bluuebunny/crossref_metadata_2025 huggingface dataset:
Labor Market Transformation: Implications of Technological Change in Society 5.0
Authors: Khoerulloh Abd. Kholik , Aziz Holis Abdul
Published: June 2024

Society 5.0 is resulting in transformation across a wide range of sectors, including manufacturing, education, healthcare, and services. Through the adoption of artificial intelligence, machines can carry out tasks that previously could only be performed by humans, resulting in significant changes in the types of jobs and skills available in the labor market. This research aims to detail and identify how technological changes affect the labor market, as well as explore solutions and strategies to capitalize on opportunities and deal with challenges that arise from these changes for society’s overall well-being. This research uses a literature study method with a predictive approach. The results showed that in the era of Society 5.0, people must improve their skills through education and training to adapt to the changes. Cross-sectoral cooperation between the government, the private sector, and the community is crucial. Joint efforts to formulate supportive policies and innovation in creating new sustainable jobs are vital to mitigating the negative impacts and harnessing the positive potential of technological change in the labor market. This research is expected to have a significant impact, ranging from policy development to improving social welfare, by comprehensively understanding how technology affects life and work in modern society.
Shadow Economy: Determinants and Its Impact on Foreign Direct Investment
Authors: Heriqbaldi Unggul , Jatmiko Salwaa Fauziyyah
Published: June 2024

The shadow economy poses a significant threat to government revenue and the effectiveness of economic policies. This paper investigates the causes of the shadow economy and its influence on foreign direct investment (FDI). Our study employs the currency demand approach, a component of the indirect method, to identify the determinants of the shadow economy in a dataset covering 105 countries from 2001 to 2017. These countries are categorized into four income groups: high-income, upper-middle-income, lower-middle-income, and low-income. Parameter estimation is conducted using the Generalized Method of Moments (GMM) model, with robustness tests incorporating reference estimates from Partial Least Squares (PLS) and Fixed Effects Model (FEM). Our findings indicate that a higher GDP and lower interest rates are associated with reduced shadow economy activity. Elevated market interest rates increase the cost of funds in the informal sector, discouraging engagement in shadow economic activities due to reduced profitability. Furthermore, higher tax revenues correlate with intensified regulatory enforcement, increasing the risks associated with shadow economy involvement. A larger workforce and lower unemployment rates similarly diminish shadow economy activity. In the context of foreign direct investment (FDI), the shadow economy positively affects FDI flows when formal institutions, including legal frameworks, property rights protection, and regulatory systems, are either weak or overly burdensome. In such scenarios, economic actors may opt for informal channels like the shadow economy, offering a flexible and cost-effective alternative to the formal sector, a crucial consideration for foreign investors.
The Influence of Economic Complexity Index on Income Inequality in G20 Forum Countries
Authors: Subekti Miftachul Jannah Meilina , Sari Dyah Wulan
Published: June 2024

This research examines the influence of the economic complexity index, GDP per capita, gross higher education participation ratio, government spending on education, and fertility rates on income inequality in G20 Forum member countries from 2010 to 2019. The dynamic panel data regression method with the two-step System Generalized Method of Moments (SYS-GMM) estimation technique is used to analyze the data in this study. The findings reveal that the economic complexity index, GDP per capita squared, government spending on education, and fertility levels have a negative and significant effect on income inequality. Meanwhile, GDP per capita and gross enrollment rates in tertiary education have a significant positive effect on income inequality. This study shows that the economic complexity index and the quality of human resources can reduce income inequality. Therefore, policies that focus on improving the quality of human resources need to be considered to encourage innovation, increase GDP per capita, and ultimately reduce income inequality.
Analyzing the Combined Role of Good Governance and GDP on Women Literacy in African Countries
Authors: Himel Mohammad
Published: June 2024

Most African countries have been affected due to economic crises regarding hyperinflation and lower agricultural productivity, civil war, lower infrastructural and transportation systems, social and domestic violence, food insecurity due to bad agricultural systems, social and religious inequality, mismanagement of governing systems with defective bureaucracy, environmental degradation and lower practice for sustainable development. In this paper, the author tries to find out two research objectives i) To measure the good governing variables on women literacy between East and West African countries ii) To measure the joint impact of good governance and GDP level between these two countries. The author uses secondary dataset from The World Bank site on quantitative analysis where the author uses multiple regression model for general regression and integrated regression model. At the time of multiple regression (Model 1), GDP, IF, P.S., R.Q. has a positive connection with women literacy for EAC. On the contrary, PHR, FLF, DOFC, R.L. create a negative effect on women’s literacy in EAC. But in (Model 2), if the good governance variable connects with GDP for long-lasting development, (GDP*PS), (GDP*RQ) has positive connection with W.L. in EAC. Reversely, L.E., PHR, G.E., R.Q., CC has positive and significant impact in WAC where these variables enhance women literacy to be improved at significant rate (Model 3) where DOFC and EFC has reverse connection with W.L. In spite of case, if GDP connects with good governance variables, (GDP*RQ) and (GDP*PS) has positive connectivi-ty with W.L. in WAC. Policy makers should highlight to promote effective R.L. and G.E. to improve macroeconomic development in EAC where governments of WAC should focus on R.L. and G.E. to ensure women empowerment and social security for sustainable development. Establishing good governance’s variables in African countries is carrying need to ensure women empowerment, lessening gender inequality, ensuring women safeness and sustainable development etc.
Persistent cosmetic dissatisfaction in rhinoplasty & management of the difficult patient
Authors: Zhang Kathy , Deane Emily C. , Becker Daniel G.
Published: June 2024

The rhinoplasty surgeon will undoubtedly encounter a cosmetic patient who is persistently dissatisfied with their results, no matter the objective outcome achieved. This article seeks to describe risk factors for postoperative dissatisfaction and highlight effective management strategies for the “difficult patient.” A literature search was performed using PubMed and Embase databases during September and October of 2023 to identify articles that analyzed factors related to dissatisfaction in rhinoplasty. Forty unique references were identified. The majority of structural aesthetic complaints related to dissatisfaction after rhinoplasty were residual dorsal hump (20%) or persistent tip dissatisfaction (19%-37%). Demographic factors including younger age, male sex, self-referral, history of body dysmorphic disorder or abuse/neglect were risk factors for postoperative dissatisfaction. Ineffective patient-provider communication, litigation due to inadequate informed consent, and surgeon inattentiveness were contributing factors to postoperative dissatisfaction. Revision rhinoplasty rates ranged from 5%-15%, where most patients sought revision surgery due to the development of a new deformity or failure to correct the original deformity, with the greatest complaints at the nasal bridge and nasal tip. Validated patient-reported outcome measures including Rhinoplasty Outcome Evaluation (ROE) and FACE-Q can be effective questionnaires to assess satisfaction. Appropriate patient selection, adequate preoperative counseling and consent, effective communication, and reaching a consensus about surgical goals are all effective strategies in the management of a dissatisfied patient.
Recent advances in Sb-based anodes for Li/Na/K-ion batteries and all-solid-state Li-ion batteries
Authors: Yoon Jeong-Myeong , Kim Deok-Gyu , Kim Do-Hyeon , Lee Young-Han , Park Cheol-Min
Published: June 2024

In recent decades, lithium-ion batteries (LIBs) have emerged as a primary focus in the energy-storage field owing to their superior energy and power densities. However, concerns regarding the depletion of non-abundant lithium resources have prompted the exploration and development of emerging energy-storage technologies, such as sodium- (SIBs) and potassium-ion batteries (PIBs). In addition, all-solid-state LIBs (ASSLIBs) have been developed to address the issues of flammability and explosiveness associated with liquid electrolytes. Among the various alloy-based anodes, antimony (Sb) anodes exhibit high energy densities owing to their high theoretical volumetric capacities that are attributable to their high densities. However, Sb anodes exhibit poor cyclabilities owing to excessive volume changes during cycling. To mitigate this issue, researchers have investigated the use of diverse solutions, including solid electrolyte interface control, structural control, and composite/alloy formation. Herein, we review and summarize Sb-based anode materials for LIBs, SIBs, PIBs, and ASSLIBs developed over the past five years (2018-present), focusing on their reaction mechanisms and multiple approaches used to achieve optimal electrochemical performance. We anticipate that this review will provide a comprehensive database of Sb-based anodes for LIBs, SIBs, PIBs, and ASSLIBs, thereby advancing relevant studies in the energy-storage-systems field.
Parameter identification of an open-frame underwater vehicle based on numerical simulation and quantum particle swarm optimization
Authors: Chen Mingzhi , Liu Yuan , Zhu Daqi , Shen Anfeng , Wang Chao , Ji Kaimin
Published: June 2024

Accurate parameter identification of underwater vehicles is of great significance for their controller design and fault diagnosis. Some studies adopt numerical simulation methods to obtain the model parameters of underwater vehicles, but usually only conduct decoupled single-degree-of-freedom steady-state numerical simulations to identify resistance parameters. In this paper, the velocity response is solved by applying a force (or torque) to the underwater vehicle based on the overset grid and Dynamic Fluid-Body Interaction model of STAR-CCM+, solving for the velocity response of an underwater vehicle in all directions in response to propulsive force (or moment) inputs. Based on the data from numerical simulations, a parameter identification method using quantum particle swarm optimization is proposed to simultaneously identify inertia and resistance parameters. By comparing the forward velocity response curves obtained from pool experiments, the identified vehicle model’s mean square error of forward velocity is less than 0.20%, which is superior to the steady-state simulation method and particle swarm optimization and genetic algorithm approaches.
Risks to CCUS Success at Scale
Authors: Meehan D. Nathan
Published: June 2024

This paper discusses challenges that may hinder or delay widespread adoption of Carbon Capture and Storage (CCS). Focusing on technical, economic, social, and regulatory aspects, it aims to pinpoint specific obstacles, such as the financial burden on energy production, uncertainties in long-term storage impacts, and the intricate regulatory framework governing CCS projects and pipelines. Reaching net zero emissions will be virtually impossible without widespread CCS; however, challenges and risks remain widespread.
Clarifying Quantitative Language in the SRMS
Authors: Harrison B. , Falcone G.
Published: June 2024

Numbers tend to have one meaning, whereas words can be open to interpretation, so one might expect the language of probability to be better at communicating the uncertainty in CO2 geological storage resource estimates to project stakeholders. However, rather than assigning a probability value to deterministic estimates, current reporting systems use quantitative expressions which may not fully convey the appropriate degree of confidence that the deterministically estimated CO2 volumes will be stored. Using published work on cognitive bias by acknowledged energy sector consultants, drawing on research by statisticians and carrying out their own tests, the authors reveal that there are different perceptions of the language of quantitative probability from those suggested in the SPE's storage resource management system (SRMS). Tests show that energy industry professionals believe the probability value that corresponds to reasonable certainty is somewhat lower than the implied 90% figure assigned to the probabilistic low estimate of recoverable volumes, generally taken as equivalent to the proven deterministic case. The divergence between deterministic language and associated and perceived probability grows when considering other storage capacity classes. Such perceptions may lead to optimistic expectations and result in sub-optimal development decisions. The assignment of probability values to deterministic storage resource classes is avoided traditionally, so perhaps the adoption of clearer and more appropriate quantitative language should be considered by altering the SRMS" text to better describe the likelihood of the outcome of an event.
GeoCogen Eclépens: Pilot Seismic 3D Acquisition for Geothermal Prospection in Western Switzerland
Authors: Baykulov M. , Hirsch Q. , Dominguez A. , Masquelier E. , Fiebig B.
Published: June 2024

In western Switzerland, a strong geothermal anomaly was detected in the 1981 Eclépens-1 well. At Swiss Geo Energy, we completed a pioneering 3D seismic acquisition project for geothermal exploration. The objective of the project is to identify reservoir targets, provide an estimation of the geothermal resources, and design wells for operating the first combined cycle geothermal power plant in Switzerland. Results of the seismic acquisition and best practices are presented in this paper.
Effects of Flow and Geomechanics Coupling in Faulted Reservoirs for CO2 Storage
Authors: Ferrari Andrea , Nardean Stefano , Monaco Silvia , Garipov Timur , Mantica Stefano , Cominelli Alberto
Published: June 2024

Accurately reproducing the coupling of fluid flow in porous medium and rock mechanics is crucial for the modelling of CO2 geological storage in order to properly evaluate and prevent the risk of inducing fault instability during injection operations. As an alternative to using monolithic flow/mechanical suites, the process can be modelled by linking individual codes, i.e., a reservoir simulator for flow and a geomechanical package to account for fluid induced stress change, which tackle the two problems sequentially. We developed a flexible numerical framework where different coupling logics can be selected, i.e., one-way coupling, two-way coupling, and explicit coupling, which are characterized by different levels of accuracy and computational costs. A multirate two-way coupling algorithm, which allows the flow and mechanical simulators to exchange information periodically rather than at every time step, is also available to reduce the computational cost of two-way coupled simulations. In this work, we employ this coupling infrastructure to perform numerical experiments aimed at defining whether sequential iterative coupling is strictly needed or not, and which less expensive logic can be used in case to attain a similar solution accuracy. First, a synthetic test case is used to illustrate the onset of fault instability during CO2 injection operations for different sets of coupling parameters (type and frequency), rock properties and fault permeability. It is thus possible to evaluate, for a reasonable range of coupling strength, which depends on fluids and rock properties, the optimal level of coupling. Results are strongly influenced by the coupling strength and two-way iterative coupling should be selected for tightly coupled systems to accurately reproduce the fault behavior. For loosely coupled system instead, the one-way approach should be the preferred choice due its lower computational cost. Later, we consider CO2 injection into a realistic formation, and we analyze the impact of the coupling frequency on the computational performance. We show that for complex cases there is no one-to-one correspondence between the reduction in the number of coupling iterations and the reduction in computational time for increasing coupling period.
The Application Framework for Geothermal Re-Circulation Proposed for Converting Hydrocarbon Wells Into Geothermal Wells in Oklahoma
Authors: Hu Zeming , Vivas Cesar , Saeed Salehi , Nygaard Runar
Published: June 2024

This paper presents an update on the project to repurpose depleted oil wells into geothermal wells and shares a comprehensive workflow for obtaining permitting in Oklahoma. The objective is to provide a structured approach that addresses regulatory aspects, facilitating the transformation of oil wells into efficient geothermal energy sources. The methodology involves assessing geological conditions, analyzing regulations, and developing tailored monitoring and operation plans. The results highlight Oklahoma's potential as a significant renewable energy source through geothermal means, particularly in the Arkoma Basin and Anadarko Basin. However, challenges exist, including the need for a clear regulatory framework for deep geothermal energy, hurdles in obtaining landowner agreements, and regulatory constraints on well selection for heat transfer fluid re-circulation. The study offers novel insights and practical guidelines for engineers and policymakers in geothermal energy development, contributing to the field of sustainable energy.
Performance Comparison of Gradient-Free Optimization Methods for Well Placement and Well Controls Optimization for Geologic CO2 Storage
Authors: Tom Imaobong , Nguyen Quang M. , Onur Mustafa
Published: June 2024

Population-based (gradient-free) methods have become attractive for solving optimization problems since they have been known to locate "optimal" (best) positions within a search space. In this study, we investigate the use of three different gradient-free optimization methods namely: Iterative Latin Hypercube Sampling (ILHS), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) for well placement and well controls optimization problem of the CO2 underground storage in a 2D saline aquifer with bound constraints on the design variables. We also consider sequential optimization of well locations and well controls using the gradient-free methods. To evaluate the performance of these algorithms, we test the methods on well-known test (or toy) problems such as the multi-dimensional (40 design variables) Rosenbrock function, as well as the Traveling Salesman Problem (TSP) with 20 cities (20 design variables). We further extend the study to a more complex problem which involves finding the optimal well types, well locations, or well controls for a CO2 injection problem in a synthetic saline aquifer compositional model built in a commercial simulator. The objective function formulated for the CO2 injection problem is the net present environmental value (NPEV) involving discrete design variables (well types and well locations) and real design variables (well controls). Results obtained from the performance comparison of these gradient-free methods on the toy problems demonstrate the superior performance of the ILHS over PSO and GA for the Rosenbrock test function, while GA outperforms ILHS and PSO in solving the TSP. We also observe that for the simple well locations optimization problems involving one or two wells, all three algorithms obtained comparable results, given the same number of samples and number of iterations. However, for more complex cases such as the sequential or simultaneous optimization problems, when multiple wells which may include injectors and producers are present, we observe significant differences in the selection of the optimal well types and well locations among the three optimizers. In the simultaneous optimization of well types, well locations, and well controls, when both injectors and producers are BHP controlled, the results obtained showed that it is more optimal for at least one producer to be present, and for the injectors to be operated at the upper bound of the injector BHP, while the producers operated at the lower bound of the producer BHP to maximize the NPEV.
Offshore Platform Revamp for Energy Transition Projects: An Integrated Monitoring System for Real-Time Asset Integrity Control and Operative Life Extension Certification
Authors: Salvi J. , Spicci L.
Published: June 2024

An integrated structural monitoring system (ISMS) for offshore platforms composed of different monitoring networks working simultaneously is presented in this contribution. The sensor networks of the proposed solution allow for covering both global and local key monitoring tasks, and a comprehensive knowledge of structural situation is ensured. It is possible to get precise information on environmental excitations and to measure their effect on the structure. Moreover, a thorough alarm service allows for efficient notification of possible critical situations both in real time and in the long term. Main outcomes obtained from first monitoring experiences on offshore platforms performed over more than five years are presented and explained in detail. The outcomes of the ISMS may represent a valuable input for a wide range of strategic tasks, such as asset integrity control, model update, digital twin. Different key activities related to the asset management can take advantage from the installation of the ISMS, especially in case of aged structures, such as life extension certification, relaxation of periodicity of inspections, energy transition projects.
Secure CO2 Sequestration: A Deep Reinforcement Learning Perspective on Injection Rate Management
Authors: Kor Peyman , Riccardi Enrico , Brumer Bratvold Reidar
Published: June 2024

Secure and efficient geological CO2 storage decisions can be supported by models of subsurface porous structure and of the fluid flow in the porous network. Geological and flow modeling is employed in order to optimize the CO2 storage process during the lifetime of the operation. The aim is to minimize the fraction of mobile CO2. To account for injection rates in target wells that are adjusted at different time intervals, a sequential decision-making model has been adopted. The method includes the future evolutions of uncertainties and includes future decisions at each time step ‘t’ until the end of operation. To address the curse of dimensionality associated decision variables (continuous and exponential growth along the time horizon), a Reinforcement Learning (RL) Approach - Proximal Policy Optimization (PPO)- is formulated. The example case of the CO2 injection model includes the 11th Society of Petroleum Engineers Comparative Solution Project, focusing on CO2 storage operations in geological settings of realistic complexity. The optimal policy suggested by the RL model provides a control policy that instantaneously maps the observed information (state) to the policy. The RL model and framework of the implementation are presented and discussed. Moreover, the research discusses the computational efficiency and scalability of the framework, providing insights into its potential feasibility for larger-scale CO2 storage projects. By employing state-of-the-art artificial intelligence methods, this research presents an efficient and realistic model for supporting CO2 injection decisions leading to effective and secure solutions for CO2 storage.
Value Creation in Sustainable Energy Transition Using Reinforcement Learning
Authors: Cheraghi Yasaman , Bratvold Reidar B. , Muhammad Ressi B. , Hong Aojie
Published: June 2024

The global challenge of climate change has prompted significant steps to reduce CO2 emissions, guided by international agreements like the Paris Agreement, which set targets for the transition from fossil fuels to renewable sources. Acting too slowly could result in future losses and reputational issues, while moving too swiftly could jeopardize shareholder value due to the marginal profitability or potential losses resulting from technology challenges and the immaturity of many renewable projects. To navigate this complex landscape wisely, energy companies will benefit from developing Sequential Decision Making (SDM) policies to maximize value creation from decision flexibility under uncertainties. However, achieving this goal is not trivial. This necessitates the use of decision-analysis and optimization methods that deliver robust decision insights without prohibitive computational costs or delays.

In this research, we propose a multi-objective SDM framework to model the dynamic energy landscape up to 2050, allowing exploration of various decision strategies related to different portfolios for allocating funds across three sectors: hydrocarbon, renewables, and CO2 reduction investments. This approach aims to maximize value during the transformation while accounting for uncertainties in hydrocarbon and renewable energy productions, energy prices, and production costs. The framework targets three main objectives: maximizing profit, minimizing CO2 emission, and enhancing competitive advantage.

Our research evaluates the benefits of using Reinforcement Learning (RL) to solve for the optimal investment decision policy for the SDM context. The sequential decisions of the energy company shape the virtual dynamic environment by influencing various variables. Subsequently, the environment responds with new variable states and immediate value feedback, reflecting the three defined objectives. Through repeated interactions, the RL algorithm navigates the extensive state space, learning the optimal decision policy amidst uncertainties.
Analytical Solution of Stresses Around a Depleting Reservoir Above a Hard Rock Layer with Application to Seismic Hazard Analysis
Authors: van den Hoek Paul J.
Published: June 2024

Surface displacements resulting from reservoir compaction owing to hydrocarbon production are often computed using the nucleus-of-strain concept proposed by Geertsma. This approach enables fast computations for arbitrarilyshaped reservoirs. It was later extended by Van Opstal to calculate surface displacements for compacting reservoirs in the presence of a rigid basement. Later authors further extended Van Opstal’s method to compute displacements, strains and stresses in the 3D subsurface (not just at surface), but those methods were still semi-numerical which significantly hampered computation speed for realistic reservoirs.

Here we propose a ‘truly analytical’ solution of displacements, strains and stresses for a compacting reservoir over a rigid basement in the 3D subsurface. Moreover, we propose a method to enable reliable computation of these quantities close to reservoir centres. The approach is applied to Seismic Hazard Analysis (SHA) in depleting (cooling) reservoirs for geometries where no analytical solutions for stress changes at the fault are available.

Our results show excellent agreement with earlier publications for compacting reservoirs over a rigid basement. Moreover, our method of applying the nucleus-of-strain concept in combination with subdividing the depleting (cooling) reservoir into thin sub-reservoirs yields stress change profiles along intersecting faults that compare well with exact analytical solutions (which are available for 2D reservoirs). Subsequently, our method was applied to more complicated (non-2D) reservoir shapes, in particular, a tilted fault with nonzero offset intersecting a cylindricallyshaped depleted (cooled) area at an arbitrary place. This is important for the SHA of geothermal doublets, where the cooled zones around the injectors generally have cylindrical shapes. Our results show that for many different geometries, the computed 2D profile of the Shear Capacity Utilisation (SCU) at the fault surface can be reasonably well approximated by a simple 1D SCU along the fault dip based on an analytical model. Furthermore, we show that the presence of a rigid basement has a limited impact on depletion-(cooling-)induced SCU for intermediately dipping faults. However, for faults with low or high dip the presence of a rigid basement does have an impact on SCU.
Implementation of a Risk Assessment Procedure to Manage Well Integrity in Underground Gas Storage
Authors: Baronio E. , Busollo C. , Abdo E. , Mauro S.
Published: June 2024

Underground Gas Storage (UGS) is an essential technique used to ensure strategic amounts of energy that can be stored at a relatively cheap price and successively used when required. Due to their strategic nature,UGS systems are usually placed close to inhabited centers which creates a highly consequential level of damagein a potential leak scenario. This delicate working envelope means that UGS wells must be maintained in good health through a rigorous Well Integrity Management System (WIMS) which will ensure that they are operating within the desired limits and with all risks identified, analyzed, and properly mitigated.

UGS wells present a characteristic behavior that makes them different from normal production wells. In fact, theyare subject to periodic injection and production cycles that apply high levels of stress on the equipment and the completion of the well. The well barrier envelope has the role of impeding an uncontrolled flow of reservoir fluids to the external environment or to other formations. The components that form it must be continuously monitored and verified to ensure that its integrity is not compromised and that it performs its final objective.

Due to the stresses applied on UGS wells, and the concepts upon which these wells are designed, it is possible to find well barrier elements that are operating at the limit of acceptability. In this case, the situation must be analyzed through a dedicated risk assessment and an intervention can be planned to recover the full functionality of the barrier. On the other hand, if the risk assessment shows that the risks associated with the current well barrier condition are under control and proper mitigations are available, the well could be run within a strict working envelope.
Appraisal of Native Hydrogen Accumulation in Geological Formations under Uncertainty
Authors: Ranaee E. , Inzoli F. , Riva M. , Guadagnini A.
Published: June 2024

Objectives/Scope: A key issue related to potential accumulation of native hydrogen (H2) in geological formations is the risk of hydrogen loss due to conversion to other chemical species such as methane (CH4). Our study tries to quantify how uncertainties linked to carbonate-clay reactions (CCR) reflect in evaluation of the geogenic methane generation and the associated losses of native H2.

Methods, Procedures, Process: We rely on a modeling workflow developed by Ceriotti et al. (2017) for evaluating geogenic carbon dioxide, CO2, generation through CCR in sedimentary basins. As a showcase, we consider a one-dimensional (vertical) model patterned after a typical sedimentary compaction setting. Such a model provides the dynamics of porosity, temperature, and pressure along the vertical direction. Outputs of the compaction model are viewed as deterministic quantities. We then consider a given mineral composition and focus on the quantification of the parametric uncertainties associated with CCR. This is reflected in the uncertainty related to the values of thermodynamic equilibrium constants of the species involved in CCR and is then propagated onto the ensuing estimated CO2 release. Underground trapping of native H2 is conceptualized upon considering the subsurface as a natural chemical reactor that consumes a mixture of H2 (generated from serpentinization of ultramafic rocks) and CO2 (from CCR) yielding a mixture of H2/CO2/CH4. Our analysis considers that (a) complete mixing of the chemical species is attained and (b) geochemical reactions can be evaluated under thermodynamic equilibrium conditions. We then perform a modelling study framed in a stochastic context and relying on a numerical Monte Carlo framework. The latter is aimed at quantifying uncertainty associated with methane production following geogenic hydrogen and carbon dioxide generation. Our results are tied to (i) shallow, (ii) intermediate-depth, and (ii) deep reservoirs. Due to its preliminary nature, the study considers uncertainty solely in the CCR process as well as accumulation reservoir depth/pressure/temperature conditions.

Results, Observations, Conclusions: Our results suggest that accumulation of H2 in geological formations entails the risk of hydrogen loss due to conversion to CH4 by methanogenesis. They also suggest that deep geological formations (characterized by high temperature and pressure conditions) tend to limit hydrogen loss due to methanogenesis reactions. Thus, exploration of native H2 accumulations could target geological formations where the residing gas has low CO2 concentrations and where the mineralogical composition of reservoir rocks contains low amounts of carbon-bearing minerals.

Novel/Additive Information: We provide a quantification of native hydrogen losses with the explicit inclusion of a stochastic assessment of some uncertainties linked to the geogenic generation of CO2.
PVT Analysis for Hydrogen and Methane Mixtures for Underground Hydrogen Storage
Authors: Tawil Michel , Borello Eloisa Salina , Panini Filippo , Loffredo Marialuna , Bocchini Sergio , Verga Francesca , Pirri Fabrizio , Coti Christian , Scapolo Matteo , Ferraro Pierpaolo , Adinolfi Luca , Viberti Dario
Published: June 2024

Hydrogen has been identified as an energy carrier that could play a major role in decarbonization. Large-scale hydrogen storage is required to face future challenges in terms of energy and environmental transition. Underground Hydrogen Storage (UHS) in depleted gas reservoirs is broadly recognized as a promising strategy to safely store large quantities of hydrogen, which can be injected into the porous rocks as a pure component (100% of H2) or as a mixture with methane. In order to properly design storage activity in a depleted gas reservoir, it is extremely important to define a reliable 3D reservoir dynamic model able to simulate the behavior of the system under all the possible considered operating conditions. To properly represent the interaction between injected hydrogen or mixture with the reservoir fluids, the Equations of State adopted in the compositional simulation must be validated against laboratory data in the pressure and temperature ranges representative of possible operating conditions, and if necessary, properly calibrated.

In this paper, we provide the results of experiments carried out on H2-CH4 mixtures within ranges of temperature and pressure conditions representative of depleted gas candidates for storage activities. Constant Mass Expansion (CME) tests were performed using a PVT cell. Pressures up to 300 bar and a temperature range from 30 to 60°C, representing the typical range for a gas storage reservoir in Italy and Europe, were applied. Four different mixtures were considered: pure H2, 50 mole% H2-50 mole% CH4, 10% H2-90 mole% CH4, pure CH4. Results are represented in terms of gas compressibility factor (z factor) curves as a function of pressure for different temperatures and compositions. Furthermore, the obtained results are compared with the GERG-2008 equation of state (EoS).

In the considered pressure and temperature ranges, the GERG-2008 EoS provides a satisfactory match with the experimental data for all the considered cases.
Experimental Investigation of the Effect of Major Impurities in the CO2 Stream on Carbon Storage in Chalk Reservoirs
Authors: Mokhtari R. , Ghahramani K. , Khojamli S. , Mihrin D. , Feilberg K. L.
Published: June 2024

Depleted oil and gas reservoirs in the Danish sector of the North Sea are predominantly composed of chalk, characterized by high porosity, low permeability, and reactivity. While storing CO2 in these reservoirs presents challenges, due to the reactivity of calcium carbonate and the low permeability, mitigating the risks could unlock substantial CO2 storage capacity. This study specifically investigates the impact of major reactive impurities such as H2S, NO2, and SO2 in the injected CO2 stream on calcite dissolution and its implications for rock integrity. These impurities can be present in captured CO2 and an important factor in carbon storage feasibility studies is the maximum tolerable levels of impurities for storage safety. Dynamic injection experiments were carried out on reservoir material from a Danish North Sea mature oil field. The experiments were carried out in a core flood injection set-up designed to mimic reservoir conditions. These experiments were designed with alternating Gas mixture/Water injection scenarios into separate core plugs, with the effluent brine samples analyzed by ion chromatography to assess rock dissolution. Porosity and permeability measurements were conducted before and after the experiments. Results revealed that calcite dissolution notably increased when SO2 was present in the CO2 stream, with H2S enhancing dissolution to a lesser extent, and NO2 exhibiting the least effect. Comparison with a base case of pure CO2 exposure showed calcite dissolution but no significant changes in porosity and permeability. The dynamic experiments demonstrated that the measured calcite dissolution occurs regardless of injection rate and takes place primarily at the gas-water interface. The experiments consider the worst-case scenario locally in the reservoir, with a relatively high concentration of reactive impurities in the CO2 allowing for a comparison between the three impurities H2S, NO2, and SO2, and the core scale and in experiments lasting several days. This research contributes novelty to the study of CO2 storage in chalk reservoirs, which remains relatively pioneering due to safety concerns. The exploration of impurity effects under dynamic conditions, as presented in this study, represents a notable innovation, addressing a gap in the literature dominated by batch experiments and thermodynamic modeling.
Evaluating Elastomer Resilience to Hydrogen for O-Ring Applications in Rapid Gas Decompression Environments
Authors: Bocchini S. , Tawil M. , Ferraro G. , Chiodoni A. , Viberti D. , Busollo C. , Baronio E. , Abdo E. , Verga F. , Pirri C.
Published: June 2024

The development of high-pressure hydrogen storage and distribution systems necessitates reliable polymeric materials that can withstand exposure to these demanding conditions. These systems utilize various components, including pressure vessels, pipelines, and fuel cell systems, each requiring specific polymeric materials with tailored properties. One critical challenge in this domain is ensuring the integrity of seals, particularly O-rings, exposed to high-pressure hydrogen gas. Rapid Gas Decompression (RGD), also known as Explosive Decompression (XDF), poses a significant threat to the performance and lifespan of these seals. This introduction explores the role of polymeric materials in high-pressure hydrogen applications and delves into the phenomenon of RGD-induced damage in elastomeric O-rings.
Prediction and History Matching of Observed Production Rate and Bottomhole Pressure Data Sets from in Situ Cross-Linked Polymer Gel Conformance Treatments Using Machine Learning Methods
Authors: Chen Yuhao , Onur Mustafa , Kuzu Nihal , Narin Onur
Published: June 2024

The objective of this study is to develop a computationally efficient methodology for the prediction of oil rate, water rate, and injection bottomhole pressure (BHP), and history matching of such well outputs to estimate important rock and fluid parameters that have a significant impact on reservoir conformance after in situ polymer gel treatment. Two different machine learning (ML) proxy methods are investigated for performing prediction and history matching of well output data such as oil production rate, water production rate, and/or injection BHP that may be acquired before and after polymer gel treatment. One of the ML methods used is the least-squares support vector regression (LS-SVR) and the other is the long short-term memory (LSTM) network, a deep learning method based on the recurrent neural network (RNN). The LS-SVR and LSTM proxy models are built on training sets of BHP and rate data generated with a high-fidelity commercial numerical simulator. The high-fidelity model is based on compositional flow simulation using double permeability fracture models. The reservoir models used in history matching are calibrated by using synthetic BHP, oil, and/or water production rate data sets before and after polymer gel treatment. The ensemble smoother multiple data (ES-MDA) method is used for history matching and prediction for the uncertainty assessment of the polymer gel treatment period, while a high-fidelity simulator is used for history matching. When the high-fidelity simulator is replaced with any of the ML-based methods, we use a randomized maximum likelihood estimation (RMLE) method where the gradients are analytically computed for the LS-SVR surrogate model, while the LSTM is replaced by the high-fidelity simulator, we compute the gradients of the LSTM by stochastic simplex approximate gradient (StoSAG) method. Results show that the LS-SVR and LSTM methods provide significant computational savings over the conventional simulation and history matching with a high-fidelity model. LSTM provides better predictions than LS-SVR for the same size of training sets. However, for larger training sets, LSTM provides a significant computational gain over LS-SVR. In addition, the results also identify the key parameters that have a significant impact on the performance of in situ polymer gel treatment. These parameters are the relative permeability curves of oil and water, absolute fracture permeability, polymer and cross-linked concentrations, and residual resistance factors (RRFT) are the key parameters in the performance of in situ polymer gel treatment.
Investigation of Physical and Mechanical Properties of Hot Dry Rock as a Geothermal Energy Source After LN2 Cooling
Authors: Longinos S. N. , Begaliyev D. , Hazlett R.
Published: June 2024

To investigate the impact of liquid nitrogen (LN2) as a stimulation method to increase reservoir contact with hot dry rock, we carried out a sequence of physical and mechanical examinations on Kazakhstani granite specimens. Granite samples were gradually heated to desired temperatures ranging from 50 °C to 500 °C. Subsequently, rapid cooling was implemented using two different coolants: water, and LN2. Following the thermal treatments, we conducted tests to evaluate both the physical and mechanical properties of the samples. Based on the experimental findings, the permeability of the heated granite samples notably rises following LN2-cooling, accompanied by reductions in P- wave velocity, and strength, as evidence of internal thermal shock fracturing. As the initial temperature increases, these alterations in properties become more pronounced. In comparison between water-cooling and LN2-cooling, the latter leads to more significant changes in both physical and mechanical properties across all target temperatures. This suggests that LN2-cooling can yield more effective stimulation of hot dry rocks compared to the water-cooling method.
Targeting accumulated apolipoprotein J in renal tubules halts the mTOR-TFEB interaction and relieves diabetic kidney disease
Authors: Sun Hung-Yu , Duan Shuangdi , Qin Nong , Pi Jiayi , Sun Pei , Huang Cheng-Pin , Wang Hsin-Tzu , Huang Mei-Chi , Wang Sin-Tian , Liu Wen-Chun , Chi Chi-Li , Liu Lamei , Ge Xingyi , Qiu Ye , Shi Liyang , Gao Qiang , Shi Junfeng , Yang Shuen-Ru
Published: June 2024

Ectopic lipid accumulation in renal tubules leads to lipotoxicity and exacerbates the progression of diabetic kidney disease (DKD). Apolipoprotein J (ApoJ) functions as a stress-induced molecular chaperone and participates in lipid homeostasis. As a hepatocyte-secreted entity, the involvement of ApoJ in the pathogenesis of extrahepatic organs remains poorly understood. Herein, serum ApoJ levels have been found to correlate with glycemic profiles, renal function parameters, and disease progression in individuals with Type-II diabetes. Accumulation of ApoJ in renal tubules was observed in mouse models of DKD and confirmed pathologically by renal overexpression of ApoJ in db/db mice. Under nutrient overload, ApoJ disrupts lipid and redox homeostasis and promotes epithelial-mesenchymal transition of proximal tubular cells by facilitating mTOR and transcription factor EB (TFEB) interaction. Consequently, targeting ApoJ promotes TFEB activation, restores autophagy, and alleviates renal injuries. By using tissue-specific knockout mice, we further demonstrated that the liver serves as a major source contributing to the ApoJ accumulated in renal tubules. Finally, the application of an MK53 peptide antagonizes ApoJ chaperone activity, thereby relieving disease progression of DKD. In summary, these findings suggest that ApoJ promotes diabetic renal pathogenesis through mTOR-TFEB-autophagy axis, and MK53 peptide emerges as a novel therapeutic strategy against DKD.
Chemical composition by PTP 1B inhibitory activity guided analysis of plant-based antidiabetic polyherbal drug used in ethnomedical systems
Authors: Dzomba Pamhidzai , Mugari Pardon , Nyoni Stephen
Published: June 2024

Background
Due to growing resistance and continued depletion of drugs that can be used as replacements, people turn to herbal medicines. A polyherbal drug was studied to investigate its chemical composition and possible mode of action.
Methods
Phytochemicals were isolated using Column and Thin Layer Chromatography, and antidiabetic activity was ascertained using an enzyme inhibition model. Active compounds identity was established using 1HNMR, 13CNMR, HSQC-NMR, UV, FT-IR and MS spectroscopy.
Results
Results showed that the polyherbal drug consisted of alkaloids and anthocyanins as the major active components. The alkaloids PTP 1B inhibition was not significantly different from that of metformin with percentage inhibition of Alkaloid 1, 78.6 ± 3.2% and Alkaloid 2, 79.6 ± 2.3. The anthocyanins inhibited PTP 1B enzyme significantly better than metformin with percentage inhibition of Flavonoid 1, 88.2 ± 3.2, Flavonoid 2, 90.3 ± 2.3 and Flavonoid 3 93.6 ± 2.9. One new alkaloid compound, Alkaloid 1, 1, 2-dimethoxy-12-methyl-7-(3-methylbut-2-en-1-yl)-12, 13-dihydro[1,3]benzodioxolo[5,6-c]phenanthridin-13-ol, one previously reported alkaloid, Alkaloid 2 berberine, IUPAC name, 9,10-dimethoxy-5,6-dihydro[1,3]dioxolo[4',5':6,7]isoquinolino[2,1-b][2]benzazin-7-ium and three anthocyanins, Flavonoid 1, malvidin 3-o-glucoside, Flavonoid 2, pelargonidin-3-O-gucoside and Flavonoid 3 malvidin 3, 5-O-diglucoside were identified as the active compounds in the polyherbal drug extract.
Conclusion
Presence of alkaloids and anthocyanins that have PTP 1B inhibitory activity showed that the polyherbal drug is a potential medicine for managing diabetes.