bluuebunny/crossref_metadata_2025_split huggingface dataset:
Drag Reducing Agent Application in Produced Water Reuse: Improved Movement Efficiency with ESG Enhancement
Authors: Price B. , Li Y. 
Published: November 2024

Abstract
In response to environmental, social, and governance (ESG) initiatives, the oil and gas industry has broadly transitioned its efforts from using freshwater for hydraulic fracturing (frac) to using produced water for hydraulic fracturing whilst minimizing freshwater usage. However, the present infrastructure in place for movement of water to frac sites limits the volume of produced water that can be transferred in many situations. As infrastructure is not easily replaced, a polymer-based drag reducing agent (DRA) can chemically alter pipe friction to allow for use of existing infrastructure with minimal supplementation. Formulating a DRA with shear stability and chemical compatibility in produced water remains a significant obstacle and requires thoughtful consideration with use of specific chemistry.
Our approach to addressing this challenge involves use of a widely known and accepted chemistry that is currently in broad use globally. Polyacrylamide polymers are used in many applications in municipal water treatment, industrial water treatment, in mining applications, in dust suppression applications, as hydration aids, as friction reducers, and as drag reducers. The broad use and availability of this chemistry makes it a great fit for application as a DRA.
The paper will discuss our methodology and several case histories that demonstrate the feasibility of this application and the inherent benefits with regard to efficiency gains and ESG enhancement.
Data-Driven Analytics for Integrated Fracture Characterization in a Lower Cretaceous Reservoir, Offshore Abu Dhabi
Authors: Hong Y. , Kirstetter O. H. , Al Neyadi A. A. , Aljaberi F. , Al Kaabi A. S. , De Ribet B. 
Published: November 2024

Abstract
Fractures are crucial factors that affect the productivity and safety of hydrocarbon reservoirs. However, conventional methods of fracture detection and characterization are often limited by data availability and quality. In this study, we present a data-driven analytics approach that integrates four different seismic methods to predict and characterize fractures in a Lower Cretaceous Reservoir in an offshore oilfield in Abu Dhabi. The four methods are: Velocity Variation with Azimuth (VVAZ), Amplitude Variation with Azimuth (AVAZ), Amplitude Fracture Estimate (AFE), and Fault Likelihood. We apply these methods to 3D seismic volumes and verify their results with well data, drilling records, and well test data.
We integrate seismic data, drilling records, and well test data to evaluate the results in characterizing the fracture zone within the Lower Cretaceous Reservoir, which exhibits strong anisotropy, significant time shifts on seismic data, and notable mud loss during drilling. The VVAZ and AVAZ methods estimate fracture orientation and density from azimuthal variations of velocity and amplitude, respectively. The AFE method measures fracture intensity from azimuthal fractional energy. The Fault Likelihood method detects fracture zones from seismic discontinuities related to faults.
The results show that each method provides different aspects of fracture information that are complementary to each other. The VVAZ, AVAZ, VVAZ & AVAZ joint inversion methods reveal a dominant fracture orientation of NE-SW, consistent with regional stress directions, and a high fracture density in the central crest part of the field where mud loss was observed during drilling and high permeability zone was detected by well pressure test. The AFE method and the Fault Likelihood method identify several fault-related fracture zones that crosscut the field, some of which coincide with mud loss and pressure anomalies.
This study demonstrates an integrated approach that combines the four fracture prediction methods and can provide a comprehensive and reliable fracture characterization in the Lower Cretaceous Reservoir. This is essential for optimizing drilling and production strategies, ensuring safe operations, and maximizing hydrocarbon recovery in line with ADNOC's strategic objectives for its offshore assets.
Saturation Distribution of Carbonate Reservoirs with FWL Tilt in Lower Shuaiba and Kharaib Formations
Authors: Ali M. Y. Al , Kobenko I. , Ebeid M. , Alkhatib M. , Khattak I. 
Published: November 2024

The Lower Shuaiba and Kharaib formations in Abu Dhabi's onshore fields present unique challenges for reservoir characterization due to their complex carbonate structures and post-depositional tilting. Understanding fluid distribution within these tilted reservoirs is crucial for accurate saturation modeling, which directly impacts reservoir management and hydrocarbon recovery.
Traditional saturation models often fall short in accounting for the effects of tilting and subsequent fluid redistribution. This paper introduces a novel approach to saturation modeling that incorporates the imbibition effects resulting from these geological processes. By leveraging the Brooks-Corey SHF model and integrating multi-domain data, we propose a simplified, yet robust saturation height model tailored for these specific formations.
Our methodology addresses the discrepancies observed in saturation levels across different reservoir zones, particularly in the flanks affected by strong imbibition. Through a comprehensive workflow that includes static rock typing, uncertainty analysis, and validation in both 2D and 3D domains, we aim to provide a practical solution that enhances the accuracy and applicability of saturation models in complex carbonate reservoirs.
Artificial Intelligence Associated Drones Solutions for Waste Disposal Management in the Process Industries
Authors: Reddy Burri Harsha Vardhan , Ramakrishnan Vijayaraghavan , Almuaikel Maha , Alanazi Khalid , Alshaikh Ahmed Ali 
Published: November 2024

Abstract
The paper aims to provide an overview of "Waste Management Solution," an Artificial Intelligence computer vision solution that can detect the location, classify, and quantify waste on a geospatial map constructed by aerial images collected with drones. The objective is to demonstrate how drones with integrated AI solutions can drive efficiency, productivity, and innovation in industrial operations. The solution comprises drones collecting aerial image data and implementing cloud-based AI/Machine learning (ML) models to detect waste materials. By integrating drone technology, AI, and mapping techniques, the solution supports industrial organizations, government authorities, and environmental agencies in achieving their net-zero goals, aligned with the Saudi Green Initiative 2030, and aiming to create a cleaner environment. The solution offers a cost-effective method for processing industries facilities and the environmental and urban planning of smart cities by digitizing waste management practices, replacing time-consuming manual industrial waste inspections. The benefits of the solution are demonstrated through our experience of an actual project conducted with the project management office of a large oil and gas operating company.
Dilution for Efficiency: Cost Reduction Strategies in Oil Production by Removing Desalter and H2S Stripper Units
Authors: Al Alawi Kathiya , Yarubi Nasser , Joshi Amol 
Published: November 2024

Abstract
This paper presents a strategic approach to optimize the ALG Project by eliminating the desalter and H2S stripper units, aiming to reduce project costs, Unit Technical |Cost UTC (CAPEX and OPEX) while maintaining operational efficiency. Alternatively, the study explores the feasibility of diluting ALG fluids with total fluids in the Main Oil Line (MOL), emphasizing cost-effectiveness, risk reduction, and simplification of both capital expenditure and operational complexity.
Desalter and H2S stripper units are essential for treating produced fluids to meet quality and safety standards. However, these units incur significant capital and operational costs.
Furthermore, the feasibility study associated with diluting ALG fluids in the MOL underscores the importance of evaluating technical, economic, and environmental considerations. It involves assessing compatibility between different types of fluids, evaluating the impact on pipeline integrity and performance, and ensuring compliance with regulatory requirements and industry standards.
The study has considered a multiple of scenarios of other stations upset since final technical proposal is based on dilution of oil with other stations fluids at the main oil line MOL.
Safe Welding Initiatives
Authors: Alsultan Abdullah 
Published: November 2024

Abstract
Maintaining safety while operation and maintenance process is a challenge. Nowadays, challenges can be tackled by new approach such as adopting 4IR technologies. One of the maintenance activities is welding process which associated with safety and risk concern such as fumes inhalation, physical hazards, heat zone radiation, and welding Hazards. To mitigate these concerns, new technology can be adapted such as "Automated Pipe welding Technology" and "In Line Weld Test Tool (IWTT)".

Automated Pipe welding Technology
Automated robotic pipe welding technology is a new generation with advance features to support welding activities. Deployment of this technology has been successfully completed, and it is a part of initiatives towards solving challenges using 4IR technologies. The Automated robotic pipe welding machine will be very useful to handle high capacity fabrication works including, but not limited to, Preventative Maintenance Shutdown, T&I workloads, emergency jobs and corroded piping replacements along with routine welding jobs. It can for example, weld 8″ OD pipe just in eight minutes with the highest accuracy and weld quality instead of four hours regular welding period using manual welding processes. The core of the Automated welding technology is its unique vision-based penetration control system. It is equipped with a video camera integrated into the welding arm that can continuously analyze the image of the root-pass welding.

In Line Weld Test Tool (IWTT)
In Line Weld Test Tool (IWTT) is a new technology supporting pipe replacement activities during the welding stage. Deployment of this technology has been successfully completed with proven the effectiveness of the technology. IWTT will be very useful to reduce maintenance turnaround time and total cost as well as maintaining oil production sustainability. IWTT provides a verified and monitored atmospheric barrier (ambient isolation) to perform welding process safely and efficiently. Also, it provides a fast and efficient method of verifying the integrity of new weld joints and to perform the hydrostatic weld test for the new weld joint. This will reduce the system downtime, minimize environmental impact, and increase the safety in the field activities. In addition, it will minimize the overall consumption of the treated water to perform hydrotest.
Offline Tubulars Bucking Service with Triplex Catwalks
Authors: Hassan T. F. , Hussain M. , Al Rumaithi Khaled , Alzaidi Saif , Al Reyami M. , Al Suwaidi M. , AlSaadi H. , Al Shamisi E. , Sumaida A. , Al Mazrooei A. Saeed , Aljabery R. K. , John J. , Al Nuaimi M. , Hamdy I. , Suleiman A. Y. , Arbadov G. , Anwar M. , Alhammadi A. , Elzeky M. , Mohammad M. A. , Hashmi M. Asef , Al Karbi H. , Alnuaimi M. 
Published: November 2024

Abstract
Significant operational drilling hazards and incidents while handling and running the drilling tubulars such as drops, hand injuries, line of fire and working at height, moreover the drilling tripping speed repeatedly started to comprise the drilling efficiency and the planned well delivery. The newly introduced offline bucking service and triplex catwalks technology is a progressive shift towards an efficient well construction operation by revolutionizing tubular handling as a significant improvement to maintain safe work environment, reduce the CO2 emission, well delivery optimization, rig automation, and mechanization.
The main objectives of Offline tubular management system a fit for purpose solutions were: Eliminate the operational HSE risks: Less personnel on the drill floor.Remote operationsDrill floor incident.Line of fire.Pinch points.Drops.Working at height.Support the environment and vision to the sustainability by: Reduce fuel consumption. Consequently, the emitted CO2.Consequently, emitted CO2.Improve the operations efficiency and well delivery by: Maximizing the tripping speed (running & pulling) for all tubulars (Drill string, Casing, and Tubing).Reducing the planned trip time.minimizing the flat time.Cost optimization by: Saving the cost of rig days.Saving the cost of other services at rig site.Simple to implement in all the operator's drilling rigs fleet.It doesn't affect the rig operation as the full operations are offline remotely at the ground ahead and simultaneously of the drilling activities
Unlocking the Potential of AI in Revolutionizing the Seismic-Driven Subsurface Workflows
Authors: Abubakar A. , Zhao T. , Hu W. , Lau L. 
Published: November 2024

Abstract
Artificial intelligence (AI), especially deep learning (DL) methods, is revolutionizing the seismic-driven subsurface workflows, enabling more accurate understanding of subsurface structures and properties. Due to the sheer size of seismic data, applying DL methods to seismic data at scale has proven to be nontrivial. In this work, with DL applications ranging from preprocessing, imaging, to enrichment, we demonstrate how AI running on high performance computation devices adds value throughout the life cycle of seismic-driven workflows. The first solution is first arrival picking that substantially reduces the total turnaround time, by minimizing the QC process that usually requires significant user interaction. The second solution is automatic salt body interpretation for seismic imaging, which uses innovative DL models and latest computation resources to eliminate the costly horizon-based manual salt interpretation. Finally, is a 2D-to-3D seismic image conversion solution, crucial for initial reconnaissance of new frontier and CCUS site screening and monitoring. Such conversion is only feasible with advanced DL workflow and GPU devices with large on-device memory. We present all three solutions with field data examples, while also discussing the importance of high-performance cloud computing infrastructure as the key enabler of such AI solutions in the subsurface domain.
Predicting Scale Depositions of Barium and Strontium Sulfates Using Novel Artificial Neural Network Algorithms
Authors: Askar Mohamed Mostafa , El Ela Mahmoud Abu , El-Banbi Ahmed H. , Sayyouh Mohamed H. M. 
Published: November 2024

Abstract
Numerous scale types normally deposit inside oil production wells; however, sulfate scales are probably the most alarming types due to their high strength and insolubility. Several company cases of slickline scratching and coiled tubing milling fail to clean and remove heavy depositions of barium and strontium sulfates. Observations of the current study show that these sulfate scales deposit due to cooling of super-saline formation waters inside offshore producers and pipelines, besides the mixing of incompatible waters.
Prediction of sulfate scale deposition is challenging. Many of the currently-available prediction software products have drawbacks in sulfate prediction due to the limited experimental data, the uncertainty of ion pair interactions, and the extremely-low solubility of these minerals. Therefore, more experimental work is still needed to investigate extreme field conditions and complex water chemistries.
Different Machine Learning (ML) algorithms are being used in the oil industry with successful applications that are adding and/or replacing the traditional methods. Therefore, the scope of the current study is to utilize ML algorithms in scale prediction. The study investigated actual field scale depositions that were collected from multiple offshore fields from 1998 through 2020 with more than 1400 data records. The available database contains 14 input features including water chemistry, water production rate, oil production rate, gas production rate, pressure, and temperature. Feature engineering was adopted to define the most important features to build the ANN models. The available data was split into training and testing datasets. Several Artificial Neural Network (ANN) models were developed to predict barium and strontium sulfate scales downhole in production wells. A comparative analysis was performed between the developed ANN models against a commercial scale prediction software and empirical correlations. The ANN models outperformed the other traditional methods concerning deposition probability or classification of scale type. Moreover, the ANN models could also predict the amount of scale with accuracy of 93% and 75% for strontium sulfate and barium sulfate, respectively. The Mean Absolute Error (MAE) of scale percentage was 3.6% and 8.2% for strontium sulfate and barium sulfate, respectively.
The paper novelty is the inclusion of actual scale deposits from different fields to build ANN algorithms capable of predicting the real composition of sulfate scales not detecting their super-saturation level. Predicting the type of scale leads to optimize company resources and dedicate personnel efforts to severe cases of hard scale depositions without the need of well intervention.
Nanotechnology Integration Excellence in Improved Oil Recovery: A Bumpy Road to Sustainable Future
Authors: Sergeev V. 
Published: November 2024

Abstract
This is a knowledge-sharing manuscript, which focuses on the methods for planning and execution of new technology integration projects in petroleum and geosystems industry. Described in the manuscript proper method of arrangement of the new technology integration projects is based on years of practicing a multidisciplinary approach to the new technology integration for improved oil recovery (IOR). This approach involves the use of knowledge of fundamentals in economics, management, marketing, law and engineering. The new technology integration in such a globally immense and inflexible industry as oil and gas exploration and production (E&P) is a challenging task. The E&P industry segment distinguishes itself from the others by its large share in global trade, capital-intensive nature, and extra-slow pace of projects for the new IOR technology integration. Obstacles to successful integration of new technology in the E&P industry are present at all scales. In the context, it may be regulatory complexities, infrastructural challenges and commodity market volatility. At the business and corporate level, it may be risk aversion, information asymmetry, corporate culture and sometimes stakeholders’ or management's skepticism. Any of these obstacles can hinder or prevent the adoption of the innovation. Therefore, even though the benefits and competitive advantages are clear, being a conceptually new technology conveys many hidden obstacles and entry barriers for integration, making this road a bumpy one. This manuscript delves into practice of the new technology integration on an example of the nanotechnology called Emulsion System with Nanoparticles (ESN) applied for subsurface flows control in petroleum and geosystems industry. Recommendations given in this paper may help both technology-developers and E&P companies overcome some of those above-mentioned obstacles and barriers. The knowledge shared in this manuscript is based on a multi-year experience of the ESN technology integration in several gigantic national oil companies and international joint ventures for oil and gas E&P in Europe, the Middle East and South-East Asia. Analysis of the accumulated international experience in the ESN technology integration helped to find common flaws in the process of pilot projects arrangements, as well as helped to develop a proper methodology for the new technology integration process. This study aims to shed light on the hidden obstacles existing nowadays and to equip decision-makers with actionable strategies to facilitate new technology integration in the global petroleum and geosystems industry.
Lessons Learnt on the Feasibility of HC and CO2 WAG Application in a Giant Offshore Carbonate Reservoir
Authors: Davidescu B. , Male-Pirker B. , Perovic I. , Valente A. , Hincapie R. E. , Masalmeh S. , Ghedan S. , Aikman M. , Porter S. J. , Chirkov M. , Hendizadeh H. 
Published: November 2024

Abstract
We explore challenges in modeling complex processes involved in hydrocarbon (HC) and CO2 water-alternating-gas (WAG) injection. It includes the topics of sector model utilization, the effect of well spacing on ultimate recovery, and the impact of utilizing a tuned Equation of State (EoS). Furthermore, reservoir simulation topics such as grid size and run times are highlighted. Understanding these challenges is crucial for effectively assessing the optimum EOR method. We offer a detailed analysis of the intricate relationship between field-scale effects, well-spacing, SCAL, and EoS in EOR modeling, addressing recoveries and economic scenarios, providing valuable insights for petroleum engineers and researchers to improve EOR strategy accuracy and efficiency. A high-level economic analysis is presented together with a discussion of some of the technical challenges that may need to be addressed in implementation of a WAG scheme.
Results showed the importance of addressing field-scale effects and optimizing well-spacing for efficient EOR methods. Highlighting the role of Special Core Analysis (SCAL) data and EoS parameters in improving EOR predictions. It has been demonstrated that SCAL plays the highest role in the ultimate recovery, even beyond small scale EOR effects. Higher recoveries have been seen up to 3-7% by using an EoS fully tuned to the appropriate lab data. Fluid modelling through EoS is crucial for determining recovery volumes in gas WAG configurations. An uncertainty workflow has been applied that showed that different sectors in the field are sensitive to different parameters. EOR economics were compared to the base case water injection economics and different scenarios have been studied in order to maximize value out of the EOR. The economic viability of the project utilizing HC/CO2 WAG is inferior when compared to the base case of water injection. This is primarily attributed to the favorable mobility ratio to water injection in the base case, compared with the substantial costs associated with the HC/CO2 WAG approach. Additionally, imposing plateau restrictions impairs the economic inefficiency of HC/CO2 WAG, as any additional production it offers occurs at a later stage.