bluuebunny/crossref_metadata_2025_split huggingface dataset:
Acoustic Anti-Scaling Technology for Sustainable Scaling Prevention in the Oil and Gas Industry
Authors: Al Munif E. H. , Alhamad L. A. , Almubarak T. A. 
Published: October 2023

Abstract
Scaling in the oil and gas industry is a common problem that increases expenses and reduces production rates. Traditional methods of scale prevention include chemical treatments that can be expensive and have environmental implications. Acoustic anti-scaling technology is an alternative method that has gained attention in recent years. This paper provides an overview of acoustic anti-scaling technology, including its principles, applications, and limitations. The paper concludes by discussing the potential for further research and developments in this area.
This review includes various laboratory studies that evaluate the potential of using acoustic anti-scaling technology in scale removal applications. A comprehensive literature review was conducted. Acoustic anti-scaling technology was also compared to mechanical and chemical scale treatment solutions currently used by the industry.
Acoustic anti-scaling involves the use of ultrasonic waves to disrupt the formation of mineral scale deposits. The ultrasonic waves create high-frequency vibrations that prevent mineral particles from attaching to surfaces and forming a solid scale layer. Typically, the frequencies used for anti-scaling are in the range of 20 kHz to 100 kHz, although some systems may use higher or lower frequencies depending on the specific equipment, composition of the fluids and the desired level of scale prevention. Acoustic anti-scaling technology has been shown to be effective in reducing scaling in various industrial systems, including desalination plants and cooling tower systems. For years, chemical and mechanical treatments have been implemented effectively to achieve scale removal and prevention. Water consumption, cost and environmental concerns have been associated with these methods.
The review also highlights some limitations of acoustic anti-scaling technology. The technology is most effective in preventing the formation of soft scale, such as calcium carbonate. However, it may not be as effective in preventing hard-scale formation, such as barium sulfate. The effectiveness of the technology may also be limited by factors such as water chemistry, flow rate, and temperature. Therefore, further research is needed to optimize the technology's performance under various conditions.
The paper provides novel information by analyzing the available literature on traditional scaling prevention methods and acoustic anti-scaling technology, and identifying the current state of research in this field. It also highlights the potential of acoustic anti-scaling technology as an environmentally friendly and cost-effective alternative to traditional methods of scaling prevention in the oil and gas industry.
In Field Referencing Study by Drone Aeromagnetic Measurement
Authors: Wiharja A. , Jufrihadi J. , Sany M. F. , Gunawan F. , Pribadi M. , Macresy L. , Momot F. , Sikal A. , Harbidge P. 
Published: October 2023

Abstract
Relief well drilling plays a crucial role in mitigating uncontrolled well incidents, ranging from minor well-control events to catastrophic blowouts. Achieving accuracy in drilling a relief well, particularly in intercepting a small target or re-entering an uncontrolled well casing, is of paramount importance. To ensure success, advanced survey management techniques are essential for precise relief well and target well positioning. Among various techniques, conducting an In Field Referencing (IFR) acquisition has proven to have a significant impact on reducing well lateral uncertainties by accurately determining the local geomagnetic field and its crustal anomalies.
In the context of relief well drilling operations, achieving timely and effective In Field Referencing (IFR) is key due to the time constraint. This paper focuses on the chosen method of deploying a drone from an offshore barge in order to conduct the IFR, highlighting its efficiency and effectiveness. The utilization of a drone enables rapid deployment and data acquisition, contributing to significant time savings. Additionally, the offshore barge serves as a stable platform, facilitating precise positioning and data collection during the IFR procedure.
Notable advantages of IFR acquisition include fine-tuning target well positioning through IFR and Multi Station Analysis (MSA), reduced lateral uncertainty of the target well, enhanced quality control thresholds for MWD data, accurate calculation of azimuth using IFR-corrected declination, increased chances of target well detection during the locate phase, improved interference detection from the target well, and enhanced interception efficiency with robust HSE management.
This article presents a comprehensive study on the implementation of IFR during the planning phase of an offshore relief well project in an offshore field. It covers various aspects, including mobilization, acquisition set-up, methods employed, procedural considerations, and the obtained results. By shedding light on the IFR process, this study aims to contribute to the knowledge and understanding of advanced survey management techniques in relief well drilling operations.
Leveraging Condition Based Major Overhaul for Higher Uptime and Profitability
Authors: Al-saedi Khaled , Al Awadhi Hesham , Kalhoro Muhammad U Ddin , Nadeem Muhammad , Al Harmi Sultan , Al Halabi Saleh 
Published: October 2023

Abstract
ADNOC's smart growth strategy necessitates all entities to maximize efficiency and profitability through transformation of business strategies and embracing the latest technologies. In oil and gas operations, major overhauling of rotating equipment is one of the main contributing factors to increasing operating cost and plant availability. However, to ensure the integrity and reliability of rotating equipment, a cost-effective maintenance strategy must be applied over the asset life cycle. In the pursuit of this objective, ADNOC Onshore Bu Hasa team worked towards optimization of major overhaul maintenance adapting a holistic approach for rotating equipment through transformation of maintenance overhauling strategy from traditional time based to condition-based overhaul. The condition-based maintenance overhaul strategy is based on accurate thermodynamic performance monitoring combined with physical condition assessment that was achieved through a customized digital solution.
Condition-based overhaul strategy was applied mainly on three different types of equipment that includes large size process pumps (centrifugal), special type Helico-axial design Multiphase pumps and centrifugal gas compressors. As a first step, revised overhaul strategy applied on water injection pumps (high pressure multistage centrifugal pumps) considering its impact on recurring maintenance cost and scheduled down-time, as on average 6 to 8 units become due for major overhaul each year based on meter-based frequency. And gradually condition based overhaul strategy was extended to other large size process pumps such as multiphase pumps and centrifugal gas compressors.
Through the transformation of maintenance major overhaul strategy for rotating equipments based on accurate condition assessment and evaluation, achieved a significant cost saving up to 30%, increased uptime by 15% extending overhaul interval based on accurate health assessment and utilizing specialist independent service providers with service base in the country and hence contributing to In-Country value (ICV) targets.
The paper provides proven case study with examples of different types of rotating equipment where condition-based major overhaul was successfully deployed and adopting similar maintenance overhaul strategy for rotating equipment will help in achieving significant cost optimization over life cycle and maximize plant availability.
The Potential CEA/CCER Market Value for Natural Gas+ System: An Assessment Using the Logistic Growth and Technology Transfer Model
Authors: Ye Xiong 
Published: October 2023

Abstract
China has committed to achieving carbon peak and neutrality targets, and ETS serves as a major and innovative policy instrument, which consists of both Chinese Emission Allowances (CEAs) and Chinese Certified Emission Reduction (CCER). Considering the appeal for restarting CCER market, this paper proposes the "Natural Gas+" CEA and CCER techno-economics system and establishes the potential market value evaluation model for corresponding CEA and CCER. By exploring the combination scenarios among revised carbon emission, Natural Gas consumption, market shares of different phases’ technologies and carbon prices, the reasonable potential market value range of the "Natural Gas+" system for CEA and CCER are obtained. The potential market value of CCER is rising year by year with range of 669.2~1367.6 billion yuan in 2050, displaying a median value of 1031.1 billion yuan without outlier. Nevertheless, the CEA market value will reach peak in 2048 with range of 71.5~162.1 billion yuan, performing extremely large outliers from 2030. The results could quantitatively support the policy recommendations for restarting the CCER market.
Innovative Practice and Development Countermeasures of Oil and Gas Cooperation Along the Belt and Road with the Background of Carbon Neutrality
Authors: Li Changyong , Tong Yi , Jiang Junshuai , Na Risu , Da Lina 
Published: October 2023

Abstract
Considering the current status of China's energy mix, requirements for the energy transition and the development level of renewables, oil and gas will play an essential role in China's long-term energy supply, with its proportion in the primary energy consumption accounting for around 30% until 2040. The goals of "carbon peak" and "carbon neutrality" put forward higher requirements for international cooperation in the oil and gas sector along the Belt and Road (B&R), together with low carbon development. Chinese state oil companies, represented by PetroChina, Sinopec and CNOOC, actively participate in the Belt and Road Initiative (BRI), collaboratively operating many world-class oil and gas development projects, as well as technological innovation and practice. The annual oil and gas equity production from these cooperation projects exceeds 200 million tons of oil equivalent, which weighs heavily in the international energy cooperation, and accelerates the energy transition. With the background of carbon peak and carbon neutrality, fossil fuels remain of critical importance to the current energy system. Cooperation in oil and gas along B&R faces unprecedented opportunities, multiple environmental supervision and carbon emission reduction pressure.
In view of above situation, countermeasures to the development in oil and gas along B&R are put forward: 1) strengthen the cooperation and development in oil and gas along B&R to enhance the international energy cooperation, natural gas production and utilization, and accelerate the energy transition to diversify China's energy mix; 2) improve energy use efficiency and optimize energy use in exploration and development system, strengthen the construction of intelligent oil fields and shore power, and implement decarbonizing measures throughout the industrial chain to increase the pace and extent of energy conservation and emission reduction; 3) actively develop carbon-negative technologies and infrastructure, and promote carbon capture, use and storage (CCUS) application represented by carbon dioxide flooding, water alternating gas flooding etc., gradually shifting to a lower carbon energy system; 4) promote the integrated development of traditional resources and alternative energy, open channels of financial support, technology and talents, actively deploy new energy business, and coordinate the development of non-fossil energy industries such as solar energy and wind power.
Estimating the Effects of Wag-EOR on Elastic Properties for Seismic Reservoir Monitoring in Abu Dhabi
Authors: Mohamed M. , Ahmed J. , Mahmood H. , Aljaberi A. , Waqas M. , Alhamedi A. , Mouri T. , Shimizu H. , Yamada Y. , Mochizuki M. 
Published: October 2023

AbstractWhen implementing CO2 WAG EOR, fluid distribution and pore pressure in the reservoir vary. The interpretation of seismic reservoir monitoring for WAG-EOR often includes uncertainty in the changes obtained by time-lapse seismic surveys because these changes are the integration of effects by several factors. The objective of this study is to estimate the impact of each factor on elastic property and investigate a dominant factor through a rock physics study at a CO2 WAG site in Abu Dhabi.To estimate the effect of pore-pressure change on elastic velocity, an empirical relation between elastic velocity and confining pressure was used. Sonic velocity using core plug samples of the reservoir was measured under multiple confining pressures to construct empirical relations. As for the effect of fluid saturation changes on elastic velocity, we estimated it through Gassmann's equation. Realistic changes in pore pressure and saturations during the WAG operations were based on reservoir simulation results. Finally, based on the input petrophysical data and the relations from rock physics analyses, elastic property changes in WAG-EOR are estimated.Density and P-wave velocity (Vp) changes caused by the fluid substitution from oil to water are more significant than those by the substitution from oil to CO2. Pore pressure changes have less of an impact on Vp variations than does fluid substitution. Based on the estimation of elastic property changes associated with WAG operations, density changes are affected mainly by the increase in water saturation because of the density difference between water, oil and CO2. Vp changes seem to be consistent with the increase of gas (CO2) saturation. Even in the upper zone of the reservoir where oil is swept by both injected water and CO2, the effect of CO2 is prominent as well-known low gas saturation effects. Vp is significantly affected by the small amount of CO2 which was observed in the comparison of Vp with and without 10% of CO2 saturation in the rock. P-impedance changes are consistent with Vp changes because of the larger changes of Vp than the density changes. From these observations, the changes obtained by seismic reservoir monitoring would mainly represent the effect of CO2 saturation changes.We quantitatively looked into the impact that changes in pore pressure and saturation had changes on elastic properties such as density and elastic velocity. Furthermore, it was determined which elements predominately control changes in elastic properties when these changes occur simultaneously in WAG-EOR operations. These findings play an important role in the interpretation of seismic reservoir monitoring for WAG-EOR in Abu Dhabi.
From Pixels to Insights- Harnessing the Power of Deep Learning for Facies Classification in Borehole Images
Authors: Mukherjee S. , Jiang X. , Al Ka'abi M. , Khan M. , Kherroubi J. 
Published: October 2023

Abstract
The objective of this study is to develop a deep-learning-based framework for automated facies classification in borehole images. We aim to address the limitations of manual facies classification, including subjectivity, errors, and time consumption. The framework should be efficient, accurate, and easily integrable into existing workflows in the oil and gas industry.
The study uses deep-learning techniques, specifically convolutional neural networks (CNN), to develop a model for feature extraction and classification. The model employed is the state-of-the-art, open-source EfficientNetB4 architecture. The study also employs data preparation techniques, such as data augmentation and Synthetic Minority Over-sampling Technique (SMOTE) to handle labelled data set class imbalance.
The study involves three main steps: data preparation, model training, and evaluation. In the data preparation step, borehole images are processed into pixel-level data, sampled at a particular resolution, labelled, and augmented. In the model training step, the model is trained on the labelled data set using the EfficientNetB4 architecture. The evaluation step involves measuring the accuracy and F1 score of the model to analyze its performance.
The results demonstrate the effectiveness of the proposed framework, achieving an accuracy of 85% on the test data within minutes. The study also shows that the model can generalize well to variations in image quality and resolution, indicating its robustness and potential applicability in real-world scenarios.
The study highlights the potential of deep learning for automated facies classification in borehole images, providing a more objective, accurate, and efficient approach to subsurface characterization. The use of artificial intelligence (AI) and analytic platforms enables efficient integration of the framework into existing workflows, supporting decision making in the oil and gas industry.
The study demonstrates that deep-learning-based frameworks can offer valuable insights for the oil and gas industry, enabling improved resource management and accelerating project delivery. The proposed framework provides an objective and efficient approach to facies classification, overcoming the limitations of manual techniques. The study also shows the potential for the framework to be applied to other image classification tasks in the oil and gas industry.
How to Properly Place a Scale Inhibitor Squeeze Treatment?
Authors: Rafie M. , Almubarak T. , Alotaibi F. , Alsunnary K. 
Published: October 2023

Abstract
When oil producers start producing water, a variety of issues arise. This includes decline in oil production rates, corrosion, emulsion, and is coupled with inorganic scale precipitation. One common treatment in such scenarios is a scale inhibitor squeeze. Scale inhibitor squeeze treatments are very effective and can prevent precipitation of scale during production for an extended period of time. This work will discuss the lab and field tests required for a scale inhibitor squeeze job in an oil producer.
Compatibility tests between the scale inhibitor and the downhole fluids were conducted using static lab experiments and an inorganic scale prediction software. Additionally, HPHT coreflood was utilized to determine the compatibility between the scale inhibitor squeeze treatment and the downhole rocks at 200°F. Moreover, thorough well completion components, completion settings, mineralogy, and productivity indices were assessed before and after the scale inhibitor squeeze job.
The results showed that the scale inhibitor squeeze treatment worked as intended, exhibiting no incompatibility issues between the used scale inhibitor and the downhole rocks or fluids such as downhole oil and downhole water. However, it was noted that when the amount of scale inhibitor was excessive in the coreflood tests, it produced significantly higher precipitation at the core inlet which led to an increase in pressure drop. Consequently, bullheading excessive amounts of scale inhibitor is not recommended. Following this analysis, a carefully controlled scale inhibitor squeeze is advised through a coil tubing targeting the zone of interest where the inhibitor was intended to be used.
This work shares the tests and the analysis that needs to be done to successfully place a scale inhibitor squeeze job in an oil producer. This study proved that a full understanding of the well components, fluids, and rock properties is critical.
Artificial Intelligence-Assisted Seismic Mapping of Mediterranean Complex Turbidite Reservoirs Depositional Architectures for Effective Gas Exploration and Development
Authors: Hafez Ahmed , Castagna John 
Published: October 2023

Abstract
Accurate mapping of internal depositional architectural elements of a Pliocene-aged gas-bearing turbidite reservoir of the Mediterranean Basin into discrete 3D geobodies has been achieved through applying innovative workflow assisted by the convolutional neural network. The mapped reservoir depositional architectures have been integrated to the acoustic and elastic properties inverted form 3D seismic data to build robust multi-realizations reservoir static models. These models have been used to optimize the appraisal and development well locations and accurately assess the gas initial in-place of the discovery. Four wells have been sited utilizing the results of the constructed models. The wells have been successfully drilled and added 175 Million standard cubic feet in a day (MMscf/d) which obviously improve the commercial value of the project. The workflow is a major step in accurate delineation of the internal depositional architectural elements of the deep-water turbidite reservoirs of the Mediterranean Basins, as well as in other locations/basins where similar settings exist. By applying the workflow, the subsurface complexities were revealed with the artificial intelligence algorithms, uncertainties were captured, risks were reduced and project commercial value was uplifted.
Operational Pigging Prediction by Machine Learning
Authors: Ramlan M. , Khabri K. A. 
Published: October 2023

Abstract
The paper demonstrates how Operational Pigging frequency is optimized to minimize production deferment and achieve Operational Excellence by predicting the required frequency via machine learning based on the operating condition of the pipeline. Currently, the Operational Pigging frequency is fixed at 3-monthly which leads to 4 times the production deferment required in a year to allow for the Operational Pigging activity to be executed.
To perform this prediction, historical data of the pipeline from 2016, which consists of oil flow rate, gas flow rate, running time, water content, and previous operational pigging record,areobtained, and analyzed. From the data, patterns and trends are developed to understand the influence of each data on the prediction model. The data is split into a 60:40 ratio where 60% of the data is used to train the machine learning model, and the remaining 40% of data is used to test the trained model.
Based on the historical data obtained, the pigging debris is selected as the target outcome of the machine learning prediction. The trained model using Poison Regression Model upon evaluation shows good accuracy and is able to predict the debris to be collected from the Operational Pigging activity. The prediction of the pigging debris reveals that the Operational Pigging activity frequency can be prolonged fromthe established frequency of 3-monthly to a bigger frequency of as much as 1-year frequency while the debris collected will still be within the acceptable limit. This ultimately allows the operator of oil field ‘K’ in Middle Eastto reduce production deferment as the number of Operational Pigging required in a year is reduced with this prediction.
From an Operational Excellence perspective, this prediction model provides a solution to minimize production deferment while ensuring the integrity of the pipeline is not jeopardized.
Unsupervised Multitask Learning for Oil and Gas Language Models with Limited Resources
Authors: Marlot Maxime , Srivastava Divya Nidhi , Wong Fui Kent , Lee Ming Xiang 
Published: October 2023

Abstract
In this paper we explore the development of an oil and gas language model (LM) using an unsupervised multitask learning approach. A large language model (LLM)enables computers to understand and generate human language. The study addresses data scarcity and domain-specific language challenges, showcasing the model's performance on specific oil and gas tasks and qualitative testing. To do that, we collected a highly diversified dataset of 33,000 documents in energy and oil and gas domains to train and benchmark our model.Our findings demonstrate that even a small model, properly finetuned on domain-specific data, outperforms larger models trained on generic corpora, highlighting the benefits of finetuning LMs in technical domains. The paper contributes to advancing natural language processing (NLP) in the oil and gas industry, emphasizing the importance of addressing domain-specific nuances and limitations for improved NLP model performance and reliability.