bluuebunny/crossref_metadata_2025 huggingface dataset:
Troubleshooting of Repetitive Liquid Fuel Nozzles Clogging Due to Liquid Fuel Coking in Dual Fuel Gas Turbine
Authors: Anwar Zeeshan , Manikandan A
Published: November 2024

Gas turbines are one of the most widely used technology for both power generation and mechanical drive applications. Their availability is a critical requirement for any plant uninterrupted operations. The subject case study details the root cause analysis done on repetitive clogging of liquid fuel nozzles due to liquid fuel coking on a dual fuel heavy duty gas turbine, that had an impact on the overall plant operation.

The site is configured with two Frame 5/1PA single shaft Gas Turbine with standard combustion system, driving generator for power production. The two Gas Turbine Generators (GTGs) are completely identical and running on same liquid fuel and gas fuel. These gas turbines can only be started with liquid fuel and then change over to gas fuel as required.

On one of the GTGs the issue of liquid fuel nozzles clogging due to liquid fuel coking happened twice in a span of six months. Few of the indications are Increase in exhaust temperature spreads and "failed to lite" during startup. Comparatively the other similar unit faced no such issue during its operations. Liquid fuel coking occurs if there is stagnant liquid fuel at high temperature over time at the Nozzles. For a duel fuel gas turbine, while it is running on gas fuel, constant air purge is provided in order to remove any residual liquid from the liquid fuel nozzles so it will not coke while running on gas fuel.
Innovative Isotopes Tracking: A Cost-Effective Approach for Monitoring of Injected CO2 Flood Advancement in Oil Reservoirs
Authors: Koksalan T. , Khan K. A. , Alwaeel R. S. , Aljneibi N. M. , Alblooshi M. N. A.
Published: November 2024

Assessing sweep efficiency and recognizing the point of breakthrough are critical for evaluating an Enhanced Oil Recovery (EOR) effort employing miscible CO2 Gas injection. This study aimed to employ CO2 carbon isotope composition and molar concentration to monitor sweep efficiency and breakthrough in production wells of an ADNOC Onshore field to optimize the project, such as adjusting injector and future production well placements. The integration of CO2 molar concentration data along with carbon and oxygen isotope readings, helped to assess the influence of CO2 Injection EOR. The project relied on the premise that using a distinct isotopic signature for injected CO2-deriving from a steel mill and notably lighter in comparison to CO2 associated with the oil-would aid in determining sweep efficiency and breakthrough levels. CO2 is injected through specific horizontal injectors in the north area of the field. Initial values of oil-associated CO2 carbon and oxygen isotope served as a benchmark to evaluate the progress of sweep efficiency and breakthrough. Frequently, compositions and isotopic values of injected CO2 are recorded. Subsequent periodic analyses of these components’ composition and isotopes in oil and water producing wells indicate the degree of breakthrough. Findings from CO2 molar and isotope measurements in both injector and producer wells demonstrate that injected CO2 tends to breakthrough preferentially in certain regions, signifying a varied reservoir quality across the field with superior reservoir quality, and CO2 injection distribution experiencing faster breakthrough. These compositional and isotopic metrics have proven sensitive enough to detect changes in producer wells. It is intriguing to note that the CO2 δ13C isotope signals a breakthrough sooner than the CO2 molar concentration. Data also indicates that CO2 WAG is a much more efficient EOR process compared with continues CO2 injection.
Enhancing Geological Studies Efficiency Through AI-Driven Analysis: A Case Study in Lower Cretaceous Time
Authors: Vaz R. , Al Muhairi B. , AlTamimi M. A. , Egorov D. , Yang F. , Li X. , AlZaabi R.
Published: November 2024

The stratigraphic framework of the Lower Cretaceous in Abu Dhabi is significantly shaped by tectonic activity and eustatic sea-level fluctuations, which have governed sedimentation patterns and diagenetic processes. A comprehensive understanding of the facies distribution, diagenetic history, and depositional settings of these formations is essential for effective hydrocarbon exploration and production, as they represent key reservoirs in Abu Dhabi's prolific oil and gas fields. Detailed thin section analysis and microscale reservoir characterization are pivotal for elucidating these dynamic geological features.

During the recent years, artificial intelligence (AI) solutions have become widely adopted in the oil and gas industry and geology. Data science algorithms and deep learning models have empowered tools capable of rapid analysis and extracting insights from geological, geophysical and production data, proving their strong capabilities across various data and task domains. For instance, previous research has shown that such approaches are extremely useful for seismic faults identification (Egorov, 2019), well log data interpretation and localization of missed oil-bearing formations (Belozerov, 2018), core photos analysis to assess reservoir heterogeneity (Egorov, 2019), hydraulic fracture design optimization (Grigorev, 2020), among other applications. This research not only demonstrates that machine learning can optimize existing business processes optimization, but also highlights its potential to extract additional insights from existing data. Hence reducing geological uncertainties and mitigating associated field development risks. Despite the proven benefits of AI for geologists in daily tasks, research on the applicability of such models for thin section analysis is limited (e.g. Bukharev, 2018 and Leseur, 2022) and requires further investigation and its capabilities assessment.

The primary objective of this study is to further investigate the application of advanced artificial intelligence (AI) techniques, particularly supervised machine learning and computer vision, to enhance microscale reservoir characterization of thin sections from carbonate rocks in the Lower Cretaceous of Abu Dhabi's oil fields. This research encompasses not only the final AI models- including model architectures, data, training pipelines, and testing methodologies- but also the challenges encountered during the research and development process. The system's high accuracy is demonstrated through both qualitative and quantitative results.
Leveraging Artificial Intelligence for Prioritizing and Streamlining Maintenance Backlogs
Authors: Abdullah Muhammad , Rahnama Ramin , Radu Alina , Stewart Kevin , Buchan Gordon
Published: November 2024

Objectives/Scope

The safety of industrial facilities relies heavily on maintenance, yet planned tasks may face delays due to factors like surging work demands or insufficient resources. This results in a backlog of overdue activities, posing operational, environmental, and safety risks to assets. Pandemic-induced challenges, shortages of skilled staff, and inflation have exacerbated this situation, compelling us to seek innovative solutions. This suggested methodology prioritizes backlog tasks that improve reliability and eliminates non-value-adding items by combining artificial intelligence and technical expertise. This analysis considers multiple factors including resourcing capacity, aging infrastructure, marginal economics, and competing priorities.

Methods, Procedures, Process

The suggested methodology in this work combines artificial intelligence, automated data processing, and operations domain knowledge to empower asset owners. By eliminating non-value-adding backlog and providing targeted reliability improvement guidance, we enhance operational efficiency. This innovative methodology integrates natural language processing algorithms and generative artificial intelligence models, with advanced reliability assessment techniques. This methodology transforms raw data into actionable insights at significantly improved pace, accuracy, and consistency.

Reliability simulations are utilized to understand the next time a failure may occur. Reliability engineers then use this data to prioritize working on equipment with poor reliability, while work on equipment with good reliability is risk- assessed for cancellation. Done at scale and with pace, our backlog solution considers a range of factors within the assessment of overdue work, including criticality, reliability, consequences of failure, and remaining life of the assets.

Results, Observations, Conclusions

This methodology enables us to prioritize the most important work by understanding how backlog items affect asset safety and reliability. The strategic focus on value-adding work leads to improved safety, higher production, and lower costs.

A recent project utilized this process and examined over 200,000 hours of maintenance backlog tasks. The project eliminated 67,000 hours of maintenance backlog that did not add value, which reduced the operation cost by more than $5M. Utilising artificial intelligence allowed us to finish the project in 8 weeks, from the start of the project to the update of maintenance plans in CMMS.

Novel/Additive Information

Traditionally, maintenance engineers relied on manual review to prioritize backlog items. However, when faced with hundreds or thousands of activities in the backlog, this task became exceptionally challenging. Our innovative approach, leveraging artificial intelligence and automation, not only streamlines the process but also enhances accuracy and consistency, revolutionizing the landscape of backlog optimization.
Enhancing WRFM Operational Excellence by Addressing High Artificial Lift Failure Index Through Comprehensive Fail-Less Strategy - A Case Study in a High Viscous Onshore Oil Field
Authors: Al Ajmi A. , Kulkarni R. , Al Farsi T. , Al Busaidi I. , Borkent E. , Al Kaabi I. , Al Jahdhami A. , Al Shukaili G. , AL Ismail i A. , Al Kindi S. , Al Noumani H. , Al Azri N.
Published: November 2024

The petroleum industry has always been in pursuit of innovative solutions to overcome challenges in oil production. One of the most significant challenges is artificial lift failures, which can lead to high subsurface deferment and, hence, result in decreased oil production and increased restoration costs. In 2019-2020, Field A in the south of the Sultanate of Oman experienced a high unscheduled subsurface deferment of 5% and a high artificial lift failure index of 30%, mainly due to erosion and corrosion caused by sand and solids production, combined with a high oil viscosity.

Therefore, to address these issues, the fail-less strategy was introduced in the field encompassing the active participation of all relevant parties to pinpoint the underlying factors contributing to the heightened failures of current pumping mechanisms within the Field. Hence, a comprehensive conversion strategy was proposed for the frequent failure wells with beam pumps (BP) and progressive cavity pumps (PCP) to electrical submersible pumps (ESP), based on successful trials conducted in the same area previously. Starting from 2020, a total of 76 wells were converted which also included pump deepening opportunities.

The implementation of the fail-less strategy resulted in significant improvements in the field's artificial lift system performance, reducing the overall field failure index from 30% in 2019 to only 15% by 2023 and reducing the unscheduled subsurface deferment to 2% by 2023 approaching top quartile performance. Furthermore, an increase in the field total net oil by 5% compared to 2019 performance was observed. This paper describes the challenges and the successful implementation of the conversion to ESP, providing valuable insights for the industry to address similar challenges in other fields.
3D Anisotropy Analysis by Using the Vertical, Deviated, and Horizontal Well in the Khufai Formation, Oman
Authors: Assous Said , Mand Bilal Arshed , Khan Kashif Mushir A , BilalSamii Shaheed , Bacciarelli Mark
Published: November 2024

By analyzing sonic data with quantified deviations from vertical and true stratigraphic dips obtained via an imaging tool in vertical, deviated, and horizontal wells, the established method computes up to five elastic moduli of an orthorhombic or transversely isotropic formation, while compensating for any tool-induced effects. The algorithm yields verifiable far-field compressional and shear moduli, unaffected by the near-wellbore zone altered by stress concentrations or rock's plastic deformation.

In this case study, cross-dipole data is evaluated to confirm the third shear slowness component derived from Stoneley waves across three well trajectories—vertical, deviated, and horizontal—in the Khufai formation in Oman, which is primarily composed of carbonate with some shale, exhibiting Vertical-VTI/Horizontal-HTI or Orthorhombic Anisotropy. Initially, the third shear slowness for each wellbore trajectory is computed to validate the method. This component is then used in 3D anisotropy analysis to determine up to five independent elastic moduli of HTI, orthorhombic, or VTI formations, using compressional, fast, and slow shear, and Stoneley slowness measurements.

The 3D Anisotropy method converts measured compressional, fast shear, slow shear, and Stoneley slownesses relative to borehole axes into anisotropic moduli aligned with the earth's anisotropy axes. This technique can produce up to five anisotropic moduli for orthorhombic or transversely isotropic (TI) formations, using sonic data that compensates for known deviations from vertical and true stratigraphic dip from imaging tools in vertical, deviated, and horizontal wells.

The method also adjusts for any tool-induced effects on the borehole's Stoneley and flexural modes. The algorithm's output delivers verifiable far-field compressional and shear moduli, not influenced by near-wellbore changes like stress concentrations or rock plastic yielding. Transverse isotropic anisotropy, or polar anisotropy, is measurable through the estimation of Thomsen parameters: ε, γ, and δ. The γ parameter is derived from borehole sonic data based on vertical and horizontal shear moduli, typically obtained from cross-dipole sonic logging.
Insights into Gas Storage Reservoir: Enhancing Energy Security and Market Stability on Giant Reservoir in Abu Dhabi
Authors: Jaber H. Bin , Al Dhanhani H. , Al-Ali R. , Albadi M. , Al Bairaq A. , Baslaib M. A. , Balobaid L. , Fathalla M. , Alawadhi F. , Al Yaaqoubi N. , Sudarev M. , Alzaabi A. , Alameri S. , Alhammadi S. , Alfardan F. , Al Shamsi A. , Mostapa M. , Alzeyoudi M. A. , Al Ameri A. , Alsaeedi A.
Published: November 2024

The implementation of gas storage reservoirs plays a critical role in bolstering energy security and stabilizing markets. This paper examines the strategic deployment of a gas storage facility in Abu Dhabi, focusing on optimizing asset utilization, managing demand variability, and mitigating supply risks. Utilizing advanced methodologies and AI-powered technologies, the study evaluates the operational efficiency and economic viability of integrating gas storage into Abu Dhabi's energy infrastructure. Results highlight significant improvements in market stability and energy system resilience, positioning Field operator as a leader in sustainable energy management.
Nature-Based Solutions as a Decarbonization Strategy
Authors: Ahmet H. B.
Published: November 2024

In the context of practical climate change action, cost effective measures to tackle emissions, either directly or through carbon sequestration, are urgently needed. This paper introduces a novel nature-based solution for wastewater treatment that also offers significant carbon sequestration benefits, and provides considerable savings over conventional approaches. This innovative solution was proposed for an unconventional gas field Mega-Project within the Middle East.

As part of the sustainability requirements of the Mega-Project, a new screening tool was developed to rapidly appraise over 370 initiatives, focusing on sustainability and decarbonization across the construction and operations phase of the project. Key themes identified included water conservation, energy efficiency, and renewable power production. A resulting comprehensive circular sustainability strategy was developed, utilizing renewables for water heating, composting for food waste, treating sanitary waste and process wastewaters via a Reed Bed, and a native tree plantation area to utilize the treated water and compost. The solution aligns with the green/circular economy ideals and offers significant multiple benefits.

The proposed Reed Bed design consists of 12 × 1,000 m2 of Vertical Flow Wetlands cells, and 3 × 9,000 m2 of Surface Flow Wetlands cells. Once operational, this nature-based solution will facilitate the reclamation of over 16 million m3 of treated water, to be used for tree plantations. The 1,000 hectares of plantations would additionally be fenced off to facilitate natural rangeland regeneration and as a barrier to increasing desertification. Based on this proposal, it was estimated that the combination of the fenced rangeland, plantations, and reed beds would sequester approximately 1.25 MtCO2 over 25 years. This initiative additionally presents a biodiversity value of over US $400 million. Further significant benefits of the scheme includes a site footprint reduction of over 800,000 m2 and a cost saving of around US $100 million. Besides these economic and environmental benefits, reed beds provide a host of other sustainability benefits.

This project will result in a strategic shift within the client organization resulting in the widespread adoption of nature-based solutions to replace conventional wastewater treatment and disposal, representing significant organizational decarbonization benefits. All of the Client's processing facilities will be evaluated for the suitability of deploying similar reed bed based solutions.

This paper presents the first in-country and in-company deployment of a novel constructed wetland systems and marks a strategic shift for the oil and gas industry. We demonstrate how the adoption of nature-based solutions over conventional wastewater treatment offers significant decarbonization benefits, cost savings, and environmental advantages. The combined benefits present a new pragmatic option for the industry's toolbox of decarbonization and waste treatment solutions.
A Journey Unlocking Additional Unconventional Resources: Extensive Integration of Subsurface & Completion. A Case Study from UAE Unconventional Oil Play
Authors: Ibe E. , Panchakarla A. D. , Uluyuz S. , Baig M. Z. , Elazab S. A. , Alharthi A. D. , Hashmi A. A. Al , Laer P. V. , Mazrzooqi H. Al
Published: November 2024

Unconventional play is associated with several challenges due to the complex nature of the reservoir. Identifying potential zones to target wells, stimulating, and planning field development to recover the hydrocarbon remains bleak without proper evaluation. This paper illustrates successful implementation of reservoir characterization, stimulation, and field development of the Shilaif formation Onshore UAE.

The Shilaif formation is argillaceous limestone, fine-grained packstones and shaly lime mudstone-wackestones. This complex lithology was deposited under intra-shelf basinal conditions during the Cretaceous period. Previous studies spe-211005-ms published elaborated on data integration approach adopted to characterize the Shilaif member, enabling identification of the potential zones for horizontal well placements and successful development of this organic-rich rock. Based on this study, extensive evaluation of geomechanical properties of the reservoir was done to guide stimulation. The hydrocarbon resource quantified was confirmed producible and pad drilling planned to fully extract the hydrocarbons.

Geomechanical modelling was done to guide hydraulic fracturing using elastic properties, unconfined compressive strength and tensile strength derived from acoustic data acquired. Overburden stress, stress profiles and pore pressure from the mechanical earth model built resulted in efficient fracturing of the reservoir to release the hydrocarbon. Successful stimulation increased permeability profile post-frac as evident from the production data. Stress profiles and orientation determination from this study also guided subsequent well planning as pad drilling was initiated with horizontal wells targeting two different benches within Shiliaf formation first time. This study well is a success as part of wells drilled with similar extensive integration of elemental spectroscopy to reduce the uncertainty in total organic carbon (TOC), nuclear magnetic resonance (NMR) to provide lithology independent porosity and distinguish producible and non-producible fluids, acoustic log for stress determination and mechanical earth modeling which guided reservoir stimulation. A pre-frac injection test carried out revealed reservoir pressure and permeability in the different frac-zones and helped stage the hydraulic fracturing exercise.

The mechanical properties estimation of reservoir rock with inputs from acoustic log, guided the staging of hydraulic fracturing and improving zone selections and optimizing hydraulic fracturing process. This has proved efficient in successful development of the organic rich rocks of the Shilaif formation.
Advanced Perforation Technology for Enhanced Remedial and Casing Recovery Applications
Authors: Lucas A. , Smith C. , Ullbrand B. , Jonsson K. , Torvestad B. T. , Geerts S. , Ledevin P.
Published: November 2024

The introduction of Perforate, Wash and Cement (PWC) techniques have delivered a step-change in annular cement remediation efficiency versus conventional methods. The development of Tubing-Conveyed Perforating (TCP) charges capable of perforating production casings without damaging the outer casing string further facilitated effective cement remediation between concentric casings in less than half the rig time typically taken by section milling. However, effective remediation via PWC techniques has been shown to be significantly challenged by relatively well-bonded annuli, since effective hydraulic communication cannot be reliably established.

A novel TCP system has therefore been developed to enhance the capability of PWC technology to effectively remediate challenging annuli by ‘rubbelizing’ or ‘pulverizing’ the annular cement, thereby dramatically improving hydraulic connectivity and enabling circulation behind casing to be more effectively established, and so permitting annular cement remediation - and indeed potentially also casing recovery operations - where it may otherwise not have been possible.

An extensive design and testing programme involving Computational Fluid Dynamics (CFD) analysis, full-scale gun System Integration Tests (SITs) and a full-scale proof-of-concept test in a test well were undertaken to develop an optimized TCP system capable of ‘rubbelizing’ annular cement between concentric casings, without damaging the outer casing such that effective remediation could be performed on well-bonded annular cement previously not suitable for PWC treatment.

Furthermore, the application of the technology to two further use cases has also been explored: (1) the use of bismuth alloy as an alternative annular remediation solution, and (2) cemented casing recovery for slot recovery operations. Both of which have demonstrated significant promise, with the bismuth alloy application now successfully tested at full-scale, and the cemented casing recovery application now successfully tested by offshore field trial.
Microbial Reservoir Souring During Engineered Water Injection with a Coupled Biogeochemical-Compositional Simulator: A Numerical Study
Authors: Fathy A. , Al-Shalabi E. W. , AlAbdullah M. B.
Published: November 2024

Reservoir souring attributed to Sulfate-reducing bacteria (SRB) poses significant challenges during waterflooding operations. Among souring mitigation schemes, engineered water injection (EWI) is deemed promising in limiting further biogenic H2S generation while enhancing oil recovery. This study is an extension of our previous work (SPE-218236-MS), where we upscale the previously generated 1D H2S bioreactor experiment model to predict the impacts of various parameters on SRB growth at a field scale. This study successfully integrates SRB activity and the effects of injection depth relevant to Oil-Water Contact (OWC) and rock wettability (intermediate-wet vs. strongly water-wet) on H2S generation into a unified 3D biogeochemical model. The bacterial growth kinetics and SRB population were simulated via partial equilibrium reaction and Monod equation incorporation. The simulation capabilities of modeling onset reservoir souring and H2S breakthrough during EWI were utilized in this study. This was possible using a reservoir simulator that offers a relatively simple yet metabolically accurate representation of the kinetic processes of bacterial populations. This model effectively captured the detailed mechanistic interactions between SRB and sulfate ions (SO42−). Additionally, the model enabled predicting the effects of injection depth relevant to OWC and rock wettability on SRB growth within a field-scale model.

Even without detailed data, the findings from this study seem to align well with the established characteristics of microbial growth. Furthermore, the results demonstrated that the injection of engineered water (EW) containing sulfate ions (240 ppm) at 20°C promoted biofilm formation near the injector, enhancing SRB activity and initiating H2S production. H2S breakthrough occurred by the end of the fifth year. Afterward, H2S production declined due to reduced SRB presence and nutrient depletion, leaving some unproduced H2S near the well. Deeper injection points delayed H2S breakthrough and decreased its concentrations, highlighting the importance of careful injection depth selection to manage risks. Both wetting conditions initially showed a rapid rise in H2S concentration, with the intermediate-wet scenario achieving faster and higher oil recovery than the strongly water-wet scenario attributed to favorable mobility due to increased water viscosity. This study provides valuable insights into predicting and managing reservoir souring. This offers significant improvements to field operations and deepens the understanding of reservoir management and engineered water injection methods by addressing fundamental mechanisms that are often overlooked.
Ethics in AI – Critical Skills for the New World
Authors: Sahoo Manas
Published: November 2024

Artificial intelligence (AI) systems are increasingly being used in consumer as well and business applications. Companies are using them, Governments are using them, public security, medical researchers, you and I – everybody seems to be somehow touched. As it becomes more advanced and ubiquitous in our lives, it is critical that AI developers and users understand the ethical implications and challenges posed by this powerful technology. This paper explores the key ethical skills and considerations that are essential for navigating the new world of AI.

Artificial Intelligence (AI) presents significant ethical dimensions across various sectors, including research, business, and daily life. It is important to appreciate the major ethical dimensions of AI and how they impact AI systems. Policies and guidelines needs to be laid out for development and usage of such systems. Automated Decisions/Responsibility: No matter how intelligent a AI system might perceived to be, it is always prone to making errors. So, AI system that make automated decisions will always be dangerous. Therefore, it is important to ensure that there is always "human intervention" or "human review" for outcomes/decisions made by AI systems. Determining accountability for AI-generated actions or content remains a significant challenge, emphasizing the need for ethical governance frameworks. There has to be human accountability.Fairness/Bias: AI systems can perpetuate existing biases present in training data, leading to discriminatory outcomes.Transparency: The opaque nature of many AI algorithms complicates accountability, making it difficult to understand decision-making processes.Privacy: AI's capability to process vast datasets can infringe on individual privacy rights, necessitating stringent data protection measuresHuman Dignity: The risk of AI-generated content resembling existing works raises ethical issues regarding originality and authorship.
Furlong Unity - A 3D Lidar Local Position Reference Sensor with Camera Augmentation
Authors: Miles Russ , Heriot Sasha
Published: November 2024

This paper introduces a novel Dynamic Positioning (DP) 3D lidar reference sensor capable of functioning with fixed retroreflectors or unaltered scenes. It aims to address challenges associated with presenting real-time 3D data to users, particularly in dynamic environments. Additionally, the paper explores the integration of a 6D inertial sensor and GNSS to enhance position tracking accuracy, focusing on the application of these technologies in real world DP operations.
Interfacial Tension of Hydrogenated and De-Hydrogenated Organic Hydrogen Carriers: Implications for Hydrogen Geological Storage
Authors: Ali Muhammad , Rawat Narendra , Alsubhi Mutaz , Alissa Faisal , Ghamdi Abdulwahab , Hoteit Hussein
Published: November 2024

Organic Hydrogen Carriers (OHCs) have emerged as a promising solution for efficient hydrogen storage, addressing one of the key challenges towards enabling hydrogen economy. As the demand for renewable energy and decarbonization intensifies, the need for reliable and efficient large-scale storage and hydrogen transport becomes increasingly crucial. OHC technology offers a promising approach that enables hydrogen to be geologically stored in a liquid form, allowing for safe and convenient handling, distribution, and utilization. The ability of subsurface formations to store geologically is determined by their wetting properties and interfacial forces. These factors significantly impact residual saturations, fluid flow dynamics, injection, withdrawal rates, and containment reliability. To our knowledge, literature has yet to report these properties for most OHCs at natural geological physio-thermal conditions. Therefore, we have measured the interfacial tension of methyl-cyclohexane (MCH; hydrogenated OHC) and toluene (de-hydrogenated MCH) in the presence of one molar NaCl solution at natural physio-thermal geological conditions (T = 298-343 K and P = 1-20 MPa). MCH and toluene were used as a surrounding phase, and one molar NaCl was used as a drop phase. The results suggest that IFT values decrease with temperature and increase with pressure. This is caused by the increased intermolecular interaction of one liquid to the other liquid molecule due to pressure increase, thus increasing the IFT.

Similarly, temperature reduces the density of both liquids, causing a reduction in IFT. However, MCH has higher IFT values than the toluene at pressure and temperature conditions similar to the toluene. This is due to the difference in density of a similar compound's hydrogenated and de-hydrogenated form. For instance, at 10 MPa and 323 K, the IFT value for MCH was 54 mN/m (density = 0.7599 g/ml) compared to the toluene = 38 mN/m (density = 0.84762 g/ml). Our findings pave the way towards optimizing safety for sub-surface hydrogen storage, offering a readily scalable solution to enhance energy security and support the transition to a sustainable, carbon-neutral future.
Northern Lights: Innovating for the Future of Carbon Transport and Storage
Authors: Belhadj Mansour
Published: November 2024

The Northern Lights project is a pioneering initiative in carbon capture and storage (CCS), representing one of the most advanced and large-scale efforts to reduce carbon emissions in Europe. The project is a key component of Norway's broader climate strategy and is supported by major industry players, including Equinor, Shell, and TotalEnergies. A crucial aspect of the project is its partnership with Microsoft, which has brought cutting-edge digital solutions to optimize the CCS value chain. This paper provides an in-depth analysis of the Northern Lights project, focusing on the role of digital technologies, particularly those provided by Microsoft, in enhancing the efficiency, scalability, and security of carbon storage operations. The paper also examines the broader implications of the project for global decarbonization efforts and the potential for CCS to play a central role in achieving net-zero emissions.
Unlocking Geothermal Potential: Advancing Exploration with Artificial Intelligence for Sustainable Energy Solutions
Authors: AlGaiar Mahmoud. M.
Published: November 2024

The objective of this study is to investigate the potential benefits and challenges of integrating Artificial Intelligence (AI) into geothermal exploration for resource identification and drilling location optimization. The scope includes investigating the application of AI algorithms in the analysis of subsurface datasets to improve prediction accuracy and reduce uncertainty in geothermal resource evaluation.

A critical review of published articles on AI applications in the geothermal industry was conducted to gain insight into the effectiveness of AI models in resource exploration. Various AI algorithms such as Logistic Regression, Artificial Neural Networks and Decision Tree were analyzed for their contributions to geophysical and geochemical data analysis in geothermal exploration. The review focused on the identification of hidden patterns in large datasets using AI algorithms to improve the understanding of subsurface properties and to locate hidden/blind geothermal systems. The analysis involved evaluating the performance of different AI techniques in predicting reservoir properties, lithology and fluid content based on geochemical and geophysical data. The results of the study demonstrate the significant potential of AI in improving the accuracy of predicting geothermal reservoir properties and optimizing drilling locations. AI models outperformed traditional expert-based and statistical methods, demonstrating their effectiveness in uncovering hidden geothermal resources and reducing exploration risks. Observations from the review highlight the importance of collaboration and partnerships within the geothermal industry to leverage multiple datasets and insights for improved efficiency and productivity in energy development. While there are challenges such as model transferability between locations, the continued growth of AI in geothermal exploration provides opportunities for further research and development of deep learning techniques. The increasing use of AI in geothermal exploration indicates a shift towards data-driven decision making and advanced predictive modelling techniques.
Optimal Matching of Multi-stage Fracturing Parameters with Geological Characteristics in Shale Gas Well Based on Deep Learning Algorithm
Authors: Luo Yang , Kang Bo , Qu Jianhua , Wang Hehua , Mi Zhongrong , Cheng Yi , Xiao Yong , Zhao Xing , Feng Yan , Guo Jianchun , Lu Cong
Published: November 2024

Multi-stage hydraulic fracturing in horizontal well is a pivotal technology for the efficient development of shale gas reservoirs. Given the pronounced heterogeneity along the horizontal section, precise matching of fracturing parameters with geological characteristics in each fracturing stage is imperative for enhancing well productivity.

In this paper, a data-driven productivity prediction model at the stage level was established based on field data and the Gate Recurrent Unit (GRU) algorithm. Fracturing and geological features in each stage was integrated as the model input matrix, and an additional mask layer was employed to accommodate varying stage numbers among different wells. To achieve optimal matching between fracturing parameters and geological characteristics, a novel variable-dimension optimization algorithm was proposed. Aiming at maximizing well productivity, this algorithm can determine the optimal number and positions of fracturing stages according to the geological profile and generate corresponding fracturing parameters.

Trained with real data from over 120 wells in Sichuan basin, China, the productivity prediction model achieved a robust performance with a mean relative error (MRE) of 10.1%. This MRE represents a 34.3%~67.7% improvement over traditional multilayer perceptron and random forest models, demonstrating the significance of extracting features in each stage. Optimization results for a representative well case indicate that to maximize productivity, the optimal stage number is 28, with stage length ranging from 51m to 91m, and fracturing parameters tailored to the geological characteristics in each stage. Furthermore, post-fracturing optimization, micorseimic monitoring reveals a 32.3%~44.5% increase in stimulated reservoir volume compared to adjacent wells, resulting in a productivity boost of 21.2%~35.7%.

Unlike manual stage division and separate fracturing parameters optimization in each stage, the approach proposed in this paper can identify the optimal stage number, stage positions and fracturing parameters in each stage perfectly matching the geological characteristics. This study provides a novel framework for the fracturing parameters optimization in shale gas wells.
Legal Insights on Energy Projects Management: Challenges of the Implementation of Liquidated Damages Provisions Under Engineering, Procurement, Construction and Commissioning Contracts
Authors: Elshazly Mostafa
Published: November 2024

Energy projects are frequently a subject of very detailed specifications and strict deadlines, poor performance or delays can throw the project and its schedules into disarray, inflate budgets, and strain relationships between the Contractor and the project company/owner. Poor performance and delayed performance of works related to the energy project often translates into increased costs due to overtime wages, penalties for missed deadlines, or additional expenses incurred by the project company/owner to rectify off-specs works and to expedite progress of the project. This may result in triggering the implementation of the liquidated damage provisions that are usually stated under the relevant signed EPCC contract/s to put the party who has not breached the EPCC contract into the position he would has been in if the contract had been properly performed. From practical standpoint, the implementation of liquidated damages provisions is a subject of argument and sometimes leads to potential dispute between the relevant parties in the concerned energy project due to lack of clear understanding of the mechanism, functions and the legal nature of these liquidated damage provisions. For instance, the parties of an EPCC contract may apply the liquidated damage clause as a penalty during the contract execution, while the intention at the earliest phases of the project during negotiating the terms and conditions of the contract was to insert the liquidated damage clause to set a pre-estimation for the losses/damages that is likely to be suffered by the affected party due to the breach of the counter party in the contract. Though, it is important to shed the light on the functions and the legal nature of liquidated damages provisions in energy contracts and to set a road map for the efficient use and enforcement thereof.
Enhanced Technology for HP Gas Condensate Wells Production Testing
Authors: Suchkov E. , Fahretdinov A. , Kim E.
Published: November 2024

Production testing of high-pressure gas condensate wells can be fulfilled by traditional test separators or multiphase meters. However, both methods have disadvantages: with a test separator the necessity to keep separation gas pressure at the level of flowline pressure of 80-140 bar does not allow for good quality of separation and measurement of C13+ components carried over with the separated gas. Dropping separation pressure would result in the requirement for flaring of separated gas thus creating large environmental impact or using expensive compression technology to raise pressure to the flowline level which is not economically feasible in most cases. Alternatively, multiphase meters introduced as an environmentally friendly and efficient method for gas condensate wells production testing have shown unstable measurement results with low accuracy and repeatability.
Successful Low Salinity Single-Well Pilot and SWCTT Operations in a High Temperature/High Salinity Carbonate Reservoir of the UAE. SWCTT Operations, Numerical Interpretation, and Integration with Well Logging
Authors: Balobaid L. , Berrahmoun H. , Tverinev A. , Shankar V. , Romero C. , Alhammadi M. , Masalmeh S. , Saxena P. , Huseby O. , Hartvig S. , Yvonnou P. , Pedersen R. , Garnier O.
Published: November 2024

This paper provides results and interpretation of an onshore Middle-East field first low salinity EOR pilot, carried out to verify the previous lab results of an extensive project to evaluate the EOR potential of low salinity water injection. It presents the design, execution and results of the extensive SWCTT project preparation of two Single Well Chemical Tracer Tests (SWCTT) and a detailed saturation logging campaign designed to evaluate the EOR potential of low salinity water injection.

Following an extensive laboratory program of fluid-fluid tests and core flood experiments. Single Well Chemical Tracer Tests (SWCTT) were designed based on the results of this experimental program. The SWCTT pilot was accompanied by a detailed saturation logging campaign to evaluate changes in remaining oil saturations (ROS) in a fractured carbonate reservoir in south-east Abu Dhabi.

The SWCTT pilot, together with PLT, noise-temperature logs and Pulsed Neutron Capture (PNC) logs provided insight into ROS distribution across the reservoir intervals.

Contributions from the different reservoir intervals contacted by the tracer were included, aligned with log results, and uncertainties in ROS determination were calculated. This paper will focus on the specially designed multi-realization numerical simulations considered by field operator as the most robust method for pilot results.

Detailed radial single-well models were built with fine geological layering, and partitioning and hydrolysis reactions were added to match historical tracer back-production profiles. From the history match of tracer profiles non-ideal flows were detected impacting the tracer's profile response and contribute to SWCTT uncertainties.

The results confirm a positive low salinity EOR effect, matching the results from the laboratory experiments, with δROS above 4% and the EOR potential of low salinity water on this reservoir.

This paper presents the field operator's first successful Low Salinity EOR pilot at HT/HS conditions. It highlights the ability of pilot result interpretation using multi-realization simulation models and demonstrates the importance of a thorough logging campaign to support and validate SWCTT modelling work.
How Drillpipe Deployed Well Integrity Measurements is Changing the Way We Log
Authors: Hawthorn A. , Steinsiek R. , Hered W. , Leslie Iain , Kamgang S.
Published: November 2024

The oil and gas industry is in a state of continually challenging the way to improve the efficiency and safety of well construction activities. An interesting path has been followed in the last 30 years in the development and use of logging while drilling tools. The measurement quality and reliability of these tools for open hole formation evaluation and geosteering has propelled an entire sequence of highly complex and highly deviated wells with enormous improvements in productivity from existing fields. You would be hard pressed to find any wells, particularly in the offshore domain, that do not utilize logging while drilling measurements that were once the province of wireline tools only. This development of technologies and tools has, as stated above, largely focused on open hole and while drilling applications only. The same cannot be said for cased hole evaluation tools in general, and for well integrity tools that measure the condition and properties of the casing and cement.

This paper will demonstrate the development of a new drillpipe conveyed tool that can both measure the casing condition and what is in the annular space behind the casing. We will show two examples that demonstrate the range of applications that the tools can be run in. The ability to run anytime drillpipe is deployed in the well therefore increases the amount of opportunity to get these critical measurements and also to be run in parallel with existing operations throughout the well construction period. As can be imagined this can lead to considerable efficiency gains as the logging does not require a dedicated run. The basic premise of the tool is that a drill collar is fitted with three circumferential pulse echo ultrasonic transducers. This allows for a full azimuthal image of the casing and annular space behind the casing if the tool is rotated, either from a downhole device such as a mud motor, or by surface rotation through the rig rotary table or top drive. The tool can be run in real-time and also in memory mode, in either case data is both processed downhole and the raw waveforms can be recovered from the tool when back on surface. Analysis of the acquired data allows for interpretation of internal caliper, the thickness of the casing, the location of casing collars and an evaluation of the material in the annular space behind the first casing string. The need to provide oriented images means the tool has magnetometers, accelerometers and a basic gyro measurement device that can be used for orientation of downhole devices such as whipstocks or oriented perforations.
Enhanced CO2 Capture from Flue Gas Using Monoethanolamine and Silica Nanoparticles
Authors: Boral Pranab , Borgohain Gogoi Subrata
Published: November 2024

Carbon neutrality is crucial in the fight against climate change, as it refers to a state in which CO2 emissions balance with measures of removal and offsetting, effectively meaning no net increase of CO2 in the atmosphere. This mandates that technologies for the capture of CO2 from flue gases a technology of the utmost importance in reducing greenhouse gas emissions from industrial sources. MEA is a very established solvent for CO2 capture, but it could be improved if nanoparticles are added to make the performance better. The primary objective of the study is to evaluate the CO2 absorption efficiency of MEA in combination with RH-derived silica nanoparticles and compare the absorption–desorption rates of the nanoparticle-enhanced MEA solution to that of pure MEA.

Adding RHSNp to the MEA solution greatly improved the CO2 capture efficiency and absorption rates. The experiment showed an 83.33% efficiency of CO2 capture using a 22 wt% MEA solution with 0.5 wt% silica nanoparticles (RHSNp), whereas 66.67% CO2 capture efficiency was observed without adding RHSNp. The rate of absorption of CO2 was calculated to be 4.5 litres per hour. During desorption, a CO2 stripping efficiency of up to 95% was realized. These results demonstrate improved solvent regeneration and CO2 capture with the nanoparticle-enhanced MEA solution.

This research work has demonstrated the potential of silica nanoparticles to enhance MEA performance for CO2 capture from flue gases. It is concluded that nanoparticle-enhanced MEA solution can be more effective and absorb at an increased rate compared to the usual MEA solution. These results suggest that this could be a promising approach to incorporate nanoparticles into amine-based solvents for process intensification in industrial CO2 capture, with benefits that are particularly prominent in scalability and application.