bluuebunny/crossref_metadata_2025 huggingface dataset:
Analyzing students’ science process skills through mobile learning using virtual laboratory
Authors: Fikriyah Aida , Ahied Mochammad
Published: October 2022

Science process skills have to be taught during learning process in order students are able to develop their skills in examining and solving science problems by leading such investigation process in which are beneficial for their future life. This study aims at analyzing students’ science process skills in the concepts of biodiversity through mobile leaning using virtual laboratory. The research included in a descriptive quantitative study. The mobile learning was conducted on November 2021 toward 24 students from 7th grade in a junior high school. Students learned the concepts of biodiversity through virtual laboratory in which the virtual laboratory has been already stated as valid and appropriate to be implemented in the learning process. In addition, the students’ science process skills were evaluated and measured using a test. Findings indicated that the students’ science process skills is 60.42±8.59. Therefore, it can be concluded that students’ science process skills reached moderate through mobile learning using virtual laboratory.
Development of integrated spermatophyta module potential of local plants on students' independence and concept mastery
Authors: Kasman Rifai , Suhartini S
Published: October 2022

Biology learning emphasizes the interaction of students with the object being studied so that the utilization of local potential needs to be done. This study aims to reveal the feasibility and effectiveness of the integrated Spermatophyta module of local plant potential in the Taliabu Island Regency, North Maluku Province. The achievement target is to increase independence and mastery of concepts. This research is development research with the ADDIE model (analysis, design, development, implementation, evaluation). The developed module is assessed by experts, and its effectiveness is tested on the dependent variable. The research population was students of SMA Negeri 4 Pulau Taliabu with a total sample of 28 students of class X. The results showed the Spermatophyta module based on the validation of learning experts 96.25 (very feasible), and media experts 68.98 (decent). The test results of the Spermatophyta module are known to have a significant effect on increasing student learning independence based on the independent sample t-test of .044 (<0.05). The N-gain value for the experimental class is 0.30 (High) and the control class is 0.07 (low). The Spermatophyta module has a significant effect on mastery of concepts known from the results of the independent samples t-test of 0.001 (<0.05). The N-gain value for the experimental class is 0.57 (Quite Effective) and the control class is 0.38 (Not Effective). The conclusion of this study, the Spermatophyta module developed is feasible based on expert validation and tends to be effective for increasing independence, and effective for increasing concept understanding. The Spermatophyta module requires further testing using a larger sample to ensure its suitability and effectiveness
Analysis of high school student’s mental model on fungi: Representation of students' conceptions
Authors: Hamdiyati Yanti , Rahman Taufik , Sulaeman Sanchia Azaria
Published: October 2022

Learning biology requires complete knowledge in understanding it, as well as in fungi. Lack of understanding of a concept, difficulty in finding relationships between concepts, and lack of teachers’ understanding are the main causes of misconceptions which can be represented through mental models. This study aims to analyze students' mental models of fungi through drawing and writing tests (DW), concept maps, interviews, and questionnaires. Using descriptive analysis, researchers use qualitative analysis and interpretation to support the quantitative results. A total of 30 students of grade 10 were given a test. The results showed that the level of students' mental models on the fungi structure was at the level of D3 W3 (46.43%) or wrong/irrelevant answers and fungi reproductive was the level of D3 W2 (17.85%) or partially correct drawing and irrelevant writing. On concept maps, as many as 60.71% of students were at emergent level and the other students were at the transitional level. Most of the answers in the interviews showed agreement with the results shown in the quantitative tests, while the questionnaires indicated the learning experiences and difficulties that students encountered in learning the functions. The conclusion of this study shows that the mental model of students on fungi is still not close to the mental model of experts.
I.Mahmud Döneminde Habsburg Elçilerine Verilen Ziyafetler [1730-1754]
Authors: AYDOĞDU Buket
Published: October 2022

Diplomasi, uluslararası ilişkileri inceleyerek mevcut problemi müzakere yoluyla çözüme kavuşturan bir kavramdır. Devletler bu kavramı bünyelerinde geliştirerek kendilerine has diplomasi kaidelerini oluştururlar. Köklü bir geçmişe sahip olan Osmanlı Devleti de zamanla kendi diplomasi kaidelerini oluşturarak elçiler vasıtasıyla diğer devletlerle olan ilişkilerini yürüttü. Devletlerarası ilişkileri yürütmede önemli görevler üstlenen elçiler, diplomasinin önemli aktörleri arasında yer aldı. Çalışmada Osmanlı diplomasisinde elçi kabullerinde uygulanan ziyafet meselesi, Sultan I. Mahmud döneminde (1730-1754) Habsburg elçilerine verilen ziyafetler özelinde ele alındı. Bu noktada araştırmanın temel amacı, Osmanlı’da genel elçi kabulleri üzerinden I. Mahmud döneminde gelen Habsburg elçilerine verilen ziyafetleri ortaya koymaktır. Çalışmada I. Mahmud döneminde Avusturya’dan gelen üç elçinin (Leopold von Talman, Anton Korfiz Ulefeld, Heinrich Freiherr von Penckler) Babıâli’deki kabulü ile bunlar için yapılan harcama ve muameleler ile verilen ziyafetlerin ana hatları üzerinde tespit ve değerlendirmeler yapıldı. İlgili tespitler için döneme ait arşiv vesikaları, kronikler, teşrifat defterleri ve diğer araştırma eserlerinden istifade edildi. Araştırma sonrasında I. Mahmud döneminde Avusturya’dan gelen elçilere Osmanlı Devleti’ndeki genel elçi kabulü temaüllerine uygun bir şekilde muamelelerin yapıldığı, farklılıkların ise dönemsel ve elçinin geliş amacı ile statüsü gibi farklılıklardan kaynaklandığı sonucuna varıldı.
La elasticidad de la oferta de trabajo en la economía española
Authors: Bellod Redondo José Francisco
Published: October 2022

En este artículo analizamos la elasticidad de la oferta de factor trabajo frente al salario real, para el caso de la economía española en el periodo 2002 – 2019. Las evidencias obtenidas son contrarias a una de las hipótesis básicas de los modelos de ciclo económico real (Real Business Cycle), esto es, la elevada elasticidad de la oferta de trabajo frente al salario real, que permite convertir los shock tecnológicos transitorios en movimientos cíclicos del empleo y, por tanto, de la producción. Los modelos VAR(p) que hemos formulado revelan una elasticidad mínima o no significativa.
SPECTRA: A Portal to Combine and Harness the Best Satellite Methane Emissions Data Available to Guide Operators on Their Decarbonization Journey
Authors: Gauthier Jean-Francois
Published: October 2022

GHGSat continues to be the only entity in the world operating satellites dedicated to high-resolution measurements of methane emissions. Every day, GHGSat makes measurements at hundreds of facilities around the world. To optimize the targeting of its satellite observations and turn its measurements into actionable insight, GHGSat developed in-house expertise in ingesting and analyzing other relevant and complementary streams of data such as public satellites and databases of information. In the Spring of 2021, GHGSat released SPECTRA, an ESRI ArcGIS-based portal to facilitate the navigation and interpretation of its high-resolution measurements and analytics layers. The purpose of this paper is to demonstrate how GHGSat proprietary data, in combination with an innovative analytical suite, can provide situational awareness on methane emissions to operators in the regions where they operate assets and provide opportunities to mitigate them.

The data analytics package under SPECTRA incorporates data and algorithms capable of providing insights into the constantly evolving O&G infrastructure. By looking at all data available, from coarse resolution regional methane concentration data to high-resolution methane measurements at facilities, from flaring data to production databases, SPECTRA provides insight on possible precursors of emission events by examining trends and patterns in the data. This paper will present results of methane emissions plumes identified with GHSGat's constellation of satellites, as well as data examples gathered with other platforms such as Sentinel 5P and Sentinel 2. The compatibility and complementarity of the different satellite platforms will be discussed, as will the ability to use low-resolution enhancements to target GHGSat's high-resolution satellite observations to identify and characterize the source of the localized higher concentration. The ability to toggle various layers of information in SPECTRA will be explored.
Estimation of Geothermal Electric Power Production from Mature Oil Fields
Authors: Negre Andres , Andjar Khadija , Muller-Shernetsky Elsa , Lantoine Martin , Marfisi Emerson , Suzanne Guillaume , Medellin Felipe
Published: October 2022

Geothermal energy development is of critical importance to meet the global challenge of energy transition. This work demonstrates that existing oil and gas industry tools can be used to evaluate the potential of geothermal energy production from mature oilfields using the heat contained in the produced fluids. This can contribute to a decarbonation strategy and be profitable since most of the costs (drilling, pumps…) are already supported by oil production operations. The only additional costs consist in surface facilities to convert thermal energy into electricity.

The aim of the study is to evaluate the potential of a mature oil field to generate electricity and predict the evolution of energy potential with time considering the current development plan for the field. This plan was designed to maximize oil production in the field and did not consider possible electricity cogeneration from geothermal energy.

The study was conducted in a sector of a mature oilfield including 15 producers currently producing about 10,000 barrels of liquid per day and with a 97% water-cut. A workflow was created to estimate the potential of electricity generation considering current and forecast liquid production rates, the nature of the secondary working fluid used in the Organic Rankine Cycle (ORC) and the minimum ejection temperature limits, defined by the operator, to avoid difficulties in surface separation processes.

This paper describes the surface process used for thermal energy to electricity conversion, and presents the workflow used to estimate electricity generated from simulation results considering uncertainty tied to some fluids and rocks parameters.
Design of an Integrated Sustainable Energy System
Authors: Anyanga Stephanie Pauline
Published: October 2022

One of the greatest industrial challenges for the oil & gas sector to address is the decommissioning of offshore assets along mature basins. The O&G Decommissioning sector highlights a key focus on low-carbon opportunities to reduce decommissioning costs, achieve ambitious emission targets, and accelerate the energy transition. This paper seeks to exploit synergies between the oil & gas and renewable sectors to reduce costs associated with oil and gas decommissioning. Firstly, the study employs a geospatial-based multi criteria site selection to identify potential energy hub locations. Secondly, assessing the techno-economic feasibility to repurpose O&G facilities as an alternative to decommissioning. A multi-criteria decision approach is employed combining weighted linear evaluation and NPV analyses on the selected offshore Southern North Sea (SNS) asset. This provides a comparative assessment of decommissioning alternatives and potential cost benefits of repurposing for renewable energy production alternatives to the ‘Business-as-usual’ decommissioning case. Repurposing the offshore platform from wind and hydrogen production with onshore methanation was found to be the most viable alternative with an estimated potential Net revenue of £146,013,922 per annum to be received as profit over an expected 10-year production. Furthermore, performing a Benefit-To-Cost ratio evaluation combined the financial and non-financial considerations to compare the benefits derived from competing alternatives. Altogether, the BTC ratios are estimated for repurposing for wind and hydrogen production, complete removal, and partial removal to obtain the values 0.000001897, 0.000002457, and 0.000005053, respectively. The BTC ratio of repurposing for wind and hydrogen production with onshore methanation presents the highest value of 0.000001438 in comparison to the other decommissioning alternatives. This paper presents a novel viable alternative to decommissioning mature oil fields through repurposing assets to renewable energy production sites. The proposed model presents the opportunity to offset carbon emissions and reduce decommissioning costs. The methodology applied to the Southern UKCS North Sea is applicable to ageing platforms worldwide given the availability of renewable resources for integration.
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

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

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

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

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

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

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

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

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

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

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.
Anti-Foam Injection Optimization
Authors: Derebe Addisu Tadesse , Al Neyadi Salem , Nasser Fadhal Mohsen , Al Nuaimi Ibrahim , Damanik Brama , Romi Ahmed Ibrahim
Published: October 2022

Habshan-5 & Sulphur Plant consists of two Rich Acid Gas Removal Units (AGRU) designed to process a total of 1340 MMSCFD of sour feed gas. These units utilize MDEA solution to produce close to H2S-free sweet gas. The objective of this study is to minimize the continuous dosing of anti-foam in the AGRUs without increasing the foaming tendencies, while maintaining sweet gas specifications. Consequently, leading to a significant reduction in the operating cost.

The method used was the reduction in anti-foam continuous dosing rate in a gradual manner. Each phase lasted for approximately one month, which gave enough time to stabilize the unit and collect sufficient reliable data. Meanwhile, several parameters were monitored on regular basis, such as foam height, foam breakdown time, absorber pressure drop and sweet gas quality. Parameters were gathered using laboratory analysis and PI ProcessBook software. These parameters were then compared against design values and trends analyzed before making decision to go ahead with the next phase or revert to earlier dosing rate.

In phase 1, anti-foam dosing rate was reduced from 90 L/hr. to 50 L/hr. Foam height remained below target of 20 cm and even showed slight reduction compared to before. All other parameters were within acceptable limits, and no noticeable changes compared to earlier data.

Anti-foam dosing rate in phase 2 was reduced from 50 L/hr. to 40 L/hr. The overall foam breakdown time for both units had a small drop, which indicated a slightly reduced foaming tendency. There were no changes in the other parameters.

The anti-foam dosing rate in phase 3 was reduced again from 40 L/hr. to 30 L/hr. All parameters were within the acceptable limits, and there were no changes compared to phase 2.

In phase 4, anti-foam dosing rate was reduced from 30 L/hr. to 20 L/hr. Foam height and breakdown time increased significantly to 100 cm and 100 seconds respectively. It was determined that maintaining dosing rate at 20 L/hr. would eventually lead to a foaming event. Thus, dosing rate was increased back to 30L/hr. permanently and trial was completed.

Overall anti-foam consumption was reduced by 53%. This initiative shows how decrease in operating costs can be achieved by optimizing chemical injection rate without jeopardizing the safety and integrity of the plant.
Evaluation of Flow Diverter Chemical to Improve Waterflood Performance as Conformance Control for Heterogeneous Reservoir and High-Temperature Field Application: An Innovative Experimental Design
Authors: Zulkifli Nazliah Nazma , Hendraningrat Luky , Razali Norzafirah , Bakri Che Nasser , Zainal Suzalina , Kechut Nor Idah
Published: October 2022

Water injection fields account for over half of Malaysia's oil production, with most of these fields at matured waterflooding stage. Low sweep efficiency due to water conformance where reservoir with high heterogeneity might cause premature breakthrough is one of the issues that leads to low oil recovery. In this study, we described the comprehensive experimental analysis to substantiate waterflooding performance in heterogeneous reservoirs by evaluating the flow diversion chemical (FDC) as a water conformance agent at high temperature. Chemical characterization, rheology, particle size analysis, compatibility, and thermal stability were evaluated for FDC according to the prevailing mechanism. Afterward, an experiment to simulate the reservoir environment was conducted to estimate oil incremental and blocking mechanism, in terms of residual resistance factor (RRF) performance in the intended field condition. The test was innovatively set up using commercial outcrop and reservoir native cores in dual-core permeability systems to test high permeability and low permeability porous media at high temperature up to 115°C temperature to mimic the conformance process. Typical single core flooding test are only able to determine the potential of the blockage mechanism by determining the RRF, without the diversion effect to quantify the additional oil recovery from the flow diversion process. The setup uses cylindrical preserved core plug samples with permeability contrast ranges from 100mD to 2 Darcy and tested at 115°C. The customized coreflood design successfully mimicked the conformance process. In our study, the RRF of 186 and cumulative oil recovery of 52% with an incremental oil recovery of >5% was obtained by the FDC injection. The study provides an innovative way in the coreflooding experiment to evaluate the performance of a conformance control agent in a heterogeneous reservoir with high temperatures.
Application of Linear Programing in Optimisation of Gas Blending Operations
Authors: Zhainakov Timur , Chong Yin Chao
Published: October 2022

Different fields and wells produce natural gas of different composition, which directly influences the value of the gas. This gas is then sent to customers depending on the individual specification. Gases from different origins are sometimes blended to make up for the minimum rate requirement. This study presents a mathematical approach, linear programming, to process big data and generate an optimized route that solves a rate allocation problem keeping in place various operational constraints.

A base model was created based on linear programming algorithm for the proof of concept. As in industry, multiple sets of constraints were applied to the model. These constraints include, for instance, maximum allowable carbon dioxide concentration of the total blend, minimum amount of gas, or target ethane concentration for various purposes. Another feature that was included in the model was Blend Specification Requirements. This feature navigates the solver to which result is more favourable and would provide higher profit. The final goal of the solver is to provide a scenario that complies with all the constraints providing the best revenue.

The results of the multiple test optimizations, generally, showed close agreement with predictions made prior the tests. Once the model was validated, more complex scenarios were evaluated. Here, the model-generated results showed completely different from the expected. This error in predictions is due to the nature of the problem i.e., rate allocation planning, where the number of considered variables directly influences the complexity of the decision, making it impossible for basic experience-based predictions. The soft-constraint Blend Specification Requirement principles proved to function and direct the solver towards the higher profitable arrangements, while complying to the hard-constraints. Soft and hard terms here relate to the level of the obligement that must be performed by the solver. Overall, the model succeeded in optimizing the relationship between the economic values of different natural gas compositions and the operational and blend constraints, identifying the most profitable gas distribution plan.

It is no longer practical to rely solely on the experience of individuals when dealing with complex rate allocation operations. This study presents a simple, reliable and elegant method to build computer-based optimisation models. Connected to an online stream of Big Data, the models can potentially contribute to oil and gas pipeline rate allocation operations, making decisions fit-for-purpose, cost effective and reliable.
Dynamic Mitigation Strategy to Flow Assurance Challenges Associated with CO2 EOR in an Onshore Abu Dhabi Field
Authors: Abdallah Dalia , Grutters Mark , Igogo Arit , Singh Manjit Kumar
Published: October 2022

A CO2-WAG (water-alternate-gas) injection EOR pilot is currently ongoing in a carbonate reservoir, in an Onshore Abu Dhabi field, as part of the company's strategy for Carbon Capture, Utilization and Storage. The pilot is an important phase to de-risk challenges associated with CO2 injection prior to full field implementation. This paper will focus on the mitigation of subsurface flow assurance challenges to handle corrosion, asphaltene and inorganic scaling risks in production wells.

As the conditions change during the CO2 pilot duration from producing dry oil to producing oil with miscible gas along with water, the flow assurance mitigation strategy must change to address the different challenges at each stage. Based on extensive lab studies and modeling work confirming the risk of corrosion, asphaltene and scale, it was decided to equip producer wells with downhole chemical injection facilities. Inhibitors for different applications were lab qualified as part of the chemical program to be injected, upon demand, during the pilot life cycle.

A comprehensive surveillance program of gauge cutter runs, logging and fluid sampling was put in place to monitor pilot performance and changes in fluid properties and conditions that warrant mitigation by chemicals. The plan was to inject corrosion inhibitor at the start of the pilot and switch to a corrosion/asphaltene combo chemical with CO2 break-through, as it tends to destabilize asphaltene and lead to deposition. Once water cut increases, asphaltene deposition starts to diminish and the risk of inorganic scale increases. At that stage the strategy is to switch back to corrosion inhibitor injection with scale inhibitor squeeze application into the reservoir to protect from scaling.

The pilot has been operational for 5 years. Water cut increased in producers from 0% to > 10% with instances of inorganic scale deposition in the lower part of the tubing. This was handled through acid washes due to favorable economics in comparison to inhibitor injection. CO2 levels at the producers increased from 6 mol% to 13-20 mol%, confirming CO2 break-through. With an effective corrosion inhibitor injection, the time-lapse corrosion logs confirmed that the tubing was in good condition. No asphaltene deposition experienced up to date hence no asphaltene inhibitor injection was administered.

This paper describes steps required to assess different flow assurance challenges during a CO2-WAG pilot and outlines the mitigation strategy including a robust surveillance program during pilot execution. This mitigation strategy should be dynamic, responding to the changes in challenges posed at each phase considering the most cost-effective solution. For scale and asphaltene deposition the frequency of occurrence should be considered to decide on whether to inject inhibitors or apply solvent soaks.