bluuebunny/crossref_metadata_2025_split huggingface dataset:
Identifying Natural Fractures in Unconventional Carbonate Source Rocks in Abu Dhabi by Comparing Borehole Image Logs and Drilling Parameters to 3D Seismic Fracture Analysis Techniques
Authors: Mykhaylenko S. , Burgess C. , Al Marzooqi H. , Baig M. Z. , Alharthi A. 
Published: November 2024

The identification of high-prospectivity areas in unconventional fields is a critical aspect of optimizing hydrocarbon recovery and ensuring economic viability. These are areas within the reservoir that exhibit higher porosity and permeability, making them prime targets for drilling and production. The integration of seismic inversion results, which provide insights into the porosity of the rock, with the identification of fractured areas that enhance permeability, offers a comprehensive approach to delineate these favorable zones.
Seismic inversion is a powerful tool that transforms seismic reflection data into a quantitative rock-property description of the reservoir. It enables the prediction of porosity variations within the subsurface, which is a key attribute in evaluating the quality of unconventional reservoirs. By integrating seismic inversion results with the identification of natural fractures or artificially induced fractures, geoscientists can better understand the reservoir's heterogeneity and fluid flow characteristics.
Fractures play a significant role in unconventional reservoirs as they can significantly enhance permeability, a critical factor in formations where matrix permeability is inherently low. The presence of fractures can transform an otherwise tight rock into a productive reservoir. Therefore, the accurate mapping of fractured zones, in conjunction with porosity data from seismic inversion, is essential for the effective exploitation of unconventional resources.
Industrial Deployment of Power Reserve Optimization
Authors: de Meyer F. , Guzman J. , Labastie E. , Germain-Vu C. 
Published: November 2024

Abstract
In remote oil and gas production facilities, power is often produced on site with gas turbine generators or internal combustion engines. The difference between the maximum available power and the current load of the group of running machines is known as the power reserve. The rationale for optimizing the power reserve is to increase the efficiency of power generation and reduce the greenhouse gas emissions. A four-steps approach to optimize the power reserve has been developed and is being deployed on all operated assets. First, all data related to power generation and consumption is collected and reviewed. Second, an optimized configuration is identified and modelled. Third, the feasibility is studied, and the business case is evaluated. Fourth, the optimal configuration is tested and implemented. The following enablers for power reserve optimization are considered in the second and third step: reduction of electricity consumption, load shedding strategy update, detailed power generation reliability study and improvement, site interconnection, hybridization and the use of a battery energy storage system. The optimization of the power reserve is under deployment on all our operated sites. The total potential greenhouse gas reduction potential identified is around 350 ktCO2eq per year. Eight gas turbine generators have already been switched off resulting in a reduction of around 130 ktCO2eq per year, as well as lower fuel gas consumption and lower maintenance costs. The main enablers are improved reliability of the power generation, site interconnection and hybridization. As part of the deployment strategy a real-time monitoring tool has been implemented (PowerBI). A key performance indicator related to power reserve optimization has also been defined for follow-up by senior management. Return of experience has been collected and shared with other sites. The paper presents a novel, structured and systematic approach to optimize the power reserve that can directly be applied to all assets. The approach often results in a reduction of greenhouse gas emissions at a low net technical cost and should thus be one of the first steps to reduce the carbon footprint of remote production sites.
Relief Well as a Derisking Tool - A Case History
Authors: Amer Mohamed , Barnett David 
Published: November 2024

Abstract
Offshore well operations are inherently complex and demand a high level of technical prowess, strategic collaboration, and adaptability. Wild Well Control exemplifies these qualities through its intricate and challenging projects. This paper provides a comprehensive look at the multifaceted challenges faced during a specific gas well operation, the strategic decisions made to address these challenges, and the invaluable lessons learned along the way. By delving into the detailed processes involved in well control and relief well operations, we aim to offer insights into the critical importance of meticulous planning, effective teamwork, and the methods required to ensure success in the field.
Drilling and Completion of the World's Longest Slim Hole Erd' with Limited Entry Liner Completions Using Water-Based Mud Saves 130 MM$ in Onshore Abu Dhabi
Authors: Varma Esha Narendra , Ditzler Theodore Jay , Sumaida Ali Ben , Mwansa Peter Levison , Husien Mohammad , Bahrom Abdul Raman Bin , Saragi Raymond 
Published: November 2024

Abstract
Extended reach drilling (ERD) can facilitate the development of untapped resources, reduce greenhouse gas emissions, surface congestion, and drilling costs. This ERD project with lower completion was started with an aim to lower well cost indicators including $/ft and $/bbl. Therefore, the challenge was to drill Slimhole ERD (6-1/8″ lateral) wells with water-based mud (WBM). WBM is more cost-effective, environmentally friendly, and less damaging to the reservoir than OBM (oil-based mud). The use of WBM instead of OBM can save $2MM per well. The major challenges in drilling Slimhole (6-1/8″ size) ERD well with lateral leg include higher torque and failure to deploy lower completion due to high friction factors.
The first pilot well was planned with a liner-less design considering the low friction factors required to drill 15,000′ of 6-1/8″ lateral hole and run the lower completion. The second pilot well was targeting a deeper and tighter reservoir zone with higher downhole temperatures. This involved drilling 12-1/4″ intermediate hole to the landing point with larger 5-1/2″ drill pipe. It enabled a push-pipe technique for drilling the lateral hole with improved weight transfer through the curved profile. The 6-1/8″ lateral hole was drilled with 4″ high-torque drill pipe, tandem high-flow circulating subs, and specially formulated drilling fluid lubricant. A conventional OBM system provides sufficient lubricity to reduce friction factors as low as 0.10. In this application, a low cost WBM system was made feasible by introducing stable high-temperature lubricant and unique hole cleaning practices.
Following this successful achievement, the 5-year business plan has been revised to include 63 similar wells with a projected total savings of ~ $250MM.
The Slimhole ERD project has demonstrated substantial value with a 35% reduction in CAPEX. The delivery of these two Slimhole ERD wells overturned conventional drilling and completion practices.
The implemented project resulted in saving up to 35% of the well cost and saved 20 days per well compared to a conventional ERD well with 8-1/2″ hole and OBM. These two Slimhole ERD (15,000′ lateral) wells were drilled with a challenging Directional Difficulty Index (DDI) of 7.2. The wells were both completed successfully by running the 4-1/2″ lower completion to reach the total depth.
Integrating Neural Operators and Transfer Learning for Efficient Carbon Storage Forecasting
Authors: Falola Yusuf , Misra Siddharth , Nunez Andres 
Published: November 2024

Abstract
Efficient forecasting involves generating accurate and reliable predictions while minimizing the use of resources, such as time, training data, and computational power. This study investigates the potential of Fourier Neural Operators (FNOs) combined with transfer learning (FNO+TL) for forecasting the spatiotemporal evolution of pressure and CO2 saturation in a large, heterogeneous geological carbon-storage aquifer. The enhanced FNO models, trained on the SACROC geological model, achieved high accuracy, with mean absolute errors (MAEs) of 0.046 MPa for pressure and 0.011 for saturation in large aquifer models. Transfer learning (TL) further enabled the adaptation of a trained FNO model to diverse operational conditions with minimal new training data. The adapted models achieved pressure prediction MAEs of 0.27 MPa, 0.51 MPa, and 0.59 MPa, and saturation prediction MAEs of 0.05, 0.056, and 0.14, under varying injection rates, schedules, and injector locations, respectively. Additionally, the FNO models generalized successfully to a geologically distinct site at the Illinois Basin - Decatur Project (IBDP), which had significantly different carbon storage operations. The models achieved prediction MAEs of 4.5 psia for pressure and 0.11 for saturation, with reductions of 87% and 80% in training data requirements, respectively. Furthermore, the FNO+TL models significantly reduced computational costs. The study concludes that integrating transfer learning with Fourier Neural Operators provides an efficient and scalable approach for CO2 storage forecasting.
First Successful Shaped Memory Polymer (SMP) Open Hole Sand Control Application in an Extended Reach Well
Authors: Antoniucci Cesare , Di Altobrando Cinzia , Gioia Valeriano , Ferrara Giovanni , Sportelli Marco , Mingozzi Michele , Derni Davide , Di Giampaolo Paolo , Fusilli Alessio 
Published: November 2024

Abstract
Shaped Memory Polymer (SMP) technology represents nowadays one of the more innovative sand control techniques available on the market for layered reservoir with water zones associated or characterized by different pressure regimes.
Even though the well configuration has not been identified as a constraint for the technology utilization, a limited number of applications has been carried out.
This paper presents the first Southern Europe deployment of the system in an extended-reach well, successfully performed.
The SMP is deployed into the wellbore like a sand screen, allowing the installation of the lower completion as a single trip assembly with a liner hanger system.
Upon downhole installation, an activation agent is pumped, causing the SMP media to expand. It conforms against the borehole, thus maintaining positive stress against the formation and stabilizing the near-wellbore region.
In order to succeed in deploying the completion in such highly deviated environment, detailed torque and drag studies have also been conducted considering the displacement and well drag friction factors (OH and CH).
This paper presents the successful deployment of the SMP in an extended-reach well, presenting a slant profile of 80° inclination while dropping to 65° over the last open hole phase (215m length), showing a final displacement at well total depth of 2900 meters.
The assembly has been customized considering four production zones separated by open hole packers with an inner string incorporating a sliding sleeve device (SSD) for each zone to ensure production selectivity. Once the SMP has been positioned at the desired open hole depth, the liner hanger is set and the activation fluid pumped. Based on detailed studies carried out considering specific well conditions such as hydrostatic pressure and bottom hole temperature, the contact time has been selected to 40 h to guarantee the full polymer expansion.
Subsequently, the liner packer element has been energized, followed by the installation of the upper completion system.
Then the clean-up operations started.
Shaped Memory Polymer technology stands out as one of the most cutting-edge sand control techniques available.
The successful deployment of the system in Southern Europe represents the first application of the technology in an extended-reach well over the few installations carried out worldwide. It validates its capability to effectively operate in highly deviated wells and confirms its viability and cost-effectiveness as an alternative to conventional standalone screens or other sand control techniques.
Impact of Industry 4.0 Technologies in Enhancing Sustainability of Maritime Logistics
Authors: Kumar U. , Dixit V. , Jha K. 
Published: November 2024

Abstract
Sustainability has become a pivotal point in the maritime industry, encompassing environmental, economic, and social dimensions. This study investigates the impact of Industry 4.0 technologies on improving maritime logistics sustainability. An extensive literature review will identify key technologies and sustainability goals across these dimensions. Using advanced decision-making frameworks like AI and ML-enabled decision intelligence or Neutrosophic-TOPSIS methods, the impact of these technologies will be quantified and ranked. The results will yield a prioritization of technologies and a strategic roadmap for their implementation, aimed at optimizing resource allocation and enhancing sustainability. This research provides an integrated approach to sustainability and technological adoption, offering a novel, industry-specific roadmap.
Selection and Maturation of a CO2 Store for Oman's First Blue Hydrogen Project
Authors: Borner N. D. , Aljiroudi F. , Al Baloshi S. , Al-Abri A. S. , Di Matteo A. , Gavnholt J. , Dhahli A. , Farajzadeh R. , Singhal M. , Al Mjeni R. , Al Bulushi N. 
Published: November 2024

In May 2022, a Memorandum of Understanding was signed between Petroleum Development Oman (PDO) and Shell to pursue the development of Oman's first Blue Hydrogen project with Carbon Capture Utilisation and Storage (CCUS), based at the Port of Duqm. To create a certified blue product, the CO2 associated with hydrogen production from natural gas must be permanently sequestered underground. This required selection and maturation of a suitable CO2 storage site in the onshore area of Oman with sufficient capacity for the project, sustained reservoir injectivity, robust containment and adequate monitorability using available technologies.
The subsurface of Oman contains a vast succession of geological reservoirs and seals, from Pre-Cambrian through to the Tertiary, many of which are proven petroleum systems. It was quickly determined that saline aquifers, rather than depleted gas fields, presented the best opportunity for CO2 storage, due to their size and separation from Oman's dense network of hydrocarbon-producing fields and infrastructure. PDO's Block 6 concession contains the most likely candidates from a geological perspective, as it covers the main sedimentary basins within the country, plus benefits from a rich subsurface dataset for evaluation of the CO2 store.
This paper describes the process of selection and subsurface characterisation of a suitable saline aquifer CO2 store in Central Oman, to meet the pore space requirements of CO2 sequestration for the lifetime of the proposed Blue Hydrogen project.
Machine Learning Accelerates and Facilitates the Prediction of Stuck Pipe Events
Authors: Katzmann L. , Mayer H. , Arévalo P. , Forshaw M. 
Published: November 2024

In the present social and economic climate, Exploration and Production (E&P) companies are under pressure to embrace low-cost digital technology that offers a dual benefit of lowering well construction capital expenditures, as presented in Fig. 1, by reducing Non-Productive Time (NPT) and Invisible Lost Time (ILT), while also aiding in meeting Environmental, Social and Governance (ESG) objectives.
One of the main sources of NPT in well construction is stuck pipe, as such incidents represent one of the most challenging and costly problems in drilling operations. In these cases, the drillstring becomes immovable in the borehole caused by different mechanisms such as differential or mechanical sticking and packing off. Such incidents can lead to not only NPT but also potential loss of equipment, hence necessitating expensive technical detours that prioritize projects over authorization for expenditure (AFE). Furthermore, stuck pipe events can significantly affect the safety and wellbore integrity. While approaches to identify stuck pipe scenarios retrospectively are well documented, this paper describes a data-driven approach to identify and predict such events at the edge in real time using machine learning.
Nowadays, the constant development of digital technologies, such as digital twins modeling behavior of the wellbore and equipment in real-time (Arévalo et al. 2021), drilling automation applications aiding in the monitoring of wellbore condition (Ait Ali et al. 2023, Arévalo et al. 2023, Arévalo et al. 2022), among others, have prepared the ground for a more accurate detection of downhole hazards, such as conditions leading to stuck pipe. This paper presents the utilization of machine learning (ML) to analyze surface data, detect stuck pipe events and classify such events into categories. The approach consists of a fuzzy logic algorithm to categorize acquired surface data and a real-time classifier to be trained. The score calculated by the fuzzy algorithm is mapped into five categories, which serve as labels for training validation and test data. A Long Short-Term Memory (LSTM) and a decision tree are trained by a sequence-to-label prediction approach to predict whether the drillstring will operate unimpeded or whether higher drag or even a stuck pipe event is expected.
Real-Time Forecasting of Subsurface Porosity During Drilling Using Advanced Time Series Models
Authors: Mohamed Badr A. , Aliyev Subhan , Ibrahim Ahmed Farid , Al-Shafloot Talal 
Published: November 2024

Abstract
Oil and gas exploration and production are critically dependent on well logging to gather essential subsurface data for effective reservoir characterization. Traditional well logging, while invaluable, incurs substantial costs and is typically confined to specific depths, thereby restricting a thorough understanding of reservoir properties across the entire wellbore. Addressing this challenge, this study harnesses the potential of various time series models, including ARIMA, LSTM, bi-directional LSTM, GRU, and simple RNN, to predict porosity in areas of the wellbore not yet reached by logging tools.
This research introduces a novel approach that involves predicting real-time reservoir porosity during drilling, using time series models to extend our knowledge into unlogged intervals. This method leverages existing log data to forecast porosity ahead of current drilling, allowing for real-time data-driven decision-making that can influence drilling directions and strategies. The methodology consists of preprocessing the data, segmenting it into training and testing sets, and training distinct models on porosity with depth serving as the time component. The efficacy of each model is rigorously evaluated using metrics such as mean squared error (MSE) and mean absolute error (MAE), with the most accurate model selected for real-time forecasting.
Results from the study highlight that while all tested models demonstrate capability in predicting subsurface properties, LSTM and bi-directional LSTM, in particular, show superior performance in modeling the complex patterns inherent in geological data. The introduction of a prediction strategy during drilling not only refines the model's accuracy but also revolutionizes how geological data is integrated into operational practices.
In conclusion, this study transcends traditional logging limitations by applying advanced time series forecasting models to predict porosity in the unlogged sections of the wellbore. The innovative approach of real-time forecasting of subsurface porosity during drilling could significantly alter operational dynamics in the oil and gas industry, facilitating more precise reservoir characterization and informed decision-making in field development and management.
Navigating the Depths: How High-Force Tractors Enable Access to Deep Reservoirs
Authors: Varghese Mathew , Woods John , Sheiretov Todor , Correia Gladwin , Altamimi Sarah , Tirado Eduardo , Ibrahim Ahmed 
Published: November 2024

Abstract
High-force wireline tractors address the conveyance challenges of ultradeep reservoirs with complex trajectories, enabling access to deep reservoirs. Accessing ultradeep wells for surveillance and intervention is highly challenging with conventional methods. Conveying long downhole tools through complex trajectories in ultradeep wells demands extreme conveyance forces, compounded by power delivery limitations associated with long cables. In this paper we provide an overview of these challenges faced by an operator in the United Arab Emirates (UAE) and a novel wireline tractor solution that overcomes these challenges to access ultradeep reservoirs with increased efficiency and reliability. The approach consists of analyzing conveyance challenges and deploying an innovative technological solution using high-force tractors to enable reliable data acquisition in deep reservoirs. The following methods are used: Model and analyze well tortuosity to estimate tractor forces and navigation challenges.Select a high-force tractor system with a bespoke motor design for the job.Choose an efficient telemetry system for reliable communication with downhole tools.Design a purpose-fit tool configuration and operation strategy to navigate complex completions.Conduct surface integration tests to assess tool capability and reliability.Provide feedback to enhance tool design and performance at the product manufacturing center.
The new high-force tractor design delivers a remarkable 70% increase in conveyance force compared to legacy tractors, as demonstrated in a surface integration test (SIT). Additionally, it achieves twice the tractoring speed under higher loads, enhancing operational efficiency and reducing intervention time. The implementation of an advanced high-speed telemetry system enables reliable surface acquisition of downhole sensor data across extended cable lengths while tractoring, maintaining a high signal-to-noise ratio. This new tractor system was successfully deployed in seven wells to access deep reservoirs for an operator in UAE. This system was first deployed to convey production logging tools in the main bore of a trilateral well with 18-ft long sidetrack windows. By combining increased force and efficiency, the tractors successfully navigated through these challenging sidetrack windows. Key achievements include secure access to reservoirs via multiple passes across sidetrack windows; a cumulative tractoring distance of more than 27,000 ft in the first well; reliable acquisition of downhole tool data over a 43,500-ft-long wireline cable using the new telemetry system; simultaneous acquisition of production log data during tractoring, which ensures high-quality data and improved operational efficiency; and accurate quantification of horizontal lateral zonal contributions from all three laterals. This successful deployment of high-force tractors opens opportunities for accessing ultradeep reservoirs.