bluuebunny/crossref_metadata_2025_split huggingface dataset:
Integrated Thermo-Hydro-Chemo-Mechanical (THCM) Analysis for Evaluating CO2 Plume Geothermal System Potential
Authors: Elsayed Touka , Okoroafor Esuru Rita 
Published: September 2024

Abstract
Objectives/Scope: The objective of this study is to conduct a thorough investigation into CO₂ Plume Geothermal (CPG) systems using a Thermo-Hydro-Chemo-Mechanical (THCM) model. It aims to unravel the complexities of supercritical CO₂ interactions within geothermal formations and to evaluate the parameters critical to optimizing geothermal output. Additionally, the research intends to refine the predictive capabilities of current models, ensuring a more accurate assessment of the geothermal potential and contributing to the sustainable development of geothermal resources.
Methods, Procedures, Process: Utilizing an integrative THCM model, we assessed the role of supercritical CO₂ in geothermal energy extraction. Geological, chemical, and mechanical parameters were meticulously quantified to simulate subsurface scenarios impacting the injectivity and productivity of CPG systems. Key chemical interactions and their impact on rock integrity were also modeled, incorporating acidification and carbonation reactions and their effect on rock strength and reservoir behavior under varying stress conditions. These procedures enable a comprehensive understanding of the multifaceted influences on CPG system efficiency.
Results, Observations, Conclusions: The study revealed that geochemical reactions, including acidification and carbonation, could alter rock integrity and affect the mechanical stress within the reservoir. These findings suggest that a careful balance of chemical and mechanical factors is essential for the optimal functioning of CPG systems.
Novel/Additive Information: This research introduces a novel THCM model, incorporating a more nuanced simulation of geothermal reservoirs, particularly in the context of supercritical CO₂ injection. By identifying the key parameters that govern the system's efficiency, this study contributes new insights to the field of renewable energy and informs better reservoir management strategies.
Use of Machine Learning in Microseismic Monitoring for Thermal Operations in Cold Lake, AB, Canada
Authors: Costin S. O. , Scaini S. , Zhao H. , Fink T. K. , Brisco C. , Feng J. , Yadav D. , Sidhu S. 
Published: September 2024

Abstract
This paper presents a novel advancement in the microseismic data processing workflow for monitoring thermal operations at an unconventional bitumen reservoir in Cold Lake, Alberta, Canada. The new enhancement to the monitoring workflow enables fast processing of the microseismic data, by filtering upwards of 94% of noise files from the daily dataset before the files are reviewed and analyzed. The software consists of a deep learning-based application trained to recognize noise files and classify event files according to their predominant source. The tool was deployed in production on an Azure-hosted data manager for almost a year, after extensive testing and achieving adequate level of performance.
Developing a Novel Machine Learning-Based Petrophysical Rock Typing (PRT) Classification: Applied on Heterogenous Carbonate Reservoirs
Authors: Abbas Mohammed A. , Al-Mudhafar Watheq J. , Alsubaih Ahmed , Al-Maliki Ali , Al-Sukaini Ali 
Published: September 2024

Abstract
Petrophysical Rock Typing (PRT) from core and well log data is a valuable tool for reservoir discrimination and recoverable reserve estimation in heterogeneous multimodal pore carbonate systems. However, the traditional PRT methods are often ineffective in these systems because of the complex pore geometry and distribution. This work introduces an innovative PRT technique that utilizes cutting-edge machine learning algorithms to categorize petrophysical rock types in a supergiant oil and gas field located in southern Iraq. The study initially employs Ward's hierarchical clustering method to define PRT from mercury injection capillary pressure (MICP) data. Ward's algorithm methodology discovers five separate groups, each of which corresponds to a different petrophysical rock type with a distinct pore geometry and system. The probabilistic neural network (PNN) approach is then used to classify PRTs from well-logging data in a cored well. The PNN algorithm is able to learn the non-linear complex relationships between well logging data and characterized PRTs, and it is able to predict PRTs with high accuracy. The confusion matrix and the total correct percent (TCP) were used as additional criteria to analyze the effectiveness of the PNN classification algorithm's performance. The study's results showed that the predicted and observed PRT had an exceptional match by attaining 94% of TCP based on the testing subset and 95 % of TCP for the entire dataset. The predicted PRT distribution is utilized as an extra independent variable to improve the modelling and prediction of core permeability and saturation height function that are essential to achieve a realistic geological distribution. The study's findings demonstrate that the suggested PRT approach can accurately categorize PRT in heterogeneous multimodal pore carbonate systems. The strategy can be easily applied to different reservoirs and has the potential to enhance the overall reservoir characterization.
Video-Powered AI Solutions for Active Monitoring and Safety Improvement in Offshore Environments: The BRAM Offshore and Altave Case
Authors: Neto Wilson Augusto Borges Leal , Gonçalves José Henrique Mouallem , Duplan Marco 
Published: September 2024

Abstract
This paper explores the utilization of video-based artificial intelligence (AI) tools for enhancing safety measures in offshore environments, specifically within the Bram Offshore fleet in Brazil. The study focuses on the application of AI-powered systems in monitoring and analyzing critical activities, identifying potential risks, and preventing hazardous incidents. It highlights the development and implementation of advanced AI algorithms integrated with video monitoring technology, showcasing their effectiveness in real-time risk detection and mitigation. By monitoring Personal Protective Equipment (PPE) compliance and managing Red Zone areas, these AI solutions significantly improve safety protocols and incident prevention. High-resolution cameras capture detailed images of operations, and the resulting dataset is used to train deep learning algorithms through techniques such as segmentation, classification, object detection, pose estimation, and tracking. Real-time insights are presented on a web platform as alerts, reports, and dashboards, with audible alarms for critical scenarios to ensure immediate interventions. The system's effectiveness is demonstrated through data from the Reedbuck vessel during the last year of operation. Key findings include enhanced hazard detection and improved safety, establishing video-based AI tools as a transformative approach to safety management in the offshore industry.
Early Wells Delivery Leads to Substantial Oil and Gas Field Development Projects Net Profit Value Gain
Authors: Vigor H. , Estvold S. , Kern S. , Vasvary A. 
Published: September 2024

Abstract
An unconventional resources operator is more likely to achieve a higher value from an unconventional field development campaign by establishing a collaborative work relationship with the lead drilling rig contractor at the outset of the early planning stages. This paper discusses the multiple benefits of early collaboration, with the end result of earlier delivery of wells and production revenues, leading to substantial project net present value (NPV) gains.
Traditionally, the field development plan is solely managed by the operator. A plan typically describes the subsurface properties of the field and outlines the development plan for it which includes drilling, completion, testing, stimulation, and production strategies. The planning stage is critical to optimize the selection of the right rigs for the job. Super spec fast-moving rigs, combined with drilling technology deployment, can help to safely and efficiently drill a well and thereby reduce the amount of time needed to complete the well program. Technologies assist to enable proper wellbore placement, directional drilling automation, rate of penetration (ROP) enhancement, and help reduce the occurrence of downhole tool failure events leading to well construction nonproductive time (NPT). In a multi-well pad environment, early and predictable well delivery helps avoid fracking delays and leads to an earlier production onset. Efficiency can be jointly optimized by defining together the following: optimal well pad layout, number of wells, distance between wells, well suspension strategy after drilling and prior to frac operations, simultaneous operations matrix between top hole drilling, batch drilling and reservoir fracking, to optimize well construction, reservoir drainage, and the frac campaign.
The analysis presented in this paper compares a one well pad Base Case scenario to multiple super spec multi-well pad scenarios, showing that optimum use of rigs on multi-well pad deployments can lead to a greater number of wells drilled within the same timeframe with lower individual cycle times.
The benefits of this close collaboration are three-fold: planning for success and performance in reducing well construction time and cost due to modern rigs and technologies deployment, optimizing rig and frac fleet deployments through proper well pads activities including simultaneous operations, and early well delivery leading to unlocking additional revenue through early production. More than 40% project NPV gain can be recorded over drilling campaigns, making it an attractive strategy for the operator.
Advancing Oilfield Safety: A Computer Vision Approach to Line of Fire Risk Detection
Authors: Lam Tammy , Santhalingam Vignesh , Abinader Antonio , Vesselinov Velizar , Krishna Dhananjaya 
Published: September 2024

Abstract
In industrial environments, the movement of heavy equipment poses significant risks to personnel. This paper presents a Line of Fire (LoF) Detection system that leverages real-time video feeds, object detection, and motion tracking to identify and alert when individuals are in the path of moving equipment, specifically pipes. The system utilizes advanced machine learning models for person detection and pipe segmentation, combined with a robust alert mechanism, to enhance safety measures.
Development of BOP API Flange Capacity Using Simulation Approach
Authors: Lu Wangming , Loganathan Sriram , Singh Yanez , Corkhill John 
Published: September 2024

Abstract
Flanges commonly selected for use in offshore equipment are based on the designs found in American Petroleum Institute (API) standards and depend on the predicted load during planned operations and capacity of the flange. The flange capacity is derived from standard analysis techniques provided by API Technical Reports 6AF, 6AF1, and 6AF2. It is however possible to conduct analysis to determine the flange capacities using more sophisticated methods to improve the accuracy of the analysis which can potentially lead to deriving higher capacity results that are less conservative. This paper presents a detailed analysis of an API 18-3/4" 15 ksi BX-164 flange capacity at ambient and an elevated temperature of 375°F. It also clarifies stress and leakage criteria and details the discrepancies in analysis methodology and capacity results between API 6AF2 and ourapproach.
To calculate the capacity of an API 18-3/4" 15 ksi BX-164 flange, a 180-degree 3D model is created to calculate the detailed load and stress distribution under different combinations of loadsincluding a flange, hub, and gasket. Both flange and hub are modeled with linear elastic material properties while the gasket is modeled with elastic-plastic material properties. For the flange capacity at elevated temperatures, a steady state decoupled thermal analysis is performed first to obtain the temperature gradient and subsequent thermal stress. The material properties are derated due to elevated temperature per API 6MET. An elastic-plastic approach is then employed to calculate the capacity under a series of load combinations of pressure, tension and bending, using the design factors defined in ASME BPVC, Section VIII, Division 2. The analysis result is based on both stress and leakagecriteria to determine the limiting capacity under various load cases.
This work presents a capacity chart that shows the relationship between pressure and bending under various tensions at both ambient and elevated temperatures and further clarifies the stress and leakage criteria. Results show that the overall capacity at ambient temperature based on this analysis is significantly higher than published API reports. Additionally, the capacity of the flanged connection at 375°F is reduced when compared to the capacity at ambient temperature. The capacity reduction from elevated temperature is more pronounced at lower pressures. The flange capacity at both ambient and elevated temperatures is governed by bolt stress criteria.
As publicly available capacity charts for API 18-3/4" 15 ksi BX-164 flanges calculated using modern finite element approaches are rare, the analysis method presented in this paper defines a process to calculate the flange capacity at elevated temperatures. This approach can potentially lead to deriving higher capacity results that are less conservative which aids in operational and well design decisions.
A New Methodology for Data Assimilation Using Capacitance-Resistance Model Combined with Fractional Flow and Aquifer Modeling
Authors: Maschio Célio , Avansi Guilherme Daniel , Schiozer Denis José 
Published: September 2024

SummaryData assimilation for uncertainty reduction (DAUR) using reservoir simulators demands high computational time and resources. Depending on the reservoir model, the process can take days or weeks. Therefore, it is desirable to speed up the process to increase its efficiency, keeping the quality of the result. Our aim in this paper is to present a new methodology for data assimilation (DA) using the capacitance-resistance model (CRM), integrated with fractional flow (FF) models, to reduce the computational time and resources in the process. The methodology brings novel contributions, such as (a) applying the CRM in a probabilistic manner for DA, (b) developing a new FF approach, and (c) proposing a new approach for aquifer modeling under uncertainty. The methodology was successfully applied in a real field case. To validate the CRM results, we compared the DA process using the CRM with the DA results using the reservoir simulator. This comparison showed a very good agreement between the results. Notably, the CRM approach was up to 279 times faster than the process using the reservoir simulator. We also validated the CRM results using several kinds of multidisciplinary geoengineering data from the same studied case, including (1) historical injection, (2) hydraulic communication analysis based on static well pressure, (3) water salinity measurements analysis, and (4) 4D seismic analysis. These analyses showed the consistency of the CRM results in terms of physical representativeness. After an extensive validation process, we can state that the CRM approach, combined with the novel FF proposed in this work, has great potential to be applied in DA, reservoir management, and production strategy optimization, thus contributing to the acceleration of the decision-making process.
Identification of Nearly Pure Leakoff Phase from Pressure Falloff Curve and Its Interpretation on Stimulated Fracture Areas
Authors: Wei Zhaoyang , Sheng Mao , Hu Zhongyu , Cheng Shizhong , Liao Qinzhuo , Tian Shouceng 
Published: September 2024

Summary
Evaluation of fractures post-hydraulic fracturing is crucial for optimizing fracturing designs. Pressure falloff analysis methods, due to their cost-effectiveness and ease of data acquisition, have been widely used in oil fields. However, traditional pressure falloff analysis methods are based on the assumption of simple double-wing fractures and often overlook the impact of early-time abnormal leakoff behaviors. While some scholars have proposed pressure falloff equations for individual abnormal leakoff behavior, establishing pressure falloff models that consider the coupling of multiple abnormal leakoff behaviors is challenging, making the assessment of complex fracture networks difficult. This paper proposes a method that identifies the phase of nearly pure leakoff pressure falloff through continuous wavelet transform (CWT) and utilizes this phase for fracture inversion. Additionally, the fracture inversion model considers the differences in closure pressure and fracture compliance between main and secondary fractures. This method is applied to a shale horizontal well, and the results show that the inversion fracture areas are 20.3% smaller on average than the traditional method after removing the disturbance of abnormal leakoff behaviors. The new method is verified by the production data of each stage. The correlation between the main fracture area of new method and the production of each stage is better than that of the traditional method, and the correlation coefficient is 0.767. It is also found that the development degree of natural fractures affects the proportion of main and secondary fractures. In addition, it is found from the time-frequency diagram after CWT that the stages with higher secondary fracture proportions have more high-frequency components in the time-frequency diagram.
Estimating Carbon Dioxide Solubility in Brine Using Mixed Effects Random Forest Based on Genetic Algorithm: Implications for Carbon Dioxide Sequestration in Saline Aquifers
Authors: Mwakipunda Grant Charles , Ibrahim AL-Wesabi , Kouassi Allou Koffi Franck , Komba Norga Alloyce , Ayimadu Edwin Twum , Mgimba Melckzedeck Michael , Ngata Mbega Ramadhani , Yu Long 
Published: September 2024

Summary
Accurate prediction of carbon dioxide (CO2) solubility in brine is crucial for the success of carbon capture and storage (CCS) by means of geological formations like aquifers. This study investigates the effectiveness of a novel genetic algorithm-mixed effects random forest (GA-MERF) model for estimating CO2 solubility in brine. The model’s performance is compared with established methods like the group method of data handling (GMDH), backpropagation neural networks (BPNN), and traditional thermodynamic models. The GA-MERF model utilizes experimental data collected from literature, encompassing key factors influencing CO2 solubility: temperature (T), pressure (P), and salinity. These data are used to train and validate the model’s ability to predict CO2 solubility values. The results demonstrate the superiority of GA-MERF compared to the other models. Notably, GA-MERF achieves a high coefficient of determination (R) of 0.9994 in unseen data, indicating a strong correlation between estimated and actual CO2 solubility values. Furthermore, the model exhibits exceptionally low error metrics, with a root mean squared error (RMSE) of 2×10-8 and a mean absolute error (MAE) of 1.8×10-11, signifying outstanding accuracy in estimating CO2 solubility in brine. Beyond its high accuracy, GA-MERF offers an additional benefit—reduced computational time compared to the other models investigated, with 65 seconds. This efficiency makes GA-MERF a particularly attractive tool for real-world applications where rapid and reliable CO2 solubility predictions are critical. In conclusion, this study presents GA-MERF as a powerful and efficient model for predicting CO2 solubility in brine. Its superior performance compared to existing methods and previous literature highlights its potential as a valuable tool for researchers and engineers working on CCS projects utilizing aquifer storage. The high accuracy, low error rates, and reduced computational time make GA-MERF a promising candidate for advancing the development of effective and efficient CCS technologies.
Quantifying the Transformational Requirements of the Electricity Sector Under Uncertain Expectations of Carbon Removal
Authors: Yang Pingjian , AFRANE SANDYLOVE , Ampah Jeffrey , ADUN HUMPHREY , Chen Jianlin , Zou Hongyang , Mao Guozhu 
Published: September 2024

Abstract

Decarbonization and carbon dioxide removal (CDR) are crucial for achieving global climate targets. The power sector is pivotal in this process, yet the role of CDR in deep decarbonization and its implications on the sector have been underexplored. Using a global multi-sector model, we quantify the effects of CDR on the transformation of the power sector under different CDR reliance levels by 2050 — high (4–10 Gt/yr), moderate (2.5-5 Gt/yr), and low (≤ 1Gt/yr)—aligned with 1.5°C and 2°C climate targets. We show that BECCS is essential for future electricity demands, particularly in Asia and Central America. High CDR pathways could require 10–20% of electricity consumption in South America and Australia/New Zealand for carbon removal. Major economies like China, the US, and India face significant investment needs, risking stranded assets worth up to US$165–225 billion by mid-century under low CDR compared to high CDR. Regions heavily dependent on coal, such as China and India, face greater stranding costs, while gas-dependent regions like the Middle East and Russia have relatively lower costs. Global mitigation efforts with limited CDR require a 15% reduction in committed emissions compared to high-CDR scenarios, with the most pronounced reduction of 65% anticipated for India.
Herbal Teat Dip preparation is helpful in reducing the mastitis-causing pathogens in Rathi Cattle
Authors: Sharma Akansha , Arora Rajni , Nehra Rajesh , Manzoor Tahir 
Published: September 2024

Abstract

Mastitis remains a significant challenge in the dairy industry, leading to substantial economic losses due to antibiotic-resistant pathogens and antimicrobial residues in milk. This study focuses on sub-clinical mastitis, a hidden threat to dairy cattle productivity. It evaluated the effectiveness of a post-milking herbal teat dip combined with oral trisodium citrate supplementation in improving milk quality and reducing bacterial counts. Twenty-four lactating Rathi cows were divided into four groups, with treatments including a herbal teat dip containing aloe vera, turmeric, castor oil, and lemon. Results showed significant reductions in Staphylococcus and Streptococcus colonies, especially in the combined treatment group. These findings suggest that herbal teat dips and trisodium citrate can effectively combat sub-clinical mastitis, enhancing milk quality and reducing antibiotic use.
Hexokinase-I directly binds to a charged membrane-buried glutamate of mitochondrial VDAC1 and VDAC2
Authors: Holthuis Joost , Bieker Sebastian , Timme Michael , Woge Nils , Hassan Dina , Brown Chelsea , Marrink Siewert , Melo Manuel 
Published: September 2024

Abstract

Binding of hexokinase HKI to mitochondrial voltage-dependent anion channels (VDACs) has far-reaching physiological implications. However, the structural basis of this interaction is unclear. Combining computer simulations with experiments in cells, we here show that complex assembly relies on intimate contacts between the N-terminal alpha-helix of HKI and a charged membrane-buried glutamate on the outer wall of VDAC1 and VDAC2. Protonation of this residue blocks complex formation in silico while acidification of the cytosol causes a reversable release of HKI from mitochondria. Membrane insertion of HKI occurs adjacent to the bilayer-facing glutamate where a pair of polar channel residues mediates a marked thinning of the cytoplasmic leaflet. Disrupting the membrane thinning capacity of VDAC1 dramatically impairs its ability to bind HKI in silico and in cells. Our data reveal key topological and mechanistic insights into HKI-VDAC complex assembly that may benefit the development of therapeutics to counter pathogenic imbalances in this process.