bluuebunny/crossref_metadata_2025 huggingface dataset:
Development of project based biotechnology teaching books
Authors: Duda Hilarius Jago , Wahyuni F Rahayu Esti , Setyawan Antonius Edy , Supiandi Markus Iyus , Bustami Yakobus
Published: October 2022

Various research states that one of the causes of misconception is textbooks or printed media that are used as learning resources. This is because the textbooks developed are monotonous and tend to present theories in full without practicing skills or stimulating the ability to think and solve problems. The textbooks developed in the research are project-based biotechnology textbooks. This certainly can answer the demands of higher education in the era of industrial revolution 4.0 where learning should be able to empower cognitive, process skills, social skills, system skills, complex problem solving and others. The depth of the material and projects selected for each topic is seen with the results of the analysis of conceptual mastery and misconceptions found. The hope is that this project-based textbook can overcome these problems. The research methodology used is 4-D development research. The 4-D development research method consists of 4 main stages, namely: define, design, develop and disseminate. The subjects of the field trials were biology education students at STKIP Persada Khatulistiwa Sintang. The percentage of validation results for project-based biotechnology textbooks by material experts, media experts, and biotechnology lecturers is 3.75 so that the textbooks have very decent qualifications. The percentage of project validation results was 3.32 with the feasible category. This textbook was tested on biology education students at STKIP Persada Khatulistiwa Sintang at the initial development testing stage. The effectiveness of this textbook is seen from the gain score of 0.54 with moderate criteria, so that it can be declared effective.
The effect of respiratory augmented reality media on analysis and evaluation skills of eight grades students
Authors: Jalmo Tri , Rakhmawati Ismi , Nuraini Kiki
Published: October 2022

The use of learning media on respiratory system material is needed so that the material presented can be understood thoroughly. One of the media that can be used to visualize the respiratory system material is Augmented Reality (AR). The purpose of this study was to determine the effect of using AR media on improving students' higher order thinking skills on the respiratory system material for class VIII at SMP Negeri 12 Bandar Lampung. The method used in this study is a quasi-experimental method with a non-equivalent pretest-posttest design. The sampling technique in this study used a purposive sampling technique. The research sample used was students of class VIII A as the experimental class and VIII C as the control class, with a total sample of 62 students. The data on the average value of pretest, posttest, and N-Gain as a result of the increase in students were analyzed by using the Independent Sample t-test. The results showed that the HOTS ability of students was in the medium category with N-gain in the experimental class of 0.58 ± 0.21 and the HOTS ability of students in the low category with N-gain in the control class was 0.29 ± 0.14. Statistical analysis with T-test showed that the use of AR media had a significant effect on increasing students' HOTS.
Development of learning video on sub materials interaction in the ecosystem based on functional feeding group of macrobenthos
Authors: Lorensa Sisi Marda , Tenriawaru Andi Besse , Candramila Wolly
Published: October 2022

Each organism has a different role as an integral part of an ecosystem. Nevertheless, this concept tends to be random and inaccurate in building a complete understanding of ecosystem interaction pattern. This research aims to develop learning video on ecosytem interaction, specifically on functional feeding group of macrobenthos. The feasibility of video as a learning medium was seen from five aspects of simplicity, audio, integration, balance, and effectiveness. The development of learning video is divided into two stages of the preliminary and the formative evaluation stage. In the preliminary stage, a field survey was conducted in the examples of interaction forms presented in 3 commonly used Biology textbooks. The formative evaluation stage was conducted to test the effectiveness of the learning product through self-evaluation and expert review. 5 validators of 2 lecturers and 3 high school biology teachers were assign. Data analysis was done with Lawshe method. An ICC (Interclass Correlation Coefficient) reliability test was carried out to determine the reliability of the validation instrument. The results of the reliability test using the ICC yielded an ICC value of 0.758 which puts it in the excellent category. All criteria from the aspects of the learning video media validation sheet are declared valid with CVR and CVI values ​​of 1. Therefore, this learning video can be considered valid to be tested in limited trials.
Student problem solving skills in PBL model: Viewed from the discourse sheet
Authors: Hidayati Nurkhairo , Idris Tengku , Handayani Peny Husna
Published: October 2022

Problem solving ability relates to a person's ability to manage and find appropriate solutions in unexpected and complex conditions. Problem solving skills are not something that is innate, but these skills can be developed when a person is given the opportunity to solve problems. This ability is very important for students, especially when they enter the world of work. This study aims to determine the problem-solving ability of students when completing the problem discourse sheet in the PBL model. The type of research used is descriptive quantitative using a sample of 37 students who take the Human Physiology Anatomy course in the biology education study program at the Islamic University of Riau. The research instrument is in the form of problem discourse and scoring rubric. Problem solving indicators consist of identifying terms, formulating questions, considering, and finding solutions from problem formulations and making and determining conclusions. The data obtained were analyzed by scoring each answer given by students. The results showed that students' problem-solving abilities differed on each indicator. The indicator that occupies the highest score is formulating questions and the lowest is making and determining conclusions. Based on the results of the study, it can be concluded that the problem-solving ability of students while completing the problem discourse sheet in the PBL model is in the very good category.
Development of a science process skills-based interactive website ‘bioenial’ on environmental pollution material
Authors: Rahmawati Atika Budi , Bowo Sugiharto , Santosa Slamet
Published: October 2022

This study aims to develop an interactive website based on the science process skill ‘Bioenial’ on Environmental Pollution material for grade X SMA/MA and identify the achievement of science process skills in the use of the 'Bioenial' website. This study was designed based on the Research & Development design from the Thiagarajan 4D development model (Define, Design, Develop and Disseminate). The instruments used in this study were media and material expert validation sheets, student worksheets, and student and teacher response sheets. The analysis technique used the Rasch analysis. The results showed that (1) the Feasibility of the 'Bioenial' website, the average percentage score of validity obtained from the three media experts was 97,4% that within the "Very Eligible" category, and the percentage score from the material experts was 76.6% or within the "Eligible" category.  The 'Bioenial' website was tested in a class of 32 students and on two biology teachers. The results of student responses according to the person measure obtained a mean of 85.5, meaning that the website is "Eligible" to use. The results of the teacher's response show that the Bioenial website is "Appropriate" for learning. (2) Science process skills can be developed on environmental pollution materials with the 'Bioenial' website observing, classifying, predicting, communicating, designing, and conducting experiments. Assessment of the skills of conducting experiments is unlikely during the distance learning period due to the difficulty to provide evaluations. Based on the study results, further research is needed to determine the effectiveness of the 'Bioenial' website in improving students' cognitive learning outcomes.
Game-based learning: An alternatif learning model in covid-19 distance learning
Authors: Susilawati Puspita Ratna , Handoyo Luisa Diana
Published: October 2022

Distance learning as Covid-19 pandemic solution has student learning boredom as a negative impact. Game application can increase students' enthusiasm and motivation to learn in distance learning. The game-based learning (GBL) model is potentially applied to create a fun learning atmosphere and overcome learning boredom. This study aims to determine the effect of GBL implementation on cognitive learning outcomes and student responses in the biological interaction chapter in Covid-19 distance learning. This research wasexperimental research (one-group pretest-posttest design). The research sample was 25 students. This research used one treatment group without a control group. The test questions were used as cognitive learning outcomes instruments. The questionnaire was used as a student response instrument. Game application used were classified as mobile games, single-player games, role-playing game (RPG). GBL syntaxes were pre-experimentation, experimentation, reflection, activity, and discussion. The results showed that the implementation of GBL in the biological interaction chapter in Covid-19 distance learning affected students' cognitive learning outcomes. The effect of this model could improve students' cognitive learning outcomes with 28% students of low category N-gain while 72% students of medium and high.
Blended-problem based learning with integrated social media-based learning media in improving students' critical thinking skills
Authors: Triyanto Samuel Agus , Wahidin W , Hartania Nina , Solihat Ai , Sutrisno S
Published: October 2022

SMAN 1 Tasikmalaya organizes education and learning that leads to 21st Century skills. One of the 21st Century skills developed is students' critical thinking skills. The critical thinking skills of class XI MIPA 1 student in the 2021/2022 Academic Year, especially in learning biology based on the results of interviews with biology subject teachers, still need to be improved, this is confirmed by the results of the students' critical thinking skills test with an average of 49.85 with the category still underdeveloped. The purpose of the study was to determine the role of Blended-Problem Based Learning with integrated social media-based learning media in improving critical thinking skills of class XI MIPA 1 student. This research was a Classroom Action Research based on Lesson Study. Data on critical thinking skills were obtained using a critical thinking test with essay type questions. Data on students' critical thinking skills test results were analyzed descriptively qualitatively by referring to the Illinois Critical Thinking Essay Test. The results showed that the critical thinking test results for students in cycle I and cycle II had increased by 9.83%, which means that the critical thinking skills of class XI MIPA 1 student in the 2021/2022 Academic Year had developed well in learning biology using Blended-Problem Based Learning with integrated social media-based learning media.
The effect of problem-based learning integrated individual rotation route on islamic character and critical thinking ability
Authors: Permana Fendy Hardian , Hi.Padu Hajriani , Susetyarini Rr Eko
Published: October 2022

Entering the 21st century the quality of human resources is focused on critical thinking because they are able to compete in the era of globalization, which is developing so fast, as well as human resources who have good character. To improve critical thinking skills, the learning model must be active, innovative, and creative. This quantitative study aims to see the effect of the Individual Rotation Route learning model based on Problem Based Learning in improving students' critical thinking skills and Islamic character. The design of this study used a Quasi Experiment with Pre-test Post-tests Non-Equivalent Control Group design which involved 2 classes, each consisting of 20 students of class VII SMP Aisyiyah 3 Malang, Indonesia. Critical thinking ability is measured using critical thinking rubrics and Islamic character is measured using character success indicators. Collecting data using pre-test, post-test, assignment, and observation. In analyzing the data on students' critical thinking skills, the One-Way ANCOVA test and the One-Way ANOVA Islamic character test were used. The results of this study indicate that there is a significant change after being given the Individual Rotation Route learning model based on Problem Based Learning on students' critical thinking skills and has an effect on students' Islamic character because students are actively involved in the learning process.
Gender-based and grade-level mapping of student genetic literacy in the midst of the covid-19 pandemic
Authors: Yanuar Alvin  Dewa , Wahyuni Sri , Fatmawati Diani , Husamah H , Waluyo Lud , Fauzi Ahmad
Published: October 2022

The Covid-19 pandemic has resulted in the face-to-face teaching and learning process being shifted to distance learning which has the potential to affect student literacy levels. This quantitative study aims to determine the effect of gender and grade level on the genetic literacy of high school students in Malang. This survey research used a statement instrument as many as 17 items involving 97 students. Data collection was carried out from November to December 2021. The sampling technique used was cluster random sampling. The research data was tested using a Two-Way ANOVA analysis. The results showed that gender did not have a significant effect on genetic literacy, while class level has significant effect. Furthermore, gender and class level did not have a significant interaction. Further research that examines the effect of other variables on genetic literacy needs to be done.
The Development of creative thinking and problem-solving skill instrument of plant growth in high school
Authors: Purwandari Bernadetta Ersi , Supriyatin S , Ristanto Rizhal Hendi
Published: October 2022

Creative thinking and problem-solving skills are needed to face the 21st century of 4.0 industrial revolution. Improving the quality of human resources to face the era of the industrial revolution 4.0 must be developed in education. Biology as one of the subjects in high school, especially in plant growth and development matters, allows to develop creative thinking and problem-solving skills because in it there is a student project on plant growth and development. It takes the right instrument to measure the achievement of creative thinking skills and problem solving skills in Biology learning, especially on the topic of plant growth and development. Two instrument questions have been developed, namely the instrument for creative thinking skills and problem solving skills. The development of this instrument was carried out through the stages of Research and Development Borg and Gall 1983. It produced 10 essay questions measuring creative thinking skills and 8 questions measuring problem solving skills. The question gets very decent expert validation criteria with a score of 91% - 97%. The results of the validation test based on the Pearson Product Moment formula show that the ten items of creative thinking skills are valid with a significance level of 0.05. The reliability test of the two instruments has a result of 0.82, above Cronbach's Alpha coefficient, which is reliable for creative thinking skills instruments. Meanwhile, the instrument of problem solving skills showed very reliable results with Cronbach's Alpha value of 0.95.
Environmental literacy profile of senior high school in Mowewe Southeast Sulawesi
Authors: Muhlis Nur Fadhilah , Yani Ahmad , Suryanni Siti Darwa , Upe Ambo
Published: October 2022

Lack of awareness and human responsibility is one of the factors that have a major impact on increasing environmental problems. The purpose of this study was to describe the environmental literacy skills of class XI students of senior high school 1 Mowewe. The method used is a descriptive method with a quantitative approach. This research was conducted in the odd semester 2021/2022 academic year (September until November), at SMAN 1 Mowewe (Southeast Sulawesi, the subjects in this study were 50 students of class XI senior high school 1 Mowewe. The questionnaire was used to measure students' environmental literacy. The data were analyzed using descriptive analysis. The results showed that the indicator of knowledge was worth 59.78, the attitude indicator of students' attitudes was worth 69.18, the ability of cognitive skills to the environment was worth 74.52, and student behavior towards the environment had a value of 67.86. Based on the results of the study, it can be concluded that the environmental literacy ability of students at senior high school 1 Mowewe, namely the knowledge indicator has a value of 59.78 with a fairly good category, students' attitudes have a value of 69.18 with a good category. category, the ability of cognitive skills to the environment has a value of 74.52 with a good category, and student behavior towards the environment has a value of 67.86 with a good category.
Implementation of PjBL using unila’s moodle v-class and its interaction of students’ intrinsic factors
Authors: Maulina Dina , Meriza Nadya , Priadi Median Agus , Sukamto Ismu
Published: October 2022

The Main Performance Indicators (IKU) of Higher Education, it has been a mandate the implementation of learning that aims to build and create collaborative and participatory classes. The purpose of this study was aims to analysis the linearity between the implementation of PjBL-based learning using Unila’s Moodle v-class with students' intrinsic factors in learning Animal Structure during online learning in realizing collaborative and participatory classes. This research is an experimental-research with posttest-only control design with the population in this study are students of biology education at the University of Lampung, amounting to 371 people. The sample was obtained using purposive sampling technique, as much as 124 students who had taken the Animal Structure course. The results showed that there was a significant difference (p<0.05) in student learning outcomes in the control and experimental classes. Nevertheless, there was no influence of intrinsic factors on PjBL results in both classes (p<0.05), all students had high intrinsic factors to take part in online learning is high, however the optimizing learning outcomes is not linear.
Factores determinantes del acceso a un empleo ajustado a la formación universitaria: diferencias entre los titulados de Grado, Máster y Doctorado
Authors: Sanjuan María Ana
Published: October 2022

En este artículo se investiga cuáles son los factores determinantes del acceso a un empleo ajustado a las características que cabría esperar para los egresados universitarios tras la inversión educativa realizada, analizando las diferencias entre Grado, Máster y Doctorado. A partir de microdatos procedentes de la encuesta de inserción laboral de una de las universidades más prestigiosas de España (UAM) y mediante modelos probabilísticos, se estima, para cada nivel educativo, la probabilidad de empleo atendiendo a cuatro características: estabilidad, salarios, ajuste horizontal y adecuación del puesto de trabajo. Los resultados muestran que aspectos como la titulación elegida, el rendimiento académico, el conocimiento informático o de idiomas, tienen un impacto positivo en los titulados de Grado, mientras que en Posgrado lo verdaderamente relevante es la rama de conocimiento cursada. Adicionalmente, las estimaciones reflejan que las diferencias de género persisten especialmente entre los titulados de Grado, a diferencia de los de Máster.
Techno-Economic Assessment of Blue Hydrogen Technologies
Authors: Dara Satyadileep , Khan Ibrahim , Al Jenaibi Eisa , Dhebar Sandeep , Srivastava Ganank , Shehata Mostafa
Published: October 2022

This study focuses on the techno-economic comparison of blue hydrogen technologies. Four technologies shown below are considered for the preliminary evaluation.

Steam methane reforming with carbon capture Auto thermal reforming with carbon capture Syngas chemical looping technology Chemical looping reforming technology

Industrial literature showed that syngas chemical looping technology and chemical looping reforming technology show high thermal efficiency and low capital and operating costs. However, challenges exist in the scale up, system integration and commercialization. Further R&D efforts may make this technology superior in near future. Steam methane reforming is the most established commercial technology for H2 production. However, when CO2 capture is included in scope, thermal efficiency decreases drastically and may become economically unviable if the H2 selling price is less than 2 $/kg. Auto thermal reforming shows attractive techno-economic indicators in terms of capital cost, thermal efficiency, and payback potential. Commercial references for this technology are limited comparatively but technology licensors do exist. Hence, this technology is identified as the recommended option for blue hydrogen production.

Indeed, ATR process is even more attractive for ADNOC due to the availability of oxygen as a waste stream in one of its facilities. To this end, it is intended to investigate the prospects of adopting auto thermal process for blue hydrogen production in ADNOC by leveraging the available oxygen supply. A further detailed study is conducted to evaluate this proposal through simulation analysis, equipment design and costing analysis and thereby verify the technical feasibility, capital cost, operating cost, thermal efficiency, emissions profile and required selling price of H2.
Carbon Footprint Reduction Through the Use of Drones for Inspection Activities
Authors: Alnumay Yazeed , Jifri Hesham , Albrahim Ahmed , Felemban Amjad , Abdellatif Fadl
Published: October 2022

Assets in the energy industry require periodic inspections to monitor their health and integrity. These inspection activities are typically performed manually by operators. However, a significant portion of the assets are also elevated and are hard to reach without the use of scaffolding. The use of scaffolding involves a complex process that includes manufacturing, transportation, and other associated logistics. These activities contribute to the carbon footprint related to inspection activities leading to increased emissions. Recently, the use of robotic and automated inspection solutions is being increasingly adopted by energy companies and the overall market as a whole. These advanced solutions could potentially play a valuable role in the reductions in carbon emissions by minimizing both the manufacturing and transporting of scaffolding for inspection activities. In this study, we aim to quantify the environmental benefit of such solutions within the energy industry.

In order to quantify the decrease in carbon emissions, we examine the carbon emissions of extracting raw materials, manufacturing, and transportation of scaffolding. We compare this to the carbon cost of manufacturing batteries and inspection technologies along with their respective power consumption.

Our initial results show that switching from traditional scaffolding techniques to drone-based solutions resulted in approximately a 30-fold decrease in emissions from 4.54kg CO2e per inspection job, if scaffolding is used, to 156 g CO2e using drone-based solutions. This was achieved by minimizing scaffolding manufacturing and transportation activities in comparison to drone manufacturing and power consumption.

Robotic and drone inspection is a recently emerging field that has captured the attention of large energy institutions around the globe. Our study aligns with the ambitious initiatives in reducing carbon emissions and driving a successful transition to sustainable energy. The novelty of this paper is that it analytically investigates the driving force behind the innovative inspection technologies that are recently emerging in the market. Additionally, it quantifies the positive impact of these technologies from the perspective of reducing carbon emissions, leading to sustainable energy.
Waste to Sustainable Aviation Fuel: How to Recover Refused Carbon and Hydrogen
Authors: Rispoli Giacomo , Borgogna Alessia
Published: October 2022

Aviation is one of the sectors, within the transport industry, in respect of which engine electrification is more challenging to be applied. Therefore, it is crucial to develop and widen processes for production of aviation fuel starting from renewable sources.

The "Waste to Sustainable Aviation Fuel" process fits this aim perfectly. The peculiarity and novelty of this process is the link between two worlds which usually work apart: the waste conversion sector and the petrochemical/chemical one. In this regard, the waste-to-chemical process is generally composed of different sections: high temperature gasification; syngas cleaning and cleaning; syngas purification; syngas conditioning; and final synthesis. The first section is the core of the process since it is a very flexible technology able to convert unsorted and/or non-recyclable waste into syngas, as well as demolition wood or other residual biomasses. Final synthesis can then follow two different paths: - methanol or ethanol; from both, SAF can be produced. Levelized of costs of methanol and ethanol coming from proposed process are highly competitive, thus leading to a competitive final SAF cost of production. SAF produced from methanol or ethanol coming from the proposed process are eligible for renewable content introduction in aviation sector, according to ReFuelEU directive which is under definition. Following mass balance approach, a fraction deriving from fossil carbon of waste is defined as Recycle Carbon Aviation Fuel, the other coming from biogenic carbon contained in residual materials - not food or feed related - is considered Advanced biofuel. The flexibility on feedstock related to the proposed technology allows to follow markets trends in relation to waste gate fee, residual biomasses cost, and relative market surplus recognized for advanced fraction.
Saudi Aramco Awards Winning Project
Authors: Abbate Piero , Sparano Silvio
Published: October 2022

Operational since 26th December 2020 on Saudi Aramco's premises in Qurayyah (KSA), TVP Solar thermal plant has been conceived as a sizeable industrial pilot for Saudi Aramco to show the potential of energy and CO2 savings offered by the solar thermal technology of the Swiss company TVP Solar.

Because of its proven performances, Qurayyah's TVP thermal solar plant has been awarded in 2021 with the Environment Excellence Award assigned to the See Water Injection Department (SWID) within Saudi Aramco and with the Energy Efficiency Award assigned both to SWID and the Research and Development Center (R&DC).

Qurayyah's TVP Solar plant has been engineered to preheat from 93 °C up to 174 °C the water fed to a set of two diesel fueled boilers used to generate steam.

Experience gained in Qurayyah shows that at this location 1 MW of peak thermal power requires 1,700 m2 of TVP Solar collectors (for about 3,000 m2 of total occupation space) to produce 1.5 GWhT/year, thus reducing fuel consumption by 200 m3/year of diesel and avoiding 520 tons/year of CO2 emissions. The specific ability of TVP Solar collectors to harvest energy from diffuse light allowed the solar field to have its performance minimally impacted by fouling, even if the panels were never cleaned during more than one year of operations.
Carbon Footprint Management Using Blockchain
Authors: Kadry Heba Ahmed
Published: October 2022

There is a broad consensus that greenhouse gas emissions management requires coordinated efforts and collaboration in all sectors and at all levels of an organization or within the value chain. However, potential conflicts of interest and lack of trust between stakeholders make this collaboration extremely challenging. Blockchain has opened the door for a series of innovative applications that can propose an efficient carbon footprint traceability and management solution.

Blockchain is a distributed secure database, called a ledger, among different parties, used to hold and verify tamper-proof records or transactions without the need to trust any participant of this process except the mechanism. In the race to net zero, the need for an advanced information and communication technology has become vital to global climate change management with increased digitalization, decarbonization, security, and decentralization challenges. Blockchain is proposed as an integrated platform for various applications, such as carbon traceability, carbon trading, certification, and value chain management.

This work presents an overview of blockchain technology and its working principles. It describes blockchain's novelty and innovation to the industry and climate action. Also, the paper investigates blockchain's potential for carbon footprint traceability and management. It explores the latest use cases and the current challenges. It concludes that enabling innovation for climate action requires digging further into evolving disruptive technologies such as blockchain.
Liquid CO2 Transportation and Storage; A Comparison of Low Pressure and Medium Pressure Solutions
Authors: Nasseri Damoon
Published: October 2022

Transportation of CO2 from a carbon capture unit to the final sequestration destination poses several technical challenges. For short / medium distances, dense phase transport via pipeline is usually the preferred solution. However, for longer distances, a more practical solution is liquid transportation (marine or rail), followed by offloading, intermediate storage and transfer to a dense phase pipeline for sequestration. The intent of this study is to perform a comparative assessment between "Low Pressure / Low Temperature" and "Medium Pressure / Medium Temperature" solutions for liquid CO2 transportation, considering overall process energy consumption, CAPEX and operational safety.

For CCS chains involving refrigerated CO2 transport, the full transportation chain from the carbon capture unit to sequestration point includes compression, dehydration, liquefaction, buffer storage, marine (or rail) transfer, and conditioning. A low-pressure solution requires a low temperature to be able to liquify CO2 (7 Barg, -46°C); while a medium pressure solution requires a medium temperature to be able to liquify CO2 (13 Barg, -30°C), with each option having its own advantages and disadvantages. To perform a techno-economic assessment, both options were simulated, equipment lists produced and a cost estimate and lifecycle cost including OPEX, developed for the entire transportation chain between the low pressure capture units and the dense phase sequestration / injection point. The assessment was performed for a study by Wood, which had a CO2 production rate of 0.6 MTPA, however the result remains valid for higher capacity as levelized cost of CO2 handling is used.

It was observed that the low-pressure option required a higher energy demand to liquify and heated the CO2, in terms of kwh/tonne of CO2. It was also noted that the low-pressure option required a material compatible with the low temperature (e.g. Stainless Steel), while Low Temperature Carbon Steel (LTCS) can be utilized for the medium pressure option. Although a higher material thickness was required for medium pressure buffer storage and marine tanks, this is compensated by the material cost difference between SS and LTCS, leading to a higher CAPEX for a low-pressure solution. Additionally, the CO2 triple point of -56°C is fairly close to the low-pressure storage conditions of -46 °C, hence any fluctuation in process conditions risks solidification of CO2. Based on the above, it is concluded that medium pressure CO2 storage (at circa 13 Barg, -30°C) is more desirable to a low pressure / low temperature solution.

This study seeks to provide a holistic view for liquid CO2 transportation, considering both the marine and traditional onshore processing and buffer storage elements, paving the way towards a standard set of guidelines for future developments.
Development of a New Ultra-Selective Solvent to Reduce the Cost to Capture CO2 from a H2S-Rich Natural Gas
Authors: de Meyer Frédérick , Cadours Renaud , Poulain Bénédicte
Published: October 2022

The contribution of CO2 present in natural gas to the total GHG emissions of the gas plant can be significant (up to 20% for Middle East Gas). When both H2S and CO2 are present, the CO2 capture cost is currently very high. The method developed to tackle this challenge is an innovative architecture of the acid gas treatment chain based on a new ultra-selective absorption process. The redesigned acid gas treatment architecture is detailed and compared with available technologies on the market. The technical cost of avoided CO2 can be reduced by up to 70% by using the new ultra-selective process compared to the available CO2 capture technology. This new acid gas treatment architecture can reduce by up to 20% the energy consumption to capture CO2. A new ultra-selective solvent has been developed using computational screening tools combined with experiments measuring physical, operational, thermodynamic, and kinetic properties. Those serve as input of a process simulation tool validated with medium-pressure absorber and regenerator pilot plant data. The new solvent is currently at TRL4 validation, a selectivity of more than 75% (CO2 slippage) has been observed on the pilot plant, with a H2S specification in the treated gas kept around 5 ppm. This result is the consequence of both a new solvent formulation (selective amine) and optimal operating conditions. There is, moreover, no increase in regeneration duty for the new solvent.
A Machine Learning-Based Data Augmentation Approach for Unconventional Reservoir Characterization Using Microseismic Data and EDFM
Authors: Leines-Artieda Joseph Alexander , Fiallos-Torres Mauricio Xavier , Alharthi Amena , Mahmoud Salah El din , Al Hashmi Abdulla , Alqaydi Maryam , Ramsay Travis , Gong Yiwen , Yu Wei , Miao Jijun , Escorcia Alvaro , Useche Franklin , Al Bannay Aamer , Fonseca Rahul-Mark , Sepehrnoori Kamy
Published: October 2022

Multi-stage hydraulic fracturing has recently gained strong interest in unconventional plays in the Middle East due to high natural gas production potential. However, prevalent characteristics of the area, including high-pressure / high-temperature (HPHT) conditions and presence of complex natural fracture networks, pose significant challenges to reservoir characterization. These challenges have motivated the development of an integrated workflow using microseismic data for the characterization of reservoir properties resulting from the interaction between natural and hydraulic fractures.

This study proposes a reliable method for modeling hydraulic fractures from scarce microseismic data. Initially, a microseismic model—based on field records of microseismic data and natural fracture spatial characterization—was developed. Issues related to limited microseismic data availability were tackled through combination of a probabilistic algorithm, Gaussian Mixture Model, and a DFN model. Then, the resulting synthetic microseismic events enabled the generation of a hydraulic fracture model using the embedded discrete fracture model (EDFM) and an in-house microseismic spatial density algorithm that captured major hydraulic fracture growth tendencies. Next, the created hydraulic fracture geometries were validated against a physics-based hydraulic fracture propagation model. Lastly, a single-well sector model—based on a corner point grid that honored the original 3D discrete fracture network (DFN)—was history matched, confirming the successful application of the proposed methodology.
Applying Blockchain Technology to Process Safety with Focus on Integrity Management for Oil and Gas Industry
Authors: Laskar Sandipan
Published: October 2022

Disruptive innovation has extensively changed human interactions, market trends and other aspects such as transportation and communication. Over last decade many new technologies (artificial intelligence, block chain technology, Internet-Of-Things) has changed the way world do business. These new technologies are playing an important role in increasing efficiency and effectiveness in businesses around the world. This paper focuses on application of blockchain technology and use of distributed ledgers to leverage process safety and integrity management program.

The focus of safety management system is to evaluate hazards and decrease risks with an assurance that the identified safeguards are effective. All the elements of process safety management program are interlinked so failure or gap in one element will adversely affect the efficacy of the overall process safety management program. The effectiveness of a process safety management depends on many factors such as management support, personnel participation, effective and tamper-proof data management and reporting. So, it takes considerable number of resources to set up and manage these systems. This paper explores use of blockchain and distributed ledgers to effectively manage elements of process safety management program with major focus around integrity management for oil and gas industry. The paper focuses on core 6 elements however the discussion could be applied for the other elements of the process safety management program. This paper provides an overview application of blockchain to different elements of process safety management program discussing the pros and cons. The paper provides a detailed scoping of application of blockchain and distributed ledgers for integrity management program.
Predicting ESP failures Using Artificial Intelligence for Improved Production Performance in One of the Offshore Fields in India
Authors: Devshali Sagun , Tripathi Shailesh Kumar , Dodda Dhiraj , Kumar Manish , Uniyal Rishabh , Yadav M. , Malhotra S.
Published: October 2022

Field X is situated at a water depth of 90 meters in the western continental shelf at a distance of 200 Kilometers from Mumbai. It is one of the few fields in the world operating entirely on Electric Submersible Pumps with 36 wells in 5 wellhead platforms producing 62907 barrels of liquid per day with an average water cut of 68%.

The performance of ESPs is being continuously monitored in the field. With continuous improvement, the run life of ESPs has increased from a few months to an average of 3 years. Despite the improvement in the run life, unexpected failures still occur from time to time. These unanticipated ESP failures cause substantial production deferment leading to considerable losses in terms of revenue and resources. This paper presents the findings of an Artificial Intelligence based model developed for failure prediction of ESPs aiming to minimize the unexpected production loss for Field X.

From the historical data obtained, 47 instances of pump failure have been identified. One of the challenges encountered during Data Exploration was missing data which in many cases was due to downhole sensor failure before the pump failure. The missing values have been inferred and imputed from the known available parameters for each pump. Various machine learning algorithms including Random Forest Regressor, Xgboost Regression, Copula-based Outlier Detection, Scalable Unsupervised Outlier Detection and Long Short Term Memory (LSTM) Autoencoder have been applied on these failure instances to develop a model for predicting the run lives of ESPs. Out of all the methods, LSTM Autoencoder model has been found to be the best suited model for anomaly detection before failure of ESPs. Autoencoders learn patterns in data over long sequences which makes them suitable for anomaly detection before the actual pump failure. The pattern recognition algorithms of Autoencoders have been able to predict the anomaly at approximately ~60 days before failure in a number of pump failure instances.

The paper discusses a proactive approach by building a predictive model for estimating ESP lifespan based on machine learning algorithms. The model's predictive accuracy can be improved over time by adding information and further improving the model components.
Transforming Well Planning through an AI-Assisted Well Trajectory Optimization Approach Applied to an Offshore Field in the Middle East
Authors: Almarzooqi Laila Zayed , Fonseca Rahul Mark , Buhindi Hani Ahmed , Binabadat Ebtesam Khaled , Al Hamlawi Imad , Dolle Norbert , Meshcheriakova Olga
Published: October 2022

This paper describes the use of Artificial Intelligence (AI) to support well planning in an Abu Dhabi offshore field. In this application, AI has been used for automated and unbiased evaluation of well trajectories, with the objective to optimize the cost, risk versus value trade-offs while considering complex issues such as anti-collision with existing wells.

A Rapid Random Tree (RRT) algorithm, well known for applications in robotics, has been used to generate well trajectories for 2 actual drilling projects. The algorithm creates a full and unbiased option space of feasible well trajectories, presented in a custom-built and collaborative digital solution.

Results demonstrate that AI-generated well trajectories were 2-5% shorter than manually planned and/or actual drilled wells. This use case also shows that an AI can design thousands of possible well trajectories in only a few hours, adhering to well design rules and anti-collision constraints. This would lead to significant time savings, and possibly material drilling cost reductions, in even more congested brownfield assets.

This paper describes a real application of AI-assisted well trajectory planning in an operational setting, with a comparison to manually planned and actual drilled wells. As such, this provides a rather unique insight into the business value-adding potential of Artificial Intelligence in traditionally manual work processes.
Remote Digital Technologies Driving Innovation - A Case Study on Subsea Operations
Authors: Bell Maxine , Sriskandarajah Anojan , Heaulme Victor , Bouillouta Fouzi , Seinuah Alex
Published: October 2022

The energy industry operates in a world where a consistent connection over real-time information is expected, however, less than 1% of available data sets are currently leveraged in the industry and usually – when monitored – most of the evaluations are based on human assessment of raw data. Today's business complexity requires an evolution in the way we share project and service information in the energy industry – from a reporting culture to true transparency and collaboration. There is a need for a digital system able to embrace raw data, perform analytics, and display key recommendations and insights. This will play a key role in turning challenges into opportunities.

Adopting a real-time modular platform for proactive asset management and instant troubleshooting can be leveraged during any stage in a field's life cycle. Built for collaboration, the platform unlocks value through minimizing equipment downtime, enabling proactive servicing and digitally optimizing the understanding of equipment needs throughout the life of field. This agile approach consolidates multiple data streams into one trusted digital source including:Autonomous monitoring of subsea wells, wellheads and xmas tree integrity without the need for active power. Designed to integrate with existing subsea infrastructure, these battery-operated monitoring systems passively monitor for a breach of material and will alert the operator of any integrity issues.A fully equipped remote communications tool that goes beyond simple audio and video chat to support effective remote support.Integrated visualization of robotic cleaning and inspection data of subsea equipment.Objective view of the overall health status asset(s), field(s) or infrastructure, considering those elements which have the greatest bearing on health and longevity, with the possibility to monitor health trends over time, prolong operable life and optimize productivity.

The implementation of the right tool, feature-rich, intuitive, and easily scalable leverages data and analytics to unlock a collaborative model of proactive servicing, prolong asset operable life and reduce OPEX. The case study demonstrates the positive outcomes that can aid the decarbonization of monitoring operations in remote locations. This approach can yield exceptional cost savings when compared to traditional methods of on-site visual monitoring. A wider overview will include the benefits of extending the window for full intervention or P&A, including lower asset risk, and protecting the environment.

This paper illustrates deployment examples of the platform on subsea fields in the North Sea and Asia, its value, approach, as well as recommendations for any operator willing to embark on a similar journey.
Machine Learning Based Prediction of PVT Fluid Properties for Gas Injection Laboratory Data
Authors: Ghorayeb Kassem , Mogensen Kristian , El Droubi Nour , Ramatullayev Samat , Kloucha Chakib Kada , Mustapha Hussein
Published: October 2022

Gas injection pressure-volume-temperature (PVT) laboratory data play an important role in assessing the efficiency of enhanced oil recovery (EOR) processes. Although typically there is a large conventional PVT data set, gas injection laboratory studies are relatively scarce. On the other hand, performing EOR laboratory studies may be either unnecessary in the case of EOR screening, or unfeasible in the case when reservoir fluid composition at current conditions is different from initial conditions. Given that gas injection is to be widely assessed as an optimal EOR process, there is increased demand on time- and cost-effective solutions to predict the outcome of associated gas injection lab experiments.

While machine learning (ML) is extensively used to predict black-oil properties, it is not the case for compositional reservoir properties, including those related to gas injection. Can we use the typically extensive conventional laboratory data to help predict the needed gas injection parameters? This is the core of this paper.

We present an ML-based solution that predicts pertinent gas injection studies from known fluid properties such as fluid composition and black oil properties. That is, learning from samples with gas injection laboratory studies and predicting gas injection fluid parameters for the remaining, much larger, data set.

We applied the proposed algorithms on an extensive corporate-wide database. Swelling tests were predicted using the trained ML models for samples lacking gas injection laboratory data. Several ML models were tested, and results were analyzed to select the most optimal one. We present the algorithms and the associated results. We discuss associated challenges and applicability of the proposed models for other fields and data sets.