bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Untold Success Stories of NEB Females Leading the Way
Authors: Qaydi Sabha Al , Meraikhi Amna Ali Al , Harthi Reem Al 
Published: October 2023

Abstract
There are countless remarkable success stories of females in the oil and gas industry who have shattered glass ceilings and made significant contributions. These females have excelled in various roles, including engineering, geology, management, and leadership positions. Their achievements showcase their expertise, dedication, and resilience in a traditionally male-dominated industry.
This paper aims to develop and enroll female youth to become an essential and integral part of the Oil & Gas operations. To achieve this, a mechanism has been established to enhance the Female leadership and decision-making skills. This involves creating a positive environment, developing a specialized tailored training program, ensuring active participation, and engagement. In addition, conducting competency assessments to identified gaps, while following up closely with their progress which will allow them to enhance their technical skills, performance and paving a robust career path for their progression.
The expected results of this implementation will allow females to successfully handle responsibilities, manage and operate field activities without any limitation. Furthermore, implementing these programs and strategies will add value to the company, such as improved implementation of safety procedures, therefore increasing our productivity. Also, this will encourage all youth to lead the responsibility of a role in the sustainability and decarbonization, energy transition & future proving.
NEB Females have been provided the required support from Dr. sultan al Jaber in year 2017, woman empowerment is very important, therefore it must be reflected in the field no matter the position. In addition, nationalization of the critical position as panel operators to be fully led by UAE national (male and female) by year 2030.
Coiled Tubing Autonomous Conveyance Improves Consistency of Offshore Intervention Operations
Authors: Segura J. , Tran V. V. , Fossati L. , Ramondenc P. , Gabdullin A. 
Published: October 2023

Abstract
The implementation of coiled tubing (CT) conveyance automation in the North Sea represents the first step toward autonomous CT operations. It is also the first documented case of autonomous conveyance in CT interventions in the world. This new technology delivers repeatable, consistent running speeds while monitoring the key safety parameters in real time, reacting automatically if any anomaly is detected. The system provides higher efficiency by achieving faster safe running speeds and is a building block towards reducing the size of the offshore crew by enabling remote operations.
The present technology leverages an advanced edge system placed in the CT unit to achieve true autonomous conveyance. Based on the job plan and equipment information, the system generates and executes the correct steps to achieve the operational goals, controlling the equipment to run at the maximum safe speed and performing tasks like pull tests or slowing down at the restrictions. An advanced real-time mechanical model calculates the safe weight operating limits and automatically slows down or stops if any anomaly is detected.
Operations using CT conveyance automation were started offshore in February 2022, and the system was tested in ten runs. It consistently achieved the required running speeds in those run-in-hole (RIH) and pull-out-of-hole (POOH) activities where it was enabled, and it kept them more constant and controlled than during manual conveyance. In one of the field test jobs, the system conveyed the CT at 25 m/min, a 25% increase compared to the maximum speed traditionally used in manual operations. During automation operations, 85% of the running meters were executed autonomously; remaining manual activities were identified and mainly corresponded to pull tests. Automation of such activities are the next target of the automation scope. The usability of the system was also demonstrated, with any user being able to operate it after a short 1-hour competency training. The users remained present and in control during the entire execution and had the ability to recover manual control at any time. The system can also fully operate without connectivity. However, when network connection is available, it also provides high-quality real-time health and diagnostics data (depth, speed, weights, and automation safety limits) for remote monitoring of operational performance.
This first worldwide implementation of CT autonomous conveyance contributes to increased operational consistency and efficiency of well interventions. The software architecture has a layer of abstraction from the specific CT hardware, simplifying the portability of the technology to other units, offshore or onshore. The results of the system implementation in offshore Norway are a reference for the rest of the industry, paving the way to autonomous CT operations and the potential benefits that they can provide.
On Raster Image Segmentation, Well Log Instance Detection, and Depth Information Extraction from Rasters Using Deep Learning Models
Authors: Gune O. A. , Lokhande A. R. , Mangsuli P. R. 
Published: October 2023

Abstract
Raster images contain regions such as plot segments, log headers, depth track etc. which provide valuable information about well logs. The automatic extraction of these regions from raster documents is necessary for well log digitization which can further open-up numerous possibilities of processing the well log data such as well log prediction and correlation. As the first step towards raster digitization, in this work, we address the raster segmentation task to automatically extract important raster regions such as plot segment, depth track, and log headers. Next, we propose to identify the number of well logs recorded in the raster. Further, we aim to extract depth information using Optical Character Recognition (OCR) in the extracted depth track region given by the raster segmentation. Specifically, we propose a novel two stage Deep Learning (DL)-based approach using conditional Generative Adversarial Network (c-GAN) for raster image segmentation where for each raster image we detect plot segments, log headers, and depth track regions. The raster image segmentation is carried out using a DL-based image-to-image translation strategy. In the image-to-image translation, input raster image is processed to get an output segmentation mask. In the next step of raster digitization, we identify different well logs by processing the log header region extracted in the raster segmentation step. We propose a faster RCNN-based model to delineate each log header instance from the log header section of a raster image. In the third step, for depth values extraction, we use the depth track region extracted by the above raster segmentation model. We use OCR to extract the depth values in the depth track image. Output from the OCR can be noisy due to inferior quality raster images. The OCR output is denoised to obtain accurate depth values at each point in the depth track region. We evaluate the proposed raster segmentation and well log instance detection model on a test dataset of variety of raster images with varying quality. We achieve an excellent mean intersection over union (mIoU) of 85.6% in the case of raster segmentation. The above results are due to the novel approach of raster segmentation using c-GAN, and data augmentation strategy used to create large data using limited training data of raster images. Similarly, we achieve mIoU of 91.82% in the log header instance detection task. Furthermore, denoising module for depth values extraction delivers robust performance in automatic extraction of depth values. We measure the performance of the depth track extraction module in terms of ability to correctly identify the extreme values (start and end) in the depth track. Our method attains an outstanding 90.5% accuracy addressing OCR failure modes (due to noisy raster images).
Aquifer Water Preservation in Water Injection Systems
Authors: Alblooshi N. , Al Memari M. , Alshamsi A. A. , Al Breik M. , Solaippan M. , Modi J. M. , Pillai G. R. 
Published: October 2023

Abstract
ADNOC Onshore has been striving to achieve the objective of developing the latest methods and sustainable approaches to enhance day to day operations that would advance the industry to environmental sustainability globally. With this prime objective, NEB Field being one of the most biodiverse locations in the UAE and has always strived for better and smarter solutions to protect the sensitive flora and fauna systems. NEB has been the pioneer across the onshore fields, to first implement an in-house project that served as the window towards changing the philosophy of operations. NEB field development team has observed a rise in its waterflood and during its phase of development, the produced water cut will continue to rise as a result of maturing wells, necessitating management of the inevitable increasing produced water. The scope of the project is to reduce our reliance on aquifer zones that is used for water injection, by alternatively reinjecting the separated produced water instead. This innovative approach preserves the natural water table and reduces our power consumption, which ultimately supports the reduction in green house gas emissions. Power consumption is optimized by reducing our dependance and requirements on electrical submersible pumps in water supply wells.
A Comparison Between ANNs and Ant Tracking Outputs for Fault and Fracture Detection
Authors: Mohammadi R. , Mohebian R. 
Published: October 2023

Abstract
Knowing the fractures and faults in a field or reservoir leads to more precise exploration and makes development strategies less uncertain. To better identify these events, in this study, the outputs of multilayer perceptrons (MLP), and unsupervised vector quantizers (UVQ), were compared with the outputs of ant-tracking. Although the specific seismic attributes and ant-tracking allow for the depiction of faults and fractures, the combination of the shortlisted attributes in the ANNs, especially MLP, reveals small-scaled and, in some areas, vertical faults and fractures more continuously and precisely than previous studies that used only conventional ant-tracking. The chaos has some deficiencies in identifying the faulted and fractured zones. Ant-tracking has a much lower performance than the MLP output. The output of ant-tracking attributes shows fault detection with less accuracy than the MLP output or even that of UVQ. To be clearly described, for the given seismic survey data, finding small-scale and in some areas vertical fractures using the integration of seismic attributes were more reliable than the output of the ant-tracking.
Improved Drilling Performance Using an Innovative Cloud-Based Ecosystem and Bespoke Operator Technology
Authors: Barry Jonathan , Lee Melissa , Bostick Tad , Cheatham Curtis , Maqui Agustin , Schneider Chris 
Published: October 2023

Abstract
This paper describes how an operator implements continuous improvement methodologies using real-time drilling data analytics in a novel cloud-based ecosystem. Standardized key performance indicators (KPIs) are benchmarked and tracked in real time for the entire drilling team to monitor and take corrective, collaborative action. In addition, the operator has implemented its own proprietary technology in the form of custom-built applications.
The methods in this paper are used in basins and environments globally. The underlying principle is simple: capture and visualize real-time data, measure and track rig fleet KPIs benchmarked against safe, iterative goals, and evaluate trends over time.
In practice, this often requires significant human resources. Advanced analytics tools now automate most of this process. Leveraging a wide range of purpose-built and custom applications to analyze real-time information and generate metrics, decision-makers can witness the effect of process change rapidly. Leadership focus on the specific KPIs emphasizes to the workforce the importance of using the noted tools to drive performance.
Selective and Reversible Water Shut-Off Technology Based on Emulsion System with Nanoparticles for Low to High Permeabilities Carbonate Reservoirs at HTHS Condition
Authors: Yonebayashi H. , Abe M. , Sergeev V. , Furuno J. , Murakami S. , Sonoda T. , Hiraiwa T. , Suzuki M. , Arief R. , Zahaf K. , Al Ameri M. 
Published: October 2023

Abstract
This paper presents the evaluation results of Emulsion System with Nanoparticles (ESN) as a water shut-off (WSO) agent. The ESN has unique advantages to overcome crucial hurdles in the existing polymer and gel materials; thermal stability in higher temperature, low sensitivity to mineralization, pseudoplastic characteristic, selectivity of blocking effects for water zone only, and reversibility of blocking effect. The surface modified silica (SiO2) nanoparticles work to reinforce the emulsion stability. The ESN make-up is convenient and quicker by utilizing the on-site oilfield materials such as crude oil and injection water. From the aspect of compatibility to reservoir, the majority of on-site material, mixed with small amount of nanoparticles-based surfactant, never causes any problem. To evaluate the ESN performance for UAE's carbonate reservoirs in high temperature and high salinity (HTHS: 130 °C and 240,000 ppm TDS) condition, physicochemical analysis and core flood tests were performed. The ESN, which was confirmed stable at the HTHS condition, achieved 1-2 orders permeability reductions for intermediate and high Swi cases of the core flood tests using intermediate-high permeability carbonate cores (50-570 mD) while no impact on the low Swi case. The relative permeability analysis confirmed the ESN reversibility by unblocking. Through comparative analysis by incorporating the previous laboratory works using tight cores (10 mD), all the test results proved the technological potentials of ESN successful application in the UAE's oil fields.
Comprehensive Steam System Optimization: A Key Element for Carbon Emissions Reduction and Energy Efficiency
Authors: Hou A. , Gomaa A. , Mita T. 
Published: October 2023

Abstract
This paper introduces a Steam System Optimization Program (SSOP) aimed at reducing greenhouse gas (GHG) emissions and improving energy efficiency in the refining and petrochemical industry. The refining industry is a significant contributor to global GHG emissions, and optimizing steam systems within refineries can play a crucial role in achieving emission reduction targets. The SSOP is designed as a continuous, annual cycle, ensuring sustained improvements in the steam system's reliability, energy efficiency, and environmental performance.
The program was successfully applied to over 220 Japanese plants across different industries, including approximately 48 refineries and petrochemical plants. Through the program's implementation over a period of 16 years starting from 2006 till now, a significant reduction of 562,000 tons of CO2 emissions per year was achieved by decreasing steam generation by 3.85 million tons annually. This resulted in substantial monetary savings of $77 million USD each year.
A 4D Geomechanics Modelling Approach Using Machine Learning for Drilling Events Predictions in the Giant North Kuwait Jurassic Field
Authors: Haas M. , Al-Hamad N. , Heiland J. , Hussein A. M. , Shekhar S. , Srivastava D. N. , Ramon J. , Abdel-Basset M. , Al-Anzi Abdullah , Al-Obaidi Ibrahim 
Published: October 2023

Abstract
Drilling new wells in the deep Jurassic formations of the North Kuwait fields has gained increased importance given its high gas production potential, which represents a strategic target for Kuwait's domestic gas independency. However, reaching these formations bears severe drilling risks and challenges caused by differentially depleted reservoir sections that lead to arbitrary stress distributions and high pore pressure zones. This study presents an integrated 4D geomechanical model that includes a machine learning model for the development of a drilling expert system. This allows to predict drilling events along any arbitrary well trajectory in the 3D field model by analysing the stress field based on the construction of a mechanical earth model. The model further assesses completion quality to hydraulically fracture the rock mass. The simulation covers a 34 million grid cell model and is based on high-resolution geological information, seismic inversion data, rock core laboratory data featuring uniaxial, triaxial, multiaxial geomechanical tests, scratch, creep behaviour and thick-wall cylinder tests as well as petrophysical logs from 54 deep Jurassic wells. Seismic acoustic impedance data served as a proxy for co-kriging the spatial distribution of geomechanical properties. In addition, fault and fracture models were incorporated at reservoir levels.
The drilling expert system allows for an easily applicable and efficient extraction of the required drilling parameters and rock properties along any arbitrary well trajectory to conclude on the probability of drilling events occurring. The derived wellbore stability analyses reduce the costly and time-consuming risks of wellbore collapses when passing through high pressure formations. The simulated 4D stress field yields a strike-slip stress regime with a minimum horizontal stress direction of 135°. Identified zones in the reservoir sections show high hydraulic fracture potential given by the under- and overlying stress barriers in the anhydrite formations for stress containment. The evaluation of critically stressed, natural fractures as well as faults lead to the conclusion of potential reactivation due to high pressure injections in the Najmah reservoir for hydraulic fracturing. The study successfully showcased the integration of domain geomechanics expertise and data science, to derive a 4D geomechanical earth model as input for training machine learning models to evaluate drilling integrity and hydraulic fracturing potential.
The Vital Role of Advanced Cutting Analysis in Unconventional Reservoir Characterization
Authors: Fahmy Mahmoud Fawzy , Nguyen Kim Long , Dasma Mohammed , Al-Mutairi Nami , Al-Morakhi Rasha , Alkandari Abrar Yousef , Quttainah Riyad , Ousididene Karim , Moustafa Ahmed , EL Masry Mohab , Magnier Caroline , Sharma Sachin 
Published: October 2023

Abstract
Advanced Cutting Analysis technique has been widely used in the industry to assist drilling operations as well as providing support in formation evaluation. Recently, alongside with the speedy development of Najmah Kerogen unconventional reservoir in Kuwait, the technique has demonstrated its vital role resulting in significant cost saving, and reliable measurements that could be used to replace other expensive evaluations from cores or open-hole logs.
The Advanced Cutting Analysis was applied in Well-H drilled recently in Kra Al-Maru field, West Kuwait (Fig. 1) that comprised of different technologies: X-ray diffractometry (XRD) for mineralogy, X-ray fluorescence (XRF) for rock chemical composition, Total Organic Carbon (TOC) analysis, and Pyrolysis for source rock characterization. The XRD-XRF technology was popular in the industry, and it could be acquired from core or open-hole log data. In this study, XRD-XRF was conducted on cutting samples. In order to prove the efficiency of this technology applied on cuttings and its potential use as alternative method to support unconventional reservoir characterization in the future, the result was validated with elemental spectroscopy wireline logs.
This paper presents the workflow used for analyzing and integrating multidisciplinary datasets in order to develop an alternative method for unconventional reservoir characterization that included the use of surface advanced cutting and fluid analysis. The aim is to:
Reduce the uncertainty of production sustainability and proper well planning of the tight fractured carbonate unconventional reservoir. Build up alternative reservoir assessment model using advanced cuttings analysis in HPHT wells, slim hole, limited logging, etc. Evaluate mineralogical composition of the rock including the formation brittleness index, which helps on the post drilling fracturing strategy. Elemental Gamma Ray as cost effective for geo-monitoring and well placement assistance as well as eliminate the risk associated to down hole effects such as: hole geometry, vibration, high mud weight used while drilling, rock mechanical stress, high temperature and pressure can lead to lost signal.
A Three-Phase Integrated Asphaltene Deposition Model in Oil Wells
Authors: Al-Safran E. M. , Aql A. A. , Ghasemi M. , Alrashedi H. G. , Qubian A. 
Published: October 2023

Abstract
This paper investigates asphaltene deposition in an oil well produces from a deep Jurassic reservoir located in Kuwait by developing a three-phase asphaltene deposition integrated model. The objective of this study is to develop and validate an integrated well model that incorporates asphaltene thermodynamic, aggregation, deposition, and transportation models coupled with a three-phase thermal-hydrodynamic mechanistic model. The proposed model is numerically solved to predict temporal and spatial asphaltene deposition and concurrently tracks oil composition changes due to asphaltene deposition. The elemental models of the integrated model are tuned against field data, and the deposition result is validated with a caliper log data. The field validation study revealed a very good match between the measured deposition behavior and model predictions. In addition, sensitivity analysis showed that although asphaltene precipitation increases as water cut increases due to higher pressure drop, asphaltene deposition decreases due to increasing shear stress and decreasing asphaltene sticking tendency on the pipe wall. Furthermore, sensitivity analysis revealed that as GOR increases, asphaltene precipitation increases; however, asphaltene deposition decreases. The proposed model shows significant sensitivity to asphaltene particle diameter, revealing that as particle diameter increases, deposition thickness decreases.