bluuebunny/crossref_metadata_2025_split huggingface dataset:
El Reglamento de Inteligencia Artificial de la Unión Europea: regulación de riesgos y sistemas de estandarización
Authors: Miranzo-Díaz Javier 
Published: November 2024

AI has been consolidated in recent years as a central line of action in the strategy of public administrations, and the AI Regulation approved by the EU intends to become a pioneering global standard for the regulation of this technology. This article analyses the nature and main characteristics of this regulation approved on 13 March 2024 by the European Parliament. The study conceptualises the EU Regulation as part of a normative trend, existing both at European and international level, consisting of the regulation of risks and situations of uncertainty. On this premise, the paper analyses the legal architecture of the norm, which revolves around a conformity assessment system that relies, in its turn, on three elements: (1) risk management instruments applicable to AI developers and implementers, (2) a procedure for the development of technical standards that will determine the specific content of the obligations of the regulation, and (3) the creation of a governance system for the implementation and verification of such obligations.
Better Predictions, Better Outcomes: How Integrated Cost & Schedule Risk Analysis (iCSRA) Reduces Costly Consequences of Project Delays
Authors: Britton Christopher 
Published: November 2024

Abstract
The success of major capital projects hinges on the balance between revenue and expenditure. Integrated Cost & Schedule Risk Analysis (iCSRA) emerges as a critical tool for enhancing predictability and mitigating risks associated with project delays and cost overruns. This paper explores the impact of iCSRA on project outcomes, drawing on actual performance data openly published by the Norwegian Offshore Directorate and other industry benchmarks.
Key findings reveal that a significant proportion of projects exceed budget and schedule estimates at Final Investment Decision (FID), with only one in seven (14%) experiencing cost and schedule underruns. iCSRA helps address these challenges by providing a probabilistic approach to risk management, enabling better alignment of project teams and more informed decision-making. The methodology involves detailed risk identification, probabilistic modelling, and collaborative workshops to refine project schedules and cost estimates.
The paper underscores the importance of robust data inputs and the need to account for biases and uncertainties in risk assessments. By integrating cost and schedule risks, iCSRA offers a comprehensive view of project uncertainties, facilitating more accurate predictions and effective risk mitigation strategies. The ultimate goal is to maximize project success by improving the confidence level in achieving both cost and schedule targets, thereby enhancing the overall value and credibility of projects.
Enhancing Plug-N-Perf Technology for Unconventional Wells
Authors: Faizov Yury , Reshetnikov Alexander , Ali J. Alshehhi Hajer Mohamed , Kumar Rakesh 
Published: November 2024

Abstract
Company has been implementing fracturing in its carbonate reservoirs since 2017, with the aim of improving well performance, reducing operational costs and risks, optimizing reservoir management. This paper reviews challenges faced and evolution of Plug-and-Perf (PnP) multistage fracturing technology implementation in Company through engineering, field execution and lessons learnt, with special focus on milling of frac plugs and surface flowback set up and operational methods to face and efficiently handle harsh high temperature high pressure (HTHP) and high H2S environment.
Methodology approach is a flexible and iterative method that allows for rapid adaptation and improvement of PnP operations. Methods involved conducting several simultaneous PnP operations with different service providers and vendors, and capturing and implementing lessons learned and best practices across all operations. Main steps for the methodology included the following: operational optimization, equipment selection and engineering, whereby engineering process of designing and implementing PnP technology based on operational optimization and equipment selection results throughout all the process.
Paper describes and shares experience and lessons learnt for surface equipment set up to run and set plugs and casing perforation, lessons for plug removal from horizontal sections via implementation of thorough engineering process, utilization of latest available technologies, such as extended reach tools, composite and dissolvable plugs, utilization of properly planned and designed surface flowback equipment to handle HTHP operations with H2S content in fluids returning to surface during milling operations.
Optimizations and improvements include, but not limited to: thorough engineering and selection of coiled tubing (CT) pipe size, milling bottom hole assembly (BHA) components, surface flowback equipment selection, milling and wellbore clean up practices and recommendations
Approaches, lessons learnt and best practices that are described in the paper will be beneficial and can be used as a reference source for other projects for PnP operations where harsh HTHP and H2S environment present, Company will continue to implement and optimize PnP technology in its carbonate reservoirs, as well as explore the potential and feasibility of PnP technology in other reservoir types and environments.
Unlocking the Potential: Enhanced Field Development Through Integrated Analysis of Pre-Drill and Post-Drill Reservoir Data in a Giant Offshore Field, Abu Dhabi
Authors: Al Zinati O. , Alyan M. , Alzaabi M. , Almessabi W. , Howell A. , Alshamsi F. , Alteneiji A. , AlTamimi M. , Al Zaabi N. , Fazal A. 
Published: November 2024

Abstract
Intensive field development is being executed with extended reach Maximum Reservoir Contact (MRC) wells in a giant offshore field in Abu Dhabi. Given the drilling complexity in brown fields with various pressure regimes, the integration between static and dynamic reservoir data is crucial to ensure that wells are optimally placed in the right reservoir zone with an optimized performance. The process helps to identify opportunity generation, and thus maximizes asset value. The process is streamlined to become efficient covering multi-disciplinary aspects for hundreds of drilled wells.
For improved outcomes, the process typically is conducted about six months after the first production date. The degree of details included varies depending on the well objectives and the asset requirements. The process involves data-driven integration of static and dynamic data (well logs, production, pressure, etc.) obtained from a newly drilled well and compare them against pre-drill expectations. Digital platforms are utilized, and multi-disciplinary team members discuss the observations after proper compilation of the data acquired. All the observations, action items (including surveillance and mitigations plan), and recommendations are summarized, documented, and shared with entire Field Development Planning (FDP) team to learn from drilled wells.
The outcomes of the process have a significant impact on decision making of field development. The process enables identifying potential opportunities to maximize production and oil recovery of the field through optimizing well placement, well spacing, horizontal length, and completion designs. The values of information are clearly addressed from data gathered and surveillance activities to support FDP decisions. For example, production logging tools gathered assure production/injection and assess stimulation effectiveness in long reach laterals. Thus, deployment of Limited Entry Liner (LEL) designs for lower completion has been remarkably successful in facilitating cost effective stimulation to maximize areal sweep efficiency. Reservoir models are also assessed as part of the process by comparing them to actual observations to ensure their quality for optimal FDP decisions. Ultimately, implementing such integration can greatly optimize field performance, achieve anticipated production growth, and realize net impact.
A Machine Learning Model for Real-Time Early Kick and Loss Prediction EKLP Application
Authors: Zhang Shang , Samuel Robello 
Published: November 2024

Abstract
Early Kick and Loss Prediction (EKLP) is crucial in drilling operations to maintain well control and prevent catastrophic events such as blowouts. Real-time detection allows for immediate corrective actions, minimizing risks and costs. This paper presents a CNN-LSTM hybrid model, leveraging Convolutional Neural Networks (CNN) for feature extraction and Long Short-Term Memory (LSTM) networks for time series analysis. The model processes real-time data from various drilling metrics, including Rate of Penetration (ROP), Weight on Bit (WOB), Revolutions Per Minute (RPM), Standpipe Pressure (SPP), flow values, pump rates, and mud pit volumes.
Trained on a dataset of normal operations and confirmed kick/loss events, the CNN-LSTM model demonstrated high accuracy and speed in predicting abnormal events, significantly outperforming traditional detection methods. The model was integrated into a real-time monitoring system for practical applications, showing superior ability to process sensor data and predict kick and loss events. This hybrid approach enhances operational efficiency and decision-making in drilling.
In conclusion, the CNN-LSTM model provides a robust and reliable solution for real-time EKLP, combining the strengths of CNN and LSTM to handle complex drilling data. This approach improves detection accuracy and system adaptability, making it a vital tool for modern drilling operations. The model's potential to address other drilling challenges, such as stuck pipe events and drill bit fouling, further highlights its versatility and utility.
CCUS CO2 Injection Well Design Using Cloud-Based Software
Authors: Mwansa P. , Hassan M. Ali 
Published: November 2024

World Nations agreed, under the 2015 Paris Agreement, to prevent increasingly dangerous and irreversible climate change impacts and limit global temperature rise to 1.5 degrees C above pre-industrial levels, which means cutting GHG emissions in half by 2030 and reaching net-zero CO2 emissions by mid-century in 2050. Carbon capture, utilization and storage (CCUS) on a grand scale is essential for the energy transition and achieving Net Zero objectives. In this aspect, it is crucial to ensure Fit-For-Purpose, safe and environmentally sound CCUS CO2 Injection Well Design.
This paper describes a process for CCUS CO2 Injection Well Design using a cloud-based software tool named WellDesign that has all regulatory well planning calculations in a single application with Open APIs to connect with third party applications and offering automation and efficiency in well planning & design. Will also present a design case that were performed during a Proof of Concept (PoC) test of digital well planning with a major Middle East national oil company.
Cloud-based computing allows businesses to use a system of remote servers hosted on the internet (the "cloud") to store, manage and filter their data. Instead of storing large quantities of data in on-premises servers. Using cloud-based software allows CCUS companies to create a more efficient and scalable work systems. It can also provide a centralized place to store and access unlimited data, making data analysis and data-driven decision-making much easier for teams within the company.
Design Considerations for Pressurized Flare and Vent Systems for Efficient CO2 Management
Authors: Rodrigues Hugo , Zaccheus Yemi , Seer Taseer 
Published: November 2024

Abstract
Carbon Capture, Utilization and Storage (CCUS) projects have gained momentum and are multiplying across the globe with more than 700 projects in various stages of development across the CCUS value chain. With the increase in carbon dioxide (CO2) related large scale projects, hazards must be well understood by the industry to avoid incidents that could hinder public acceptability or further development of these technologies. The hazard management of CO2 poses several risks such as physical property predictions, thermal expansion, low temperature, solid formation, corrosion, toxic effects, system vents and others.
This paper presents a discussion of the design challenges encountered in CCS and high CO2 gas field development projects. It introduces an innovative concept of flare segregation, which is not common in the petroleum industry but is expected to gain prevalence in the coming years with the expansion of CCS projects. Most commercial process simulators face challenges when it comes to handling high CO2 content at high-pressure conditions, such as those found with dense phase. Thermodynamic equation of state selection and solid formation prediction are key requirements for process simulation tools.
Design calculations show that the pressure maintained by the pressurized flare/vent can vary between 18barg to 6barg. The accurate assessment of this limit hinges on the precise prediction of the process-side fluid temperature, requiring an appropriate model of the thermodynamic and process system behavior. The subsequent Joule Thompson effect from the worst-case composition can be captured and used to calculate the tailpipe and header fluid and metal temperatures, as well as estimate dry ice formation. Design constraints such as flare capacity and API 521 15 min depressurization time may require staggering systems and procedural decreases in pressurized flare pressure.
Research on Substitution Path of Clean Energy for Production System in Oil and Gas Fields: A Case Study of Tarim Oilfield
Authors: Deng Qianxing , Wen Zhang , Ren Yongcang , Wang Kelin , Li Junyi , Guo Xinyong , Yong Xuemei , Xin Chen , Miao Xinyue , Zhao Zhiyong , Huang Chunjian , Gao Jieyu , Wei Zhiqiang 
Published: November 2024

Abstract
Under the goals of carbon peak and carbon neutrality, oil and gas fields enterprises undertake the tasks of oil and gas production and energy conservation and emission reduction. It is imperative to accelerate low-carbon transition and green development. However, energy consumption of production system in Tarim Oilfield Company of CNPC is mainly composed of natural gas and electricity, accounting for 92% and 8% respectively. Moreover, total energy consumption continues to increase, and the trend of carbon emission is rising under the existing production mode. Thus, clean energy such as solar energy, geothermal energy, and waste heat are utilized to produce clean electricity and heat energy. But the proportion of clean electricity and heat consumed is still less than 6% in structure of energy consumption. This is far from the transition goals of CNPC. It is urgent to speed up the development of clean energy to replace fossil energy consumed of production system.
The solutions and measures were determined after many discussions. Firstly, the demand of energy were reduced by energy-saving measures. Secondly, clean electricity and thermal energy were developed to increase the proportion of non-fossil energy in the energy consumption structure. Distributed photovoltaics and waste pressure of natural gas were utilized, and green electricity was purchased to decrease consumed grid electricity and natural gas. In order to cut down consumed natural gas, a method for selecting clean heating equipment was formulated, low-cost solar thermal equipment were developed and applied, and waste heat and low-temperature air heat were explored to recycle. Then, existing processes were electrified to build low-carbon oil and gas production system coupled with the characteristics of clean energy.
The results of cleaner production are very significant in the Oilfield. Overall, energy consumption was reduced by about 74,000 tce(tons of coal equivalent) and natural gas was replaced by about 4×107 m3 in 2023. As for green and low-carbon transition, while oil and gas commodities increased, total energy consumption and total carbon emission declined by about 2% and 3.5% respectively year-on-year. The electrification rate increased by 10% compared with last year. As for clean substitution, the production capacity equivalent of clean energy reached 53,000 tce, year-on-year rises of 114%. Clean heat consumption increased to 16,700 tce, a year-on-year increase of 108%. At the same time, clean electricity consumption increased to 7.4×107 kWh(kilowatt-hours), which was 615 times than that of last year. Then, six low-carbon demonstration areas and 10 carbon neutral wellsites or stations were built. As these measures are gradually implemented, it is expected that the results will be more obvious in 2024.
Total Emissions Estimation for Oil and Gas Production Facilities Using Aerial Gas Mapping LiDAR
Authors: Thorpe M. J. , Donahue C. P. , Kreitinger A. , Roos P. A. , Brasseur J. K. , Losby B. , Greenfield N. , Carre-Burritt A. , Kunkel W. M. , Altamura D. T. , Dudiak C. D. , Oberoi K. , Hengst V. 
Published: November 2024

Abstract
We present Gas Mapping LiDAR (GML) for methane emissions monitoring, mitigation, and informatics for oil and gas infrastructure. GML is deployed on small-manned aircraft for periodic monitoring to achieve high-accuracy and cost-effective detection, localization, and quantification of methane emissions. GML combines spatially scanned topographic and methane concentration LiDAR measurements with navigation data and aerial photography to create geo-registered methane plume imagery, LiDAR topography, and photographic mosaics. Data and results based on several years of single- and double-blind controlled release testing conducted with academic and industry partners is presented. Results demonstrate the high sensitivity LiDAR measurements allow detection of methane emissions at rates below 1.5 kg/h with greater than 90% probability of detection in most deployment conditions, allowing the detection of more than 90% of emissions from typical oil and gas production basins. The high spatial resolution of the LiDAR scans provides geo-location of emission sources, typically to within 2 m, for targeted LDAR response. The LiDAR methane concentration measurements, coupled with gas flow speed information, enable single-pass quantification error ranging from −65.6% to +78.9% (95% CI). However, when multiple plume measurements are aggregated from a set of facilities, total emissions rate quantification error below ±10% is achievable, supporting the development of accurate measurement-informed inventories. We present a method for estimating total emissions that combines models for interpreting individual source measurements and infrastructure data in a statistical analysis that is stratified by equipment type and facility class. We demonstrate the method by applying it to aggregated and anonymized emissions data from nine scans of 1350 facilities in the Permian basin and 320 facilities in the Eagle Ford shale play, spanning 2022 to 2023. Back-to-back scans of the facility sets are analyzed to evaluate the repeatability of the total emissions estimates relative to the uncertainty estimates produced by the method. A time series of total emissions from the two facility sets highlights the emissions reductions impact and cost-effectiveness of GML LDAR programs. In the Permian basin, annualized total emissions uncertainty for the scanned facilities was 24% in 2022 and 25% in 2023 with a year-over-year emissions reduction of 30% [7%, 54%] (95% CI). For the Eagle Ford shale play facilities, the annualized total emissions uncertainty was 13% in 2022 and 10% in 2023, with a year-over-year reduction of 20% [5%, 35%] (95% CI).
Reliability Study of Data Microchips for Downhole Data Acquisition Subjected to Pseudorandom Vibrations
Authors: Tella Sherif , Li Sanguo , Li Jibo , Zhan Guodong 
Published: November 2024

AbstractDownhole data acquisition is a critical aspect of drilling operations for maintaining optimized and safe drilling conditions. However, the real-time downhole data readings while drilling are best captured using affordable drilling data microchips. Typically, microchips include an SoC (System on a chip), secondary memory, sensors, and an on-chip battery. But during drilling and releasing procedures into the well annulus, the data microchips may be subjected to random stresses that cause interconnects and components to fail, which ultimately results in damage, loss of stored data, and a low recovery rate after being released. Since random vibration loads can limit the efficient performance of drilling microchips, it is crucial to study the peak responses of the device when exposed to such stresses or a designed response spectrum. The development and validation of simulation modelling approaches is the initial step in this direction. This paper presents the numerical analyses and stress development mechanisms of the data microchips subjected to pseudorandom acceleration by a specified Power Spectrum Density (PSD). The highest computed RMS acceleration values are estimated to be about 25 g, 16 g and 18 g in the X, Y and Z directions respectively. However, assuming that the allowable peak value is thrice more than the RMS values, the peak acceleration can be evaluated to be 75 g, 48 g and 54 g in the X, Y and Z directions respectively. This indicates that there may be damage and loss of stored data on the memory chip. The permitted accelerations for the impacted components should be examined due to these high peak values which are more than the allowable vibration levels for downhole drilling tools. Hence, this device may require vibration absorber system. It is also necessary to estimate the margin of safety from the projected peak values. The pre-stress on the supporting unit is evaluated to be 60 MPa assuming 1 kN of force, the margin of safety is evaluated to be 7.6, 12.0 and 6.7 for the excitations in X, Y and Z directions respectively. In all excitations, the margin of safety is positive, indicating that the support might not break free from the housing unit and demonstrating how much stronger the support is than what is required to hold the actual vibration.