bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Drift-Flux Model for Upward Two-Phase High-Velocity Flow in Large Diameter Pipes
Authors: Lee Woochan , Waltrich Paulo 
Published: October 2022

Abstract
This study proposes the evaluation and development of a drift-flux model for upward two-phase high-velocity flow in large diameter pipes. A case where the proposed model is applicable is WCD (Worst-Case-Discharge) calculations for offshore wells. WCD assumes relatively larger pipe diameters and higher flow rates than the flow experiments at laboratory conditions utilized to develop most of the flow models available in the literature.
Most of the two-phase flow models describe flow regime transitions as discrete processes by assigning the required void fraction or velocity for each flow regime transition. Therefore, for each flow regime, flow-regime-dependent correlations or fixed drift-flux parameters are applied, which lead to a wide variety of flow regime maps and complications of flow models.
Unlike others, the model proposed does not have sudden pressure surge or reduction along with flow regime transitions from bubbly to non-bubbly flow regimes. Also, it doesn't have fixed drift-flux coefficients for each flow regime. Further, rather than assigning a fixed void fraction or velocity for bubbly/churn flow transition, it applies the minimum pressure gradient estimated by the bubbly flow model to determine bubbly/non-bubbly flow transition.
The proposed drift-flux model is validated for a wide range of experimental and field data and compared with pressure loss models in commercial software. Among all the models, the proposed model gives the lowest average absolute pressure error (2%) and standard deviation (4.5%) for the tested 279 field data points.
Power Generation Through Back-Pressure Steam Turbine Utilizing Waste Process Steam
Authors: Rege Atul , Dahagam Ravi Chandra , Mukherjee Rahul 
Published: October 2022

Abstract
ADNOC Gas processing fractionation facility have majority of steam consumers at low Pressure (LP) level. Steam is produced at medium pressure (MP) level with superheat to optimize the plant design and piping. Steam generators produce Medium Pressure steam followed by Pressure Letdown Stations (PLS) with de-superheating at various places to cater the demand of Low Pressure (LP) steam. Thermodynamically, use of PLS are viewed as less efficient from energy perspective, while the industry applied efficient alternative is the use of Back Pressure Steam Turbine (BPST) to recover the lost energy in PLS.
How Drone Data Capture and a Visual Intelligence Platform Offer Unparalleled Insight into Capital Project Delivery
Authors: McGinn Barry 
Published: October 2022

Abstract
The construction industry is recognising the increasingly important role of digital technology in delivering capital projects on time and within budget. Over 95% of major projects (over $1bn) are delayed or over budget, according to a report by Accenture. Project owners are under pressure to become more efficient, innovative, and ambitious in how they operate, they are turning to digital technologies to enable full project visibility and evidencing data to make more informed decisions.
The execution of capital projects within the energy sector is typically complex and often relies upon information being shared between the site and globally dispersed project teams. Determining a holistic view of a capital project can be challenging. Storing and interpreting key data associated with planning resources, equipment allocation, utilization and coordinating the delivery of raw materials only compound the challenge of effective reporting.
Traditionally, assessing the status of a project was heavily reliant on paper-based processes. This was a difficult method to collate and manage valuable project information centrally and key stakeholders often had to make decisions based on partial data. The process relied on management's presence frequently onsite and a ‘boots on the ground’ approach to visual inspection. Large volumes of data were often captured by quality inspectors and construction engineers which was an arduous and time-consuming task.
Factory Drilling and Fracturing Technology for Tight Sandy Conglomerate Oil Reservoir in the Junggar Basin
Authors: Chang Long , Zou Lingzhan , Wang Haige , Yu Yangfei , Liu Qi , Zhuo Lubin , Yu Jing , Wu Baocheng , Xu Jiangwen , Shi Jiangang 
Published: October 2022

Abstract
The Triassic Baikouquan Formation reservoir in the Mahu Sag, Junggar Basin is characterized by tight sandy conglomerate oil reservoir with long drilling cycle and great fracturing difficulty. The factory drilling and fracturing techniques of horizontal wells for the Ma131 Well Block of Mahu Oilfield have been determined by regional geological characteristics research and optimization scheme design.
The key technologies for horizontal well drilling speed improvement were explored by designing small three-stage wellbore structure, customizing the PDC bit properly, optimizing drilling tool combination, and selecting the drilling fluid with inhibition and plugging ability. The factory reservoir stimulation technique carried out horizontal well hydraulic fracturing with small well spacing for two sets of beds, and adopted bridge plug + clustering perforation staged fracturing and slick water + gel composite fracturing technology.
12 wells in a drilling pad were arranged in the demonstration area with the depth of 5000m, the distance between adjacent sections was 150m or 100m, with the horizontal section length of 1800m. Batching drilling operation pattern was adopted, with 3⁃well drilling for each of rig. Rapid inclining prevention drilling through vertical sections adopted PDC bit + screw + MWD, with high rotary speed and high flow rate. In the build-up section, radical drilling parameters was used to improve the top drive rotary speed and release the torque. In the horizontal sections, rotary steering tool + low speed high torque screw was adopted. The application of this technology achieved good results, with average drilling cycle of 48.3 d and average ROP of horizontal section increased by 77%. The fracturing construction adopted high pad fluid ratio, high sand ratio and high pump rate. More than 10,000 m3 fluid was injected in a single well with high-intensity proppant injection. The factory operation was carried out in the order of construction from the two sides to the middle well with microseismic monitoring. Factory fracturing with small well spacing generated stress interference and improved the fracture reconstruction effect. Higher shut-in pressure, more microseismic events and large-scale stimulated reservoir volume had been carried out in horizontal wells fractured later.
The factory drilling and fracturing techniques of horizontal wells for the Ma131 Well Block have achieved the goals of improving drilling speed and volume fracturing, which provided technical support for the efficient development of the tight sandy conglomerate oil reservoir of the Mahu Oilfield.
De-Carbonisation Through Energy Management
Authors: Somayajula Harish 
Published: October 2022

Borouge, established in 1998 in Abu Dhabi by the Abu Dhabi National Oil Company (ADNOC) and Austria based Borealis, is a leading petrochemical company that provides innovative and differentiated polyolefin solutions. Combining the strengths and experience of its majority shareholders ADNOC and Borealis, Borouge serves a wide range of industries including energy, infrastructure, mobility, advanced packaging, healthcare and agriculture. As a strategic and successful partnership at Borouge, we employ more than 3,100 people with over 50 nationalities, serving customers in over 50 countries across the Middle East, Asia and Africa.
Abu Dhabi Polymers Company Limited (Borouge) ("ADP"), headquartered in Abu Dhabi and the sales and marketing joint venture, Borouge Pte Limited ("PTE"), headquartered in Singapore. ADP consists of the main manufacturing activity of Borouge, whereas PTE consists of the marketing arm of the Borouge business.
Our petrochemicals and polyolefins manufacturing plant is located in Ruwais at a distance of about 250 km west of Abu Dhabi City. The facility is now one of the largest fully integrated single-site polyolefins complex in the world, with an annual capacity to produce 5 million tonnes of polyethylene (PE) and polypropylene (PP). The complex is also the largest Borstar® process technology-based plant in the world, providing enhanced innovative bimodal polymers for a broad range of polymer applications. We remained on track to increase our production through Borouge 4, the next mega-project expansion that will significantly increase our production capacity by 2025. Moreover, we have already started-up our fifth polypropylene plant (PP5) in Ruwais.
Facilitate Geoengineering and Completions with Machine Learning Methods: Case Study from Ordos Tight Oil Field
Authors: Li Jian , Lei Qihong , He You‘an , Han Xueliang , Qi Yin , Mu Lijun , Liu Yuan , Li Haoyan , Ma Fujian 
Published: October 2022

Abstract
Geoengineering is an important integrated approach for oilfield development. Conventional geoengineering focuses on geology and reservoir studies using analytical or numerical methods. A new geoengineering approach uses data mining and machine learning as input to completion optimization. Compared to conventional practice, this innovative approach allows field development optimization to be based on statistics instead of numerical models.
The application of geoengineering to completion means converting geology and reservoir understanding into a form that supports the well completion engineering. Conventional geoengineering work is heavily associated with building geology models and populating reservoir property grids; these require a huge amount of data and human effort to set up and sustain. In addition, in conventional geoengineering for completion, the engineered completion design, production modeling, and pressure matching can show high levels of uncertainty.
The data-driven approach incorporates data mining and machine learning methods to improve the understanding of the reservoir from wireline measurements, geological modeling, and completion practice. Because this approach is based on data and algorithms, the potential exists for improving efficiency and accuracy.
In a case study, data from 130 existing horizontal wells in the Ordos tight oil field were collected for analysis. These data included logging data, well engineering parameters, and production performance data. Beyond data mining, which focused on the data cleanup and visualization, the typical machine learning methods, including analysis of variance, linear discriminating analysis (LDA), and artificial neural network (ANN), were used to improve the logging evaluation, to define the effective sand body and geology quality, to calculate effective lateral length, and to demonstrate trends leading to a completion optimization strategy.
The high efficiency and accuracy of data mining and machine learning contribute to upgrading our understanding of the logging data, reservoir properties, completion engineering, and production performance. With the support from those powerful tools, we can use geological and reservoir input quantitatively for completion engineering, which enables better well planning and better production evaluation.
This case shows examples of data mining and machine learning used in geoengineering for completions. Compared to conventional geoengineering, which is based on analytical and numerical modeling, this innovative approach built on data, statistics, and algorithms has the potential to be more efficient and accurate.
How to Build a Sustainable Offshore Platform: A New Design Philosophy
Authors: Maxwell Stewart 
Published: October 2022

Abstract
It is an unpalatable truth that all engineering and construction works, either onshore or offshore will have an environmental impact, during all the design, fabrication, and operational phases. Balancing the worldwide need for hydrocarbons with the desire to maximise the ESG benefits of an offshore development require a change in the way we approach both platform design and project implementation.
In this paper we highlight the main points in this approach to platform design, and the direct and indirect benefits gained. A case study is presented, where sustainability was at the core of both the clients desires but also in the design philosophy used. The outcome of this project and the benefits gained are also outlined.
The next steps to be considered in this philosophy are also discussed, where additional benefits gained from the use of Digital Twins can be leveraged to provide further potential reductions in the overall carbon footprint.
A System to Inspect Corrosion Under Insulation via Temporal Thermography & Artificial Intelligence
Authors: Al-Jarro Ahmed , Alshehri Ali , Amer Ayman , Althobaiti Abdulrahman , Saiari Hamad , Alsalman Fatima , Albaqshi Muntathir , Chan Godine , Kakpovbia Anthony , Kairiukstis Edvardas , Odisio Matthias , Qian Weiwei , Roy Arjun , Saad Bilal , Shapiro Vladimir 
Published: October 2022

Abstract
In this work, we present a novel Artificial Intelligence (AI) powered non-destructive testing (NDT) system for the detection of potential corrosion under insulation (CUI) inspections, code named DPCUI, developed by the Research and Development Center (R&DC) at Saudi Aramco in partnership with Baker Hughes. This inspection system enables fast external thermographic screening of large facilities by covering many condition monitoring locations (CML), and without any contact with the asset surfaces. It examines temporal thermography datasets that are collected using a high-resolution IR camera, such as those provided by FLIR, and on one or more RGB images that provide context of inspected areas. The collected data is analyzed by a dedicated AI engine to detect the presence of abnormal heat transfer signatures that occur due to defects present within the targeted CMLs. The novelty of this AI powered technology has several advantages. It provides a contact-less, smart, easy, fast, automated, safe, and reliable risk-based repair and maintenance decision making on the integrity of assets, enabling asset owners to efficiently prioritize their operations and processes in a seamless manner while the assets are kept online. Here, we enhance and extend the performance of our AI models to predict not only the presence of potential CUI or not, i.e. binary classification, but also various types of potential CUI, i.e. multi-class classification, a first of its kind.
AI and Digital Transformation: Integrity and Maintenance
Authors: Al Aamri Amran Hamed , Al Fahdi Khalid Khamis 
Published: October 2022

Abstract
Moving forward to Operation & Maintenance excellence in OQ and as per the continuous improvement approach to improve maintenance activities at sites in terms of safety, quality, cost, and time which will reflect on mitigation in risk and redaction in maintenance cost. The quality and the culture of the maintenance team on how to execute the maintenance activities on the field have been improved by the execution of a digitizing project. We moved to digitize field activities MCPs (maintenance procedure craft) as QA and digital collective data (Quantitative/Qualitative) and allowing us to apply QC protocol in maintenance tasks in a proper way. Therefore, the Reliability section started digitalizing maintenance activities via Asset Performance Management (APM) tool to enhance compliance and the Quality Management System of maintenance activities.
Implementation Challenges in Automation of Gas Lift Optimization Workflow: A Case Study for Digital Fields Journey
Authors: Mokhlis Mohamad Mustaqim , Basarudin Mohd Ashraf , Hassan Muhammad Firdaus , Fuenmayor Roberto , Trivedi Rajesh 
Published: October 2022

Abstract
The oil and gas industry has evolved and embarked into the new era, it includes digitalization and cognitive computing, cyber-physical systems, and cloud computing or so-called industry revolution 4.0 or IR 4.0. This industry has been experimenting with artificial intelligence on any task that is time-consuming, requires precise results, and minimizes human error. Most of the giant operators have difficulty managing huge amounts of data that stream from the field every second. Thus, having a good workflow or system that automates the process, reliable data set, and the right people mindset is a huge game changer. These three main components are the main ingredient for digital transformation. Nevertheless, transforming the right people mindset is key for this objective to be obtained and sustained for long run.
Main operator in Malaysia has urge for going digital as part of their transformation journey. By means of collaboration and pace, any operation process or standard operating procedure (SOP) should be reviewed and align with their transformation objective. Nonetheless, managing gigantic data with diverse types and sources from 43 fields is bizarre. This requires a careful plan that consumes lots of resources and effort.
Bringing additional barrels with minimal cost and less effort is the key component of digital field objective. Gas lift optimization workflow is introduced as a workflow that automates the process of optimum gas lift injection rate for maximizing the production using real time data. Gas lifts are being re-distributed across all active producers based on the availability of gas lift supply to identify opportunities to improve production behavior at field level.
Consecutively to accomplish this objective, a special project team is established for initiation, planning, assessment, evaluation, development, deployment, implement and lastly suitability of the digital ecosystem. Through this journey, few challenges were encountered especially when most of the fields are matured fields with lack of real time technologies and some still in analog mode. For marginal field to invest on such technologies will cost them enormous budget and not feasible for the long run.
The objective of this paper will detail out the challenges raised during the project implementation. Some methodologies and strategies of how to overcome the respected challenges will be described further. This paper should be beneficial to all engineers and technologists, especially main operators that plan to take the challenges for digital transformation.
Enhance Compressor Performance by Value Engineering
Authors: Saxena Ankit , Al Hammadi Hamed 
Published: October 2022

Abstract

Background
The continuous operations of axial and Centrifugal type of blowers and Compressors are critical for the Gas Processing facilities and Compressor Surge which can cause catastrophic failure of the equipment, leading to loss of containment and production upsets
Whenever there is no insufficient forward flow through a compressor, surge occurs. Surge is the inability for the compressor to sustain the forward flow and flow reversal happens very quickly.
The purpose of the Anti-surge valve (ASV) is to protect the machine from surge conditions by increasing the suction flow. To sustain the forward flow, a recycle line is provided with ASV which quickly opens as per the requirement with minimum dead time and hysteresis.
Hence the response of Anti surge valve plays an essential role in compressor performance and compressor life cycle. In current set up, sometimes the response time of anti-surge valve was not achieved, and this caused machine going to surge or process upset. To overcome this certain minimum opening of ASV is kept, which introduces continuous recycle and energy wastage.

New technology
Considering the above limitations, a new actuator technology has been reviewed. The newly introduced Electraulic technology is combined between both, Electric mechanism and hydraulic system, to achieve faster response time, almost negligible overshoot and dead time. This enabled anti surge valve to open very fast on demand which comes and bring the machine out of surge quicker.
As a Pilot project, this actuator technology has been implemented in one of the anti-surge valve 552FV1583 of the combustion air blower 552B101A, which is critical equipment in Habshan -5 sulphur recovery unit. This new technology actuator was commissioned and in operation since Oct-2021.

Conclusion & Recommendation
Various tests have been carried out to confirm the smooth and fast performance of the ASV and the results are found to be satisfactory.
This has enabled to tune the anti-surge controller settings which caused reduction in surge margin. This has resulted in consistent energy savings due to the less or minimum recycle operation compared to the previous operating scenario with conventional pneumatic actuators. Valve performance parameters are consistently exceeded compared to the KPI /Benchmark for this application.
Reduction of motor power and equivalent Energy savings 2800 MW per year observed, which will result in the direct cost savings of fuel consumption to generate the electricity. This saving of motor power is equivalent to saving a 1139 tons of CO2 emission per year.
Based on the successful outcome of this Pilot, this technology can be considered for similar applications with existing poor performance of anti-surge valves. Also, for new anti-surge valves in the Project, where this is found to provide the required cost benefit.