bluuebunny/crossref_metadata_2025 huggingface dataset:
Lessons Learnt on the Feasibility of HC and CO2 WAG Application in a Giant Offshore Carbonate Reservoir
Authors: Davidescu B. , Male-Pirker B. , Perovic I. , Valente A. , Hincapie R. E. , Masalmeh S. , Ghedan S. , Aikman M. , Porter S. J. , Chirkov M. , Hendizadeh H.
Published: November 2024

We explore challenges in modeling complex processes involved in hydrocarbon (HC) and CO2 water-alternating-gas (WAG) injection. It includes the topics of sector model utilization, the effect of well spacing on ultimate recovery, and the impact of utilizing a tuned Equation of State (EoS). Furthermore, reservoir simulation topics such as grid size and run times are highlighted. Understanding these challenges is crucial for effectively assessing the optimum EOR method. We offer a detailed analysis of the intricate relationship between field-scale effects, well-spacing, SCAL, and EoS in EOR modeling, addressing recoveries and economic scenarios, providing valuable insights for petroleum engineers and researchers to improve EOR strategy accuracy and efficiency. A high-level economic analysis is presented together with a discussion of some of the technical challenges that may need to be addressed in implementation of a WAG scheme.

Results showed the importance of addressing field-scale effects and optimizing well-spacing for efficient EOR methods. Highlighting the role of Special Core Analysis (SCAL) data and EoS parameters in improving EOR predictions. It has been demonstrated that SCAL plays the highest role in the ultimate recovery, even beyond small scale EOR effects. Higher recoveries have been seen up to 3-7% by using an EoS fully tuned to the appropriate lab data. Fluid modelling through EoS is crucial for determining recovery volumes in gas WAG configurations. An uncertainty workflow has been applied that showed that different sectors in the field are sensitive to different parameters. EOR economics were compared to the base case water injection economics and different scenarios have been studied in order to maximize value out of the EOR. The economic viability of the project utilizing HC/CO2 WAG is inferior when compared to the base case of water injection. This is primarily attributed to the favorable mobility ratio to water injection in the base case, compared with the substantial costs associated with the HC/CO2 WAG approach. Additionally, imposing plateau restrictions impairs the economic inefficiency of HC/CO2 WAG, as any additional production it offers occurs at a later stage.
AI-Enabled Annexure Parsing for Oil & Gas Project Exhibits
Authors: Mohamed Anwar Ali , Mohammed Azharuddin , Saud Shah , Veedu Anjana Kavassery , Zubair Syed Nizamuddin , Al Sayari Amal Salem
Published: November 2024

In the Oil & Gas industry, the effective management of capital project exhibit documents is essential for ensuring compliance, tracking progress, and making well-informed decisions. This paper presents a comprehensive methodology for developing a Large Language Model – Retrieval Augmented Generation (LLM-RAG) framework leveraging LangChain that enhances the accessibility and usability of these documents using advanced machine learning (ML) techniques. A Llama2-RAG model that incorporates Llama-2-70b large language AI model for advanced language understanding and generation is proposed in this paper. Project exhibit documents are uploaded via user-friendly Streamlit application, and the text is extracted and pre-processed to smaller chunks that are converted to vector embeddings and indexed in FAISS database for efficient retrieval. The integration of FAISS with LangChain creates a robust pipeline that retrieves relevant document sections and generates contextually relevant responses to user queries.

One of the primary issues addressed by this study is information overload, where the sheer volume of documents makes it challenging to find specific information promptly. The Llama2-RAG model alleviates this by providing precise and contextually relevant responses, reducing the time and effort required to sift through extensive documentation. Additionally, the study tackles the problem of version control, ensuring that stakeholders always have access to the most current information. The Llama2-RAG model also addresses the technical complexity of the documents, making them more accessible to non-technical stakeholders. By translating complex technical language into simpler terms, the model improves understanding and communication among project teams. The system is also designed to manage project-specific terminologies and document structures, offering a high degree of customization to meet the unique needs of each project. The study also highlights the development of the Streamlit application, which not only facilitates document uploads but also provides an intuitive interface for user interaction.

The integration of advanced AI and machine learning techniques ensures that this system remains adaptable and scalable, providing continuous improvements in accuracy and relevance over time. By addressing key challenges such as information overload, version control, cross-referencing, and technical complexity, the Llama2-RAG model promises to enhance the overall effectiveness of project document management, benefiting all stakeholders involved.
Advances in Inorganic Scale Prediction – Development and Validation of a Kinetic Model
Authors: Stalker R. , Nichols D. , Fyfe A. , Zarareh A. , Boyde D. , Graham G.
Published: November 2024

Understanding and predicting the impact of chemical kinetics and metastability on inorganic scaling in oil and gas production systems has been a significant challenge, particularly in many Arabian Gulf production environments where many systems are only moderately scaling. Development of a kinetic model can lead to significant improvements in predicting risk, occurrence and location of inorganic scaling in oil and gas production systems, resulting in significant cost savings when designing scale mitigation treatments.

A combination of fluid flow dynamics (FFD) modelling and laboratory tests were used to understand the influence of kinetics and metastability on the location and propensity of scale formation in oil and gas production systems. Extensive laboratory works were conducted using small scale high shear laboratory experiments and larger scale "pilot rig" tests under field representative conditions designed using CFD (computational fluid dynamics) modelling to recreate field flow environments using conditions prevalent in both Arabian Gulf and North Sea production environments. Subsequent validation of results was obtained by comparison with field experience.

In moderate or low scaling environments, under conventional (lower shear/lower turbulence) flow conditions mineral scale often either isn’t seen in the production system or forms high up in the production tubulars even though the produced fluids are oversaturated further upstream. However, if the well inflow patterns are modified (e.g. by the introduction of ICDs, ICVs and / or slotted liners), this can change the local fluid flow dynamics such that the location of inorganic scaling moves further downhole, often leading to scale formation in the most undesirable places – such as around SSSVs, ICDs/ICVs, ESPs etc. The impact of increases in shear and turbulence can also have important consequences for chemical scale inhibitor treatments or other remedial treatments, which now may have to be applied further downhole in the wells as opposed to in the production tubulars or topside equipment. The impact of kinetics and fluid flow dynamics on inorganic scaling can also require increased volumes of inhibitor to prevent scaling.

This paper focuses on the development of the first truly kinetic model for scale prediction, including fluid flow dynamics considerations and the concept of the Critical Saturation Ratio. The results presented here demonstrate how careful understanding of FFD, and use of a kinetic model can lead to significant improvements in predicting the risk, occurrence and location of inorganic scaling in oil and gas production systems, resulting in significant cost savings when designing scale mitigation treatments.
A Decade of Challenges and Successes in Well Construction at Majnoon Oil Field – Southern Iraq
Authors: Dange Mikhil , Ridha Hussein , Khudair Abdullah Alwan , Cabrera Rodolfo Carlos , Monterio Arnold , Mohamed Sherif , Kumar Animesh
Published: November 2024

Majnoon Oil Field is situated in the southern region of Iraq and stands as one of the world's most prolific oil reserves. Its strategic importance lies in its contribution to meeting global energy demands. However, this field, once a battlefield, presents unique challenges and remarkable successes. Over 70 wells have been meticulously drilled and brought into production. Upon completion, the operator faces the critical task of ensuring well integrity, which hinges on the cement's performance against each section of the casing under scrutiny.

There are two types of well designs with an S-shaped trajectory: slim design and fat design (also known as big-bore). These well designs traverse fourteen separate hydrocarbon-bearing zones, primarily consisting of carbonate and clastic reservoirs with a host of challenges: Lost Circulation: Caverns, vugs, and large fractures,Cement Level Drop: In the intermediate section,Differential Sticking: The UER formation poses challenges,Washout Sections: Several washout sections impact the efficiency of cement displacement.Sustained Casing Pressure (SCP): Gas influx from the Ghar formationShale Instability: The Shiranish formation

Considering the challenges, multiple solutions were engineered from the initial design of cement slurries to the completion of the cement job. To combat lost circulation, a thixotropic and acid-soluble cement was tailored to remedy losses and prevent accidental sidetracking. The fluid parameters of the cement were optimized to minimize fluid loss (less than 10cc) in high-permeable zones. Additionally, shale stabilizers and anti-shrinkage additives were included to enhance the cement properties. A novel spacer system was introduced, enhancing displacement efficiency, and providing competent isolation in the intermediate sections.

Furthermore, the integration of an expandable liner hanger with carefully tuned fluid properties in the production sections, along with computer-aided simulation for analyzing torque and drag limits, ensures the highest cement coverage around the liner. This approach aims to achieve a competent cement sheath and meet the criteria for zonal isolation acceptance.
Case Study of Human Capital Dashboard and Robotic Process Automation (RPA) implementation at HPCL
Authors: Zode V. V. , Sorte D. , Sonawane S.
Published: November 2024

The contemporary business landscape is undergoing a profound transformation, driven by the relentless march of technological advancement. This digital revolution has permeated every facet of organizational operations, demanding a corresponding evolution in strategic approaches. Among the critical functions within an enterprise, Human Resource Management (HRM) has emerged as a focal point for technological transformation. The traditional role of HR, once predominantly administrative and transactional, is now expanding to encompass strategic business partnering, talent management, and organizational development.

Central to this transformation is the increasing adoption of data-driven insights and automation. Human Capital Management (HCM) systems, with their ability to capture, analyze, and report on human capital data, have become indispensable tools. A subset of HCM, the Human Capital Dashboard, offers a visual representation of key HR metrics, enabling data-driven decision-making and strategic workforce planning.

Concurrently, Robotic Process Automation (RPA) has emerged as a disruptive technology, capable of automating repetitive, rule-based tasks. Its application in HR has the potential to significantly enhance efficiency, accuracy, and employee experience. By liberating HR professionals from mundane tasks, RPA empowers them to focus on higher-value activities such as talent acquisition, development, and employee engagement.
Oil Spill Detection and Understanding Using Deep Learning and Large Language Models
Authors: Tharayil Sarafudheen M. , Qarni Shouq Al , Thubaiti Leen Al , Dossary Basim Al , Shahada Marwa
Published: November 2024

The primary objective of this research work is to harness the advanced capabilities of Artificial Intelligence (AI), specifically Deep Learning (DL) and Large Language Models (LLMs), to develop a comprehensive system for detecting and understanding the causes of oil spills. Our approach involves utilizing deep learning algorithms to detect oil spill incidents from images, extracting relevant factors from these images, and feeding these factors into LLMs to determine the causality of the incidents. This research is motivated by the increasing frequency and environmental impact of oil spill events globally, and the lack of existing mechanisms to accurately monitor and explain these incidents. By enabling rapid detection and causality analysis, this system aims to enhance environmental protection efforts and prevent future oil spills through informed decision-making and timely intervention. The methodology of this study involves several critical steps. We began by utilizing an industrial dataset comprising labeled images of oil spills. Initial preprocessing steps included resizing and normalization of the images, followed by extensive data augmentation to enhance the dataset's robustness. We then employed advanced deep learning models, where images are considered as a grid of cells, with bounding boxes. We trained the Convolutional Neural Networks (CNNs) model to identify oil spill incidents by extracting key features from each image. These factors were then fed into a Large Language Model (LLM), to analyze and determine the underlying causes of the oil spills. The study demonstrates the effectiveness of integrating deep learning and LLMs in environmental monitoring and analysis. Our approach achieved a considerable increase in the accuracy of oil spill detection compared to traditional methods. Additionally, we attained a better accuracy rate in identifying contributory factors to oil spills. These results underscore the ecological importance of promptly identifying and mitigating oil spills, highlighting the system's potential to significantly enhance sustainable resource management strategies. By moving beyond traditional methods that focus solely on visual data, our innovative approach leverages LLMs to conduct a comprehensive analysis of oil spill causality. This integration allows for profound insights into the multifaceted nature of oil spills, addressing an urgent environmental concern with advanced AI methodologies.
An Efficient Quantum Neural Network Model for Prediction of Carbon Dioxide CO2 Sequestration in Saline Aquifers
Authors: Rao Xiang , Luo Chenjie , He Xupeng , Hyung Kwak
Published: November 2024

This paper introduces the first-ever assessment method for the dissolution volume of carbon dioxide (CO2) in saline aquifers, based on Quantum Neural Networks (QNN). It explores the application effectiveness of QNN in the classification of reservoir flow issues. The QNN model in this study employs angular encoding and padding techniques to transform input layer data into corresponding quantum states. This is followed by a quantum circuit ansatz with variable parameters, and the output of the QNN is obtained by implementing a Pauli Z operator measurement on the first quantum bit. An optimizer in a classical computer optimizes the cost function of the sample data, updating the variable parameters in the ansatz, thereby achieving a hybrid quantum-classical training of the QNN. This paper designs two dual-variable classification problems for the dissolution volume of CO2 in saline aquifers, investigating the impact of reservoir temperature and mineralization degree, as well as permeability and saturation, on the CO2 storage potential in saline aquifers, with other parameters held constant. It is found that the QNN model in this paper can achieve a rapid decrease in the cost function and a swift rise in the accuracy of both the training set and the validation set within a smaller number of optimization steps. The well-trained QNN model can accurately reflect the influence of reservoir temperature, mineralization degree, permeability, and porosity on the dissolution volume of CO2 in saline aquifers, revealing the significant application potential of the QNN method in reservoir engineering.
CycloneCC: The Technology to Accelerate Industrial Decarbonisation
Authors: Bumb P. , Alhameli F. O. , Al Maqtari A. , Al Blooshi G. , Neeliesetty G. K. , Gillions L.
Published: November 2024

Carbon capture technology plays a critical role in reducing emissions. In retrofit projects, where space constraints are common, shrinking equipment size can offer significant advantages – making carbon capture possible for hard-to-abate industries.

Large, costly, and complex CCUS projects will only chip away at global emissions. Deploying modular, standardised carbon capture solutions across many industrial sites will deliver more for climate mitigation and local economies.

CycloneCC is Carbon Clean's next generation carbon capture technology that makes carbon capture scalable and more affordable – bringing it within reach of a huge number of industrial emitters. The technology overcomes traditional barriers to widespread adoption by reducing the overall capital expenditure (CapEx) and the physical footprint of carbon capture by up to 50% when compared to conventional solutions.

It achieves this through a combination of two proven process intensification technologies – Carbon Clean's advanced, proprietary amine-promoted buffer salt solvent, APBS-CDRMax®, and rotating packed beds (RPBs).

The RPBs contain a disk of packing material which rotates around its axis. The centrifugal force generated through this rotational motion is significantly greater than the gravitational force seen in conventional packed columns, making RPBs far more effective in mass transfer operations.

APBS-CDRMax® has been developed by Carbon Clean and tested at the world's largest CO2 capture pilot and demonstration facilities, capturing up to 240 tonnes of CO2 per day utlising our CaptureX technology. The solvent has been proven to require up to 30% less energy for regeneration when compared to the industry standard 30 wt.% monoethanolamine (MEA), at comparable capture rates. In addition, it has been validated that APBS-CDRMax® has low solvent and ammonia emissions which provides an improved environmental performance relative to benchmarked solvents. Whilst the improved solvent stability to oxidative and thermal degradation pathways, results in lower solvent make-up rates. Overall, the improved performance of the solvent means that operating expenditure (OpEx) of APBS-CDRMax® can be reduced, relative to that of 30wt% MEA.

Built on a pre-fabricated skid mount, every CycloneCC unit can be delivered ready to install, avoiding a lengthy and complex design, build and engineering process. There is minimal disruption to operations and safety risks onsite are significantly reduced compared to conventional solutions.

The technology has been successfully pilot tested at 1 TPD in the UK and US, where it has been proven to reduce the packing volume required to capture CO2 by 10 times. It is currently being demonstrated in the Middle East where it will be capturing CO2 from an industrial facility owned by ADNOC at Fertiglobe, their Fertilizer plant in Ruwais. The CO2 is being captured from Reformer flue gas stack and the captured CO2 is used for Urea production.
Mitigating Drilling Risks with NMR Technology and Oil-Based Mud: A Case Study from Abu Dhabi
Authors: Soliman A. , Deng L. , Elabsy E. , Sharma A. , Hammadi A. A. , Salawu M. , Ali Zuraiqi M. , Al Maskari S. , Seddik I.
Published: November 2024

In this paper, we will discuss the use of Nuclear Magnetic Resonance (NMR) technology in oil-based mud to drill through the overburden section, along with thick shale cap rock. This approach allowed us to land the well in the reservoir and attain important formation evaluation without radioactive source.

Drilling through overbalanced environments and reactive shale layers poses a sizeable danger of having the Bottom Hole Assembly (BHA) stuck, which is increased while the BHA carries a radioactive source. If the BHA becomes caught and can't be retrieved, the radioactive source can be lost or deserted in the wellbore, posing a serious threat to employees, the surroundings, and device. To mitigate those risks, oil-based mud may be used to save you the swelling or weakening of reactive shales, lowering the likelihood of mechanical sticking. Additionally, changing radioactive LWD with NMR can provide similar formation evaluation information even as casting off the risk of dropping a radioactive source inside the wellbore. NMR logging measures the reaction of hydrogen nuclei inside the formation to a carried out magnetic field, presenting accurate data on fluid properties and distributions within the pore space. Logging While Drilling (LWD) NMR is a specific utility of this technology that entails taking measurements even as the well is being drilled.

Our case includes many wells that used the same workflow and BHA to attain their objectives without the need for a radioactive source in an oil-based mud environment. We will reveal how NMR, with oil-based mud, mitigated the threat of shale reactivity and eliminated the risk of having the BHA stuck while containing a radioactive source and achieving well placement as well as formation evaluation objectives. This approach provides a safer and extra efficient method for drilling through complicated geological formations.

NMR is an effective device for formation evaluation. In addition, LWD NMR also can be used for landing wells. By the usage of LWD NMR in conjunction with oil-based mud, it's possible to mitigate the threat of shale reactivity and mitigate the hazard of getting the Bottom Hole Assembly (BHA) stuck whilst containing a radioactive source. This approach offers a safer and even more informative method for drilling and evaluation of complicated geological formations.
Downhole Power Generation: Status, Problems, and Prospects
Authors: Tao Fu , Qinghai Yang , Bairu Shi , Deli Jia , Chenglong Liao
Published: November 2024

Downhole power generation is an important technology for the oil/gas industry. It provides a steady source of power for all types of operations, including drilling and production applications. The technology has been used in the oil/gas industry for many years, but recent advancements have made it increasingly popular and efficient. This paper will discuss the current status of downhole power generation, its benefits, the challenges it's facing, and its potential impact on the industry. Because of the different working environment, technical requirements and energy sources of downhole power generation devices, the way of downhole power generation varies. This paper introduces in detail the existing downhole power generation technologies from the aspects of technical principles, classification, structural design, application status, advantages and disadvantages, focusing on two of the most promising downhole power generation technologies, turbine generator and piezoelectric generator. Based on the current research and application status of downhole power generation technology, combined with the actual production status of the oilfield, recommendations for improvement of downhole power generation technology are proposed. The first is to improve the technical adaptability and reliability of existing traditional equipment based on requirements of field application. The second is to improve the efficiency of power generation by combining multiple power generation methods. The third is to combine power generation equipment with other downhole monitoring/control equipment to develop intelligent self-powered downhole tools. The fourth is to extensively absorb the research results of power generation technologies in other fields and use new materials and new technologies to promote the advancement of high-efficiency and high-energy density downhole power generation technology. With the advancement of the technology, more and more operations can take advantage of reliable, efficient, and safe power generation. This will lead to improved safety and efficiency in operations, increased cost savings and improved efficiency in production, and reduced downtime. Moreover, with more reliable and efficient power available, operations will be more resilient and can better weather disruptions and unexpected events.
Unlocking the Potential of Mud Logs and LWD in the Gulf of Thailand Using Machine Learning
Authors: Palviriyachote S. , Misra S. , Moelyono S. , Ekkawong P. , Tonburinthip T.
Published: November 2024

The gas field in the Gulf of Thailand stands as one of the largest natural gas reserves in Thailand, with over 30 years of development history and more than 1,000 penetrated wells. With the power of machine learning and availability of historical data, there are opportunities to revolutionize traditional processes. Use of machine learning for log synthesis can help reduce expenses and operational risks associated with traditional well logging methods, including service fees, rig time, and potential retrieval challenges. This study aims to utilize machine learning techniques to predict well logs by analyzing Mud Log and Logging While Drilling (LWD) data.

The proposed machine learning workflow utilizes raw mud log data (gas show content) and LWD measurements (gamma-ray and resistivity) to predict essential well logs—bulk density (RHOB), neutron porosity (NPHI), and compressional sonic travel time (DTC). This study employs various machine learning techniques, including Random Forest Regressor, Gradient Boosting Regressor Neural Networks and Support Vector Regression (SVR). The data sources come from the wells in the Gulf of Thailand, ranging from depths of 1,000 to 2,800 meters TVDSS. With a resolution of 0.1 meter, the training dataset will consist of 10,000 to 28,000 points for each well. These wells exhibit porosity levels between 13% to 30% and permeability below 100mD. The formations mainly composed of channel and point bar sands within a fluvio-deltaic system. Challenges arise from variations in reservoir properties across different depositional facies, clay content, and fluid presence, affecting prediction accuracy between original training data and predicted wells.

The model effectively predicts well logs utilizing gamma-ray, resistivity, gas content data, and applies feature creation through polynomial transformation as input. Among the models tested, the Random Forest Regressor and Gradient Boost Regressor models prove to be the most accurate predictor. The random forest model achieves prediction accuracies with Mean Absolute Percentage Errors (MAPE) for bulk density (RHOB), neutron porosity (NPHI), and compressional sonic travel time (DTC) of 0.965%, 9.27%, and 2.536%, respectively.

Such a predictive model is suitable for existing wells with only LWD and Mud Log data, lacking open-hole logs due to operational issues encountered during drilling. The cost savings from reducing well log fees and rig time in one of the fields could amount to $1 million USD per month. Beyond log synthesis, the methodology opens avenues for identifying reservoir and fluid types, enhancing decision-making in oil and gas exploration and production activities in the Gulf of Thailand.
Comparative Study of Nitrogen, Carbon Dioxide, and Hydrogen Pore Occupancy, Connectivity and Ostwald Ripening in Carbonate Rocks
Authors: ALZaabi A. , Alzahrani H. , Alhosani A. , Bijeljic B. , Blunt M. J.
Published: November 2024

High-resolution three-dimensional micro-CT imaging is used to investigate nitrogen, carbon dioxide, and hydrogen injection in reservoir carbonate rocks. The images were analysed to ascertain gas saturation, pore occupancy, ganglia size and connectivity across the sample. The experiment studies a water-wet reservoir carbonate rock that is filled with synthetic brine before and between each gas injection. The experimental conditions involve unsteady-state gas injection at a pressure of 9 MPa, a temperature of 60°C, gas injection at a rate of 0.005 mL/min, and brine injection at a rate of 0.1 mL/min to remove all the gas in the sample. The sample is firstly saturated with brine, followed by N2 injection, and then brine injection. Images are taken throughout each injection phase and after waiting 24 hours after gas injection. This procedure is replicated for the CO2 and H2 cycles. The saturation of gases in the sample after injection were as follows: N2 around 25%, CO2 approximately 21%, and H2 around 18%. While the N2 saturation slightly decreased after 24 hours, the CO2 and H2 saturation remained relatively stable over time. The saturation profile reveals that N2 and H2 experience more significant changes during the waiting period caused by Ostwald ripening compared to CO2 as the gases propagate to the centre of the sample. Capillary pressure measurements based on interfacial curvature at around 80% brine saturation indicate a significant decline for N2, from 1.46 kPa to 1.18 kPa after 24 hours. CO2 experiences a decrease in pressure from 1.17 kPa to 1.05 kPa over 24 hours, while H2 shows a minimal decline in capillary pressure with time remaining at approximately 1.04 kPa. Pore occupancy and ganglia analysis indicate N2 occupying pores mostly above 25 µm radius, and the saturation increase in the larger pores after 24 hours. Throat radii larger than 18 µm show an increase in N2 saturation, indicating improved connectivity, as supported by the normalized Euler number. CO2 predominantly occupies pores from 30 to 40 µm with minimal change. H2 occupies pores larger than 19 µm with minimal changes with time and throats larger than 8 µm with increased saturation with time that suggests enhanced connectivity. Overall, this study provides a useful reference for comparing N2 and CO2 displacement and trapping behaviour after waterflooding which have been studied extensively, with those for H2, and is applicable to gas storage projects within carbonate reservoirs.
Automated Refractory Dry-Out Operation
Authors: Sengupta Subhendu , Goveas Vincent , Al Seyabi Mathla , Karunakaran Raghesh
Published: November 2024

This paper is based on successful implementation of automated heater refractory dry out operation in Habshan-5 SRU using Yokogawa's Exapilot software, in continuation of past success of digitalisation of critical batch operation like SRU start-up & shut down, Ethane recovery /rejection change over in NGL unit. Objective was to automate SRU heater refractory dry out operation (with or without refractory repair) as per standard operating procedures to ensure process safety & asset integrity by minimizing human error. Besides it aims to reduce dry out time & thus energy required for dry out operation.

Scope is to integrate Habshan-5 SRU heaters like reaction furnace, Incinerator heater dry out automation with existing SRU start up automated modules.

Procedure

Heater refractory dry-out is critical operation in SRU startup & carried out manually prior to automation.

Drawback

Maintaining temperature profile as per OEM dry out curve poses challenges in manual operation.

Custom solution based on Exapilot & past dry out data used for automating SRU heaters refractory dry-out.

Two modules for each heater were built. Viz.

Each module consists of

Prediction block

Which predict air, fuel demand for every target temperature based on past & current data

Monitoring block

This block scans all critical parameter related to dry-out & alert operator if deviation

Adaptive control

This sub module adjusts dynamically the controlling parameter like Air/Fuel based on feedback to maintain target temperature within its range. Conceptually it learns from past action & improve to follow the temperature profile.

Validated modules were deployed in Expilot Server and seamlessly integrated with rest of SRU start up modules which enabled operator easy access to perform dry-out operations during start up in an efficient manner exactly as specified in the OEM given dry out procedures. Post testing on U552 heaters dry out & subsequent data analysis & operation feedback revealed the following benefit.

Business Benefits

This data driven & prediction enabled, heater refractory dry-out automation, first of its kind in ADNOC highlights the success stories of Habshan-5 & Sulphur digital transformation journey of critical batch operations.
Development of Searching Strategy for Micro-Leakage in Natural Gas Stations Based on TDLAS Methane Detectors with PTZ Controller
Authors: Xiao Fei
Published: November 2024

Abstracts

Detecting and locating micro-leakages pose significant safety challenges in the gas and petroleum industries. Typically, a single Tunable Diode Laser Absorption Spectroscopy (TDLAS) methane detector equipped with a PTZ controller can effectively mitigate the risks associated with potential leaks at natural gas stations. However, the lack of a dedicated search and localization function often results in operators spending considerable time pinpointing the source of a gas leak. To address this issue, a series of experiments and simulations were conducted to investigate integral concentration and evaluate its characteristics and influences. This led to the development of three search algorithms, which were assessed in a virtual environment: Global Searching Based on Steady Concentration (GSSC), Global Searching Based on Real-Time Concentration (GSRC), Spherical Search Based on Real-Time Concentration (SSRC). These algorithms were tested under field conditions, focusing on accuracy and response time across various scenarios. The results indicate that integral concentration, affected by factors such as optical path and concentration distribution, exhibits distinct characteristics. Notably, the highest values for both integral and gas concentrations consistently occur at the leak source. In the simulated leak scenarios, all three proposed algorithms successfully identified the leak locations. Among them, the SSRC algorithm demonstrated superior efficiency and accuracy by accounting for real-time concentration dynamics and the evolution of gas cloud diffusion. In field tests conducted at natural gas stations, the SSRC strategy effectively located leak sources with rates exceeding 300 ml/min and integral concentrations above 3000 ppm·m, achieving this within a 15-minute time frame.
A Comparative Study of High-Modulus Polyethylene (HMPE) and Steel Wire Ropes for Offshore Subsea Lifting and Installation
Authors: Schlömlich Stefan , Vinther Morten Loell , Eijssen Marc , Kanters Marc
Published: November 2024

Offshore lifting and deployment operations are integral components in the lifecycle of subsea infrastructure, encompassing activities from initial construction to maintenance and eventual decommissioning. The choice of lifting and deployment accessories, such as lifting slings and deployment lines, significantly influences the safety, reliability, efficiency, and cost-effectiveness of these operations. Traditionally, steel wire ropes have been the industry standard due to their robustness and reliability. However, recent advancements in synthetic materials have introduced material such as high-modulus polyethylene (HMPE) ropes, specifically those made with Dyneema® fibers, as a compelling alternative.

This paper presents a comparative analysis between HMPE and steel wire ropes, focusing on how advanced material modelling can capture important response mechanisms, and demonstrating this with practical applications in the offshore field of use. The study aims to guide the selection of materials in various operational scenarios by evaluating key performance factors.
ELMA (Export Loading Monitoring & Analysis): Transformative Innovations in Custody Transfer Monitoring and Analysis at PETRONAS Export Terminals
Authors: Daud Nurul Shaidatul Shima Mohamad , Shahrun Datu Darul Anwar Datu , Fauzi Nur Melatee Mohd
Published: November 2024

The ELMA (Export Loading Monitoring & Analysis) system represents a significant advancement in custody transfer operations at PETRONAS Export Terminals. By integrating automation, machine learning, and real-time data processing, ELMA addresses key challenges in maintaining accurate and reliable custody transfer measurements. The system enhances the reliability of these measurements by automating key monitoring tasks, reducing reliance on secondary measurement methods, and providing operators with analysis trends and actionable insights. This paper discusses the development, deployment, and impact of ELMA, focusing on its four key modules: Meter Factor Control Chart, Meter Factor Prediction, Loading Performance, and Prover and Tank Status. The implementation of ELMA has led to improved measurement accuracy, reduced reliance on secondary measurement methods, and enhanced early detection of critical issues. The early results highlight ELMA's effectiveness in optimizing custody transfer operations, positioning PETRONAS as a benchmark for innovation in the oil and gas industry. Despite its success, challenges related to data quality, system integration, and ongoing maintenance remain areas for future improvement.
Empowering Petrophysics: Elevating Thin-Bed Analysis with Integrated Advanced Logging and Digital Workflows
Authors: Tolioe W. A. , Cavalleri C. , Lerdsuwankij T. , Hakimi S. , Yusuf N. A. , How L. , Zakaria M. H. B. , Kassim M. S. A.
Published: November 2024

In response to domestic and international energy demands, oil and gas companies are positioned to discover new reserves by making investments in exploration activities. The oil and gas discovery must meet economic criteria and be feasible for development. Low- resistivity pay reservoirs remain a topic of interest because the characterization of these reservoirs containing laminated shaly sand hydrocarbons and thin-bed layers have traditionally been challenging. The standard resistivity techniques often underestimate hydrocarbon assessments due to presence of conductive laminated shale, resulting in the field or block to fall into marginal or sub commercial categories.

Understanding reservoir properties is essential to estimating the hydrocarbons in place because assessing net pay and hydrocarbons saturation presents challenges in reservoirs characterized by high-shale content. This paper presents a novel approach for integrating logging measurements, including nuclear magnetic resonance (NMR), 3D resistivity, and borehole imaging, with advanced laminated sand analysis (LSA) to refine reservoir characteristics and determine accurate formation properties. The LSA method incorporates 3D resistivity and NMR data, employing probabilistic methods to calculate true reservoir properties of sand fraction and resistivity, and quantify laminated shale content. LSA results undergo standard petrophysical interpretation using deterministic approaches, ensuring consistency across the analysis. Formation evaluation employing probabilistic techniques further refines mineral volumes and corrected porosity, essential inputs for interpreting integrated well logs. NMR-factor analysis across the entire interval, with T2 cutoff values distinguishing clay and free fluid, enables computation of clay-silt-sand volume and effective porosity to help calibrate the petrophysical model.

Advanced techniques, including 3D resistivity anisotropy, dip-corrected outputs, NMR poro-facies, and factor analysis contribute to precise reservoir property determination. Digital workflows enable rapid data processing and analysis, providing valuable insights within time constraints.

This comprehensive approach significantly enhances reservoir understanding, producing improved hydrocarbon estimation. The results indicate substantial increases in net pay and hydrocarbon pore volume, highlighting the effectiveness of this innovative thin-bed analysis approach to maximizing hydrocarbon recovery potential in challenging shaly sand environments.
Structured Energy Trending and Performance Assessment for Combustion Engine (SETPACE©) - An Innovative Approach to Decarbonize Combustion Emissions from Upstream Operations
Authors: Ali Z. , Azuhan A. , Lee Y. , Hakim M. Abdul , Taib N.
Published: November 2024

This paper introduces a state-of-the-art tool known as SETPACE© (Structured Energy Trending and Performance Assessment for Combustion Engine) which has the capability to validate authenticity of fuel gas flow meter reading, improving fuel gas accounting; quantify fuel efficiency (thermal efficiency) degradation by comparing theoretical vs actual fuel consumption for individual machines based on current operating parameters; quantify fuel gas reduction from various improvement opportunities and generate near-to-real fuel gas forecast. PETRONAS Upstream Energy Efficiency (EE) journey, which began in 2021, aims to reduce energy consumption without affecting production, thereby increasing gas sales and decreasing Greenhouse Gas (GHG) emissions. By 2023, SETPACE© had been widely implemented across all domestic assets in Malaysia. More than 90% of fuel gas (energy) within these assets is consumed for gas compression and power generation. The assessment of over 170 turbomachines across more than 80 oil and gas facilities has revealed that approximately 18% fuel gas reduction from year 2023 conventional forecast is discovered through accounting and forecasting improvement. Baseline results show gas turbines’ fuel efficiency estimate ranging from 4% to 32%. Fuel inefficiency in machinery typically stems from two (2) main factors: performance degradation and low operating loads. Machines with significant deviations in fuel efficiency often consume more fuel than necessary due to deteriorated performance. Addressing this through performance restoration, typically as part of routine planned maintenance, can reduce fuel gas consumption. This approach is particularly viable if the cost is offset by fuel gas savings, with an estimated maximum reduction of 11% from the actual fuel gas reported in 2023. Gas Turbine Generators (GTGs), designed for redundancy and to manage upstream uncertainties, may operate at low loads, leading to inefficient fuel use. While this operational philosophy ensures reliability, it results in higher fuel consumption. Optimizing GTG train operations is estimated to achieve an additional 5% reduction in fuel gas consumption. Furthermore, energy demand optimization and partial electrification is estimated to contribute an additional 11% reduction in fuel gas usage. Decarbonizing combustion through Energy Efficiency (EE) measures is anticipated to reduce GHG emissions by approximately 0.8 to 1.4 million tons of CO2 equivalent per year and recover energy, resulting in an estimated annual savings of USD 20 to 35 million.
Influx-Free Barrier for Oman Salt High-Pressure Water Zones
Authors: Khan Anum Yousuf , Salazar Jose , Bogaerts Martijn , Peng Fei , Ashraf Shameed , Jain Bipin
Published: November 2024

A unique case of salt cementing occurs in the subsalt wells in the south of Oman, which contains pressurized water-bearing zones across salt beds. To achieve zonal isolation, the cement must overcome the influx of water during the cement hydration period, create strong bonding with the salt formation to prevent the flow of formation fluids following placement, and be able to address the stresses of salt creep to avoid loss of isolation.

A three-step approach was developed to address each of the challenges during the cementing of a 7-in. liner. First, a detailed salt cementing study was performed to determine the sensitivity of the slurry design to contamination from the salt formation. Second, using a stress simulation, a fit-for-purpose flexible and expandable slurry design was proposed to overcome wellbore stresses. Third, a novel laboratory testing procedure was developed to confirm the shear bonding of the cement slurry to the halite salt formation.

In the event that the 8.5-in. openhole section across the salt formation encounters high-pressure water zones, mud weight can be increased to 18.5 lbm/gal to prevent influx. Therefore, a high-density cement slurry is required to maintain overbalance during the cementation of the 7-in. liner. The effect of contamination was determined in the laboratory by comparing various concentrations of formation salt and the optimal design using potassium chloride (KCl) in the base fluid.

Using the outcome of proprietary stress simulations, the slurry was optimized to have a low Young's modulus and expanding properties to prevent any failure of the cement sheath integrity and migration of formation fluids resulting from a micro annulus.

To confirm bonding between the slurry and the salt formation, salt cores were used in the laboratory to confirm shear bonding between the slurry and the formation.

The final slurry design was optimized in the laboratory to the specific job conditions in terms of cement transition time and setting time. Cement placement and displacement procedures were adjusted to avoid large pressure changes in the wellbore that could potentially result in the debonding of the cement from the casing.

A fit-for-purpose high-density, flexible cement slurry was developed to overcome the challenges encountered while drilling the salt formation. The slurry has been used on seven 7-in. liner jobs since 2023. Since the implementation of the new slurry design, all of the wells have achieved zonal isolation across the salt sections and have isolated the high-pressure water zones, enabling the operator to drill the low-pressure reservoir interval below the salt formation without the need for remediation, saving time and lowering costs.