bluuebunny/crossref_metadata_2025_split huggingface dataset:
Optimize Computational Throughput of Reservoir Simulation Workflows in a GPU Based High Performance Computing Environment
Authors: Soffientini S. , Panfili P. , Ferrari A. , Dovera L. , Rossa E. Della , Fragola D. , Abbate E. , Ortega A. , Rosa A. , Khait Mark , Zhang Yongpeng , Cominelli A. 
Published: February 2024

Abstract
Decision making workflows in the energy industry require the capability to simulate hundreds if not thousands of models in a practical timeframe. In this paper we show how these capabilities can be achieved by exploiting top-class high performance computing resources available on premise.
A GPU reservoir simulator can maximize the efficiency and effectiveness of computational kernels for solving partial differential equations and phase equilibria on modern devices, hosting thousands of floating-point units with an unprecedented memory bandwidth. It incorporates functionalities to model recovery processes, and includes capabilities for coupled reservoirs and network simulation. Recently, we added the possibility to exploit GPU Multi Process Service and run field scale, multiple-reservoir simulations on a single device.
The Simulator enables the possibility to simulate multi-million cell models on a single GPU, and then execute reservoir workflows where ensembles of many hundreds of models may run altogether, maximizing the overall throughput. In this work we present the most relevant applications of a specific GPU reservoir simulator, highlighting its efficiency and effectiveness in tackling everyday business requests and the benefits it provides with respect to traditional CPU-based software. In more detail we will show the application of the tool for the simulation of: 1) an uncertainty quantification of a compositional dual porosity-dual permeability super-giant carbonate reservoir; 2) the history matching of a black-oil reservoir through ensemble multiple data assimilation followed by screening and optimization of injectors location; 3) revisiting an integrated asset model uncertainty quantification including five black-oil reservoirs. This work demonstrates how the availability of a GPU centric reservoir simulator combined with high performance computing resources was instrumental to the deployment of the most complex and computationally intensive workflows processes inside our Company. The process combines focus on the leading-edge company hardware and care on current company needs.
Underground Hydrogen Storage in Saudi Arabia: Opportunities and Challenges
Authors: Alanazi Amer , Ye Jing , Afifi Abdulkader , Hoteit Hussein 
Published: February 2024

Abstract
Hydrogen (H2) is anticipated to play a crucial role in Saudi Arabia's transition to a low-carbon economy as an alternative clean fuel. The conversion of fossil fuels through steam methane reformation produces blue H2, with captured carbon dioxide (CO2) being stored in geological formations. Saudi Arabia's strategic location and recent policies promote renewable energy and green H2. However, establishing an industrial-scale H2-based economy necessitates a suitable large-scale storage solution. Underground hydrogen storage (UHS) emerges as a prominent option, offering significant storage capacities in the Giga- and Terra-Watt-hour range, effectively addressing seasonal fluctuations in supply and demand from renewables.
Therefore, the present work aims to evaluate the opportunity of UHS in Saudi Arabia and assess potential geological formations (salt caverns, deep saline aquifers, and hydrocarbon reservoirs) and key technical challenges to be addressed for UHS integration in the energy grid. This includes criteria for site selection, storage capacity calculations, and other critical scientific research areas to be studied. The paper reviews the geological settings in Saudi Arabia that are potentially suitable for UHS, Red Sea basins, and sedimentary formations in the eastern basins at the Arabian plate.
The results highlight the requisite fundamental experimental and numerical studies for a complete understanding of H2/brine behavior within formation rocks, including geo-bio-chemical reactions prone to occur during the UHS process. The analysis of H2 thermo-physical suggests a more operational challenge than storing CO2 or natural gas. Commercial demonstration of UHS is crucial, while all the ongoing field tests of UHS (pure H2) worldwide are still in their early stages. Regionally, deep salt caverns and saline aquifers with closed structures or regional seals provide the best structural traps for UHS due to their tight and secure seal system. Down-dip aquifers and sedimentary packages in the eastern basins at the Arabian platform are more attractive and safer options. The discussed analysis of UHS potential in Saudi Arabia sheds light on its integration possibility into the circular carbon economy (CCE) framework to achieve a net-zero emission by 2060.
Process Control Optimization on Water Injection Pump Control Valve to Reduce Energy Consumption and Optimize Waterflood Injection Rate in Gathering Station Facility Pertamina Hulu Rokan
Authors: Prakoso C. B. , Rekayana E. , Susetyo B. , Arifin S. , Zaradan N. 
Published: February 2024

Abstract
A typical Gathering Station Facility in Pertamina Hulu Rokan operates a Water Injection Pump (WIP) to inject produced water back into the reservoir subsurface. Each pump has a pressure control valve (PCV) at the discharge line. The Pump is operated at maximum capacity with an applied single setpoint. A level control valve (LCV) is installed at the return line to maintain the water tank level. This control system configuration when the water supply rate is lower than the discharge pump rate, tends to decrease the water tank level and the control system will respond to this condition by opening the LCV return line, which results in wasted energy due to the water returned to the tank and impacted unstable injection line header pressure. This Process Control Optimization (PCO) provides efficient and reliable WIP system operation by implementing automation of the control valve setpoint adjustment, enabling the WIP to increase or reduce its flow rate based on water tank fluid level (Smart System Control) to eliminate waste energy or reduce return flow when the water supply is lower than pump injection rate, improve pump reliability where the pump is not always operating in maximum capacity and also improving injection rate performance.The method is implemented by applying function logic modification on the Programmable Logic Controller (PLC) and Human Machine Interface (HMI) system which controls WIP operations. The objective is to provide efficient and reliable injection pump system operations, where the WIP flow rate setpoint shall consider tank level, while the control valve in the return line shall have the capability to operate as both level and mainline injection pressure control. The PCO is made to provide a multistage set point that can change automatically, it will change the set point WIP control valve following the operation condition based on the water tank level so that the return control valve will not open immediately when the tank level starts to decline. By implementing the PCO system in two gathering station facilities, the project team has successfully improved the WIP operability and waterflood injection process. The benefit of this system comes from reduced energy efficiency where it was successfully eliminated return flow ~ 13,000 BWPD which equivalent to 13,200 kWh/year, improved 30% stability injection pressure in the header line for supporting water flood / EOR system, decreasing WIP unplanned downtime ~ 50 % and increase injection capacity up to ~ 3000 BWPD which support to mitigate capacity constrain and successfully reactivation oil production wells ~ 70 BOPD.
Successful Deployment of Electro-Mechanical Tractor to Maximize Wellbore Reach in Highly Deviated Wells
Authors: Mashhad H. S. , Jassem H. M. 
Published: February 2024

Abstract
The advancement of drilling technology has made horizontal wells with maximum reservoir contact a possibility. These highly deviated horizontal wells pose huge challenges with respect to wellbore reach during well intervention jobs. The successful implementation of the advanced electro-mechanical tractor to maximize wellbore reach in highly deviated or horizontal wells is presented with the result of several trial test attempts.
The utilization approach of electro-mechanical tractor is based on electro-mechanical drive over hydraulic conveyance approach. Moreover, this advanced fit-for-purpose technology incorporates real-time communication and control while tractoring, including casing collar locator, wheel speed, wheel torque, inclination and relative bearing. Faster operation made possible by the ability of the tractor to move 100 feet per minute. Electro-mechanical tractor was utilized to extend the wireline reach during several well intervention jobs in high inclinations of more than 60 degree.
The several deployments of the advanced electro-mechanical tractor during well intervention jobs in high-angle and extended-reach horizontal wells effectively met the desired objectives and resulted in higher reach along with faster operation. Across several attempts, in which the angles higher than 85 degrees and debris/scale type of restrictions present, the tractor demonstrated an excellent reach result compared to gravity conveyance approach that are limited to 45 to 60 degrees. Moreover, this advanced fit-for-purpose technology surmounted the reach limitations and restrictions, thus unleashed the potential of logging the entirety of target interval to obtain the valuable data for better future decisions.
This paper presents and details the successful deployment of advanced fit-for-purpose technology. Furthermore, electro-mechanical tractor demonstrated its ability to maximize reach in cased hole environment, and provided an economical conveyance solution for logging diagnostic, monitoring, perforating, and mechanical intervention in high- angle and extended-reach horizontal wells.
Advancing Hydrogen Storage in Depleted Gas Reservoirs: A Comprehensive Evaluation of Dominant Trapping Mechanisms
Authors: Alhotan M. M. , Machado M. V. Barbosa , Delshad M. , Sepehrnoori K. 
Published: February 2024

Abstract
The utilization of depleted hydrocarbon reservoirs for Underground Hydrogen Storage (UHS) presents a promising and competitive solution for addressing large-scale hydrogen storage challenges. By repurposing existing infrastructure, UHS can provide a cost-effective and sustainable approach to integrating hydrogen storage within the energy sector. This study aims to identify and quantify the dominant trapping mechanisms that contribute to hydrogen loss during UHS, offering insights for optimizing deployment and ensuring efficient integration into the energy landscape. To achieve this objective, a real-field geological model of a depleted sandstone gas reservoir with a shale cap rock was employed. The Peng Robinson equation of state as well as the viscosity and solubility models were tuned using published hydrogen properties. Recent relative permeability, capillary pressure, and hysteresis data from the literature were also integrated into the simulations to capture the reservoir's dynamic behavior during hydrogen injection, storage, and withdrawal processes. The mechanisms examined include hydrogen residual trapping and hysteresis, diffusion in water and the formation, solubility trapping, geochemical reactions, and the impact of salinity, all of which can influence hydrogen retention and recovery efficiency. The results of this comprehensive analysis revealed that residual trapping was the most dominant mechanism, accounting for majority of the hydrogen loss. This finding emphasizes the need for precise relative permeability data to accurately model and predict reservoir performance during UHS. The geochemical loss was found to be highly dependent on rock composition, with negligible impact in sandstone reservoirs. Furthermore, diffusion in the fluid and the formation contributed to less than 3% of the total hydrogen loss, while the impact of solubility trapping was minimal. This study presents an analysis of dominant trapping mechanisms during Underground Hydrogen Storage in depleted gas reservoirs within a real-field geological model. What differentiates this study is the systematic incorporation of all loss mechanisms using a tunned fluid model, providing engineers with a more holistic understanding of UHS. The findings offer practical insights into how these factors can be optimized for improved hydrogen retention and recovery efficiency. This research is pivotal for engineers seeking data-driven methodologies and best practices in UHS. By delivering actionable knowledge, this study supports the integration of hydrogen storage into the existing hydrocarbon infrastructure, contributing to the development of a sustainable energy sector.
An Improved Method for Oil-Water Contact Determination in Connected Fractured-Caved Reservoir Modeling Restricted by Data of Oil Displacement by Cyclic Water Injection
Authors: Zhang Qi , Li Mengqin , Cao Wen , Niu Ge , Zhang Jing , Wang Qi 
Published: February 2024

Abstract
With complicated accumulation mechanism of fractured-caved carbonate formations, characterization of reservoir and fluid distribution has great uncertainty due to varied oil-water contact and discontinuous reservoir, which brings difficulties to refined reservoir characterization and development index prediction. However, wells are always completed in upper part of the reservoir, instead of drilled through the pay zone, leaving the oil-water contact unknown below the well. It's urgent for a reservoir unit to confirm the oil-water contact. In the study, a method is proposed to constrain the pore volume and aquifer volume in a fractured-caved model based on the projection of water injection data. A material balance equation set of cyclic water injection in the fractured-caved reservoir is used to calculate the total volume of the connected reservoir drained by wells and distinguish the volume proportion of water in the fluid, according to the formation pressure recovery caused by the certain water injection volume in each cycle. A geological model is built via multi-attribute modeling technology and then modified by the outcome of former dynamic reserve evaluation. The seismic attribute threshold of reservoir extraction is controlled by the total reservoir volume, thus the volume of geological model determined. The depth of oil-water contact is adjusted to fit the oil and water volumes related to the water volume ratio, finally a reasonable oil-water contact obtained. Fractured-caved reservoir model and oil-water contact depth are further verified and corrected by drilling and completion data, oil testing and water production data. The feasibility and reliability of the method are demonstrated by a case study in Tarim Oilfield, where the depth of oil-water contact is deduced as −6342m from the first producer in the reservoir unit, and the oil testing of second producer supports it. The method can be applied to single wells or well groups in fractured-caved reservoir that have not yet seen water breakthrough after water injection. It can improve the geological understanding of the reservoir, evaluate the development potential of water injection, and predict the water breakthrough, helpful to the adjustment of development measures.
Fiber Monitoring of TRSSV to Monitor Health and Forecast Failures
Authors: Murdoch E. , Asthana P , Bukhamseen A , Ghamdi T 
Published: February 2024

Abstract
Sub-surface safety valves (SSSV) are critical to the safe functioning of a well. Whilst they are tested regularly, they have limited diagnostics, valves that are experiencing reduced performance are not detected until they fail a test. This typically requires the well to be shut in until remedial action can be undertaken. This paper will set out a new philosophy to use fiber optic sensing to monitor the health of valves and use Artificial Intelligence (AI) to forecast failures and reduce unplanned shut-ins.
Like all machines, failures in SSSV don't just appear. There are typically signs of degrading performance ahead of time. But given the sub-surface location of these valves, it is very hard to detect these signs. In a parallel application, in-well fiber monitoring systems are now deployed routinely for leak detection, flow characterization and many other applications. These fiber monitoring systems can be used to monitor the performance of the SSSV during function testing and establish a health condition of the valve.
This paper will describe the process used to test the concept of monitoring a SSSV using fiber monitoring system first in the work shop. A number of different sized valves will be used. Failure conditions will then be simulated in the test valves and they will be again monitored during function testing in failed condition. This provides a base line to provide indication of SSSV health. The machine learning algorithm will then be trained with these example data. Various coupling methods will be also tested and a final design optimized. The model will then be taken to the field to be trialed in wells that have had fibers pre-installed. Each well will be monitored whilst the SSSV function test was carried out. The results will then be analyzed and a health assessment given. These results will be further fed in to the machine learning model to optimize algorithm. Over time this model will provide a more accurate forecasting of failure and allow better scheduling of workover operations.
This is the first time that a fiber monitoring system has been used to monitor the health condition of a SSSV. This also represents an excellent integration of real time data collection and machine learning interpretation to better schedule workover operations and reduce unplanned shut ins. This safety valve surveillance kit is a strap-on solution for existing hydraulic valves already installed in any wells that have fiber monitoring systems.
Direct CO2 Mineralization Through Foaming Cement and Concrete
Authors: Santra Ashok , Johnson Kenneth , Martinez Roland 
Published: February 2024

ABSTRACT
The use of carbon dioxide (CO2) in the development of products including concrete and oil well cementing is attracting great attention of industry, academia, and government in mitigating climate change. Out of total global energy-related production of ~ 31.5Gt CO2, 8% is by cement production plants. Herein we demonstrate possibility of utilizing a total of 0.378Gt CO2 or 1.2% of total global CO2 by foaming into cement and Concrete. Hydration of Portland cement results in production of 15-20% of Ca(OH)2 (Calcium Hydroxide or Portlandite). All these Calcium Hydroxide can be mineralized into CaCO3 by CO2 foaming. We have developed a proprietary laboratory size blender that can blend CO2 at up to 1000psi. The testing allows us to monitor irreversible conversion of CO2 to CaCO3. We were able to prepare very light weight cement designs by using foamed CO2 technology, with up to 20% enhanced compressive strength compared to commercially available technology without carbonation, such as water extended, and/or silica fume based. Laboratory testing has been performed to optimize the CO2 foaming efficacy and quality. Thermogravimetric analysis has been performed to quantify the percentage of carbonation. Permeability and compressive strength measurement have been performed to ensure that the carbonation does reduce the former and enhance the latter, wherein both changes are improvement towards longer term integrity of cement and concrete. Direct incorporation of CO2 into the matrix of cement and concrete blends reduces the hardening time significantly which is another way of reducing loss of net productive time (NPT) towards sustainability. In addition, it also maximizes the amount of CO2 that can be utilized in this process. The CO2 foaming into cement and concrete is a novel process and have huge potential to directly mineralize large amount of CO2 in useful products.
Prediction of Two-Phase Flow Regimes in Vertical Pipes Using Tree-Based Ensemble Models
Authors: Al-Sharifi H. A. , Alkhudafi A. M. , Al-Gathe A. A. , Baarimah S. O. , Al-Ameri Wahbi , Alyazidi A. T. 
Published: February 2024

Abstract
The multi-phase fluid transfer pattern in vertical flow through pipelines is a significant parameter to be predetermined for predicting the pressure gradient, liquid holdup, and other flow properties. In the present study, the prediction of two-phase flow patterns in vertical pipes using ensemble machine-learning classification models is presented.
For this purpose, ensemble machine learning techniques including boosting, bagging, and random forest have been applied. A decision tree-based classifier is proposed, such as Random trees (RT), J48, reduced-error pruning decision trees (REPT), logistic model trees (LMT), and decision trees with naive Bayes (NBT), to predict flow regimes. Datasets consisting of more than 2250 data points were used to develop the ensemble models. The importance of attributes for different models was investigated based on a dataset consisting of 1088 data points. Feature selection was performed by applying six different optimization methods. For this task, training, and cross-validation were used.
To check the performance of the classifier, a learning curve is used to determine the optimal number of training data points to use. The performance of the algorithm is evaluated based on the metrics of classification accuracy, confusion matrix, precision, recall, F1-score, and the PRC area. The boosting approach and random forest classifiers have higher prediction accuracy compared with the other ensemble methods. AdaBoost, LogitBoost, and MultiBoosting algorithms were applied as boosting approaches. Multiposting has a better performance compared with the other two techniques. The random forests provided a high level of performance. Its average precision, recall, and F1 scores are 0.957, 0.958, and 0.949, respectively. It is concluded that comparing the results of single classifiers, the ensemble algorithm performed better than the single model. As such, the accuracy rate of the prediction of flow regimes can be increased to 96%.
This study presents a robust and improved technique as an alternative method for the prediction of two-phase flow regimes in vertical flow with high accuracy, low effort, and lower costs. The developed models provide satisfactory and adequate results under different conditions.