bluuebunny/crossref_metadata_2025_split huggingface dataset:
Revolutionizing the Perforation Process Via an End-To-End Automated Perforation Toolbox in the Gulf of Thailand (PerfSight)
Authors: Tonburinthip T. , Norasaed W. 
Published: November 2024

Abstract
This paper presents an innovative solution to generate the composite log using the in-house auto depth-shifting program and addresses the challenge of determining the optimal perforation depth for reservoir engineers to achieve optimal hydrocarbon recovery and reservoir performance. The traditional manual process of depth shifting is not only time-consuming but also prone to human errors, requiring collaborative efforts from multiple stakeholders.
To address these challenges, Perfsight was developed, an in-house programming tool. It incorporates three key modules: auto depth matching, perforating depth optimization, and cement verification. The first module utilizes grid search to align depths between open-hole and cased-hole logs, accounting for variations in weight tension. The second module employs linear programming to optimize perforation interval selection, considering practical constraints. By assessing key factors such as resistivity, gamma rays, density, and neutron, our automated method ensures optimal perforation intervals. Lastly, the cement verification module utilizes a cement score equation to automatically identify the "good cement zone," mitigating the risk of flow behind the casing.
Our study demonstrates a perforation enhancement process, transitioning from depth matching to cement verification in the perforating process. Our depth matching module achieves a commendable similarity score of 0.93, facilitating automatic correlation between open-hole and cased-hole logs. Following this, our tool utilizes linear programming to suggest perforating depths for each sand, considering operational constraints such as interval length and patching area to prevent water-coning. The model selects optimum intervals by prioritizing prolific, porous, clean, and top sections of the sands, resulting in a 20% increase in prolific score compared to manual selection. Subsequently, the model evaluates cement quality using a developed equation, identifying intervals with a cement score exceeding 80 as "good cement zones" to prevent water-flowing behind the casing. Our tool significantly streamlines the perforating process, reducing processing time from 4 hours to just 20 minutes, while also automating error-prone tasks such as cement quality verification. When integrated, these components produce a comprehensive one-stop service, generating a composite log.
E-Navigate Aids for Smart and Adaptive Planning, Scheduling, and Excursions
Authors: Yahia A. O , Qahtani M. A , Alsalim A. T 
Published: November 2024

Navigation aid is a crucial component in offshore oil and gas operations which directly affect not only the cost of the operation but also and more importantly the health and safety of agents. Annually the number of vessels offshore increase due to the high demand in the industry. With that being said, navigation and maintaining the offshore equipment, such as buoys, becomes inherently more difficult.
In any operation, maintaining the different equipment and artifacts that contribute to the end goal is critical. However, it does come with a challenge. On one hand there are cases where the equipment in question is due for a planned maintenance. On the other hand, equipment fails suddenly or require and immediate response, which is the less desirable scenario.
Certified LNG Bunker Metering System for Maximum Transparency
Authors: Author Stephan Natter , Author Sam Miller 
Published: November 2024

Abstract
The demand for Liquefied Natural Gas (LNG) as a marine fuel is surging as ship owners strive to comply with emission reductions set by the IMO. Transparency during bunkering operation is key to ensure acceptance by the different stake holders involved. The measurement equipment which is used today often misses on measurement traceability and custody transfer certification for the quantity measurement. Short deliveries and disputes between buyer and seller are the result.
Complex and maintenance intensive gas analysis equipment is used to measure the LNG composition and requires frequent maintenance.
This paper outlines the benefits of utilizing a custody transfer approved bunker metering system based on MFM (Mass Flow Meter) in combination with Raman spectroscopy.
The transferred quantity of LNG is measured with custody transfer approved Coriolis mass flow meter of type Promass Q. This mass flow meter type has a mechanically optimized design for cryogenic liquids which leads to high temperature stability and accuracy. The quantity measurement includes LNG, boil off gas (BOG) and fuel gas consumption. Raman spectroscopy is used to measure the LNG composition in real-time without the need for vaporization and sampling. Custody transfer approved flow computers calculate the amount of energy transferred and provide the reporting for commercial transaction. In addition, the methane number is calculated following different mathematical models.
The quantity measurement shows significantly better accuracy than required by OIML R117. The operation time is reduced due to real-time LNG composition measurement. This LNG bunkering system allows compact installation on board the bunker tanker because no vaporization and sampling equipment is needed. The Raman analyzer does not need any calibration gases for frequent verification. This reduces the effort and costs for maintenance. The real time quantity and quality measurement provides a high degree of transparency during the bunker operation. The combination of accuracy, transparency, custody transfer approval and real time information gives all stakeholder a high degree of confidence into the received measuring results.
New is the combination of custody transfer approved MFMs with a Raman analyzer for metering LNG bunker transfers. The custody transfer certification for metering the LNG bunker quantities sets the corner stone for transparency and measuring accuracy. It lifts LNG bunkering on the same level of system integrity as we know it from the oil bunker industry using MFMs already today.Body text 1 paragraph.
Machine Learning Assisted Framework to Improve Well Rate Estimation Processes for Enhanced Production Surveillance
Authors: Gireesh Greeshma , El-Bakry Amr , Garrison Brett , Smith Richard , Singh Vikash , Avlani Jainam , Khera Kashika 
Published: November 2024

Abstract
Robust well rate estimation, one of the most important requirements for production surveillance, is essential to understand the performance of a hydrocarbon field and plan optimization activities that can assist in additional production uplift. This paper is concerned with developing a novel framework to combine multiple data sources in order to produce highly reliable well rate estimates. The focus is on improving meter water-cuts (water to total liquid ratio) during measurements using manual spin cuts. Accuracy of in-line automated water-cut meter readings, which measures water-cut during well testing, gets reduced with presence of gas. Water-cuts calculated from spin cut samples, which are manually collected from well test set-up, are more reliable as they do not depend on gas content. Different machine learning modeling approaches were evaluated to estimate a new corrected meter water-cut reading for every well test, using historical spin cut data and closest meter water-cut and temperature readings recorded during collection of these spin cut samples. To account for fluctuations in meter readings resulting from manual calibration of meter, a weighted correction factor ("bias correction") was also added to the improve the predictions. While comparing raw meter reading and model estimates to water cut from spin cut samples, from one year of unseen data, a significant reduction (from 5.6% to 0.5%) in mean error was observed. From evaluations, Tree Bagger models, which are Random Forest models without random selection of features, were found to have the best performance. Moreover, the model was able to capture and respond to sudden shifts in meter readings, possibly originating from manual calibration of the meter. While considering individual wells (or well groups which are tested together through the same meter), poor model performance was observed for few wells where spin cuts consistently recorded values lower than the corresponding meter readings. Model performance was also impacted on well groups where (a) spin cuts taken in close time proximity showed higher variation in water-cuts and (b) spin cut data was sparse. The corrected water-cuts and corresponding phase rates received positive feedback from the operators, underscoring their significance in well rates estimation. This approach uses multiple data sources, real time and historical meter data and, latest and historical manual spin cut data for a surveillance system that runs every few minutes, thus correcting water-cut measurements and coupling it with real time well rate estimations. Additionally, an innovative bias correction method was implemented to improve accuracy of measurements. This framework generates substantial value for upstream businesses with higher well counts (1000+ wells) where data driven approaches are proven to enhance surveillance.
An Integrated Approach of Thin Carbonate Reservoir Prediction and its Application
Authors: Jianfang Yang , Shanbo Sheng , Jiawen Song , Bao Li , Mengze Li , Tiejun Sun , Jiangong Chen , Xueyin Ding , Zichang Tang , Qiang Li , Ailing Wang , Guibin Dong , Yong Liu 
Published: November 2024

Abstract
Controlled by sedimentary diagenesis, the carbonate reservoir in the N Block of the eastern margin of Pre-Caspian Basin are mainly 3-5 meters thick. The accuracy of reservoir prediction is low, posing significant challenges to exploration. To address the problem, a new integrated thin carbonate reservoir prediction technique was summarized based on geological genetic analysis and Seismic Meme Inversion.
The integrated thin carbonate reservoir prediction workflow mainly includes 3 steps. 1) Integrated sedimentary microfacies classification based on core, well logging, seismic and palaeogeomorphology to identify favorable facies (Inner shoal) for the development of thin carbonate reservoirs; 2) In the favorable facies, generated the characteristic physical parameters which can reflect the thin carbonate reservoir sensitively by curve reconstruction technique; 3) Utilized the method of Seismic Meme Inversion with high resolution to complete the thin carbonate reservoir prediction quantitatively based on reconstructed well logging and high quality seismic data.
According to the certification of new drilling wells, this technique has been applied successfully in carbonate reservoir in Block N on the eastern margin of Pre-Caspian Basin. The 3-5 meters carbonate reservoir can be effectively identified, with a drilling coincidence rate of over 90% in thin reservoir of 5 meters or more, as verified by new drilling well, greatly improving the drilling success rate.
The integrated thin carbonate reservoir prediction workflow integrated geological analysis and high resolution Seismic Meme Inversion improved the accuracy and reliability of thin reservoir prediction. Under the guidance of geological analysis, the geophysical exploration technology takes full account of the genetic mechanism of geological body, which can greatly reduce the multiplicity of reservoir prediction by using seismic data.
Advancing Portfolio Project Delivery and Operation Handover Assurance Through Digitalization in the Oil and Gas
Authors: Chime Kingsley Chidiebere 
Published: November 2024

ABSTRACT
Consistently delivering success in the dynamic Oil and Gas industry is dependent of the effective identification and development of hydrocarbon opportunities. Research from Senseye Predictive Maintenance shows that the average oil and gas producer loses up to $128 million per year (3). Unplanned downtime and production deferment from missed Asset handover transition continues to add up, and in today’s energy environment, that magnitude of loss just isn’t sustainable.
The recent energy crisis and the resulting focus on energy supply and security have led to a recovery in oil and gas demand. Capitalizing on that demand, however, isn’t so easy. Industry Operators are filling the gap and getting more performance out of existing assets through expansion and modification projects. While the timely execution and delivery of cost effective engineering projects is critical, the actual transition from Project completion to Asset Handover acceptance is considered the cornerstone of this success. This paper explores the role of Digitalization in advancing First-Time-Right Project-to-Asset Handover Assurance in Oil and Gas sector, to enable the efficiency, accuracy, and transparency of this Transition Phase. The harmonization and automation of multiple Project Management business processes, and integration with Digital Tools, have enabled real-time data exchange, collection, and analysis, and ensure that all project deliverables meet the required specifications before handover. This approach provides auditable trail of activities, enhance compliance with related Technical Standards, and provide basis for proper stakeholder oversight of project status. Digitalization have shown to represents a paradigm shift, and an imperative for companies aiming to enhance operational efficiency, reduce risks, and achieve sustainable growth in a highly competitive industry where the Unit Cost of Production needs to be kept at the minimum. This paper would reflect on the enhanced Project to Asset Handover Framework, as implemented in Petroleum Development Oman (PDO), and how it transformed Flawless Project Delivery.
Utilizing AI to Maximize Equipment Availability at Minimum Operational Cost & Reduce Emission Impacts
Authors: Mohamed K. A. , Aboutaleb A. , Ghosh R. , Mahasseb M. , Altayer A. 
Published: November 2024

Organizations striving for operational excellence must constantly evolve their ability to deliver customer value while increasing the cost-efficiency of their operations. The essence of this endeavor is to boost equipment ROI (Return On Investment) by prolonging its useful life and lengthening intervals between major maintenance overhauls, thereby reducing equipment renewal and operational costs while ensuring availability and integrity.
AON's maintenance & reliability department has devised an optimal procedure called RBMO (Risk-Based Maintenance Optimization) to meet these goals. The process starts with a pre-selected list of equipment scheduled for next year's major overhaul according to OEM (Original Equipment Manufacturer) guidelines. Each piece of equipment undergoes four different assessments: AI-predicted performance using the CPAD (Computerized Predictive and Diagnostic) system, condition monitoring techniques (like vibration, oil analysis, thermography, etc…), historical reliability data, and a comparison of energy versus maintenance costs. Should all evaluations yield favorable results, extending the equipment's service life can be done safely.
The development of the process began in 2023. The application of the new process commenced in the fourth quarter of 2023, resulting in an assessment of 11 pieces of equipment and deferring the maintenance overhaul (MOH) for 9 items to the following year. This decision was based on risk assessments that led to a savings of $1.9 million per year in maintenance costs and generated an additional $900K annually in production availability. In the first quarter of 2024, further assessments were conducted on 26 pieces of equipment, with 23 equipment MOHs being postponed to the subsequent year, saving another $875K in annual maintenance costs and enhancing production availability by $2.6 million. In total, these actions have saved $2.775 million per year in maintenance expenses.
Enhancing Offshore Personnel Transportation Efficiency Through Digital Logistics Management
Authors: Larsen F. , Wickholm R. 
Published: November 2024

Executive Summary
The offshore industry is characterized by its inherent complexity, with numerous interconnected components requiring precise coordination to ensure smooth and efficient operations. One of the most critical aspects of these operations is the transportation of personnel, which is often plagued by challenges such as lack of collaboration among multiple stakeholders, reliance on manual processes, disconnected systems, and communication barriers.
This whitepaper examines the various challenges and inefficiencies associated with current offshore personnel transportation processes. It highlights the transformative potential of adopting a digital logistics management system, particularly through a Software-as-a-Service (SaaS) solution. By integrating advanced digital tools into the planning and execution phases of personnel and cargo transport, organizations can achieve substantial improvements in several key areas.
Firstly, operational efficiency can be significantly enhanced through better coordination and real-time visibility of logistics activities. Secondly, cost savings can be realized by optimizing resource utilization and reducing unnecessary expenditures. Thirdly, workforce allocation can be improved, ensuring that personnel are deployed where they are needed most, thereby minimizing delays and enhancing productivity.
In summary, this whitepaper underscores the importance of digital transformation in the offshore industry. By embracing SaaS solutions, organizations can overcome the limitations of traditional methods, leading to more streamlined, cost-effective, and efficient operations. The insights provided herein aim to guide industry stakeholders towards adopting innovative digital strategies that will drive future success and sustainability.
Key Benefits Highlighted: Operational Flexibility: Enhanced ability to adapt to unforeseen disruptions.Seamless Communication: Improved collaboration and information flow among stakeholders.Accommodation Optimization: Better planning and utilization of offshore accommodation resources.Equipment-Staff Alignment: Synchronized arrival of personnel and equipment at offshore sites.
Regional Globigerina Limestone Characterization by Fluid Replacement Model and Pre Stack Inversion for Predicting Future Exploration and Development in Northeast Java Basin Indonesia
Authors: Sumantri Indra 
Published: November 2024

Abstract
Since 1980, Globigerina limestone reservoirs have been explored and successfully discovered biogenic gas. At least, 15 wells were drilled with 13 of them are contributing to energy sustainability in Madura and East Java communities of Indonesia. Gas accumulation of this unique reservoir is very well pronounced in seismic data, either found as seismic flat spot, seismic dim spot, and seismic bright spot. Each of this seismic response correlated to its reservoir characteristic, where porosity ranges up to 55% with permeability up to 500mD. The facies of Globigerina limestone foraminifera are varied from grain-stone to pack-wackestone. Even though the success ratio of this play is high, due to its marginal resource, a thorough reservoir characterization has to be established, or the field may end un-economic when the properties are far worse than expected. Regional AI - Vp/Vs cross plot is the initial tool to define the filled gas reservoir qualitatively among existing wells, roughly describe the reservoir quality through rock density and p-wave velocity. By using the Zoeppritz equations, the reflection coefficients are calculated and generated P-impedance, S-impedance, and density as outputs. Others elastic moduli such as bulk modulus and LMR were derived from these individual parameters for further discriminations among lithology, porosity, and fluid effect. The aim of this regional study is to deliver the workflow in dealing with such unique reservoir, with very limited deposition over the world. This workflow can be used as exploration or appraisal and development tools where size of the field is quite challenging yet still tempting to look and economically valuable for investment in Indonesia.
Integrating Seismic Inversion with Machine Learning for Porosity Cube Estimation: A Synergistic Approach
Authors: Rauf R. , Zubair B. , Vega E. G. 
Published: November 2024

Abstract
This study investigates porosity prediction by integrating seismic inversion with machine learning, employing chi angle-based and Acoustic Impedance (AI) cube-based techniques to estimate porosity values. A systematic approach involving data collection, preprocessing, and seismic inversion is utilized to extract rock property information crucial for accurate subsurface characterization. Advanced machine learning models are developed and rigorously trained on diverse, relevant datasets, with porosity estimated using both methodologies. The efficacy of these methods is thoroughly evaluated through cross-validation and comparison with well-log data, ensuring robust validation. Results indicate that the chi angle-based method excels in capturing subtle, intricate patterns for precise porosity predictions, particularly in complex geological environments, while the AI cube-based method provides robust and reliable predictions across diverse geological settings. A detailed comparative analysis highlights the complementary nature of these two techniques, showing how their integration enhances porosity characterization, providing deeper insights than traditional methods alone. This innovative approach offers valuable insights for subsurface exploration and reservoir management, emphasizing the significant benefits of combining seismic inversion with machine learning for improved porosity prediction, enhanced accuracy in reservoir assessment, and more informed decision-making in exploration and production strategies.
Fiscal Allocation of Partially Stabilized Crude Oil for Marginal Fields
Authors: Saadawi H. , Al Mheiri F. 
Published: November 2024

Abstract
Al Dhafra Petroleum operates marginal fields in remote desert locations southeast of Abu Dhabi. Produced fluids are exported via a multiphase flow pipeline to a nearby sister company for further processing. Multiphase Flowmeters (MPFMs) are used for fiscal allocation. The MPFMs have their limitations and inherent accuracy in flow measurement. A 10 % deduction on oil production was applied for fiscal allocation between the companies.
To enhance fiscal measurement accuracy, new facilities for measurement of single-phase partially stabilized crude oil were recently implemented. The facilities were commissioned in January 2023 and are currently operational and used for custody transfer between the two companies.
This paper addresses the experience gained from this unique application. The approach and methodology adopted in measuring the partially stabilized crude oil are described. Testing, calibration, and proof of the new metering facilities are also discussed. Based on the experience discussed here, measurement of partially stabilized crude outlet from a 3-phase separator offers a reliable alternative to MPFMs by largely reducing the uncertainty, or inaccuracies, associated with measuring multiphase flow.