bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimization of ERW Well Drilling In Real-time: Integrating Geosteering Techniques and Drilling Parameters for Reduced Geological Uncertainty
Authors: Carrillo L. A. , Flores R. , Roa D. G. , Vargas A. J.
Published: October 2024

During the drilling of extended reach wells (EWR), understanding the stratigraphic position and apparent dip of the local geological structure is crucial for decision-making. It poses a significant challenge due to the variability of local dip angles along the wellbore, hindering correlation with neighboring wells and the determination of optimal drilling parameters. The proposed methodology aims to optimize EWR drilling by reducing uncertainty in the knowledge of the stratigraphic position and automating the analysis of drilling parameters. Using geosteering and drilling software technologies supported by machine learning, real-time parameter comparisons from a geological perspective were conducted, allowing the identification of the expected main fault in the geological model and estimation of apparent dips from conventional gamma ray and resistivity logs. This provided a detailed understanding of vertical layer displacement post-fault in the subsided block, along with obtaining true vertical thickness (TVT) logs to define geological boundaries and control stratigraphic position correlatable with neighboring well drilling parameters. This facilitated forward projections, determined casing setting depths, enabled effective entry into the main target, and recommended parameters for optimal performance, thus expediting real-time decision-making. Precise setting adjustments were achieved, continually optimizing the drilling process by varying parameters informed through the integration of both technologies. The combination of model-based geosteering techniques and stratigraphy, along with the integration of drilling parameters, has significantly reduced geological uncertainty in drilling highly deviated wells. This methodology has demonstrated notable improvements in operational decision-making. This unique combination could offer an innovative and practical perspective for practicing engineers, providing them with more precise and effective approaches for real-time decision-making and continuous drilling process optimization.
Feasibility Assessment of Tiltmeter Monitoring for an Offshore Monoclinal Dipping Saline Aquifer CCS Site: Insights from Coupled Geomechanical Modeling
Authors: Ashida A. , Tsusaka K. , Mochiji S. , Coelho G. , Himeno M. , Iino A.
Published: October 2024

This study investigates the feasibility of tiltmeter monitoring for an offshore monoclinal dipping saline aquifer CCS site. The objective is to quantitatively evaluate the response of seabed deformation that can be monitored by tiltmeters through coupled geomechanical modeling. Additionally, the study explores the potential of integrating tiltmeter data with coupled geomechanical modeling to provide insights into pressure propagation and potential plume movement during CO2 injection. A 3D geomechanical model was constructed by applying a machine learning technique to a 1D geomechanical model and seismic attribute data. This model was coupled with a reservoir simulation model to perform one-way coupled analysis, simulating the injection of CO2 at a constant rate over 25 years. The analysis involved extracting predicted deformation amounts on the seabed and calculating tilt changes. The predicted seabed tilt changes were then compared with the CO2 plume and pressure front to investigate their correlations. As a result of coupled simulation, the predicted seabed deformation was consistently found to be less than 1 centimeter and within the expected range of similar injection operations. The tilt changes were also very small, on the order of several hundred nanoradians. The predicted deformation of the seabed and tilt changes were observed even though pressure changes occurred only within the reservoir. The regions exhibiting tilt changes aligned with the CO2 plume and pressure propagation areas, indicating that continuous tilt monitoring could capture spatial and temporal CO2 advancement behavior. Notably, there is a point where the trend of the tilt change over time varies in the tiltmeter near the injection well. At the timing when the trend of tilt change deviates, pressure changes gradually propagate towards shallower depth, and it is observed that pressure progresses to the secondary seal formation. The results indicate the potential for monitoring local vertical pressure propagation through tiltmeter monitoring. This study demonstrates that utilization of tiltmeters as monitoring tools not only allows for the measurement of seabed deformation induced by CO2 injection but also enables the prediction of the advancement behavior of the CO2 plume and pressure front when interpreted in conjunction with coupled geomechanical simulation.
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

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

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

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

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

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

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

For 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

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

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.
Sand Control Prediction and Advisory Automation: Malaysia's Cost-Effective Digital Transformation that's Contributing to Sustainability
Authors: Yap Bee Ching
Published: October 2024

Malaysia Petroleum Management (MPM) has embarked on a digital transformation journey of cloud-based sand control prediction and advisory automation with the support of PETRONAS Carigali and data science teams to address the oil and gas wells sanding pain points. Leveraging on its local regulatory advantage, consisting of petroleum engineering expertise and mass data from all the operating fields, the digitalisation of offset data into machine learning (ML) has enabled the nation's predictive solution to improve sand management strategy decision making.

The intelligent sand management system is developed through reliable ML interpretation principle to tackle the nation's sand control challenges. The system is trained to analyse rock mechanic data using several key parameters to predict unconfined compressive strength (UCS) value as the pre-requisite for sand production prediction. It analyses the subsurface sand management data lake and, with the system calculated UCS value, the suitable downhole sand control (DHSC) method will be advised based on well design inputs. The proposed DHSC design's high level well cost is also generated based on benchmarking.

Sand encroachment from producing sand reservoirs is one of the key issues in oil and gas wells. The erosion consequences in production facility due to sand production normally result in well integrity concern, loss of primary containment (LOPC) risk and subsequently the domino effect of undesirable health, safety and environmental (HSE) incident(s) at operators' producing asset.

Conventional well completion design addressing sand control requires significant time and resources to gather and perform sanding analyses and risk ranking from scattered subsurface data. Besides that, a costly reservoir sand core sample test and time-consuming studies for its strength normally will be performed to assist in better well design sanding risk profiling. It is common for the executed downhole sand control selection to be sub-optimum, resulting in earlier end of well productivity or costly remedial works.

However, the developed system can significantly reduce the sand control selection analyses and decision-making duration. It serves as a solution for the unavailability of core samples for UCS tests, or cost saving for marginal operator on rock mechanic test avoidance. The optimised DHSC algorithm provides better long-term sand control success rate and well life longevity. The system has proven data analytics result of above 0.91 R-Squared (R2) accuracy.

A similar approach can be referred by the industry to minimise sand control issue in current cost saving and digital business operating rhythm. This digital transformation approach has also contributed to reducing equivalent greenhouse emission from carbon dioxide (CO2) compared to traditional UCS laboratory test and lengthy DHSC method analysis processes.
Research on the Fracturing Technology of Three Steps Method for Tight Sandstone Gas: A Case Study in Songliao Basin, China
Authors: Duan Yongwei , Shao Guangchao , Hao Chuncheng , Wang Yanling
Published: October 2024

Tight sandstone gas is the main gas source for increasing storage and production in Songliao Basin, China. Due to the deep burial depth, poor physical properties, and small pore throats of the reservoir, large-scale fracture network fracturing leads to uncontrolled fracture height, resulting in effective fracture length, limited coverage, and poor stable production capacity.

A "three Steps Method" fracturing technology has been developed based on the concept that "fracture-controlled reserves are well-controlled reserves". three-stage fracture network stimulation technology controls near fractures and expands far fractures, conducting the network fracturing by taking advantage of the relationship between the two-dimensional stress difference of reservoirs and the net pressure of fracture extension. This technology splits fracturing into three stages: fracture forming stage, network forming stage, and fracture supporting stage. In this paper, the "Three Steps Method" fracture network stimulation technology is implemented in the YS303-3 well. The underground microseismic monitoring data are used to compare the high displacement fracture network stimulation of adjacent wells. The results show that the length and width of the fractures in the well after implementing the technique increased by 1 and 0.7 times, respectively. The volume of the stimulated reservoir increases by twofold and the daily gas production of this well rises to 3.9×104m3, marking an increase of 30% in gas production compared to the adjacent well. The three-step fracturing method is subsequently applied to 6 wells, which increases the average stable gas production to 5.3×104m3 per well, accounting for a production increase of over 50%.

The "three Steps Method" fracture network stimulation has emerged as a critical method for optimizing storage and production in tight gas reservoirs. It offers opportunities for advancement and implementation, providing technical support and reference for the effective utilization of unconventional resources.
Research and Application of Fracturing Huff and Puff Technology in Low Permeability Reservoirs
Authors: He Dingkai , Huang Tianjie , Mao Jianying , Li Dongxu , Sun Yanyu
Published: October 2024

As low-permeability reservoirs are being developed, the formation pressure and production gradually decrease, and the effectiveness of using conventional huff and puff techniques to improve single well production is deteriorating year by year. To address this issue, this article proposes a fracturing based huff and puff technology.

Fracturing huff and puff technology is mainly divided into three stages. The first stage is energy storage, in which low displacement water is injected to supplement formation energy, the second stage is oil washing, in which high displacement water is injected into nano micro lotion to improve formation oil washing efficiency, and the third stage is capacity expansion, in which high displacement water is injected to replace nano micro lotion to expand oil washing coverage.

Fracturing huff and puff technology was implemented in 10 wells in the low-permeability reservoir, resulting in an average cumulative increase of 203.65 tons of oil. Taking the typical well M130-4-8 as an example, firstly, during the energy storage stage, the injection rate is optimized to 2m3/min and the liquid volume is 1000m3 through reservoir conditions and numerical simulation. Secondly, in the oil washing stage, through indoor performance evaluation and numerical simulation optimization, 1700m3 nano-microemulsion with oil washing efficiency > 40% and solution particle size <15 nm was injected with a displacement of 3 m3/min. Finally, during the capacity expansion stage, numerical simulation was used to optimize the injection rate of 3m3/min and liquid volume of 600m3.

The use of fracturing huff and puff technology for energy storage, oil washing and capacity expansion has significantly increased the production capacity of a single well, effectively supporting the development practice of fracturing huff and puff technology in low-permeability old area reservoirs. The next step is to expand its application in low-permeability old area reservoirs.
Effect of Methyl Orange and Methylene Blue on the Wettability of Organic Acid Aged Sandstone and Carbonate Formations: Implication for CO2 and H2 Geo-Storage.
Authors: Fatemah Alhammad , Mujahid Ali. , Stefan Iglauer , Alireza Keshavarz
Published: October 2024

Underground storage of carbon dioxide (CO2) and hydrogen (H2) in geological formations has been considered an effective method for the energy transition towards a low-carbon industry. The wettability of the rock is a significant parameter for underground gas storage, determining both the storage capacity and containment safety. This study focuses on using two chemicals, methyl orange (MO) and methylene blue (MB), as wetting agents at different concentrations (10 to 100 mg/L) to change rock wettability and improve the storage capacity of CO2 and H2.

To achieve this, the contact angle measurement technique was utilised to measure the advancing (θa) and receding (θr) contact angles under reservoir conditions, with a constant pressure of 13 MPa for the hydrogen system and 20 MPa for the CO2 system, temperatures of 25°C and 50°C, and a brine salinity of 0.3 M NaCl. To mimic reservoir conditions, the surfaces of both calcite and quartz samples were treated with stearic acid before being exposed to the wetting agent chemicals. Although the surfaces of these treated samples are hydrophobic, modifying their surfaces with even a very trace concentration of MO or MB significantly alters the wettability from hydrophobic to hydrophilic. This demonstrates that the presence of organic acids can affect the storage capacity of CO2 and H2 in reservoir rock. However, injecting even a very diluted amount of MO or MB into sandstone and carbonate geological formations can increase CO2 and H2 storage capacity.
Multi-Lateral Level-5 Completion: A Cost-Effective Solution for Marginal Oil Fields Development, First Business Case
Authors: Arif Naveed , Almuhairi Sharina , Abdulla Ruaa , Alkhalaqi M. F. , Jamal Muna , Wibowo Satrio , Alnuaimi Mohamed Ahmed , Ali M. , Talal Hend , Salam F. A
Published: October 2024

This paper aims to analyze the impact of appraising and developing marginal fields with multiple stacked reservoirs which is quite challenging in terms of techno commercial value. The development of such marginal reservoirs using conventional single horizontal wells drilling and completion is uneconomical. Therefore, it was necessary to engineer a solution that can enhance the commercial value of the project by reducing CAPEX and OPEX. This paper will present the first comprehensive business case, where multiple stacked reservoirs with marginal reserves were studied to produce independently using multilateral completions, granting full accessibility of the laterals while achieving production monitoring and reservoir surveillance.
The Synaptic Architecture of Layer 5 Thick Tufted Excitatory Neurons in the Visual Cortex of Mice
Authors: Costa Nuno Macarico da , Bodor Agnes , Schneider-Mizell Casey , Zhang Chi , Elabbady Leila , Mallen Alex , Bergeson Andi , Buchanan JoAnn , Bumbarger Dan , Dalley Rachel , Gamlin Clare , Kapner Dan , Kinn Sam , Seshamani Sharmishtaa , Suckow Shelby , Takeno Marc , Torres Russel , Yin Wenjing , Bae J. Alexander , Castro Manuel , Dorkenwald Sven , Halageri Akhilesh , Jia Zhen , Kemnitz Nico , Lee Kisuk , Li Kai , Brittain Derrick , Joyce Emily , Mahalingam Gayathri , Jordan Chris , Lu Ran , Macrina Thomas , Mitchell Eric , Mondal Shanka Subhra , Mu Shang , Nehoran Barak , Popovych Sergiy , Silversmith William , Turner Nicholas , Yu Szi-chieh , Wong William , Wu Jingpeng , Celii Brendan , Campagnola Luke , Seeman Stephanie , Jarsky Tim , Ren Naixin , Arkhipov Anton , Reimer Jacob , Seung H. Sebastian , Reid R. Clay , Collman Forrest
Published: October 2024

The neocortex is composed of microcircuits built from distinct cell types. Despite significant progress in characterizing these cell types, understanding the full synaptic connections of individual excitatory cells remains elusive.
This study investigates the connectivity of arguably the most well recognized excitatory neuron in the neocortex: the thick tufted layer 5 pyramidal cell also known as extra telencephalic (ET) neurons using a 1 mm3 publicly available electron microscopy dataset.
The analysis reveals that in their immediate vicinity ET neurons primarily establish recurrent connections with a specific group of inhibitory cell types. The most common excitatory targets of ET neurons are layer 5 IT neurons and layer 6 pyramidal cells, whereas synapses with other ET neurons are not as common. When ET cells extend their axons to other cortical regions, they tend to connect more with excitatory cells. Our results highlight a specific circuit pattern of a L5ET neuron using an automated approach for synapse recognition and automated cell typing, which is publicly available offering a framework for exploring the connectivity of other neuron types.
Clients’ Experiences and Satisfaction with an Integrated Intensive Outpatient Program for Substance Use Disorders
Authors: Bador Kourosh , Johansson Catrin , Axelsson Ida , Nilsson Maja , Kerekes Nóra
Published: October 2024

Comorbidity between substance use disorders and other mental health conditions is common, yet existing treatments often fail to address its full spectrum. Opportunities for integrated treatment are limited, and the effects of such treatment remain relatively unexplored. This study aims to describe the experiences of individuals with substance use disorders who received treatment at an integrated intensive outpatient addiction-care clinic. Utilising a survey with quantitative measures and qualitative questions with free-text responses, the experiences of 65 clients who had completed the programme between 2015 and 2021 were evaluated. The findings indicated that most clients were highly satisfied with the treatment. The analysis revealed three main categories: strong relationships and a comprehensive treatment programme; engaged and knowledgeable staff who lead with warmth; and self-development through new experiences. Overall, clients reported high levels of satisfaction with the integrated intensive outpatient programme. Despite these positive outcomes, this study highlights the need for further research on, knowledge about and competence in the development of integrated treatment interventions for individuals with substance use disorders.
Spinal signatures of entrenched and treated neuropathic pain
Authors: Neely G , Loo Lipin , Fujikake Kazuma , Saad Sasha , Clark Teleri , Hamoudi Zina , Manion John , Caron Leslie , Davis Olivia , Tavares-Ferreira Diana , Shiers Stephanie , Powell Joseph , Price Theodore
Published: October 2024

Chronic pain arises from an imbalance between excitation and inhibition. For chronic pain caused by peripheral nerve injury, sensory neuron damage leads to changes in spinal pain transduction, where enhanced excitatory and/or loss of inhibitory pathways can drive pain diseases. At the cellular level, the transcriptional changes occurring in entrenched chronic pain are largely unknown. Here we use single nuclei and spatial transcriptomic approaches to investigate the cellular and molecular causes of entrenched and treated neuropathic pain. Our data rules out a loss of spinal inhibitor neuron populations or transcriptional downregulation of inhibitory machinery, and instead we show that nerve injury leads to broad upregulation of spinal synaptic and excitatory pathways in dorsal neuronal and glial populations. We confirm these core populations are conserved in the human spinal cord, and show an effective pain therapy acts by dampening these neuropathic transcriptional programs. Overall, our data provides the first high resolution transcriptional analysis of the neuropathic spinal cord, highlighting known and novel pathways that may help us better manage pain diseases.
UDP-Glucuronosyltransferases 2A3 as a biomarker for ulcerative colitis and colon cancer
Authors: Chen Hao
Published: October 2024

Background
Ulcerative colitis has a serious impact on the quality of life of patients and is more likely to progress to colon cancer. Early diagnosis and timely intervention of ulcerative colitis are of great significance.
Methods
Gene expression data of active ulcerative colitis were downloaded from the GEO database, and genes with significant differential expression were identified. Biochemical markers with diagnostic significance were selected through machine learning methods. The expression differences of the selected markers between COAD and normal control groups in the TCGA database were also understood, and the diagnostic efficacy of these markers for COAD was observed. In addition, the correlation between the selected markers and clinical indicators, as well as their predictive efficacy for the survival of COAD patients, was explored.
Results
Through machine learning and LASSO regression analysis, UGT2A3 was finally determined as a diagnostic marker for ulcerative colitis. It showed good diagnostic efficacy in both the training set and the external validation set. Meanwhile, UGT2A3 was significantly downregulated in COAD tissues compared to normal control tissues. The ROC curve suggested that UGT2A3 could serve as a diagnostic marker for COAD with high diagnostic efficacy, with an AUC of 0.969. Immune infiltration analysis indicated a significant negative correlation between the expression of UGT2A3 and neutrophils. Correlation analysis suggested the association of UGT2A3 with the pathological classification of colon cancer. Survival analysis showed that UGT2A3 is negatively correlated with OS, PPS, and RFS in colon cancer.
Conclusion
We identified UGT2A3 as a diagnostic marker for ulcerative colitis through bioinformatics methods, and verified its significant downregulation in colon cancer, as well as its predictive role in the survival of COAD patients. It may serve as a diagnostic marker for ulcerative colitis and colon cancer, and also provide certain guidance for the prognosis of colon cancer patients.
Ethnic differences in efficacy of drug treatment in patients with an acute manic episode: an individual patient data meta-analysis of randomized placebo-controlled trials
Authors: Storosum Bram W.C. , Cohen Sem . E. , Steinz Cedrine , Mattila Taina , Welten Carlijn , Brink Wim , Roes Kit , Haan Lieuwe , Denys Damiaan A.J.P. , Zantvoord Jasper B.
Published: October 2024

Background
Little is known about the effect of ethnicity on drug treatment in patients with an acute manic episode. The aim of this study is to determine whether ethnicity moderates the response to drug treatment in patients with an acute manic episode, and whether this moderation is independent of potential confounders.
Methods
We analysed ten short-term placebo-controlled registration trials of atypical antipsychotics and anticonvulsive mood stabilizers in patients with an acute manic episode (n = 2199). A one-step random effects individual patient data meta-analysis (IPD) was applied to establish the moderating effect of ethnicity on symptom improvement on the Young Mania Rating Scale (Y)MRS and on response defined as 50% (Y)MRS symptom reduction. These analyses were corrected for baseline severity, age, and gender. A two-step IPD comparing these outcomes between White, Black and Asian patients. Additionally, a conventional meta-analysis was performed to determine the effect size of drug treatment separately for these ethnic groups.
Results
In the complete dataset, 60.4% of the patients was White, 8.0% was Black, 12.7% was Asian, 33.7% was of other ethnicities. Ethnicity did not significantly moderate the efficacy of drug treatment: pooled beta-coefficient (β) for the interaction between treatment and the ethnicities White, Black and Asian, varying from 0.889 to 0.899 with overlapping confidence-intervals ranging from 2.356–2.430 in the main analysis. The drug treatment effects were significant in all three analysable ethnicity groups compared to placebo.
Discussion
In White,Black, and Asian patients with an acute manic episode drug treatment is equally effective.