bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abatement of Operational Discharges from Offshore Vessels and Structures in an Emerging Zero Spill Regime
Authors: Bassey Bassey Okon , Ana Ekpenyong Anakari 
Published: October 2022

Abstract
The petroleum industry must satisfy energy demand while safeguarding the environment as global interest in sustainability rises and more zero discharge regimes emerge. Operational discharges from offshore vessels and structures cause untold damage to ecosystems but rarely make news headlines as these acts are mostly perpetuated in the open sea, at night and impacts are less visible due to advection. Oil spill response is complex, expensive and long-lasting. Chemical releases are more difficult to clean and the environment is seldom completely restored, making prevention more desirable. This paper was informed by this scenario and identified gaps in the literature, which mostly reports accidental spills. The nature, sources, causes and effects of operational discharges are highlighted; existing preventive and remediation measures are critically evaluated, and the imperatives of recent regulatory changes for process safety are examined. Live cases are cited from across the globe to assess typical solutions and/or penalties deployed. Relevant local, regional and international regulations are juxtaposed with developments in design, operating procedures and asset integrity measures for selected case studies. Since the ratification of MARPOL 73/78 by certain countries, subsequent decades have seen the continual operation of tankers without segregated ballast tanks, double hulls and other spill prevention design features at major oil loading and unloading hubs across the Gulf of Guinea. Several discharges go unreported and the penalties are barely enforced by regulators. Taking a cue from mature zero discharge regimes such as Norway, discharge prevention measures would be economically viable, address environmental considerations and enhance the corporate image of operators. In a comparative analysis of the value of energy production, social and environmental concerns often indicate great disparity. Creating a good balance on the economic scale will possibly show a different and more realistic perspective on the cost of production. This paper advocates a proactive and preventative, rather than the reactive and curative approach typical of most previous studies. Implementation of recommended engineering and administrative controls are hoped to aid the industry's drives towards zero harm, self-regulation and social license-to-operate in a world apprehensive about oil and gas. They will strengthen regulators in developing economies with heavy dependence on hydrocarbons and poor enforcement, where operators often overlook regulations.
Technical and Economic Analysis of Mini LNG from the Utilization of Gas Flare by Optimization of Liquefaction Process
Authors: Khoir Khoir Lazuardi , Ade Ade Rafsanjani , Widi Widi Hernowo 
Published: October 2022

Abstract
Nowadays, the world is facing global warming as one of its main issues. This problem can be caused by a rise in CO2, CH4, and other GHG emissions in the atmosphere. On the other hand, flared gas is very similar in composition to natural gas and is a cleaner source of energy than other commercial fossil fuels where is 142 bcm of natural gas was flared in 2020 globally. Around 265 Mt CO2, almost 8 Mt of methane (240 Mt CO2-eq), black soot, and other GHGs were being directly emitted into the atmosphere as the result. Currently, Mini-LNG facilities could have been eliminated from gas flaring. This study analyses the LNG chain concept which can be used for the monetization of small volumes (0.05 -10 MMscfd) of associated gas.
The methodology of this study reviews both technical and economic aspects. Technically, two typical types of small-scale natural gas liquefaction processes in skid-mounted packages were designed and simulated. After simulating the LNG production unit, economic unit estimation was calculated and performed.
Small Scale LNG has good prospects for its development, especially to eliminate gas flaring or reduce gas emission, providing advantage economic and social e.g. to address increasing demand for energy as a peak session (peak shaving), monetization of stranded gas, and overcome the distribution of fuel to remote areas that are not yet connected to the electricity grid and comprehensive gas pipeline network. The cost calculation uses two approaches, both levelized cost and cash flow. The gas price of levelized cost is US$ 2.49/MMBTU, while the cash flow is US$ 2.95/MMBTU. The cash flow method is higher in gas costs because it takes into account the cost of money, DMO for the State, inflation, and depreciation. While ROI and NPV were obtained at US$ 29,692,691 and US$ 29,692,691.
Trust in AI for HSE
Authors: Al Ghafri Saif , Hinai Younis , Eldred Morgan , Thatcher Jimmy , Suboyin Abhijith , Gee Ivan , Adriana Monika , Anjum Hassaan 
Published: October 2022

Abstract
Health, Safety and Environment (HSE) operations can be greatly enhanced by reducing the uncertainty inherent in quantifying human activity. One way to do this is by using artificial intelligence (AI) techniques, in particular natural language processing (NLP), on HSE data to identify hidden trends, uncover and quantify textual data and, ultimately create a smart system that can alert users to risk before that risk is realized. This allows an organization to better target their resources, such as safety coaches, more effectively to prevent an adverse event, protecting vital equipment and potentially saving lives. One important aspect inherent in this process is the need to establish trust in the AI system among the users. This is especially the case during data collection, system rollout and user adoption.
In order to realize the goal of an AI driven HSE workflow, a system was implemented which allowed for a) the easy collection, structuring and preprocessing of data associated with the performance of management observations, and for b) the development and implementation of a robust set of AI tools that allowed the users to enhance their existing workflows to better be able to identify, quantify and address HSE risk. This created a new set of leading indicators for HSE awareness. While management observations were the first set of data considered as they were the most robust data set, the adaptable system was constructed in such a way that work orders, audits, and near misses could be easily incorporated in the future.
This study is part of a pilot project with Petroleum Development Oman (PDO) for IHTIMAM, which is a process that creates a safety partnership between the workforce and management that continually focuses everyone's attention and actions on their own and others daily safety ‘behaviour’. It was unique and innovative, because it took an auto ethnographic approach in the analysis of the AI, drawing on a team with multidisciplinary expertise.
A novel, comprehensive approach for HSE data collection and development of industry specific AI models to is presented. In addition, a novel approach was identified to leverage user adoption and use case in terms of both method and application.
Full-Stack Machine Learning Development Framework for Energy Industry Applications
Authors: Chen Jie , Yao Changqing , Devarakota Pandu , Sidahmed Mohamed , Dong Zexuan , Kanuparthi Sai , Wei Jia 
Published: October 2022

Abstract
Machine Learning (ML) has proved successful in various applications and delivered tremendous value across numerous domains. ML turns data into knowledge and intelligence, that can be used to make the right business decisions. The application of ML in the energy industry is increasing rapidly. This includes but is not limited to manufacturing, refining, energy distribution, and other related domains. Due to the unique and diverse domain requirements, various ML and AI solutions in the energy industry must be extensively customized. These specific requirements usually lead to different approaches to solutions and further introduce difficulties in developing, deploying, and scaling. These difficulties lead to higher costs and longer cycle time in productization. To reduce cost and save time, there is a growing business need to develop, deploy, and scale these energy industry ML applications in an agile way. To achieve this goal, we developed a full-stack ML development framework, which makes it easier to develop and deploy (energy industry) ML solutions with high efficiency, reproducibility, and scalability. It has been demonstrated that projects using this full-stack ML development framework successfully saved both turnaround time and costs.
Opportunities for Producing Hydrogen and Low-carbon Fossil Fuels from Venezuelan Conventional and Unconventional Hydrocarbon Reservoirs: An Idea in Times of Energy Transition to Net Zero-Carbon
Authors: Rodriguez Fernancelys , Belhaj Hadi , AlDhuhoori Mohammed 
Published: October 2022

Abstract
Venezuela has a big potential for the generation of energy from renewable resources (e.g. water, wind, solar, biomass, etc.), geothermal reservoirs, and from its huge reserves of hydrocarbons in conventional and highly viscous oil reservoirs. Due to the great efforts being made by several countries worldwide to reduce the emission of greenhouse gases and global warming, the production of clean fuels (e.g. hydrogen) with low-carbon content to reach net zero is a great challenge, but yet of keen interest, for the Venezuelan industry. The objective of this article is to review most of the relevant techniques and propose potential scenarios/technologies for the reduction of greenhouse gases and the generation of clean fuels from reservoirs in Venezuela.
The methodologies that have been envisaged in various industry and research segments in hydrogen and low-carbon fuels include, but are not limited to, steam reforming, pyrolysis, gasification, combustion, CCUS, oil, etc. A detailed description of each technology along with relevant scenarios and main conclusions are given. Also, the processes and procedures undertaken in these clean fuel generation technologies are addressed in this article to cover advances made in various industry disciplines and to highlight potential future breakthroughs.
For the Venezuelan particular case, the following major scenarios are predicted for the generation of clean fossil fuels: 1) production of hydrogen from natural gas with CO2 capture and storage in aquifers or in depleted hydrocarbon reservoirs, 2) use of the produced or sequestered CO2 for Enhanced Oil Recovery (EOR) in reservoirs with production decline, 3) production of clean fossil fuels from upgrading techniques and applicable EOR methods (e.g. in-situ combustion), 4) perspectives for the generation of hydrogen from renewable resources, among others. The application of each of these technologies/scenarios is closely linked to underlying technical feasibility and economics.
This article presents an approach to producing hydrogen and low-carbon fossil fuels, upstream and downstream, with CO2 capture and storage. The sequestered CO2 could then be used in miscible gas floods in conventional oil reservoirs for improved recovery, despite the possible effect on asphaltene precipitation and related flow assurance issues. Reconversion schemes for existing flow lines and new infrastructure designs would be necessary for the application of hydrogen and low-carbon fuel technologies, which would involve significant investments in terms of OPEX and CAPEX.
Physics-Based Structural Digital Twins to Minimize Downtime & Maximize Lifetime
Authors: Sierra Simon , Wong Jerry , Podskarbi Mat , Knezivic David , Bell John , Guidoum Mohamed 
Published: October 2022

ABSTRACT
Oil and gas owners and operators are facing a dual challenge: extracting more value from their current operations and preparing for a net zero society. One promising approach that could address this challenge is the deployment of structural digital twins for critical assets. This technology can be used to pinpoint potential structural issues early enough to prevent equipment failure and avoid production losses. The new technology must be holistic enough to cover the whole asset and fast enough to ingest sensor, inspection and process data. This is critical in order to provide near-real time asset condition, and highly detailed insights to enable reliable and high value O&M decisions.
We present a sensor-enabled physics-based methodology to help operators: 1. understand the ‘as-is’ structural condition of the asset in near-real time to drive process optimization 2. make informed decisions on optimizing inspection, maintenance, and repair activities and 3. extend operational life of assets. We share some use cases on delayed coker units, hydrotreating units, and pressure vessels – where we discovered important insights that minimized production loss, protected the asset integrity and extend its remaining useful life.
The engineering simulation approach presented combines powerful mathematics with field data to deliver levels of insights that were previously not possible due to inherent technological limitations of legacy simulation tools.
Optimization and Economic Evaluation of Unconventional Reservoirs Hydraulic Fracturing Treatment Based on a Combined Simulation and Machine Learning Approach
Authors: Siyal Amaar , Qi Chuangchuang , Belhaj Hadi , AlAmeri Waleed , Al-Shalabi Emad W. 
Published: October 2022

Abstract
This study introduces a novel and refined workflow for optimizing the fracturing operations based on simulation-assisted machine learning technique. Large datasets, ample efforts, and intensive time-consuming pose uncertainty and risk that human cognition is incapable of deciphering using direct simulation techniques only. The objective of this paper is to investigate the enhancement of a project's NPV through fracture parameters optimization. This includes fracture extensions, conductivity, distribution, fracture fluids, proppant types, and fracture job design. Specific UCR case study was used, and results were analyzed to verify the validity of the proposed workflow. Tight gas reservoir of 1 mD has been used for this case study. The first step of the proposed workflow started with data acquisition and data input in MFRACTM fracturing simulator. A set of 25 proppants was chosen according to the fracture closure stress, schedule, conductivity, and project NPV. Afterwards, fracture fluid was optimized based on fluid loss coefficient, treatment schedule, productivity, etc. Furthermore, the CMG commercial simulator was utilized to generate the required mathematical model using optimized fracture proppant and fracturing fluid. Finally, a designated machine learning-assisted random forest algorithm was used to select the effective fracturing parameters.
Optimization efforts showed the best proppant and fracturing fluid selection. Among the 25 proppants tested, HSP proppant resulted in the highest Net Present Value (NPV). This proppant enhanced the well productivity to 2.46 times of the original productivity and boosted the NPV to more than $13.88 Million. This optimized proppant was used for the investigation of different fracturing fluids. Among which H006 resulted in the highest fracture half-length and NPV as opposed to the other fluids. CMG simulator was used for data generation and an assisted machine learning Algorithm was used for optimizing other fracture parameters, such as the number of fractures and anisotropy that were found influential for the fracturing treatment. Neither over-fitting nor under-fitting was observed during training and testing of the ML model with a coefficient of determination value of 0.9795. This study provides more insight into optimization of hydraulic fracturing design through a combined simulation and machine learning approach. The paper promotes further application of hydraulic fracturing jobs in unconventional reservoirs based on the proposed efficient workflow.
Chemical EOR Efficiency for Ultra-Low Permeable Shale Deposits
Authors: Ushakova Alexandra , Groman Andrey , Kasyanenko Anton , Bakulin Denis 
Published: October 2022

Abstract
The purpose of the article is the search and development of water-soluble chemicals for the successful drainage and impregnation of low-permeable shale oil formations. The main problem for shale oil deposits is ultra-low water permeability. We are dealing with the cores of the Bazhenov formation, which have permeability of 10 mkD, nano-porous matrix with natural cracks. In the paper we have collected and examined the mechanisms that prevent water from penetrating into core samples with low permeability, and suggest chemicals that can help overcome this problem. Complexes of different surfactants, polysurfactants, nanoparticles with modified surface were investigated. Wettability, interfacial tension measurements, adsorption, micelles formation and phase behavior experiments were held in order to select the most effective chemical agent. A "funnel" of experimental search was performed and two best surfactants and composition of surfactant with nanoparticles was figured out. Then, for the first time, displacement experiments were carried out through the consolidated core samples of the Bazhenov formation, and the displacement efficiency of 53% was obtained. Due to the fact that the injection pressure turned out to be very high, and there were also problems associated with the adsorption of surfactants. The further steps to improve the experimental study were discussed.
A Machine Learning Based Accelerated Approach to Infer the Breakdown Pressure of the Tight Rocks
Authors: Tariq Zeeshan , Yan Bicheng , Sun Shuyu , Gudala Manojkumar , Mahmoud Mohamed 
Published: October 2022

Abstract
Unconventional oil reservoirs are usually classified by extremely low porosity and permeability values. The most economical way to produce hydrocarbons from such reservoirs is by creating artificially induced fractures. To design the hydraulic fracturing jobs, true values of rock breakdown pressure is required. Conducting hydraulic fracturing experiments in the laboratory is a very expensive and time consuming process. Therefore, in this study, different machine learning models were efficiently utilized to predict the breakdown pressure of the tight rocks. In the first part of the study, a comprehensive hydraulic fracturing experimental study was conducted on various rock specimens, to measure the breakdown pressure. A total of 130 experiments were conducted on different rock types such as shales, sandstone, tight carbonates, and synthetic cement samples. Rock mechanical properties such as Young's Modulus E, Poisson's ratio, Unconfined Compressive strength (UCS), and indirect tensile strength sigma_t were measured before conducting hydraulic fracturing tests. Machine learning models were used to correlate the breakdown pressure of the rock as a function of fracturing experimental conditions and rock properties. In the machine learning model, we considered experimental conditions including injection rate, overburden pressures, and fracturing fluid viscosity, and rock properties including Young's Modulus, Poisson's ratio, Unconfined Compressive strength (UCS), and indirect tensile strength, porosity, permeability, and bulk density. Machine learning models include Random Forest (RF), Decision Trees (DT), and K-Nearest Neighbor (KNN). During training of ML models, the model hyper-parameters were optimized by grid search optimization approach. With the optimal setting of the ML models, the breakdown pressure of the unconventional formation were predicted with an accuracy of 95%. The proposed methodology to predict the breakdown pressure of unconventional rocks can minimize the laboratory experimental cost of measuring fracture parameters and can be used as a quick assessment tool to evaluate the development prospect of unconventional tight rocks.
Quantitative Multi-Barrier Evaluation Through Dual Completion Using Time-Domain Electromagnetic Measurements
Authors: Daghmouni Hamdi Bouali , Al-Shanti Said Ahmed , Vorozhikhin Sergei , Iqbal Pervaiz , Dutta Sushant , Ismail Mostafa 
Published: October 2022

Abstract
An innovative electromagnetic tool is presented which utilizes transient or pulsed eddy current measurements to provide individual quantitative evaluation of downhole corrosion in up to four concentric pipes. Case studies from dual string completions are presented that compare the results from passes logged through short string (SS) and long string (LS) tubing.
The novel instrument utilizes transient electromagnetics or pulse eddy current technology and contains three sensors that achieve a trade-off between high-resolution measurements of the inner barrier and high radial depth of investigation for up to five barriers. Each sensor induces coaxial outward-diffusing rings of eddy currents in multiple concentric tubulars and measures the consequent time-varying response. The full transient responses from multiple sensors are then interpreted to obtain individual tubular thickness profiles.
Case studies are presented where the thickness profiles of outer barriers are obtained with the new tool. Logging was carried out in two passes; one pass was logged through SS tubing and the second pass was recorded through LS tubing. The comparisons show that the new electromagnetic instrument provides accurate corrosion evaluation while logging through tubing, even in the presence of a second tubing string. This feature of the electromagnetic tool is important for proactive well integrity management because electrochemical external corrosion, which is the primary corrosion mechanism in these wells, causes the outermost casing to fail first and then continues to penetrate inwards. Therefore, the new electromagnetic tool allows early identification of the outer tubulars with potential weak zones in the completion string without the need or cost of pulling completions for this purpose.
This paper describes the advantages and limitations of state-of-the-art multi-sensor pulsed eddy current measurements for individual barrier evaluation of up to five concentric strings even in the presence of dual tubing strings. The presented case studies in dual completions support these conclusions, with additional evidence from other diagnostic tools.