bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Comprehensive Study on in-Situ Gasification of Hydrogen Generation from Heavy Oil Pyrolysis Catalysed by Clay Minerals
Authors: Luo Chen , Liu Huiqing , Zhou Song 
Published: October 2024

Abstract
In-situ gasification (ISG) has been recognized as a highly promising technology for hydrogen generation. One of the key methods for generating hydrogen from crude oil is through the pyrolysis gasification of heavy oil. Clay minerals, found in the rock matrix, act as natural catalysts with exceptional adsorption capacity, ion exchange capacity, and acid sites. These properties greatly enhance the process of in situ gasification of heavy oil, leading to efficient hydrogen generation.
This study examines the impact of different clay minerals on the ISG of hydrogen generation from heavy oil pyrolysis. We investigate the catalytic effect of clay minerals on hydrogen generation from crude oil pyrolysis gasification using thermogravimetric mass spectrometry (TG-MS) analysis. They determine the amount of hydrogen generated and hydrogen generation efficiency (HGE) through equivalent characteristic spectrum (ECS) analysis. Additionally, we calculate the kinetic parameters using kinetic models such as the Friedman method and DAEM method to assess the influence of clay minerals on the activation energy of the hydrogen generation process in crude oil pyrolysis gasification.
The oil samples containing clay minerals exhibit greater mass loss during the pyrolysis stage and higher thermal conversion compared to the oil samples without clay minerals. Through MS and ECS analysis, it is observed that the oil samples generate hydrogen during both the pyrolysis and coking stages, which can be attributed to the acidic catalytic and ion-exchange effects of the clay minerals. The Lewis and Brønsted acid sites of the clay minerals play a role in advancing the temperature range of hydrogen generation from oil samples pyrolysis gasification. The HGE analysis indicates that the strong adsorption of the clay minerals contributes to a more complete pyrolysis gasification of heavy oil, resulting in the generation of more hydrogen. The kinetic analysis of hydrogen generation reveals that the activation energy tends to increase with the conversion rate. Notably, montmorillonite, a type of clay mineral, significantly reduces the activation energy for hydrogen generation during heavy oil pyrolysis gasification and enhances the hydrogen conversion rate. The ISG of heavy oil represents a significant stride in advancing methods for hydrogen generation from heavy oil, thereby facilitating the transition of fossil fuels to cleaner energy sources like hydrogen. The efficient conversion of hydrogen generation is achieved through the utilization of natural clay minerals, acting as catalysts in the process of catalytic ISG of crude oil.
Core Description and Depositional Environment for Saline Aquifer CO2 Storage in the Malay Basin, Malaysia
Authors: Widyanita Ana , Nicholson Uisdean , Busch Andreas , de Jonge-Anderson Iain , Doster Florian 
Published: October 2024

The development of hydrocarbon (HC) fields with high CO2 content requires Carbon Capture and Storage (CCS) due to the prohibition of CO2 venting in Malaysia. Mitigating CO2 emissions is also important for addressing climate change concerns. There are several potential offshore storage sites in offshore Peninsular Malaysia (PM), such as depleted fields and saline aquifers. The currently identified depleted fields are insufficient to provide the required storage capacity volume for current development plan of HC fields with high CO2 content, and eventually for wider-scale storage of CO2. Therefore, saline aquifers are required to provide additional storage sites.
Identifying the most suitable regional saline aquifer for carbon capture and storage (CCS) presents many challenges due to the limited understanding of their distribution and characteristics within the basin. The primary challenge, compared to hydrocarbon fields, lies in ensuring containment and sealing effectiveness, as hydrocarbon fields have already demonstrated entrapment within their structures. To mitigate the risks and uncertainties in identifying regional saline aquifers, it is crucial to analyze the seals and assess the suitability of potential storage sites. This includes evaluating the distribution, continuity, and properties of the storage reservoirs, as well as the distribution, continuity, integrity, and effectiveness of the seals.
Predicting the Lifespan of Electric Submersible Pumps Using Random-Forest Machine-Learning
Authors: Arukhe James O. , Kalu-Ulu Torty C. , Anazi Ammal F. , Almarri Hamad M. 
Published: October 2024

Abstract
The primary objective is to describe the application of a robust method and system for predicting the lifespan of Electric Submersible Pumps (ESPs) using advanced machine learning techniques. This system aims to enhance the efficiency and reliability of ESPs by leveraging data from various categories and employing a Random-Forest Machine-Learning model to provide accurate lifespan predictions.
The system begins by collecting comprehensive data related to the ESP from diverse categories, including operational parameters, environmental factors, design specifics, historical data, and equipment specifications. This data is then fed into a machine learning model, specifically a random forest algorithm, which has been trained on a vast dataset to recognize patterns and correlations. The model processes the data, analyzing the intricate relationships between different parameters, and subsequently predicts the remaining expected life of the ESP. The predicted lifespan, along with any relevant insights, is then reported to the user or system administrator.
Upon implementation, the system demonstrated a high degree of accuracy in predicting the lifespan of ESPs. The utilization of the Random-Forest Machine-Learning model proved to be particularly effective, given its ability to handle large datasets and consider multiple variables simultaneously. Observations indicated that certain parameters, such as operational and environmental factors, played a more significant role in determining the ESP's lifespan. The system's predictions not only provided insights into the expected operational life of the pumps but also offered actionable recommendations to extend their lifespan. In conclusion, the integration of machine learning into ESP lifespan prediction offers a promising avenue for enhancing pump efficiency, reliability, and extended operational longevity.
The innovative integration of the Random-Forest Machine-Learning model into ESP lifespan prediction presents a groundbreaking approach in the field. This method offers practitioners a proactive tool to preemptively address potential issues, optimize pump operations, and ultimately extend the life of their equipment, leading to significant cost savings and operational efficiency.
Machine Learning for Enhanced Production Optimisation and Management
Authors: Bost Samuel 
Published: October 2024

Abstract
This study introduces an innovative framework for harnessing Machine Learning (ML) within production engineering. The objective is to offer engineers a comprehensive framework for utilising ML-based modelling across core production engineering tasks to elevate operational efficiency, reduce expenses, and advance sustainability efforts.
Methodology: 1. Build a Model Register: Catalogue existing modelling approaches used across the production engineering function, detailing inputs, outputs, assumptions, dependencies and associated decision-making. 2. Data Review: Assessment of data accuracy, completeness, and interdependencies. 3. Task Mapping: Mapping current tasks to existing approaches with the addition of an ‘ML Opportunity’ column marked as enhance, augment, or replace. 4. ML Opportunity Matrix: A matrix calculation to prioritise ML applications. 5. Implementation Strategy: Strategy for incorporating ML into existing workflows.
Results: This study presents a framework for incorporating Machine Learning (ML) into production engineering. By systematically cataloguing existing models and assessing data processing and utilisation across various decision-making processes, we identified pivotal areas where ML can deliver value, such as in enhanced production optimisation and control, turndown management, and maintenance and intervention planning. The creation of an "ML Opportunity Matrix" prioritised the selection of ML techniques for specific engineering tasks, pinpointing supervised learning as a critical tool for enhanced estimation and predictability in the management of dynamic, integrated production systems. The proposed framework presents a systematic approach for prioritising ML-based modelling for specific production engineering tasks as well as identifying the opportunity for alignment and collaboration with associated disciplines such as reservoir engineering, drilling and completions engineering, and control systems engineering. ML-based modelling is recommended as a significant enhancement to traditional simulation and physics-based modelling techniques, for assets that have collected operational data, to enable more efficient, data-driven decisions in production system optimisation and management.
Benefits: This paper introduces a novel framework for production engineering teams to be able to systematically identify, evaluate, and integrate Machine Learning (ML) solutions into core production engineering tasks. This study equips engineers with a framework for understanding the modern digital tools available to develop strategies that not only optimise operations but also yield cost reductions and environmental improvements.
Technical Evaluation of Microbial Approach as an Efficient Enhanced Oil Recovery Method
Authors: Manap A. A. Abdul 
Published: October 2024

Abstract
Sustainability initiatives have been the buzz word over the last few years with energy transition gaining momentum. However, fossil fuel, particularly oil and gas still has major roles to play in meeting global energy and consumer products demand. Hydrocarbon producers have been more vigilant in lowering greenhouse production, with carbon capture, utilization and storage thrusted to the forefront of the industry. This trend pushed greener technology and operational efficiency agenda to complement increase in hydrocarbon recovery.
Microbial enhanced oil recovery (EOR) is a tertiary oil recovery technology utilizing micro-organisms to facilitate, increase or extend oil production from reservoirs (Abd Karim et. al 2001). PETRONAS first attempted feasibility study at laboratory scale, followed by well stimulation mode field trial with mix results (Abd Karim et. al 2001, Sabut et. al 2003). This development led to expanded research and development (R&D) project to study feasibility of microbial EOR flooding mode. This paper focuses on microbial based enhanced oil recovery (EOR) evaluation at coreflood scale. Overall, it showed potential with minor technical success, but might require more work to scale up for a field application.
Strategic Realignment: Empowering Non-Governmental Organizations (NGOs) for Lasting Community Impact
Authors: Santoso Gagok Imam , Prasetyo Arief , Hermawan Ida Bagus , Nengkoda Ardian , Ismoyo Hadi 
Published: October 2024

Abstract
The paper aims to communicate the insights gained by the Society of Indonesian Petroleum Engineers in Saudi Arabia, a Non-Governmental Organizations (NGO) on effectively managing community involvement and growth during the pandemic. To maintain organizational structure while introducing strategic initiatives, we underwent a comprehensive restructuring, resulting in impactful programs that benefited the Indonesian community in Saudi Arabia.
In response to pandemic constraints, the organization proactively developed strategic pillars through an extensive analysis involving seven key stakeholders: the Ministry of Energy, Embassy Missions, University Students, Members, Volunteers, External NGOs, and other profession-based organizations. This process led to the creation of a new value proposition, articulated through four strategic pillars: Member Focus, Capacity Development, Embracing Energy Challenges, and Social Responsibility. These pillars were actualized through impactful activities facilitated by an alignment matrix, ensuring structured project management from initiation to completion.
The realignment had a notably positive impact on the organization, fostering active engagement among stakeholders. A significant achievement was the successful completion of a school renovation project on the small island of East Lombok, Indonesia. By adopting a "coalition of associations" approach, our organization partnered with other organizations sharing a similar mission to provide value to students in Indonesia. A key consideration throughout the project was the use of environmentally friendly materials for the school's construction. Following the school's inauguration, a coaching program was implemented to enhance the teaching skills of local educators. Additionally, other activities were adapted to a virtual setting, enabling the organization to feature distinguished speakers from various disciplines, including those outside the field of Petroleum Engineering. These social initiatives enriched the member experience and were instrumental in maintaining organizational cohesion during challenging times.
The strategic realignment undertaken by the organization represents a significant advancement for the organization, utilizing a novel framework uncommon in the NGO sector. This framework, if adopted by other NGOs and organizations, has the potential to generate more meaningful programs for members and volunteers while driving greater community impact.
Revolutionizing Resource-to-Reserve with COREAI: Unlocking New Opportunities and Accelerating Success
Authors: Ramli A. , Gupta M. , Turiman H. 
Published: October 2024

Abstract
This paper presents the development and application of COREAI, a machine-learning-based platform designed to manage and analyze Malaysia's extensive legacy petroleum engineering core data. The platform transforms data from 23,000 core plugs across 608 wells into predictive models that estimate rock and fluid properties, validate subsurface analyses, and reduce uncertainty. COREAI has been deployed in 25 fields, achieving significant cost savings, enhancing reservoir modeling, and optimizing field development, particularly for marginal fields. This study demonstrates how digitization and advanced analytics can unlock the value of legacy data, providing accurate and timely insights for subsurface evaluations. The objective of this project is to develop an intelligent platform using Azure web applications to manage and analyze Malaysia's extensive legacy petroleum engineering core data. Figure 1 shows the regional availability of core analysis data in Malaysia. By integrating machine learning, the platform predicts rock and fluid properties, validates subsurface analyses, and reduces uncertainty. These 23,000 core plugs and associated analyses is transformed into a robust reservoir properties knowledge model, enhancing reserve estimation accuracy, optimizing field development, and significantly reducing costs and data-related inefficiencies.
CCS Development Optimisation via a Cost-Effective Reservoir Simulation Technology: Full-Field Simulation of CO2 Sequestration with Conductive Faults by Embedded Discrete Fracture Model
Authors: Iino A. 
Published: October 2024

AbstractFor the robust planning and implementation of Carbon Dioxide Capture and Storage (CCS) projects, capacity and injectivity of target reservoirs and containment capability of cap rock formation need to be evaluated thoroughly through high-fidelity flow simulations with various hypothetical subsurface scenarios. Impact of conductive faults, which may act as high permeability conduits for the injected CO2, should be carefully assessed as the fault conductivity is typically highly uncertain. However, it is challenging to incorporate numerous faults with high conductivity and complex multi-phase displacement process in a practical framework of flow simulations. In this study, we present an efficient workflow using an Embedded Discrete Fracture Model (EDFM) to simulate CO2 flow via conductive faults and its application to the full-field scale assessment.The EDFM approach has been proposed to efficiently incorporate discrete fractures in flow simulations. Based on the pre-defined permeability, geometry and location of discrete fractures, transmissibility is computed (1) between each fracture and reservoir matrix and (2) at intersections of two fractures. In our application, conductive faults were assumed as discrete fractures. Extra grid blocks are added to the original reservoir grid system to represent discrete fractures by assigning the computed transmissibility via non-neighbor connections (NNCs). This approach involves no upscaling, no unstructured gridding and no local grid refinement and can be coupled with any commercial reservoir simulators. Multi-phase displacement process through individual faults can also be modeled.We demonstrate the efficiency and robustness of our proposed approach for synthetic and field scale examples. We first validated the CCS simulation with the EDFM approach for a producing gas reservoir using an illustrative 3-D example. Next, we present the application to a full-field simulation model where more than one hundred faults were interpreted based on the seismic data, to evaluate the CO2 breakthrough risk to up-dip gas producers. The simulation results show that the injected CO2 preferably migrates along conductive faults. leading to the earlier breakthrough as compared to the case without conductive faults. However, it was also confirmed that the CO2 production rate was not significantly increased even after the breakthrough.This study showed the effectiveness of the EDFM approach to simulate CO2 flow via numerous conductive faults in both edge aquifer and gas zones. This approach is also simple and easy to implement, which allows efficient application to full-field simulation models.
Conformance Control and Oil Displacement Dynamics: The Role of Size-Controlled Microbubbles in Enhanced Oil Recovery
Authors: Tianshuang He , Yibo Li , Jinzhou Zhao , Bo Zhou , Xin Yu , Gangde Xie , Wei Lv , Jianshan Li , Shitou Wang , Hao Gao , Yongqiang Chen 
Published: October 2024

Abstract
During the development of low permeability reservoirs, strong heterogeneity often results in low displacement efficiency. Microbubble flooding technology has received significant attention and has been introduced into tertiary oil recovery due to its smaller diameter and high stability. To further explore the reservoir adaptability of microbubbles in low permeability reservoirs and analyze the interaction mechanisms between microbubbles and crude oil, this study focused on the generation of microbubble by microfluidic method and the conformance performance characteristics of microbubbles. The results indicate that the generated microbubble sizes are from 50 to 220 μm. Small gas-liquid ratio, small necking sizes, and low surface tensions are the main factors to influence the size of microbubbles. According to the results of microfluidic experiments, size-controlled microbubble produced by adjusting gas-liquid ratio can enter to low permeability area and increase the seepage resistance in the high permeability area, which can improve the sweep efficiency significantly. Three major oil displacement mechanisms also were identified: wrapped liquid film, direct displacement, and co-directional liquid film flow. The study proves the flexibility of microbubble technology in low permeability reservoirs and provides a theoretical basis for its wider range of applications.
Well Productivity Decline During Oil & Water Production due to Fines Migration
Authors: Russell T. , Nguyen C. , Loi G. , Shafian S. R. Mohd , Zulkifli N. N. , Zeinijahromi A. , Bedrikovetsky P. 
Published: October 2024

Summary
The aim of this work is to characterise well productivity decline during two phase production due to fines migration. Modelling is performed separately for homogeneous and heterogeneous reservoirs, allowing for quantitative predictions across a range of applications.
In homogeneous reservoirs, progressive flooding of individual pores drives particle detachment, leading to permeability decline. Utilizing the pore size distribution allows for explicit modelling of the distribution of phases and the progressive formation damage during water-cut increase. For heterogeneous reservoirs, we extend the Dietz model for layer cake reservoirs to develop pseudo relative permeability functions that account for the formation damage occurring as water encroaches into the production zone. The Dietz model relies on the permeability distribution of the reservoir; however, a technique is developed to predict formation damage without requiring this information. The two models are compared with production data from two Malaysian fields.
The result of both models are explicit formulae describing the decrease in well productivity as the water-cut increases. By presenting the data in a new diagnostic plot, impedance vs water-cut, the formation damage characteristics of the reservoir can be determined simply, and further calculations performed. Comparison with field data from Malaysian fields shows good agreement, validating the models. The models allow for straightforward extrapolation to higher water cuts for predictive analysis and well design, including the planning of well stimulation or remediation in cases of high formation damage. The heterogeneous case produces a linear curve on the new diagnostic plot, which is shown to be general for all permeability distributions.
The models for the first time resolve quantitatively the widely reported phenomenon of formation damage due to fines migration occurring during periods of increasing water-cut. The models allow for history matching and predictive analysis.