bluuebunny/crossref_metadata_2025_split huggingface dataset:
Fingers of Fire: Understanding Hand and Fingers Injuries in Oil and Gas Industry
Authors: Kusumawati D. , Erwandi D. , Puspa W. 
Published: October 2023

Abstract
Hand and finger injuries are prevalent in the oil and gas industry, resulting in significant human and economic costs. Understanding the underlying causes and contributing factors of these injuries is crucial for their prevention and mitigation. This paper explores the use of human factors analysis and classification systems in comprehending hand and finger injuries in the oil and gas industry. It highlights the key elements of a comprehensive human factors approach, examines the major factors contributing to hand and finger injuries, and presents a classification system for analyzing and categorizing these injuries. By integrating human factors principles into safety management systems, Company can effectively identify and address the root causes of hand and finger injuries, leading to improved safety performance and reduced occupational hazards.
Smart Field Technology / Automation and New Technologies Applied to Projects
Authors: Sasipriya Rajendran 
Published: October 2023

Abstract
The smart field technology / Automation and New technologies used in Oil and gas projects attained specific attention from researchers because smart field technology and automation techniques improve the smart way of managing the plant process by communication between objects, machines, and people. The evolved technique of Cloud-based SCADA is one united system consisting of the things of the real world or combined process activities connected to the Internet via wired and wireless network structures. This structure of automation helps humans to manage plant operations in an effective and efficient manner by achieving required safety and improving plant processes with improved productivity. This paper has discussed the requirement of automation in the oil and gas industry and the benefit of Cloud-based SCADA using VPN in Oil and gas plants. The automated plant with Cloud-based SCADA using VPN can serve the function of a single node in a global setup. However, an automated oil and gas plant should be a forerunner of Cloud-based SCADA using VPN, by which the structure/characteristics can provide a solution to a lot of research challenges of Cloud-based SCADA.
Inter-Well Similarity Analysis as a Key Pre-Processing Step in Stratigraphy Interpretation Workflows
Authors: Subbotina M. V. , Bukhanov N. V. , Tirikov E. M. , Michael N. A. 
Published: October 2023

Abstract
High-resolution and high-quality stratigraphic markers are fundamental to geological modelling and operations. Stratigraphic interpretation workflows, however, are time-consuming due to the processing of massive amounts of data collected from geological surveys. This paper presents highlights of the importance of inter-well similarity analysis on the preprocessing stage for stratigraphic interpretation. The proposed methodology involves the use of common time series similarity metrics for quantitative estimation of pairwise similarity between wells to select appropriate ones for further interpretation. Experiments were conducted on the data collected from Groningen Gas field and petroleum basin in the Middle East. The impact of data selection on the final results was estimated based on the prediction quality metrics and computational time between baseline models (XGBoost classifier and simple Feedforward Neural Network) trained on the whole set of wells and models trained only on selected wells. The integration of similarity-based wells selection step allowed us to improve computational time of models, while preserving or even outperforming prediction quality. With the results of our experiments, we demonstrated the importance of the usage of only representative data for stratigraphic predictions and custom-made models’ creation for the target well. The proposed workflow can be beneficial for traditional manual correlations as well.
Augmented Learning Parameter Advisor for Wellbore Domain Interpretations
Authors: Rekik Karim , Bouyghf Abdelkabir , Zened Olfa , Kontsedal Tanya 
Published: October 2023

Abstract
The Parameter Advisor introduces an AI-powered solution for automating the selection of optimal parameter values in wellbore data interpretation. The aim is to reduce effort and time required for accurate interpretations. The software leverages machine learning algorithms, a comprehensive knowledge base, and collaboration among experts to enhance the interpretation process.
The overall approach includes data gathering, quality control, and validation. Relevant data is collected and stored in a cloud storage system. The software applies statistical techniques and unsupervised learning algorithms to ensure accuracy and identify patterns in the data. Once the database is established, the software provides recommendations for future analyses based on past interpretations and expert knowledge.
The results of tests conducted in the GRONINGEN and CASABE fields showed 92% accuracy compared to manual interpretation. The execution time for a Shale Volume interpretation was reduced by 64%. Collaborative studies with AkerBP in the Valhall field demonstrated an interpretation time reduction of approximately 70%.
This study presents a novel approach in the petroleum industry by automating parameter initiation using machine learning and cloud computing. It improves the speed, accuracy, and efficiency of wellbore data interpretation. The software's ability to recommend optimal parameter values based on previous interpretations and expert knowledge contributes to better decision-making. The findings emphasize the effectiveness of machine learning in automating interpretation tasks and enabling non-experts to interpret data accurately.
In summary, the proposed software streamlines the wellbore data interpretation process, reduces errors, and saves time. It enhances collaboration among experts, captures expert knowledge, and improves decision-making. The solution adds valuable insights to the petroleum industry by showcasing the power of machine learning in interpretation tasks and demonstrating its potential for transforming the field.
Bringing Old Non-Integral Wells Back to Life. Latest Unconventional Maintenance Techniques to Improve Asset Reliability, Sustainability and Reduce Carbon Footprint
Authors: Basit P. Eng. Abdul 
Published: October 2023

Tree Valves and wellhead cavities provide critical barriers to control the flow of oil and gas from wellbore to the production system. To guarantee secure and Integral operation of wells in accordance with HSE requirements, implementation of an effective maintenance strategy is essential. Conventional methods might not always be successful in tackling various challenges faced in preserving and reestablishing the integrity of critical barriers at the Tree and wellhead. The industry has therefore resorted to cutting-edge unorthodox procedures in order to revive and maintain the best performance of these wells.
Three novel ways of maintenance that have yielded promising outcomes are:
An Experimental Study of Shale-Drilling Fluid Interactions to Enhance Long-Term Shale Inhibition
Authors: AlArfaj Mohammad , AlRashead Mohammad , AlOtaibi Mohammad 
Published: October 2023

Abstract
The objective of this paper is to discuss shale-fluid interactions while drilling clay-rich shale formations. Shale swelling, dispersion and sloughing are frequently encountered problems that may lead to excessive non-productive time. Comprehensive understanding of the behavior of the shale sample helps in mitigating shale drilling problems.
To understand the behavior of a shale sample when exposed to fluids, different types of characterization tests have been conducted starting with mineralogy identification using X-ray diffraction, X-ray fluorescence and also the conventional swelling tests. It went further to study the organic carbon content using thermogravimetric analysis.
After carrying out X-ray diffraction and thermogravimetric analyses on two shale samples to determine clay content and organic carbon content, a series of swelling tests were conducted using different types of non-inhibitive and inhibitive fluids. Linear shale swelling results are represented as the percentage of volume expansion as a function of swelling time while shale. Results showed high variance among shale inhibitors in terms of the ability to inhibit shale particles for prolonged time intervals
This paper provides a review of various factors that influence shale-drilling fluid interactions Due to the presence of some unique conditions, such as loss circulation, sometimes there is a need to use a low cost non-inhibitive mud instead of a high cost inhibitive mud to drill the next formation after the shale section. If the durability of original inhibition is not long enough to ensure smooth drilling of the the second zone, the previously drilled inhibited shale formation starts reacting again with the non-inhibitive drilling fluid. Hence, the reactive shale zone needs to be isolated by a casing string before drilling ahead. However, if inhibitive muds with long-term inhibition potential such as 24 hours or more are available, multiple formations including the shale formation can be drilled and cased in one hole section.
Lithostratigraphy Modeling with Transformer-Based Deep Learning and Natural Language Processing Techniques
Authors: Jacinto M. V. G. , Silva M. A. , de Oliveira L. H. L. , Medeiros D. R. , de Medeiros G. C. , Rodrigues T. C. , de Montalvão L. C. , de Almeida R. V. 
Published: October 2023

Abstract
In the Geosciences, sequence modeling algorithms such as long short-term memory - LSTM - have been widely used in various natural domains, like in earthquake and rainfall forecasting. A new architecture called Transformer has been used by the recent state-of-the-art models, capable of outperforming classical methods. Using this technology, this paper brings a modern approach that applies Transformers-based algorithms to solve a lithostratigraphy prediction challenge. We propose modifications to the original technique in order to embed geological information. In this sense, the lithological sequence is encoded as a sequence of integers and mapped by an embedding layer into a richer representation (a numerical vector per lithological element). We also incorporate the relative position of the lithological samples by adapting the original encoding. The database comprises four different wells located in the same onshore basin: two were used for training, one for validation and the last for test. They were filtered to guarantee that all wells’ data would have the same depth range. We ran 38 experiments with varying hyperparameters (number of transformer blocks, embedding size, learning rate and parallel attention heads). It was found that higher values of those variables indicate a higher model’s performance (in terms of F1-Score, Accuracy, Precision and Recall). The results achieved have accuracy and F1-Score concentrated between 0.89 and 0.92, showing consistency and good generalization capacity. Visually, we observe the model can approximate the lithostratigraphy within the geological wells’ context. Moreover, we developed 2 metrics in order to assess the model’s ability to detect lithological transitions, named ‘Transition Accuracy’ and ‘Expanded Transition Accuracy’. It was applied in 10 randomly selected intervals in the test data. Statistically, the ‘Expanded Transition Accuracy’ shows the model misses the transition in 0.3048 meters at most. Finally, we consider this paper presents a proof of concept in the use of transformer-based technologies for the modeling and prediction of lithostratigraphic sequences, a successful adaptation of NLP techniques to solve a geoscientific challenge.
Influence of Nanoparticle-Based Drilling Fluids on Egyptian Shale Swelling – An Experimental Investigation
Authors: Mady Ahmed , Abdelhafiz Mostafa M. , Dahab Abdel Sattar , Hegazy Gehad M. , Mahmoud Omar 
Published: October 2023

Abstract
Shale swelling is a major challenge in drilling operations and can lead to severe problems. The use of shale swelling inhibitors and plugging additives in drilling mud is a common solution to mitigate these problems. This study aims at investigating the influence of aluminum oxide (Al2O3) and copper oxide (CuO) nanoparticles (NPs) as mud additives on Egyptian shale swelling. Two formations were studied, Duwi and Dakhla shales. X-ray diffraction (XRD) was utilized to reveal their clay mineralogy. Low-solid non-dispersed mud (LSNDM) was tested as the base fluid. LSNDM is a common choice to drill an intermediate section which may contain minor strikes of shaley and problematic formations. Al2O3-NPs (15 nm) and CuO-NPs (40 nm) were added at different concentrations (0.1, 0.3, 0.5, 0.7, and 1.0 wt%) and the rheological and filtration properties were tested using standard viscometer and API filter press. Oedometer tester was used to examine the effect of NPs-based LSNDM on shale swelling. Adding NPs to the LSNDM yielded better rheological behavior. An increase in the yield point (YP) was obtained when using 0.3 and 0.5 wt% of Al2O3-NPs and 0.1, 0.3 and 1.0 wt% of CuO-NPs, which implies less solids sagging, higher cuttings carrying capacity and thus, more efficient hole-cleaning. Furthermore, the gel strength (GS) of the LSNDM showed improvements when adding NPs compared to the base. Also, the Herschel-Bulkley model was found to best fit the rheological behavior of the NPs-based LSNDM. Moreover, using both NPs yielded better filtration properties, especially at 0.3 wt% of Al2O3-NPs and 1 wt% CuO-NPs. The scanning electron microscopy (SEM) images showed better surface morphology and less porous microstructure of the filter cakes containing 0.3 wt% of Al2O3-NPs and CuO-NPs compared to the base fluid. Also, a decrease in the slope of the filtrate volume curve after 30 minutes (up to 90 minutes) was observed compared to the base fluid, which confirms better NPs dispersion. The composition of the clay minerals ranged from 80% montmorillonite to low kaolinite content (19%) for Dakhla shales, and from 48% montmorillonite to low kaolinite content (22%) for Duwi shales as revealed by XRD, which suggests high swelling ability, especially for Dakhla shales. The Oedometer swelling measurements showed a reduction in Duwi shale swelling by 30 and 35% when using 0.3 wt% of Al2O3-NPs and CuO-NPs, respectively, compared to the base. In addition, a significant reduction in the swelling of Dakhla shale was obtained when using 0.3 wt% of Al2O3-NPs and CuO-NPs by 52 and 63%, respectively. This paper discusses a new application concerning the efficient use of NPs in the drilling fluid industry, which may promote using NPs-based mud to drill the Egyptian oil and gas wells.
13 Years of Comprehensive Reservoir Monitoring and Surveillance in South of Oman EOR Polymer Project
Authors: Al Sawafi M. S. , Al Ghailani T. A. , Al Kindi O. , Al Abri M. , Al Hinai S. 
Published: October 2023

Abstract
The largest polymer flood field in the Middle East is operating since 2010 with more than 45 polymer patterns and around 300 wells. Throughout the last 12 years, the project went through several challenges such as injection uptime sustainability, polymer viscosity stability & polymer pumps uptime which have direct consequences on the field polymer oil recovery.
Running such a complex project requires detailed insight of all the dynamics of fluid movement. To understand the reservoir’s behavior, a thorough surveillance plan is carried out each year enabling the team to review the performance and take proper actions accordingly. There has been a significant amount of data collected to monitor the polymer performance from both surface and subsurface aspects such as wells production data, polymer and water injection rates, polymer viscosities, back produced polymer concentration, static and dynamic pressure, injection tubing head pressure, polymer daily consumptions and many others.
The big flood of data requires to be analyzed periodically. To achieve this, the team has come up with a set of automated tools to aid in visualizing the performance of the polymer flood at any point of time. The tools have pre-set logics that pull surveillance data and produces them as readable charts and tables, which helps the team to focus the efforts to the areas of concern.
In addition, the follow up on wells and patterns is run through a logic called exceptional based surveillance (EBS), where alarms are triggered when something falls out of the pre-set operating envelope.
All of the above has led to realizing more recovery from the polymer flood. The surveillance activities have demonstrated clearly how the data-driven and focused efforts lead to better performance. Alongside operational excellence, an oil gain of 50% was achieved in the last 3 years.
The paper will showcase examples of the data acquired, analyses done, and actions implemented to achieve oil gain.
Autonomous Parametric and Machine Learning Estimation Models Revolutionize Capital Expenditure Process of Engineering and Construction Project in Pttep
Authors: Srivaranon Suchart 
Published: October 2023

Abstract
Brownfield Cost Excellence is one of PTTEP digital transformation project; to resolve pain points of project cost estimation process for engineers. This project contains 2 development phases, 1st Phase (in 2022) is to provide the calculative features of cost estimator using autonomous parametrical techniques modelled by cost estimation relationships (CERs) of data with dependent variables by plotting the relationships of data on TIBCO Spotfire and using Natural Language Processing (NLP) such as IronPython and R-script to build up calculative features and shaping up Parametric equations of Major Equipment and key bulk material cost curves with prediction of residual.
In 2023, 2nd Phase project is focusing on development of cost estimation web-based application which have 6 modules to deliver Major Equipment cost, Bulk Material cost, Construction cost, Tariff cost, Allocation cost including Contingency cost. The calculative of Tariff cost (PTTEP engineer man-hour) is delivered by Machine Learning algorithms which developed by Multiple Regression Equations by joining of 3 different data sources (PTTEP TimeWrite, Project Tracking Information and historical transactions of project expense from SAP). The calculative of allocation cost (builds from historical transaction data of Helicopter/Vessel transportation gathering from legacy logistic system of PTTEP) is also delivered by Machine Learning algorithm developed by connecting of several data sources as similar as the development of Tariff algorithm. Lastly, the contingency cost is aiming to be developed further by collecting gaps between estimated project cost versus actual spending cost of project to diversify its distribution prior to statistically construct contingency algorithm based on each type of project. Ending results given from the cost estimation web-based application are readiness for budget preparation of engineering and construction project which is compiled to AACE estimate class III (Typical purpose of Brownfield cost excellence project is for preparing budget of engineering and construction project in the accuracy range of +30%/-20% in a proper manner).
A Data-Driven Approach for Forecasting Static Bottom Hole Pressure Using Machine Learning Methods
Authors: Alasiri S. , Ureiga S. , Madan M. 
Published: October 2023

Abstract
This research is proposing a new machine-learning approach for predicting static bottom hole pressure (SBHP) using localized injection and production ratio (IPR) data and related parameters. The methodology identifies key features, compares several machine learning techniques, and demonstrates the potential of the suggested approach to improve the precision and effectiveness of SBHP estimate using the available dataset. The goal is to offer an innovative methodology that can help with better SBHP forecasting using the data already available.
The machine learning approach involves a comprehensive methodology for forecasting SBHP in individual wells. Key parameters considered in this study include well depth, reservoir and wellbore properties, production and injection rates, time, and various flow and pressure data.
This research incorporated various types of features, including static, dynamic, numerical, and categorical data. To ensure optimal performance of the machine learning model, the data underwent preprocessing. The methodology employed decision trees, random forests, and gradient boosting as machine learning algorithms. The evaluation of the model involved the utilization of the coefficient of determination (R-squared), mean squared error (MSE), and root mean squared error (RMSE). Training the model relied on a dataset comprising more than 13,000 data points gathered from diverse fields and wells.
The results of this study indicate the precision of machine learning algorithms in forecasting static bottom hole pressure (SBHP) using individual well injection/production ratio and other essential parameters. By combining static, dynamic, numerical, and categorical the proposed approach achieved a remarkable 97% accuracy in SBHP prediction. Decision trees, random forests, and neural networks, which were the machine learning algorithms employed in this research, demonstrated encouraging outcomes in SBHP forecasting. Notably, gradient boosting regressor algorithm exhibited superior performance, delivering highly accurate SBHP predictions.
Integrated Solution using Coriolis Flowmeter, Borehole Image, and Deep Shear Wave Imaging to Cross-Validate Open Fractures in Tight Carbonate Reservoirs
Authors: Nguyen Kim Long , Fahmy Mahmoud Fawzy , Al-Mutairi Nami , Al-Morakhi Rasha , Dasma Mohammed , Al-Mijrin Lulwa , Quttainah Riyad , Bouchou Ramdane , Kerrouche Nawal , Chenglong Wang , Ousididene Karim , El-Masry Mohab 
Published: October 2023

Abstract
Wellbore natural fracture systems characterization is a key element to achieve the successful exploitation of tight reservoirs, where one of the dominant aspects is permeability. Carbonate reservoir fracture evaluation is challenging in terms of type, density, aperture, and extension. Traditionally, fractured reservoirs are evaluated based on Borehole imaging (BHI) data. However, the borehole drilled with oil-based mud (OBM) poses constraints in the image quality of the logs. The oil-based mud has higher resistivity than the conductive mud that limits the depth of investigation and reduces the ability to identify surface features and formations. Ultrasonic imaging tools provide higher resolution than OBM imaging tools due to its capability to read deeper into the formation. However, the ultrasonic imaging tools are specialized equipment with high operational cost and the quality of measurement is affected due to noises from drilling or other sources. Moreover, the meaning of features observed from image logs is a matter of interpretation at the borehole wall only. This introduces a degree of uncertainty, which might be greatly reduced by integrating other measurements such as Deep Shear Wave Imaging (DSWI) & Coriolis micro mud-loss detection data. The first is to map the near borehole environment for fractures as well as their extension far away from the borehole (within ~150 ft vicinity), while the latter provides information on the presence of open fractures while drilling by detecting minor well kicks and mud-losses. Therefore, integration of the multiple sources of information significantly enhances the benefits of all.
This paper aim to present an integrated workflow, which combines above-mentioned methods to enhance the confidence of fracture interpretation at & away from the wellbore, resulting in a detailed description of the natural fracture system in a deviated well drilled through a tight carbonate reservoir in Kra Al-Maru (KM) field recently that supports the development strategy.