bluuebunny/crossref_metadata_2025_split huggingface dataset:
Experimental Investigation of Degradable Solids Diverter Efficiency for Different Completions Used in Matrix Acid Stimulation
Authors: Zhao H , Tavarez F. A. , Gomez M. P. , Almubarak M. , Hill A. D. , Zhu D. 
Published: November 2024

Abstract
A known challenge in carbonate acid stimulation is achieving a uniform acid distribution in long wells along a heterogeneous reservoir. Completion strategies such as running a limited-entry-liner may not be sufficient to achieve the desired conformance. In recent years, degradable solids have become one of the most common diverters for acid stimulation. However, their behavior in uncemented completions is not well understood. In this study, parallel dual-core flow tests were conducted to evaluate the impact of completion type (cased hole vs. open hole) on diverter efficiency.
In the dual-core flow setup, two limestone cores with a permeability ratio of 30:1 were used, and experiments consisted of three main flow stages. First, 15wt% HCl was injected, which tended to create a wormhole in the high-perm core. A specified volume of a solids diverter slurry was then injected to improve the flow distribution. The final stage consisted of injecting 15wt% HCl until breakthrough. The flow rate of each core was recorded throughout the test to evaluate the diversion effect for both open hole and cased hole completion scenarios. CT scans of both cores were obtained to visualize the wormhole geometry after each acid stage.
Test results showed significantly different diverter performance for the cased hole vs. open hole scenarios, leading to insightful guidelines on diverter efficiency depending on completion type. After the first acid stage, a dominant wormhole was created in the high-perm core only, mainly due to the large permeability ratio. This behavior was very similar for both completion scenarios. In the open-hole test, the diverter formed an external filter cake on the injection face of the high-perm core. The second acid stage easily broke through this external filter cake and continued to propagate the original wormhole. As a result, acid diversion was limited, and a relatively short wormhole was created in the low-perm core. However, in the cased-hole test, a relatively higher volume of diverter entered the perforation / wormhole structure, leading to a more effective diversion and hence a more even stimulation of both cores after the second acid stage was injected.
The large-scale dual core flow setup in this study is the first of its kind and using it in conjunction with the implemented workflow has provided valuable insights into how various completion types will impact solids diverter efficiency during matrix acid stimulation. As completion lengths continue to get longer, a good understanding of solids diverter efficiency, especially for open hole completions currently being implemented in the Middle East, is necessary to develop reliable models that can be used for a more effective treatment design.
Retrieving Operation Insights with GenAI LLM: Comparative Analysis and Workflow Enhancement
Authors: Lee M. X. , Wang Z. 
Published: November 2024

Abstract
Identifying drilling risks for upcoming wells demands insights from offset wells. While some risks may lead to non-productive time (NPT), others remain latent within daily drilling reports (DDRs), which is crucial for comprehensive analysis. Manual extraction from DDRs is often time-consuming. In this paper, we systematically explore the methods to leverage the Large Language Models (LLMs) with enhanced workflows to automate insights extraction and improve risk identification accuracy. We compared LLMs customization approaches and state-of-the-art NLP models to validate LLMs efficiency in the drilling domain. We show that the adaptation of prompt optimization, Retrieval Augmented Generation (RAG) with the pre-trained LLMs before fine-tuning the model, already shows a significant improvement, achieving an accuracy ranging from 80-85%, with precision and recall surging from less than 50%, as observed from our benchmark oil and gas finetuned GPT-2 model (Marlot et al., 2023), to over 70% with our customized model. We addressed the challenge of imbalance training examples during the finetuning of the state-of-the-art GPT-2 model, which caused the underperformance of the benchmark model, particularly in addressing rare drilling event classes.
This paper demonstrates LLMs able to improve the generalization and reliability of the imbalance drilling risks, with proper workflow enhancements. Subsequently, we also finetuned both on-premises LLMs (Llama2, Llama3, Mistral), and cloud LLM GPT-3.5 Turbo, which exhibits promising outcomes, achieving better performance to over 90% accuracy, and over 80% precision and recall, compared to the earlier approach using prompt and RAG. This paper contributes to advancing the application of LLMs in the oil and gas industry, for domain-specific tasks, addressing the common challenge of imbalance operation training data, and demonstrating our enhanced workflow outperforms both the benchmark finetuned GPT-2 model, pretrained GPT-4 model, and the finetuned GPT-3.5 Turbo.
Succesful Pilot Water Conformance Control Project at The X Field of The North Caspian Depression in Kazakhstan
Authors: Aliyev A. , Koszhanov A. , Samenov E. 
Published: November 2024

Abstract
Polymer used for water shut off (WSO) and conformance control (CC) is required to withstand both the temperature and salinity of the reservoir over prolonged timescales of months. For these reasons it is typical to find them as copolymers of acrylamide and acrylic acid, otherwise known as partially hydrolyzed polyacrylamides (HPAM).Selection of a polymer depends on more than the stability; it also depends on the application and desired effect. All lead to the final CC or WSO design.Type of application CC vs WSOLow MW vs high MW polymerChargeconcentrationPermeability and Homogeneity of the reservoirconcentration, topside viscosityDistance to the targeted treatment zoneconcentration of polymer, crosslinker, retarderHistory of the reservoirWell logs for streaks etc.production issuesconing or viscous fingeringWSO polymers tend to be lower molecular weight to be able to penetrate deeply into smaller pores. lower viscosity on topside facilities also allows higher concentrations. Down hole, these are able to then set up strong well held rigid gels that prevent water flow
One of the best tools to identify the zones were WCC can give a better result is tracer test. Tracer applications in the oil industry began in the mid-20th century (Hutchins et al. 1991; Serres-Piole et al. 2011, 2012). Today, tracers and nanoparticles are integral to various oil and gas drilling and testing operations. These substances are introduced into borehole fluids to trace fluid pathways, migration mechanisms, and well connectivity. Tracers are extensively used in drilling, water flooding, and E&P stages to assess hydrocarbon migration, optimize flooding strategies, and estimate residual oil saturation (Sor) and enhanced oil recovery (EOR) (Bjornstad et al. 1990; Clayton 1967; Michael Shook et al. 2004; Michael Shook et al. 2017; Sun et al. 2021; Sun and Ertekin 2020).
The X field is at a late stage of development of its only production target - the Valanginian horizon. The development of this horizon is envisaged with waterflooding. The reservoirs of the Valanginian horizon are mainly composed of 3 layers. Despite some differences in capacitive parameters, they are all hydrodynamically connected.
Drone Measurements Ensure Global OGMP 2.0 Level 5 Compliance
Authors: Smith B. J. , Cooper I. M. 
Published: November 2024

Abstract
The need for globally deployable, consistent emissions measurement programs is increasing as clean energy feedstock demands and regulatory pressures continue to rise. Many programs, such as Oil & Gas Methane Partnership (OGMP) 2.0, require high-accuracy and precision ‘bottom-up’ emissions calculations and facility-scale ‘top-down’ measurements. The challenge the industry faces is deciding where to allocate resources between abatement and measurement/reconciliation of GHG emissions. This work shares the deployment of a fit-for-purpose global emissions measurement program that is critical for measurement and reporting frameworks.
All surveys were conducted with an independently validated, field-proven, drone-mounted miniature tunable diode laser absorption spectrometer (TDLAS) designed to measure methane emissions in the atmosphere from energy-industry sources emission sources. High-fidelity anemometer data and high-accuracy and -precision methane measurements are necessary to ensure low uncertainty methane fluxes calculated using mass-balance algorithms. Facility-level emission rates are reported through a single measurement or the aggregation of several equipment group-level measurements. Standard operating procedures relevant to the application of any new "advanced" methane technology were developed to scale for global demand of methane measurement and reduction, ensuring consistency of solution across the globe and across operators.
Surveys were scaled for numerous operators across 6 continents and growing from 0 to 24 global drone service providers (DSPs) over 7 years. Logistical challenges were overcome to ensure technology was transported into the regions of interest and quality of service maintained. Regional DSPs were trained and certified to ensure consistency of service operation and excellence in execution. The measurements from these campaigns show a dramatic increase in impact over time, and begin to highlight, in resolution, the differences and similarities between regional emission footprints, both onshore and offshore. Many of the emissions measurements shared with operators have resulted in change of behavior, accelerated repairs or the inclusion of validated data to internal/external methane measurement initiatives. Reconciliation between top-down and bottom-up emissions can be challenging, showing the importance of "continuous improvement" initiatives such as OGMP 2.0.
This work shares the challenges of deploying novel technologies for the novel emissions measurement globally, highlighting operator-accepted cost-effective solutions that ensure global consistency across multiple assets in multiple countries, onshore and offshore, facilitating swift and transparent reconciliation.
Mechanistic Model for In-Situ Diverting Acids Flow in Carbonate Formations: From Lab to Field Treatment Optimization
Authors: Ali Mahmoud T. 
Published: November 2024

Abstract
In-situ diverting acids are commonly used in carbonate acidizing treatments to improve acid coverage by redirecting acid to lower injectivity formation. Most of the models to represent the flow of these in-situ diverting acids, in literature are semi-empirical with very limited predictive capability. Some studies use numerical simulations to accurately capture the physics and dynamics of the process, however, they are computationally intensive, even on lab scale.
This study presents a computationally fast physics-based model to simulate and predict the pressure response of in-situ diverting acids. The model divides the formation into three zones, namely, wormhole, disturbance, and virgin zones. The change in acid concentration is tracked in the wormhole and porous media by calculating the overall reaction rate and acid velocities. An abrupt increase in viscosity occurs as pH rises above 1 or 2, which generates disturbance/turbulence in the flow. A Forchheimer-like equation is used to account for the increase in pressure due to flow turbulence.
Core flow experiments were conducted at 175°F and 250°F using limestone and dolomite cores treated with in-situ gelled acid (ISGA) and VES-based acid to validate the model. The comparison with linear experiments revealed that, unlike semi-empirical models, the current model captures the complete pressure response observed experimentally. Additionally, it scales very well with the injection rate. Radial experiments from the literature were used to validate the radial version of the model. Remarkably, the model is not only in agreement with radial experiments but also scales well with radial core dimensions. Synthetic one-layer and two-layer radial models were built, and both models revealed that the initial permeability and permeability range have a strong effect on divergence. A multi-layer example is presented to shed light on the model's ability to capture the impact of diversion on fluid distribution.
This study describes the development of a mechanistic model for in-situ diverting acid flow under linear and radial geometries. The model specifies the effect of formation permeability contrast, temperature, mineralogy, and wellbore diameter on chemical diverter selection and on acid strength, volume, and injection rate optimization. This study provides useful recommendations on how to design the placement of in-situ diverting acids and the critical factors that need to be considered.
Advancing Geoscience with Multi-Modal AI: A Comprehensive Copilot
Authors: Altynova A. Y. , Kozhevin A. A. , Dubovik A. S. , Khudorozhkov R. L. , Suurmeyer N. , Martin T. Jones 
Published: November 2024

In geology, LLMs (Large Language Models) have been applied to various tasks, including document analysis, geological consultation, workflow planning and reservoir simulation [1, 2, 24]. However, existing applications often focus on single-modality data or specific subdomains within geoscience, limiting their overall effectiveness in addressing the full spectrum of industry needs. As a result, LLM-based agents are not yet fully present in all the stages of the seismic exploration pipeline.
The objective of the multi-modal AI copilot extension presented in this paper is to address these limitations by creating a comprehensive, integrated system capable of processing and analyzing diverse geoscience data types. This extension aims to enable seamless interaction between users and their geoscience projects (including but not limited to seismic processing, interpretation and well data analysis) through natural language interfaces, while executing complex commands and analyses on multi-modal data, including seismic volumes in SEG-Y format, through integration with 3D visualization software APIs.
Electrifying Offshore Oil and Gas Assets with Floating Offshore Wind: A Demonstrator for Future Energy Solutions
Authors: Benhamou A. , Forby B. , Barrier P. , Ester F. , Crews C. , Barral S. , Marmier R. , Villette S. , Mazevet N. 
Published: November 2024

Abstract
TotalEnergies is a global integrated energy company that produces and markets energies: oil and biofuels, natural gas and green gases, renewables, and electricity. The company affirms its ambition to be a major player in the energy transition and shares a vision of what its activities could be to achieve carbon neutrality by 2050, together with society. In order to achieve its ambition, the company undertakes numerous actions to reduce its carbon emissions and produce more low-carbon electricity.
TotalEnergies has launched a project to demonstrate the possibility of electrifying existing oil and gas assets in the North Sea via the installation of a Floating Offshore Wind Turbine (FOWT), which will be connected to the existing Culzean Field gas platform located in the Central North Sea area approximately 230 kilometres east of Aberdeen. The involved TotalEnergies affiliate is leading the design, construction and installation project, strongly supported by the OneTech branch which is supporting the follow-up of the engineering and takes the benefit of the electrification project to develop a R&D program.
This project involves conducting a hybridization showcase on an offshore oil and gas asset and qualifying a novel semi-submersible floating substructure WTG concept.
This paper presents the specifics of the project, the challenges, and the benefits brought by the electrification of an offshore oil and gas platform with a Floating Wind Turbine. In particular, the following topics are addressed:
A comprehensive project overview, Design, fabrication, and installation challenges of a floating wind turbine adjacent to an oil and gas asset, Modifications of the existing oil and gas platform to accommodate the connection of the floating wind turbine, Quantified greenhouse gas (GHG) savings and implications for future energy projects.
This publication presents a novel approach to reducing carbon emissions from offshore oil and gas assets by demonstrating the possibility of electrifying them with Floating Offshore Wind Turbines. The project showcases the viability of such hybrid systems for future projects everywhere else in the world, and provides valuable insights into the design, construction, and installation challenges of floating wind turbines adjacent to oil and gas assets.
Assessment of Static Models by Performing Fast-Track History Matching
Authors: Parra H. , Singh M. , Jeddou H. Ben , Su S. , Kloucha C. Kada , Al-Kindi Z. S. , Mustapha H. 
Published: November 2024

Abstract
Implementation of robust geological analysis for static reservoir characterization does not provide a guarantee about the prediction of dynamic behavior in the reservoirs. The reservoir properties are often populated using geostatistical methods in a static model. These methods are dependent on input data density and quality. The fluid movement inside this reservoir is simulated during dynamic modeling. In the case of many complex reservoirs, it is observed that the dynamic model response doesn't match with observed historical well data and modification in a static model is needed. This loop of dynamic-static modeling impacts the project timeline significantly. The objective of this work is to provide a dynamic validation tool used in a very early stage to perform quick assessments of the static model by doing a fast-track history matching.
In this paper we have developed a solution that provides fast-track assessment of static models to check the response of dynamic properties before starting the full history-matching exercise. The solution uses the static reservoir properties and observed data as its main input. It provides a list of fluid properties based on some basic fluid types. It enables use of core constants to define special core analysis (SCAL) function and rock compressibility to define rock compaction.
After running the fast-track simulation, the key performance indicators (KPIs) are checked to assess the quality of the model. The liquid production rate and pressure in all the wells have been chosen as KPIs to demonstrate the impact of static model properties on the dynamic model. A mismatch analysis is performed on these KPIs in all the wells. The wells are classified as good, acceptable and needs improvement, categories based on different thresholds in mismatch analysis. This mismatch is analyzed in map view, which is used to identify key challenges in static model for improvement.
This approach provides a quick assessment of static models and their modifications before starting the detailed dynamic modeling exercise. It saves significant time in the projects, enabling geoscientists to have an early evaluation of the behavior of the models from the dynamic point of view, reducing the number of future iterations in the history-match phase.
The Future is Here. Utilizing Physics-Informed AI Models to Optimize Highly Intricate Water Injection Systems Within a Mature Oil Field
Authors: Franco F. , Sepulveda W. , Marchán L. , Manzo B. , Cattani M. , Medina J. , Aguilar Ledwin , Somale J. , Guamán J. C. , Florez F. , Kartoatmodjo G. , Correa R. , Vallejo R. , Pinto T. , Ontaneda N. , Gimenez E. , Johnson L. , Jacobsohn G. 
Published: November 2024

Abstract
This paper discusses the development of an integrated model-based solution for a mature oilfield. The solution integrates state-of-the-art hydraulic models with cutting-edge Physics-Informed AI, to create a solution for the optimization of the water injection and disposal Network, which is impacted daily by the dynamics of fluid composition and the changing operational parameters in wells and surface flowlines across the field. The solution is capable of thoroughly assessing production and injection systems for improved forecasting and operational excellence
The primary goal in developing this solution is to enhance asset management and improve decision-making by considering the interactions and synergies between injection wells, surface flowlines, and facilities constraints. The solution was developed using calibrated well and flowline models built within the automatic Flow Assurance Smart System (FASS), a digital solution that utilizes high-frequency data, machine learning insights and automatic updates of the hydraulic predictive models. This digital solution allows for the continuous evaluation of injection/disposal components including injection wells and hundreds of kilometers of surface pipelines.
The implementation of this solution led to notable enhancements in the overall field injection and disposal systems, driving significant improvements in operational efficiency. The solution enables a comprehensive evaluation of various operational scenarios by integrating the number of injector wells with network model and aligning these with the broader field strategy. This integrated approach supports the identification of immediate optimization opportunities and provides insights into the long-term dynamic impact on field development.
The system's advanced capabilities play a crucial role in identifying bottlenecks within the field's injection and disposal processes. By optimizing water injection rates and ensuring the efficient disposal of produced water, the system enhances the reservoir's pressure maintenance and overall productivity. This level of optimization is essential for achieving the aggressive oil production targets set for the asset. Moreover, the ability to evaluate and adjust to multiple scenarios allows for a more resilient and adaptive field management strategy, ensuring sustained performance and reliability in meeting the asset's goals
An Award-Winning Automation Strategy Taking PDO To Top Quartile Performance
Authors: Al Mawali S. , Al Alawi A. 
Published: November 2024

Abstract
The global demand for energy continues to rise, with hydrocarbons remaining the primary source until renewable technologies mature. The upstream oil and gas industry faces increasing complexity due to depleting traditional fields and stricter regulations, necessitating sophisticated operations. Despite significant investments in automation, many plants underperform due to deficiencies in process control performance.
In 2020, Petroleum Development Oman (PDO) launched a transformative automation strategy to enhance control performance, reduce process upsets, alarms, and interventions, and ensure sustainable remote operations. This manuscript provides an overview of PDO's strategy, detailing the methodology, implementation phases, and the significant value realized.
Key steps included establishing KPIs for controller uptime and compliance, setting performance targets, and deploying digital tools for real-time monitoring. By 2021, 100% of PDO facilities were covered, with controller uptime improving from 78% in 2019 to 95% in 2024, and compliance from 73% to 91%. These improvements led to a 30% reduction in alarm rates and a 90% reduction in operator interventions.
PDO's strategy demonstrates how a structured, data-driven approach can optimize automation investments, enhance operational efficiency, and achieve significant financial benefits. The insights gained provide a benchmark for other organizations seeking to improve their automation performance.