bluuebunny/crossref_metadata_2025_split huggingface dataset:
Produced Fluid, WTP and BFW for Steam Generation Management at Mukhaizna Heavy Oil Field
Authors: Al-Asimi M. , Al-Habsi A. , Al-Qalhati A. 
Published: April 2024

Abstract
The Mukhaizna heavy oil field in Oman is one of the world's largest steamflood operations. With more than 3,000 wells drilled (producers and injectors), gross fluid production has surpassed 900K bbl/day, with steam injection of 600K bbl/day and water disposal of 300K bbl/day after 17 years of successful steamflooding.
These large volumes of produced fluids undergo various treatment processes to prepare them for either sale, boiler feedwater for steam generation, or disposal. With water cuts exceeding 90% and fluid chemistry changing with new fluid streams added from exploration areas, technical difficulties compelled a multidisciplinary team to share operational intelligence to optimize the processes of dehydration, de-oiling, water treatment, and disposal.
To address the challenges posed by the substantial volumes and high temperatures of viscous produced fluids, free water knockout vessels, skim & dehydration tanks, and induced gas flotation are used, enhanced by a carefully optimized selection of chemicals, resulting in a reasonably efficient dehydration and de-oiling processes.
The water treatment plant (WTP) uses a sophisticated treatment process to comply with the specifications for boiler feedwater. This process involves chemical precipitation softening methods, ion exchange with strong acid cation (SAC), a reverse osmosis membrane, and thermal desalination using one of the world's largest Mechanical Vapor Compressors (MVC).
The high volumes of multiple disposal fluids demand a complex and ecological disposal system. The current process includes conventional deep subsurface disposal and segregated evaporation ponds. Other environmentally friendly solutions are being considered, including autonomous inflow control devices/valves and the use of reed beds for wastewater treatment.
. Due to the operation's size and complexity, the team examined leveraging modification of the facility to improve produced flu id cost and to recycle selective waste streams. This approach would alleviate bottlenecks, resume production of shut-in high-water-cut wells, optimize chemical expenditures, and enhance the water quality entering the WTP's SAC inlet.
The team has recommended both mechanical and chemical solutions to unlock the full potential of these mature assets, leading to savings of more than 20% on some chemicals and a big gain in production from re-opening the shut-in wells.
Synergistic Integration of a Photovoltaic Park and an Electrolysis Facility for Sustainable Green Hydrogen Production
Authors: Oprisescu-Dulgheru Andrei-Stefan 
Published: April 2024

Abstract
In today's quest for energy optimization and reduction of carbon emissions, hydrogen has emerged as a promising solution for capturing and storing surplus energy. With the increasing production of renewable energy leading to negative energy prices, hydrogen plays a pivotal role in efficiently utilizing this surplus energy. This research presents an advanced and comprehensive model for the synergistic integration of a photovoltaic park and an electrolysis facility, aiming to harness excess energy and produce sustainable green hydrogen.
The integration model revolves around the utilization of an advanced Maximum Power Point Tracking (MPPT) Charge Controller. This controller plays a vital role in optimizing energy extraction from the photovoltaic park by ensuring that the solar panels operate at their maximum power point, thereby enhancing the overall efficiency of the park. Furthermore, during periods of high solar energy production, the surplus energy is intelligently directed to the electrolysis facility instead of being wasted or negatively impacting energy prices.
By coupling the photovoltaic park with the electrolysis facility, the surplus energy is effectively converted into hydrogen through the process of electrolysis. This conversion allows for the storage of potential energy in the form of green hydrogen, which can be utilized for various applications, including power generation, transportation, and industrial processes. The production and utilization of green hydrogen not only facilitate energy storage but also contribute to the reduction of carbon emissions, promoting sustainable and environmentally friendly energy practices.
The proposed integration model addresses the challenges associated with surplus energy management and offers significant advantages. By effectively utilizing surplus energy through hydrogen production, it mitigates the financial implications of negative energy prices and maximizes the utilization of renewable energy resources. Furthermore, the adoption of green hydrogen as an alternative to conventional fossil fuel-based energy sources supports the global transition towards a low-carbon future. By efficiently managing surplus energy and harnessing the potential of hydrogen as an energy carrier, this integration model not only optimizes renewable energy utilization but also promotes a more sustainable and environmentally conscious energy landscape.
Ocean Bottom Node Seismic Data Changes Occidental's Plan for Lucius Field in the Gulf of Mexico
Authors: Barnett D. B. , Manning J. P. 
Published: April 2024

Abstract
Geological risks and uncertainties must be considered when evaluating and deciding whether to drill an offshore prospect. Recent technological advances in seismic data acquisition, processing and interpretation techniques have provided geoscientists cleaner data sets and new tools to help make such decisions. This paper will highlight the technological advances of deep-water seismic data at Lucius and the value gained through optimizing the field development plan.
The advent and application of Ocean Bottom Node seismic acquisition has greatly improved subsurface imaging and helped geoscientists reduce geological risks and better quantify subsurface uncertainties. Occidental has recognized the value of OBN seismic surveys, having employed it recently with immense success across multiple Gulf of Mexico assets. These new surveys, along with innovative processing tools such as Least Squares RTM, Full Waveform Inversion, and attribute analyses, have greatly improved how geoscientists measure the risk and uncertainty of prospects.
Learnings from the OBN survey at the Lucius field in the deep water GOM have helped Occidental optimize the field development plan by shedding light on the structural complexity of new fault blocks, better defining the salt/sediment boundary, and improving reservoir characterization. These learnings drove changes in and additions to future drilling locations and identifying possible stranded hydrocarbons.
Investing in seismic technology is often overlooked in mature assets, but at Lucius, the OBN data set has led to more integrated and robust interpretations and has already delivered to co-owners over four times the return on investment. OBN implementation has provided a step change in subsurface understanding.
A Systematic Approach to Multistage Hydraulic Fracturing Optimization for Production Improvement with Geomechanical Modelling
Authors: Jia Jing , Wang Lichang , Zhang Xin , Fan Wenli , Meng Xianhang , Zhang Hanyue , Yu Yanbo , Yang Chunxi , Huang Xingning 
Published: April 2024

Abstract
In order to advance the sustainable development strategy of oil fields and cope with the crude oil prices fluctuations, the oilfield operators adopt a risk-sharing development mode of project contracting and EUR sharing for inefficient blocks with high barrel oil costs to reduce the investment risk. Focused on "single well production enhancement" and "cost control", improve the accuracy of geological and engineering sweet spot predictions via geology and engineering integrated study. Given the non-homogeneity and non-uniformity of hydraulic fracturing of unconventional reservoir, the application of hydraulic intelligence neural network technology is crucial. Fully leverage fracture information obtained from imaging logging and the reflection of various seismic attributes on the fracture to quantitatively study the distribution of fractures. The evaluation of the characteristics of stress, geomechanics, and the interaction between hydraulic and natural fractures are essential for more accurate simulation and prediction of the hydraulic fracturing. Via conducting hydraulic fracturing simulations based on differential stress distribution in horizontal wells, optimizing parameters such as fracture spacing, length, height, cluster combination, and pumping scale to achieve the best match between engineering technology and reservoir conditions, ultimately realizing efficient development.
Using a Genetic Algorithm to Estimate Bingham Equation Parameters for Rate of Penetration Prediction
Authors: Eltrissi Mohammad , Yousef Omar , El-Banbi Ahmed , Khalaf Fouad 
Published: April 2024

Abstract
Bingham rate of penetration (ROP) model offers a reasonably accurate and easy-to-compute equation for ROP prediction. However, the model requires estimating the local empirical parameters varying by region and formation encountered. This requirement may cripple the effective utilization of the model when new entrants drill in a location. The standard method for estimating these empirical parameters is by iterations to find the optimum values.
This work presents a methodology for estimating localized modified Bingham equation parameters using the Genetic Algorithm (GA). The case study was conducted on a large dataset collected from 260 wells covering the range of formations in the Western Desert of Egypt. The dataset provides more than 390,000 records for twenty-eight different formations in two different geological basins in the Egyptian Western Desert. The data was pre-analyzed by two different data processing techniques prior to its introduction to GA.
The parameters of the modified Bingham model for every formation were determined using the entire dataset. The outcome range of the dataset prediction error ranges between 5.3 m/hr and 26.1 m/hr, with an average error of 13.91 m/hr for all 28 formations. In addition, it was observed that the model had increased accuracy with depth despite some anomalies that could be found in a few formations. The resultant empirical parameters for the modified Bingham ROP model of the case study can be used for any Egyptian Western Desert ROP prediction application, while the introduced algorithm can be applied elsewhere to find the value of its local empirical parameters.
Decarbonizing Drilling Operations Using a More Efficient Power Source, Electrical Utility Power
Authors: Landry B. , Trueheart L. , Stopkoski C. , Vaughan D. , Dotson D. , Landacre K. , Benedicti P. , Posse P. Riva 
Published: April 2024

Abstract
Electrification has proven to be an effective way to lower operational costs and reduce the carbon footprint of drilling operations. This paper provides a technical analysis of highline (grid) power utilization on drilling rigs through the lens of an innovative technology that connects a rig to a power grid, reducing Scope One emissions to nearly zero.
Traditionally, diesel engines and generators have been used to produce electricity, both direct and alternating current (DC and AC) for drilling operations. This power source is effective and reliable, but it has drawbacks that can be addressed using highline electrical power, which is more affordable, efficient, and clean.
Recently introduced technology allows drilling operations to be powered by an electrical utility grid using a substation transformer to step down highline power voltages to rig voltage and interface the utility grid with the rig's powerhouse. An electrical transfer switch enables the rig to switch between highline and generator power quickly and easily. In situations where grid power cannot be utilized, the highline transformer module allows the existing power generation system to remain integrated to the drilling rig. The system is compatible with both Variable Frequency Drive (VFD) and Silicon Controlled Rectifier (SCR) drilling rigs and has minimal impact on rig move timelines.
Powering the drilling rig with electricity instead of generators decreases on-location emissions and noise levels to near zero, and highline power can reduce the overall carbon footprint of drilling operations even more when utility power is created by more efficient and lower polluting methods such as natural gas power plants, nuclear power, or renewable energy (solar, wind, geothermal, etc.). Field data collected from more than 20 highline transformer modules deployed on rigs across the Lower 48 United States demonstrates an average daily reduction in Scope One emissions of 20 metric tons of CO2e per rig.
With highline powered operations becoming more commonplace in the United States, technological advancements are starting to make their way to global markets.
Optimizing Well Placement in Heterogenous Carbonate Reservoir Utilizing a New Generation of High Definition Mapping Tool
Authors: Al Araimi A. , Al Yahyai H. , Taher A. 
Published: April 2024

Abstract
Field development plans and oil recovery can be significantly enhanced by the full understanding of the geological structure, petrophysical properties and reservoir behavior. Detailed subsurface understanding can depict strategies for reservoir and field development. Building a detailed picture of the reservoir requires a combination of measurements and data acquisition processes usually starting with seismic data to build a geological model which is refined during drilling operations with a combination of logging while drilling (LWD) and reservoir mapping sensors.
Conventional LWD sensors usually measure formation properties around the well trajectory, Deep azimuthal electromagnetic (EM) tools are often used to map boundaries further away from the wellbore to improve well placement. The resistivity of the surrounding formation and resistivity contrast with the adjacent layers are two critical factors that influence the depth of detection and ability to map multiple layers. As the resistivity contrast decreases, it becomes more difficult to identify boundaries at a distance. A Deep Azimuthal EM tool with enhanced sensitivity to resistivity variations was deployed along with a mapping inversion algorithm in the case study. In addition to utilizing density and neutron tools, to provide a detailed picture of the reservoir and refine the geological interpretation near well-bore.
A case study from the North of Oman where a carbonate reservoir with anticipated thickness variation and variable reservoir quality, overlain by shale identified from offset data was selected for the deployment of a high-definition multi-layer inversion mapping tool. The varying thickness of the carbonate target and the small contrast in resistivity laterally towards the toe with the shale above imposed additional challenges for conventional deep resistivity tools due to their limited depth of detection and, were anticipated as being a challenge to the well placement operation, limiting the depth of investigation and the ability to detect the critical boundary and ensure there was no exit in areas of thinning.
The Realtime inversion output showed the Resistivity degradation across the lateral section ranging from 12 ohm-m to 2.5 ohm-m, the thickness of the reservoir showed substantial variation varying from 32ft at heel to 8ft at toe. The continuous detection of bed boundaries along with lateral deterioration of resistivity provided a detailed view of the reservoir thickness and dip variation to optimize well placement decisions A novel approach for utilizing a new generation of high-definition multi-layer inversion mapping tool, the successful mapping of the boundaries in low resistivity contrasts expands the environments in which the technology can be confidently deployed and ensure optimum well placement with increased reservoir understanding.
Machine Learning Model for Efficient Surveillance of Electrostatic Pump Operations & Predictive Analytics for Early Alarming System
Authors: Alrowayyeh Jarrah , Dhote Prashant , Alqenaei Abdulmohsen 
Published: April 2024

Abstract
In today's fast-paced technological world, Oil and Gas E&P industry must embrace innovative solutions for boosting efficiency and minimizing expenses. One promising direction involves investigating smart systems that can supervise, notify, and foresee the malfunction of electrical submersible pumps (ESPs). By leveraging artificial intelligence (AI) and machine learning (ML), we can transform how we handle the upkeep and administration of these vital elements. Adopting AI and ML-driven solutions offers significant advantages, such as preventing expensive failures and promoting eco-friendly resource management. By detecting and resolving potential issues early, we can extend equipment life and reduce our operations’ environmental footprint.
The widespread application of Electrical Submersible Pumps (ESPs) in Kuwait Oil Company (KOC) has recently experienced a surge in pump failures due to increased water production from maturing fields. This issue prompted the exploration for adopting intelligent systems, and integrating AI and IoT, to improve ESP monitoring and predictive maintenance, ultimately enhancing efficiency and reducing costs. The study employed a novel workflow connecting field, well, and detailed ESP operational data, such as intake and discharge pressure, wellhead and flowline pressure, motor current and voltage, motor temperature, reservoir temperature, and vibration for failure diagnosis. Predictive analytics were utilized to proactively identify potential issues before they manifested, thereby mitigating the impact of these failures.
The study results show that utilizing advanced technologies such as Machine Learning and Automatic Surveillance Systems can significantly reduce ESP failures. Key conclusions from the analysis include,
A streamlined solution is provided to identify and predict ESP failure causes. ESP real-time data is digitized and converted into a computationally friendly form. Early anomaly detection enhances well uptime and minimizes ESP failures. Model parameters can be fine-tuned for better results with a successful proof of concept (POC). The proposed workflow aids in cost reduction for workovers and maintains production. Decision-making is improved through the monitoring system.
This approach offers an accessible and user-friendly platform for non-specialists within organizations. Oil companies can adopt this workflow to boost well performance and pinpoint areas for improvement. Ultimately, implementing digital transformation will elevate organizations’ competitiveness in the global industry.
Evaluation the Effect of Temperature and Reaction Time on Hydrothermal Conversion of Oil Shale: Insights into Synthetic Oil Generation and Micro-Scale Pore Structure Change
Authors: Saeed Shadi A. , Al-Muntaser Ameen A. , Hakimi Mohammed H. , Kadyrov Rail I. , Morozov Vladimir P. , Abdullah Mohammed A. , Suwaid Muneer A. , Nguyen Hung T. , Varfolomeev Mikhail A. , Nurgaliev Danis K. , Al-Fatesh Ahmed S. , Nazimov Nafis A. 
Published: April 2024

Abstract
This research presents a systematic study on the hydrothermal conversion (artificial thermal maturity) performance of oil shale from Domanik Formation (Russia). The experiments were conducted at different conditions including temperatures of 300°C, 350°C, and 400°C, and reaction times of 1, 3, 6, 12, and 24 hours. For this purpose, the quantity and quality of produced synthetic oils, the composition of released gases, as well as changes in pore size and structure of shale consolidated core samples by X-ray computed tomography were evaluated. In addition, SARA group composition, carbon number distribution including light fractions (C10 – C20, with a boiling point of 216–342°C, representing petroleum, kerosene and diesel cuts) and heavier fractions (C21 – C32, with a boiling point of 356 – 458°C), elemental analysis, and the compositions of obtained synthetic oils (including aliphatic and aromatic hydrocarbons) and the conversion degree of kerogen were analyzed using multiple techniques including, Low Field - NMR, GC, GC–MS, and Rock-eval pyrolysis. The obtained results confirmed that the high quality of synthetic oils is characterized by a higher amount of light fractions (C10 – C20) compared to heavy fractions (C21 – C32), with a higher amount of saturate and aromatics than resins and asphaltenes. The degree of OM conversion varies depending on the temperature and reaction time. Among 300, 350, and 400 °C, the highest conversion rates were 25, 70, and 99% for reaction times of 24, 24, and 6 h, respectively. Hydrothermal conversion at 300°C for 24 hours, 350°C for 24 hours and 400°C for 3 hours showed the highest quality of synthetic oils. Notably, hydrothermal treatment caused a substantial change in pore size, leading to an improvement in porosity and the formation of connected pore channels. The porosity increased approximately 4 times (from 2.6 to 11.9%). In general, the promising obtained results technically demonstrate the feasibility of developing Domanik oil shale by in-situ hydrothermal retorting.
Vision Inspection of Power Lines with Deep Learning
Authors: Alotaibi Najd , Dursun Serkan 
Published: April 2024

Abstract
The purpose of this technical paper is to introduce a novel approach to inspecting power lines using computer vision and deep learning algorithms. Traditional inspection methods are often time-consuming and costly and can be dangerous for human inspectors. This paper presents a new workflow for power line inspection that leverages machine learning algorithms to automate the process.
The proposed workflow for vision inspection of power lines involves capturing high-resolution images of power lines using drones or other unmanned vehicles. These images are then processed using a deep learning algorithm, which is trained to identify potential condition such as good, problem, and unknown for various components in the power lines. The workflow utilizes cutting edge object detector models, such as YOLOv5 and YOLOv8 to analyze the images, and output the prediction of bounding boxes, abjectness scores and probabilities for each detected object.
This paper presents a novel approach to power line inspection that utilizes deep learning algorithms to identify and localize objects not only by using axis-aligned bounding boxes but also with oriented bounding boxes, which can significantly reduce the time and cost associated with traditional inspection methods. The use of drones and other unmanned vehicles also increases safety by eliminating the need for human inspectors to climb power line structures.
The vision inspection workflow presented in this paper has the potential to revolutionize the power line inspection industry. By utilizing deep learning algorithms and unmanned vehicles, inspection times can be reduced, costs can be lowered, and human safety can be improved. This approach can also lead to more efficient maintenance and repair, prolonging the lifespan of power lines and reducing the risk of power outages. Future research can explore the potential of using this workflow for other types of infrastructure inspection as well.