremendous potential for large-scale CO2 storage. To gain a better understanding of how CO2 is trapped in saline aquifers, it is important to create robust and speedy tools for assessing CO2 trapping efficiency. Therefore, this study focuses on using machine learning techniques to predict the efficiency of CO2 trapping in deep saline formations as part of GCS. The methodology involves simulating the CO2 trapping mechanisms using a physics-based numerical reservoir simulator and creating a dataset based on uncertainty variables. The study used a numerical reservoir simulator to simulate CO2 trapping mechanisms over 170 years, with uncertainty variables like petrophysical properties, reservoir physical parameters, and operational decision parameters being utilized to create a large dataset for training, testing, and validation. 722 reservoir simulations were performed and the results of residual trapping, mineral trapping, solubility trapping, and cumulative CO2 injection were analyzed. A deep neural network was applied to predict the CO2 trapping efficiency. The results showed that the deep neural network model can predict the trapping indices with a coefficient of determination above 0.95 and average absolute percentage error below 5%.

INTRODUCTION
Carbon capture and storage (CCS) is a technology that captures carbon dioxide (CO2) emissions from power plants and industrial sources, transports it to a storage site, and injects it deep underground into geologic formations, such as saline aquifers. The aim of CCS is to reduce greenhouse gas emissions and mitigate climate change (A. Z. Al-Yaseri et al., 2016; Arif et al., 2016; Gholami & Raza, 2022; Harp et al., 2021; Johnson et al., 2004; Rutqvist, 2012; Tariq et al., 2022; Wen et al., 2021, 2022; Yan, Harp, Chen, & Pawar, 2022; Yan, Harp, Chen, Hoteit, et al., 2022). The need for underground storage of carbon dioxide is crucial in our current era as it helps reduce the emission of greenhouse gases into the atmosphere. The focus on reducing the effects of these emissions and transitioning to net zero carbon energy sources has been a global concern for many years. As the world's population continues to grow, energy demand will increase and the world will not soon reach its peak in energy consumption. This has led to discussions among the energy industry to address the impact of climate action on business sustainability, as greenhouse gases are a major contributor to global warming and climate change.
Sealing Capacity of Caprocks for CO2 and H2 Storage in Illinois Basin
Authors: Kim H. , Makhnenko R. Y. 
Published: September 2023

ABSTRACT
Permanent geological carbon dioxide (CO2) storage is a promising pathway to mitigate the greenhouse gas levels. Recently, seasonal underground storage of hydrogen (H2) has become of interest to fulfill energy demand uninterruptedly by periodic renewable energy supply. Caprock formations should be studied for both types of the storage to inhibit the leakage of buoyancy-driven CO2 and H2. Cores of two caprock representatives – Eau Claire and Maquoketa groups – are collected from CO2 storage sites in Illinois Basin and tested for the breakthrough pressure with liquid CO2. The direct method is time-consuming due to the necessity of achieving full saturation and stepwise injection, such that the indirect method is often utilized to estimate the breakthrough pressure via the capillary pressure-saturation curve. The comparison between the two methods indicates that the direct method provides a higher value of the breakthrough pressure. The H2 breakthrough pressure of a given caprock is estimated by the indirect method to be approximately 2.4 times higher than that of CO2. However, we expect that the H2 breakthrough in very tight rock, like Maquoketa Shale, would occur through different leakage scenarios compared to CO2 breakthrough, suggesting that direct measurements under representative in-situ conditions should be conducted.

INTRODUCTION
Increased greenhouse gas levels in the atmosphere have a direct impact on climate change. Geological carbon dioxide (CO2) storage is one of the essential tools for mitigating its atmospheric level by storing CO2 captured at coal- or gas-fired power stations and industrial facilities in deep reservoirs (at depths below 800 m; IPCC, 2005). Hydrogen (H2) has higher energy content per unit mass compared to other gases such as methane and natural gas (Heinemann et al., 2018), such that converting surplus energy into H2 and storing it in a porous reservoir is a promising way to fulfill energy demand uninterruptedly by periodic renewable supply. The main goal of CO2 storage is its removal from the atmosphere, such that the withdrawal stage is not required and CO2 must remain in the subsurface for thousands of years. On the other hand, hydrogen is stored seasonally (about one full injection and production cycle per year). However, the most abundant geological formations for CO2 storage are similar to those for H2 storage: a porous reservoir saturated with saline water and overlying low permeable caprock.
Numerical Modeling Application in the Design of Regional Pillars for an Ore-Replacement Project: Underground Platinum Mining Case Study
Authors: Mutsvanga C. , Zvarivadza T. , Adoko A. C. , Onifade M. 
Published: September 2023

ABSTRACT
Pillars are critical to the stability and profitability of mining operations and their proper design is essential in ensuring that these objectives are met. A number of tools are available in designing pillars and numerical modelling is one of those tools. It is important to note that numerical modelling is not design in its self as wrongly assumed by many, but a tool that is used to aid the design process. This paper evaluates numerical modelling application in the design of regional pillars for an ore-replacement project, underground platinum mining case study, using three different numerical modelling programs. The three numerical modelling programs used in the design process were Examine 2D, Map3D and Lamodel. These programs were used to determine the average pillar stress on the designed pillars and the values obtained were then used to determine the most feasible pillar design. The results obtained were then validated by comparing them with a design based on an empirical method (Stacey and Page pillar strength equation) and underground observations. The advantages of numerical modelling versus the traditional empirical methods are that they can cater for different geometries and scenarios that were identified as limitations in the use of empirical methods. The difference in average pillar stress values obtained between empirical design and numerical modelling and indeed between the different numerical modelling programs themselves give a compelling argument that it is prudent not to base the design of any underground layout on only one method. An attempt should be made to use as many different available methods as possible, then from those, use the most optimal one in terms of cost and extraction ratio based on the design criteria.

INTRODUCTION
Numerical Modeling plays a key role in enhancing the confidence in designs undertaken using empirical approaches. Empirical approaches have been found to be also reliable since they are tethered in reality and can account for design parameters which may be difficult to account for using numerical modeling (Benyoucef et al, 2023; Jarufe, 2022; Li et al, 2021; Chikande and Zvarivadza, 2018; Zvarivadza and Sengani, 2018a; Zvarivadza and Sengani 2018b; Chikande and Zvarivadza, 2016; Chinyowa and Zvarivadza, 2015). A detailed study on empirical approaches in the design of regional pillars in platinum mining is given by Mutsvanga and Zvarivadza (2020).