bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Scalable Parallel Compositional CO2 Geological Storage Simulator
Authors: Di Chaojie , Wei Yizheng , Wang Kun , Shen Lihua , Xue Zhenqian , Chen Zhangxin 
Published: September 2024

Abstract
Storing CO2 in deep saline aquifers is one of the most promising methods for achieving carbon neutrality. Numerical reservoir simulation can assist researchers and engineers in comprehensively understanding and effectively managing the CO2 sequestration process, thereby ensuring its safety and effectiveness. Most of the current CO2 storage simulators use a CO2-brine fluid model which only has water and CO2 components. When impure CO2 is injected, the subsurface fluids form a multi-component gas-brine system, whose phase behavior cannot be accurately described by a CO2-brine fluid model. The current gas-water compositional models include a very limited number of gas components. In this study, we developed a fully implicit parallel CO2 storage simulator for distributed memory computers based on our in-house parallel platform. This simulator uses a fully compositional fluid model which treats water as a component and incorporates it into phase equilibrium calculations. The gas components include not only CO2 but also other common components in captured impure CO2, such as CH4, N2 and H2S. The cubic Peng-Robinson equation of state was used to predict the fugacity and PVT properties of CO2-rich phase. The fugacity in aqueous phase and brine properties are calculated by Henry's law and empirical correlations respectively. Meanwhile, the effect of dissolved gas influence on aqueous viscosity is considered which is ignored by current commercial simulator. The finite difference (volume) method is applied to discretize the compositional fluid model. Numerical experiments show that our simulator is scalable, stable and validated to simulate large-scale CO2 storage problems with hundreds of millions of grid blocks on a parallel supercomputer cluster.
Optimizing Image Logging Acquisition with Deep Learning
Authors: Guimaraes C. , Piazza R. 
Published: September 2024

Abstract
With the latest advances in well logging technology, data can now be obtained dynamically during drilling (LWD) with quality comparable to that obtained under controlled conditions, using wireline equipment. When data is successfully acquired during drilling, a significant amount of time and resources are spared by the operator. However, of all the tools typically found in the bottom-hole assembly, the bore-hole image tends to be the most sensitive to the noisy environment encountered during drilling. Considering that wireline acquisitions occur after this period, it should be possible to decide whether to also run the image tool in the wireline assembly, based on the quality of the LWD data collected. Unfortunately, due to the large size of the data, image logs demand a significant time to be processed and analyzed by a professional specialist, typically exceeding the time available between the operations. Furthermore, the evaluation of the images by different professionals is subject to inconsistent interpretations for different operations. Therefore, the objective of the work is to automate and standardize the quality assessment of images acquired via LWD, to support the decision of whether to run a wireline image log.
To achieve this goal, it is necessary to efficiently analyze multiple criteria that would ordinarily be dependent on the speed and subjectivity of human interpretation. The chosen solution involves the combination of convolutional neural networks with feed forward networks, to extract and automatically classify features from the image. Using LWD image logs from eleven different wells and their respective quality annotation logs, provided by a group of professional image specialists, as well as the application of data augmentation techniques, a total of 14,000 dependent and independent variables were generated for the training and validation processes. Additionally, a blind test was conducted to confirm the robustness and reliability of the model.
The automation of image log quality assessment enables real-time decisions to be made. Consequently, unnecessary expenses on redundant acquisitions are avoided and critical resources, such as wireline image tools, are preserved. Additionally, the developed model can prevent the operator from leaving a location without having acquired proper image information, which could ultimately lead to flaws in the reservoir development and in the completion of the well. Finally, the assessment's reliance on a trained model ensures consistency in evaluations across different wells.
A plugin compatible with different petrophysical software has been developed and incorporated into operational routines. This has enabled real-time assessment to be performed in scenarios where it would not have been previously possible. This has consistently led to optimized wireline acquisition designs in terms of costs, resource allocation and well log quality.
Assessment of Geothermal and Hydrocarbon Wells Using Supercritical Fluids for Heat Production
Authors: Ratnakar R. R. , Gupta S. S. , Hackbarth J. , Livescu S. , Dindoruk B. 
Published: September 2024

Abstract
Subsurface geothermal resources are estimated to hold several orders of magnitude more energy than hydrocarbon resources. It is renewable, abundant, and has a small carbon footprint, however, its commercial-scale development faces several critical problems from reservoir to the final delivery point. To resolve these issues, it is essential to understand and analyze the transport and thermodynamics aspects in the geothermal well, which is the main objective of this work.
Here, we extend the previously proposed mathematical model for closed-loop wells in enhanced geothermal systems by including (i) time- and space-dependent fluid/flow properties and temperature, (ii) heat-inflow from formation undergoing transient cooling, and (iii) local-property dependent heat transfer coefficients for the two-phase flow. A detailed sensitivity studies were performed to show the effects of several transport and geometric parameters (such as flow rate, well length, inner tubing and annulus diameters, geothermal temperature, overall heat transfer coefficients) on the thermal output.
The result of this work contains two folds. In the first part, we show that the fluid properties and transfer coefficients are local-property dependent and must be included in the model. The second part was focused on sensitivity of thermal output of the well, where we observe significant difference in the power generation when local-property dependent fluid properties and transfer coefficients were included. Depending on the assumed boundary conditions and other simplifying assumptions, numerical simulations of coupled heat transport and fluid flow in well and reservoir may help predict more accurately the thermal output and the long-term economics, only when essential physics are included in the models.
The novelty of this work lies in non-isothermal fluid flow modeling of coupled well and reservoir with local property-dependent transfer coefficients and fluid (PVT) properties for enhanced geothermal systems. The work is crucial for accurately evaluating the thermal output and economics of geothermal or hydrocarbon wells for power generation, especially with supercritical fluids.
An Invention of Special Liner Completion Technology with Deeper Stimulation Capabilities for Tight Carbonate Reservoir Productivity and Injectivity Enhancement
Authors: R. V. Subba Ramarao , Alshehhi Shamma , Alhebsi Abdulaziz Ali , Mogensen Kristian , Chehabi Wissam , Punnapala Sameer , Al Jasmi Sami , Zubair Muhammad , Chang Ernesto Lenin Barragan , Reddy Ratnakar 
Published: September 2024

Abstract
Tight carbonate reservoir productivity and injectivity enhancement along with sustainability has been a challenge in the oil and gas industry for an extended period. Limited entry liner technology is one of the technologies that can stimulate the horizontal section up to 4 ft only. This technology is a onetime application only and allows near well bore penetration of acid to connect the sublayers and natural fractures within the target zone limiting the sustainability.
New liner completion technology, consisting of partly liner subs to introduce special needles that can penetrate deeper into the formation up to 40 ft, and remaining liner subs with special holes that can jet the acid up to 4 ft is under piloting. This innovative technology has the capability to connect sublayers and natural fractures that improves the poor vertical communication (low Kv/Kh) within the target zone, and simultaneously can stimulate the horizontal section to bypass the near well bore damage. In addition, this technology provides a flexible option for deep stimulation of the reservoir during installation, followed by near well bore stimulation using liner holes as a second step after a certain production duration. This flexibility provides a backup option for regaining sustainability after a production period.
This innovative technology pilot is under implementation at field A. The reservoir is a tight carbonate reservoir with a permeability range of 1-2 md. The present strategy is to install limited entry liner completions as part of the field development. However, the successful implementation of the new pilot with special liner completion expected to increase the production rate by three times, providing the opportunity to revise the field development plan with special liner technology in the upcoming new well drilling campaign. This paper will provide the details of the technology, well selection, completion design, implementation steps, pilot results along with lessons learned for inclusion as part of the next phase of implementations. It will further discuss the well monitoring plan, data collection and performance evaluation of the pilot. In summary, this technology will provide a backup option to conduct stimulation in case the sustainability of well productivity needs to be reestablished and overcome the current limitation of advanced completion stimulation techniques. This technology is an invention that combines deep acid stimulation technique and near well bore jetting with an option to execute them either together or separately.
Horizontal Open Hole Gravel Pack Challenges and Lessons Learned from More than 60 Offshore Wells Performed in Peregrino Field
Authors: Colbert F. C. , Pedrosa H. A. , Boldrini A. C. , Garcia F. M. , Rezende D. M. , Carrara M. , Gois S. C. , Steensland O. , Santos A. 
Published: September 2024

Abstract
The Campos basin is a sedimentary basin located in offshore Brazil, between the north coast of Rio de Janeiro State and the south coast of Espírito Santo State and is composed of several fields. Among, Peregrino is made mainly of a high-permeability sandstone reservoir in a water depth of 120 m, producing a 14° API grade oil at a moderate reservoir temperature (60° C). To maintain the reservoir pressure, the production drive mechanism is composed by reinjection of produced water and pressure support from the aquifer. Artificial lifting method is required to improve production. The well construction design used, to keep production economically feasible, was to drill and complete long horizontal wells passing through the reservoir.
In unconsolidated sandstone reservoirs, sand control methods are mandatory due to the risk of sand production. In the case of Peregrino field, oil producing wells required the use of screens and gravel pack while injector wells required stand-alone screens. Thorough engineering was required, and many lessons learned were acquired during the gravel packing of 66 wells since the first well was completed in 2011. Although the wells are part of the same field, different completion scenarios were observed in each well, requiring specific design in conjunction with the lessons learned and the best practices to overcome those challenges.
Considering that most of the wells have long open-hole lengths from 1,300m to almost 2,300m, a big challenge was the high friction during the alpha and beta wave propagation. The balance between increasing the pumping rate to prevent proppant settling and the increase of friction pressures without fracturing the formation was a key to the success. Beyond this challenge, many others were faced such as high losses prior to the pumping, shale areas in the interval, use of autonomous inflow control devices (AICD) screens, changes in proppant type and concentration strategy, vessel mobilization strategy change, introduction of a new rig in the field, etc. The success of this project is clearly based on gravel pack packing statistics as ∼95% of the completed wells were fully packed and on the improvement in the operational safety over the years of operations.
This paper outlines and describes the design, completion and lessons learned that were used in the past 13 years of gravel pack operations in the Peregrino field, which can be used as reference and benchmark for other offshore horizontal open hole gravel pack projects around the globe. To a large extent focus on simple and robust solutions and operations. Peregrino has contributed with many good learning points for gravel pack operations.
Fast History Matching with a Customized Physics-Based Data-Driven Flow Network Model GPSNet: Application to a Giant Deep-Water Gas Field
Authors: Guan X. , Wang Z. , Ren G. , Hui M. , Kazanov D. , Wen X. -H. 
Published: September 2024

Abstract
History matching with full-fidelity reservoir models could be a computationally expensive task. We have previously implemented a novel physics-based data-driven surrogate model (GPSNet) and used it for rapid history matching and optimization for waterflood and steamflood applications in mature fields. In this work, we construct a GPSNet model with customized network configurations to capture reservoir volume and connectivity distributions with associated uncertainties and demonstrate a successful application to a deep-water giant gas reservoir in Northern Carnarvon Basin offshore Australia.
This GPSNet model incorporates both current wells in-production and pseudo wells placed across the reservoir. A flow network is constructed using a network of one-dimensional (1-D) connections that link production-production wells, production-pseudo wells, and pseudo-pseudo wells. Both pseudo wells and connections in the network were designed following our best understanding of reservoir connectivity and uncertainty. Connections are grouped into different categories to account for regional variations at different scales. Reservoir properties are calibrated from historical data through a sequential history match process typical for GPSNet modeling: The average reservoir properties of each connection group were first inferred using Design of Experiment (DoE) and Generic Algorithm (GA); Then, excellent history matching results were achieved by adjusting pore volumes and transmissibilities to match build-up pressures and by adjusting completion-level producer well index and non-Darcy effects to match flowing bottom hole pressures using Ensemble Smoother with Multiple Data Assimilation (ESMDA).
With 200 connections among 39 producers and pseudo wells, the typical run time of this GPSNet model is several minutes which is almost two orders of magnitude faster than a full-physics 3-D model. The integration with a commercial simulator also makes it feasible to extend GPSNet to depict complex processes such as non-Darcy effects and well control logic. GPSNet is also flexible and can be customized to capture regional variations with different uncertainty levels which was a key factor in improving the history matching quality. The history-matched models can be used for fast and reliable decision-making as well as reservoir management.
First Worldwide Application of New Generation Single-Phase Retarded Acid in High-Temperature Carbonate, Mexico
Authors: Munoz Y. , Chavez M. Gonzalez , Cerron D. , Artola P. , Andrade E. , Macedo L. Vazquez , Valtierra J. Camarillo , Altuve A. Gomez 
Published: September 2024

Abstract
This work presents the first worldwide application of a new-generation, single-phase, low-viscosity retarded acid developed to effectively stimulate wells in High-Pressure, High-Temperature deep carbonate reservoirs. While single-phase retarded acid has shown success since implementation, the retardation performance is compromised at temperatures above 300°F, limiting acid concentrations to 20% HCl or lower depending on the bottomhole static temperature and well conditions. A new generation of single-phase retarded acid (Danican et al, 2024), capable of performing at temperatures up to 350°F was employed for the first time to stimulate deep and hot carbonate formations in Mexico.
The well selected for this first-time application is an openhole completed in a naturally fracture dolomitic reservoir with 905 meters openhole section and 367°F bottomhole static temperature. After dedicated laboratory test to qualify the fluid and complete placement computer modeling, we successfully executed a matrix stimulation treatment with the new single-phase retarded acid as main stimulation fluid. The initial production test, immediately after the stimulation, resulted in 3.6 production-fold increase; exceeding expectations and demonstrating that the acid system employed could create effective wormholes at this challenging conditions. This success has created opportunities to enhance stimulation outcomes in similar completions in this and other fields.
Natural Radionuclides Behavior of 238U, 232Th and 40K in the Prometeo-1 Well: A Case Study from the Middle Magdalena Valley Basin, Colombia
Authors: Echavez R. A. , Cardenas J. C. , Obando A. A. , Rueda J. P. , Perez E. R. 
Published: September 2024

Abstract
The development of research about preservation of the human health and environment have taken a relevant place in several industries around the world. In Colombia, recently, has been developing some studies respect to the behavior of natural radionuclides in the hydrocarbon areas in zones of interest at the Middle Magdalena Valley Basin (MMVB), these results have been considerably variables and dependents of the multi elemental composition, type of rocks, rock-fluid interactions, among others. The Prometeo-1 Well, has showed values of radioactivity concentrations from the chains 238U, 232Th, and 40K between 58 – 82, 14 – 67, and 185 – 287 Bq/kg, respectively, from formation rocks at depth between 8180 and 9260 ft; these results are related with concentrations of chemical elements such as calcium, silicium, and aluminum, and minerals such as calcite, quartz, and pyrite. The relationship between radioactivity concentrations and multi elemental composition is so important in the results for this well in the Absorbed Doses Rate (92 nGy/h) and the Annual Effective Dose Equivalent (112 µSv/y); these values are very close to the world average and suggest a specific review to know the true radiological risk. In this way, this work shows an evaluation of the natural radionuclide behavior in rocks from the Prometeo-1 Well through measurement of radioactivity concentration using gamma spectrometry in the formation members Lower Lidita and Salada, subsequently, with the results obtained using XRF and DRX a concentration relationship was made allowing to know the radioactive content, besides, the results in both formation members were compared, measuring the radiological risk using defined math equations. Finally, the results allowed to obtain a general view of point about natural radionuclides behavior in the well and create a technical criterion related to a possible radiological risk, waiting the definition of the radioactive baseline at the MMVB.
Optimizing Hydrogen Storage in the Subsurface Using a Reservoir-Simulation-Based and Deep-Learning-Accelerated Optimization Method
Authors: Eltahan Esmail , Albadan Deema J. , Delshad Mojdeh , Sepehrnoori Kamy , Alpak Faruk Omer 
Published: September 2024

Abstract
This paper addresses the challenge of optimizing subsurface hydrogen storage in porous media, a crucial component for advancing energy transition. The multifaceted nature of this challenge stems from the complex physics governing the process, coupled with operational limitations, and subsurface geological uncertainties. We apply a stochastic gradient-based optimization method with novel deep-learning acceleration components, tailored to maximize the efficiency of hydrogen storage by tuning well locations while honoring operational constraints.
The key objective of optimization is to maximize the amount of recoverable hydrogen while maintaining operational constraints. We adopt a robust optimization approach that maximizes the mean objective function over a set of realizations representing subsurface uncertainty. The objective function, defined as the hydrogen deliverability index, is calculated using a compositional reservoir simulator with high-resolution grids to minimize numerical dispersion. Our approach leverages a deep-learning-accelerated-gradient (DLAG) method alongside these simulations. This method is applied to the Brugge field case study, which is divided into two distinct optimization scenarios. In the first case, we evaluate the effectiveness of the optimization method with only one subsurface realization, optimizing the placement of eight storage wells and comparing outcomes with and without the application of DLAG. In the second case, we extend the analysis to include five different subsurface realizations and impose specific location constraints on each of the storage wells to optimize their placement. In the first case, the application of the DLAG method showed a clear advantage over the non-DLAG approach, resulting in faster convergence. The optimization of hydrogen storage well locations in the Brugge field model yielded notable improvements in storage efficiency, demonstrating the practicality and effectiveness of our approach.