bluuebunny/crossref_metadata_2025 huggingface dataset:
Seismic Performance of a Large Underground Cavern at Different Excitation Direction
Authors: Zhu Yimo , Zhang Qianbing
Published: September 2023

Underground infrastructures can be adversely affected by earthquake-induced rock engineering vibration causing damage problems. These impacts are even more significantly affected for large underground caverns due to the large span and complex structures. Currently, time-history numerical simulation is still the most popular method for seismic design of underground infrastructure, however it is based on the plane-strain model and assumes the vertical incidence of seismic waves. In fact, the propagation direction of seismic wave is arbitrary and the effect of excitation direction has not been understood well. In this paper, a 3D full-scale hydropower station is built by FLAC3D, and several sets of seismic waves based on the real earthquake motions are applied from different excitation direction. The results show that the wave excitation direction directly influences the result of the large underground cavern assessment, which is manifested by traditional vertical wave applied at bottom underestimating the response, especially for the cases of high lateral stress.

INTRODUCTION

Underground infrastructures are being built at a fast rate to meet the expanding space needs of densely populated urban areas and megacities. Australia is witnessing historic investment across transport, utilities, and social infrastructure. In total, more than $300 billion of major transport projects will be delivered in the next 10 years. The two major subsectors within transport (road and rail) account for around 75% of total Major Public Infrastructure Pipeline resource demand. There will be a 25% increase in tunnelling projects in the next five years (Infrastructure Australia, 2022). Considering the future development scale, existing project volume, unit construction cost and the important role of these infrastructures in modern society, even minor earthquake effects and related downtime may lead to significant direct and indirect losses, for example, In 2021, an earthquake (M5.9) hit near the town of Rawson, which is the biggest earthquake on record in Victoria. About every ten years or so, Australia experiences a potentially damaging earthquake of magnitude 6.0 or more (Geoscience Australia, 2021). In 2023, the central-southern turkey hit by two more powerful earthquakes (M6.4 and M5.8) two weeks after disaster (M7.8 and M7.5), destroying railway infrastructure there, including 446 bridges, 6161 culverts and 175 tunnels (Thomas Johnson, 2023), damaged roads, border crossings and critical infrastructure, severely hampering aid efforts (Elias A.A., 2023). Therefore, it is essential to carefully consider the impact of seismic loads on tunnel design, construction, operation and risk assessment.
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

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

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

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

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

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

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

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

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).
Precipitation of Calcite in a Rock Fracture
Authors: Asem P. , Detournay E. , Guzina B. , Labuz J. F.
Published: September 2023

A promising technology for permanently storing CO2 in the subsurface is through mineralization in mafic (e.g. basalt) and ultramafic (e.g. peridotite) rock, which are abundant in the earth's upper crust. Indeed, recent field experiments in Iceland and Washington State have demonstrated the ability of trapping carbon in mineral form by injecting CO2-charged water in the subsurface. One key question to be addressed in assessing this technology is whether the mineral replacement resulting from the combined dissolution / precipitation process will either clog the pore space or induce cracking that will expose fresh mineral surfaces to, and provide new pathways for, the reactive fluid. In other words, is the process self-limiting (clogging) or self-sustaining (cracking)? To explore this question, we designed a suite of experiments that involves injecting a solution supersaturated with respect to calcite in a rock fracture. The mixture was injected via a central hole drilled in a granite block that was fractured hydraulically. The paper reports on preliminary results of the precipitation of calcite on fracture surfaces, and resulting changes in fluid flow and fracture aperture.

INTRODUCTION

One option to reduce the release of anthropogenic carbon dioxide (CO2) to the atmosphere is the capture and storage of CO2 in geologic formations (Orr 2009). Although sequestration of CO2 via structural trapping in fluid-saturated porous sedimentary rock has gained much interest in the recent years, maintaining seal integrity remains a persistent issue (Benson and Cole 2008). To resolve problems associated with conventional methods for CO2 geologic storage (e.g. structural trapping; solubility trapping), permanent storage of CO2 as thermodynamically stable carbonate minerals has been suggested (Seifritz 1990; Kelemen and Matter 2008), where CO2-charged fluid is injected in Mg- and Ca-rich silicate rock. The carbon is fixed as MgCO3 or CaCO3

(Equation)

Sustainable and large-scale storage of CO2 in geologic formations by mineralization (Equation 1), however, requires (i) continuous circulation of CO2-charged fluid in the rock mass, and (ii) access to fresh minerals enabling dissolution and precipitation to move forward. Potential clogging of the fracture network through vein formation, therefore, can pose significant limitations to carbon mineralization. Understanding the process of vein formation and whether a sufficiently permeable fracture network can be maintained over time by the pressure that growing crystals exert on the crack walls (Taber 1916; Rothrock 1925; Durney and Ramsay 1973; Wiltschco and Morse 2001) is an important question that should be addressed.
Natural Fracture Pattern Development Using 3D Coupled Numerical Model: Mechanics and Diagenesis
Authors: Lee B. T. , Olson J. E.
Published: September 2023

In the earth's crust, while tectonic processes can mechanically create natural fractures, cementation can simultaneously grow on the surfaces of the fractures. This crystallization can affect the growth of the fractures. To more accurately address the modeling of natural fracture development, both mechanical deformation and chemical diagenesis should be carried out in an integrated manner. We achieve this with a three-dimensional (3D) simulator that couples mechanical and chemical processes for natural fracture pattern development in layer confined situations. Tectonic loading over geologic time is mimicked by displacement driven boundary conditions in a 3D displacement discontinuity method (DDM) scheme. Quartz diagenesis competes with mechanical fracture opening, resulting in three different cementation conditions - open, bridging or fully infilled – which define fracture element stiffness that is used as feedback within the 3D-DDM code during propagation. An important milestone of this work is that this coupled structural diagenetic model can reproduce power-law aperture population statistics which cannot be generated using mechanics alone. More accurately estimated mechanical fracture apertures and their cementation condition is a critical step toward better permeability estimation and fluid path analysis for subsurface reservoirs.

INTRODUCTION

Natural fractures are important to characterize for many purposes, such as stimulation of unconventional shale reservoirs, enhanced oil recovery methods in tight carbonates, and the design of geothermal injection operations, among others. The presence of open fractures may be critical for low permeability formations because the fracture network could be a highway for fluid flow. Additionally, the existence of natural fractures may augment permeability arising from hydraulic fracturing operations (e.g., Weisenberger et al., 2019). Observation of subsurface fracture systems is inherently challenging (e.g., Laubach et al., 2019), so subsurface reservoir characterization often relies heavily on models. While stochastic-based modeling studies using discrete fracture network (DFN) are widely used, models that account for the physics and chemistry of pattern development have many additional potential benefits (Laubach et al., 2019).
Subsurface Characterization Using Bayesian Deep Generative Prior-Based Inverse Modeling for Utah FORGE Enhanced Geothermal System
Authors: Bao Jichao , Lee Jonghyun , Yoon Hongkyu , Pyrak-Nolte Laura
Published: September 2023

INTRODUCTION

For the subsurface inverse problem (Carrera et al., 2005; McLaughlin and Townley, 1996; Oliver et al., 2008), spatially distributed geologic parameters such as permeability and porosity are estimated from noisy and sparse hydrogeophysical and geomechanical measurements such as pressure, temperature, displacement, seismic responses, and so on. Due to ill-posedness in the inverse problem, probabilistic frameworks have been implemented in order to account for the uncertainty that provides both unknown subsurface parameters and their corresponding uncertainty in a Bayesian statistical framework (e.g., Kitanidis, 1995, 2010; Lee et al., 2016). Typically pressure measurements from injection-extraction well operations are used for the site characterization, but unless many injection/extraction/observation wells are available, the subsurface characterization with sparse pressure measurements leads to poor predictions of other relevant quantities such as heat transport. To overcome this issue, different sensing data can be used for joint inversion of pressure, temperature, displacement, and seismicity to identify the subsurface geologic connectivity during the geothermal energy recovery operations (Lee et al., 2018)

Still, one of the challenges in subsurface characterization is to identify highly heterogeneous permeability fields such as geothermal reservoirs with fracture networks. For such cases, traditional techniques using the Gaussian prior lead to smoothed, low-resolution images of subsurface properties with high estimation uncertainty due to the diffusive nature of the governing equations. Machine learning techniques such as deep generative models (Kang et al., 2022, 2021; Yoon et al., 2022; Kadeethum et al., 2021; Kim et al., 2021) have been actively studied to enforce the subsurface solution space on the relevant prior distribution constructed through multipoint geostatistics-based training images.

In this work, we present an application of a recent inverse modeling method based on the deep generative model for characterizing the geothermal site at a reduced computational cost with improved accuracy compared to traditional inversion. The multiphysics model MOOSE-FALCON (Podgorney et al., 2021) for simulating THM processes at the geothermal site is combined with our proposed variational latent space inversion method. A synthetic application with pressure and heat data is considered in this work for a two-dimensional, strongly heterogeneous geothermal site. Inversion of synthetic pumping tests is performed to show the usefulness and practicality of the proposed approach for geothermal site characterization with reasonable accuracy. We will incorporate the geomechanical data to improve the accuracy of the inversion in the near future.
Produced Fluid Induced Mineralogy and Elemental Alterations of Caney Shale, Southern Oklahoma
Authors: Awejori G. A. , Xiong F. , Katende A. , Radonjic M.
Published: September 2023

This study involves batch reactor experiments and subsequent analyses of samples from Caney Shale in the Ardmore Basin of South-Central Oklahoma. Samples include mainly rock cores and cuttings recovered from two wells respectively drilled vertically through and horizontally across the Caney Shale. Mineralogical compositions are obtained by X-Ray Diffraction (XRD) measurements whilst microstructure and elemental distribution are acquired by Scanning Electron Microscopy/Energy Dispersive Spectroscopy (SEM/EDS) respectively. Batch experiments are then conducted using selected rock samples and produced fluid from the Caney Formation. Deionized water is also reacted with some samples to serve as standard. Experiments are conducted at 95°C and ambient pressure for 7 and 30 days to assess the geochemical rock-fluid interactions.

Results show rock mineralogical compositions are predominantly quartz, feldspar, carbonates, and clay with minor pyrite. Post-experimental mineralogical changes observed in samples include increased amorphous entities especially within the clay portions of XRD plots and dissolution of feldspar and carbonate minerals and formation of new mineral phases, mostly clays and salts. These are corroborated by EDS elemental analyses which show decreased elemental compositions. The implications of reactions mentioned above include but not limited to, scale formation, clay fines migration and shale softening all of which pose significant permeability impairment on formation over time.

INTRODUCTION

Shale reservoirs account for a large share of unconventional reservoirs in the world (Lyu et al., 2015). However, ultra-low permeability and high clay compositions pose significant challenges when producing from these reservoirs (Dawuda and Srinivasan, 2022, 2023). Producing from these reservoirs therefore requires horizontal drilling and hydraulic fracturing technologies which have proven their efficacy in generating substantial permeability in reservoirs to ensure production (Fujian et al., 2019; Liu et al., 2018). Even after expensive horizontal drilling and hydraulic fracturing, geochemical reactions between engineered fluids and formation leads to fracture constriction and adversely impact petrophysical properties (permeability and porosity) of the reservoir. These technologies are therefore under constant development and improvement in various aspects to ensure fine tuning for specific reservoirs. Under present conditions, much of the hydrocarbon reserves in unconventional shale reservoirs are left unproduced due to rapid decline in permeability following resumption to production after hydraulic fracturing. It is therefore essential to understand the range of geochemical reactions that cause rapid depletion of permeability after hydraulic fracturing and apply these to each shale reservoir to ensure substantial recovery rates.
Integrated Investigations on the Refracturing Process in Tight Oil Reservoirs
Authors: Ruan Qi , Peng Huanhuan , Yang Lu , Tang Huiying , Zhao Yulong
Published: September 2023

Refracturing is one of the most effective methods to solve the problem of rapid production decline and low recovery in tight oil reservoirs, especially in the late production stage. The decrease of pore pressure and changes of in-situ stresses during early production might result in inefficient refracturing due to the high leak-off or fracture reorientation. Thus, the volume and timing of fluid injection is important for the effectiveness of refracturing. To properly predict the geometries of re-fractured hydraulic fracture, it is necessary to consider both the stresses and pore pressure changes before refracturing. For Changqing oil field, both new and existing fractures are fractured during the refracturing process. It is also important to understand which kind of fractures should be re-fractured to bring greater production improvement. To investigate the above problems, an integrated simulation procedure is conducted to optimize the refracturing processes, which couples the initial fracturing, production, refracturing and post production after refracturing with a 4D geomechanical model. The comparisons between fracturing existing and new fractures, and optimal fluid injected volume are discussed. According to the 4D geomechanical model results, after the initial fracturing and production, the pressure around the hydraulic fractures is gradually recovered with fluids injection. When the amount of fluid injected is increased to 400 m3 per 100 meters, the pressure can be well recovered and the pressure depletion during the well shut-in process is small. Moreover, for the area with high remaining oil saturation after liquid injection, it is beneficial to select the old perforations with good pressure and stress recovery and perforate new fractures in the well section without obvious stress variations to improve the efficiency of refracturing. The integrated numerical simulation of the refracturing can provide important guidance for the future designs of refracturing in tight oil wells.

INTRODUCTIONF

After a period of production in oil and gas wells, a pressure depletion zone will gradually form around the hydraulic fractures, which will change the magnitude and orientation of in-situ stresses and even inverse the direction of minimum and maximum horizontal stresses. The decrease of pore pressure could results in the loss of fracture conductivity, resulting in rapid decline of oil production (Yi and Sharma, 2016). Refracturing, which injects a large volume of fluid into the depleted reservoir again, can effectively supplement the pressure depletion, increase the fracture complexity and conductivity, hence maintaining the stable production of oil wells.(Shah et al, 2017 and Ren et al, 2020)

Typically, three kinds of stimulation strategies are used for fractured wells to enhance the production, including direct refracturing, huff and puff and fluids injection before refracturing.
EFEKTIFITAS MEDIASI DALAM PENYELESAIAN PERKARA HARTA BERSAMA DI PENGADILAN AGAMA BATULICIN
Authors: Farid Ishlah
Published: September 2023

Para pihak yang bersengketa dipengadilan kerap dapat terselesaikan oleh teknik mediasi, Keberhasilan praktik mediasi terletak pada mediator yang ulung dalam menjalankan perundingan ketika para pihak bersih kukuh pada pendapatnya masing-masing. Umumnya permasalahan yang membutuhkan mediasi adalah penyelesaian sengketa harta bersama (harta gono-gini) Pada Pengadilan Agama. Penelitian ini akan mengupas bagaimana efektifitas mediasi dapat terjadi pada tataran praktik, dan untuk mengukur kefektifitasan mediasi tersebut, peneliti mengambil sample objek perkara pada Pengadilan Agama Batulicin. Metode yang dilakukan dalam penelitian ini adalah penelitian campuran (mix method) artinya peneliti menerapkan teknik penelitian normatif dibersamai dengan penelitian empiris. Dengan pendekatan konseptual (conceptual approach) dan pendekatan undang-undang (statute approach) penelitian ini ingin mengungkapkan hasil dari dua permasalahan, permasalahan. Adapun permasalahan pertama yakni bagaimana efektifitas mediasi dalam menyelesaikan perkara harta bersama pada Pengadilan Agama Batulicin, dan permasalahan kedua apa saja faktor yang mempengaruhi mediasi itu menjadi efektif pada sebuah pengadilan. Hasil penelitian menunjukkan bahwa efektifitas Mediasi yang diterapkan pada Pengadilan Agama Batulicin menuju pada arah yang optimal, hal ini didasari pada landasan hukum Peraturan Mahkamah Agung Nomor 1 Tahun 2016 tentang Prosedur Mediasi di Pengadilan. Dan faktor yang mempengaruhi keberhasilannya praktik mediasi yang ada di Pengadilan Batulicin adalah mendominasinya populasi mediator yang telah bersertifikat pada lingkungan Pengadilan Agama Batulicin, dan berdasarkan hasil penelitian sertifikat mediator yang dikeluarkan oleh Badan Penelitian dan Pengembangan, Pendidikan dan Pelatihan Hukum Peradilan Makamah Agung Republik Indonesia menjadi tolok ukur seberapa besar praktik mediasi penyelesaian perkara harata bersama dapat berjalan secara optimal.
PENGARUH KUALITAS PELAYANAN DAN KEPUASAN PELANGGAN TERHADAP LOYALITAS PELANGGAN PADA SWALAYAN SURYA DI SUKABUMI BANDAR LAMPUNG
Authors: Putri Ariza Karina
Published: September 2023

This study aims to determine the effect of service quality and customer satisfaction on customer loyalty at Surya Supermarkets in Sukabumi, Bandar Lampung. Furthermore, the data collection technique used in this study was a sampling technique taken by a set number of 50 respondents. Data collection was carried out using a questionnaire and hypothesis testing. In this study the researchers used the t test method and the Coefficient of Determination test with the help of the SmartPLS3 program. Based on the table of test results for the coefficient of determination test, the magnitude of R Square (R2) is 0.700. These results indicate that 70% indicates that the variable service quality and customer satisfaction, the customer loyalty variable is 70%. The results show that the variable Quality of Service with Customer Loyalty shows a path coefficient value of 0.247 with a t value of 2.804. This value is greater than t table (0.60). This result means that Service Quality has a positive and significant relationship to Customer Loyalty. And the results of the second test show that the relationship between customer satisfaction and customer loyalty variables shows a path coefficient value of 0.638 with a t value of 6.517. This value is greater than t table (0.60). This result means that Customer Satisfaction has a positive and significant influence on Customer Loyalty Performance.  Keywords : Service Quality, Customer Satisfaction, and Customer Loyality
PENGEMBANGAN WIRAUSAHA DAN GREEN ECONOMY DI INDONESIA
Authors: Efrenza Muhamad Aldo
Published: September 2023

The purpose of this study is to determine Entrepreneurial Development and Green Economy in Indonesia. The data used in this study is secondary data. Furthermore, the method used is this research using analytic observational research using a case control or case control research design. This research is included in the type of research that uses a qualitative approach and the analysis in this study is included in the type of descriptive research. Descriptive research seeks to describe, write, and report a situation, an object or an event as it is, and in the form of disclosing facts. Research containing entrepreneurship for environmental interests needs to be implemented in the business competition law system in Indonesia for various reasons, namely to reduce the adverse effects of climate change in Indonesia, increase and promote Green Economy in business transactions in Indonesia, provide legal certainty and legal benefits to companies and stakeholders who want to make agreements that prioritize green economy. Keywords : Entrepreneur, and Green Economy
The Influence of CEO Character on Tax Avoidance In Property And Real Estate Companies Listed on The Indonesia Stock Exchange 2020-2021
Authors: Resqy Annisa Nanda , Hasnawati Hasnawati
Published: September 2023

This study analyzes and obtains empirical evidence on CEO character, including CEO age, CEO tenure, CEO education level, and CEO gender in tax avoidance. This study uses a sample of the property and real estate sectors listed on the Indonesia Stock Exchange (IDX) during the 2020-2021 period. The number of companies that are sampled are 64 companies with observations for two years using the purposive sampling method-the hypothesis testing of this study uses multiple regression models. The results showed that the CEO's education level and CEO's age had a positive effect on tax avoidance. While CEO tenure, gender does not affect tax avoidance.
The Determinants of Sustainable Apparel Products In Indonesia Exploring Consumer “Attitude-Purchase Intention” Gap
Authors: Zahroh Fitriatul
Published: September 2023

Fashion products have a short cycle that results in harmful environmental damage, a shift to more sustainable consumption purchase is needed in Indonesia. Although public interest in sustainability has increased and consumer attitudes are positive, their purchase intentions are inconsistent with attitudes. This study aims to reveal the psychological drivers and barriers (utilitarian consumption, social norms, attitudes) as well as the price-saving orientation of Indonesian consumers' purchase intentions towards thrifting clothing products by exploring the attitude-purchase intention gap. Online survey data were used to examine the moderate impact of utilitarian consumption, social norms and price-saving orientations on the relationship between Indonesian consumers' attitudes and purchase intentions towards thrifting products, particularly the greater Jakarta area. The results of the PLS-SEM analysis show that (1) attitude has a significant influence on purchase intention, (2) utility value negatively moderated the relationship, (3) and social norms negatively moderated the relationship, (4) while price saving orientation has a positive relationship to purchase intention. This study does not validate the attitude-purchase gap model in sustainable consumer purchase, however it does discuss how the current findings can help researchers and practitioners in the Apparel and Textile field to refine and improve sustainable programs and marketing strategies in Indonesia.
Pengaruh Literasi Keuangan Dan Media Sosial Terhadap Perilaku Konsumtif Mahasiswa Institut Agama Islam Tafaqquh Fiddin Dumai
Authors: Nur Israyana Pipin
Published: September 2023

Penelitian ini bertujuan untuk mengetahui pengaruh variabel literasi keuangan dan media sosial terhadap perilaku konsumtif. Metode penelitian yang digunakan adalah metode penelitian kuantitatif dengan data primer yang diperoleh dari data kuesioner yang diukur menggunakan skala likert. Penelitian ini dilakukan kepada seluruh mahasiswa Institut Agama Islam Tafaqquh Fiddin Dumai dengan populasi sebesar 407 mahasiswa dan jumlah sampel yang didapatkan yakni sebanyak 105 mahasiswa. Penelitian ini menggunakan teknik simple random sampling. Data dalam penelitian ini dikumpulkan dengan menggunakan kuesioner yang kemudian dianalisis menggunakan analisis regresi berganda dengan bantuan software SPSS 21.0. Hasil penelitian ini menunjukkan adanya pengaruh signifikan dan positif dari variabel literasi keuangan dan media sosial secara bersama-sama terhadap perilaku konsumtif mahasiswa Institut Agama Islam Tafaqquh Fiddin Dumai. Yakni sebesar 25,3% dan 74,7% dipengaruhi oleh variabel lainnya.