bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Data Driven Machine Learning Approach to Predict the Nuclear Magnetic Resonance Porosity of the Carbonate Reservoir
Authors: Ayyaz Mustafa Ayyaz , Zeeshan Tariq Zeeshan , Mohamed Mahmoud Mohamed , Abdulraheem Abdulazeez 
Published: February 2022

Abstract
Carbonate rocks have a very complex pore system due to the presence of interparticle and intra-particle porosities. This makes the acquisition and analysis of the petrophysical data, and the characterization of carbonate rocks a big challenge. Neutron porosity log and sonic porosity logs are usually considered as less accurate compared to the NMR porosity. Neutron-density porosity depends on parameters related to rock matrix which cause the inaccurate estimation of the porosity in special cases suchlike dolomitized and fractured zone. Whereas NMR porosity is based on the amount of hydrogen nuclei in the pore spaces and is independent of the rock minerals and is related to the pore spaces only.
In this study, different machine learning algorithms are used to predict the Nuclear Magnetic Resonance (NMR) porosity. Conventional well logs such as Gamma ray, neutron porosity, deep and shallow resistivity logs, sonic traveltime, and photoelectric logs were used as an input parameter while NMR porosity log was set as an output parameter. More than 3500 data points were collected from several wells drilled in a giant carbonate reservoir of the middle eastern oil reservoir. Extensive data exploratory techniques were used to perform the data quality checks and remove the outliers and extreme values. Machine learning techniques such as random forest, deep neural networks, functional networks, and adaptive decision trees were explored and trained. The tuning of hyper parameters was performed using grid search and evolutionary algorithms approach. To optimize further the results of machine learning models, k-fold cross validation criterion was used. The evaluation of machine learning models was assessed by average absolute percentage error (AAPE), root mean square error (RMSE), and coefficient of correlation (R).
The results showed that deep neural network performed better than the other investigated machine learning techniques based on lowest errors and highest R. The results showed that the proposed model predicted the NMR porosity with an accuracy of 94% when related to the actual values. In this study in addition to the development of optimized DNN model, an explicit empirical correlation is also extracted from the optimized model. The validation of the proposed model was performed by testing the model on other wells, the data of other wells were not used in the training.
This work clearly shows that computer-based machine learning techniques can determine NMR porosity with a high precision and the developed correlation works extremely well in prediction mode.
Local Manufacturing of Oilfield Chemicals- Mitigating Potential Future Disruptions While Supporting Local Content Program
Authors: Al-Abdullatif Abeer , Khan Aman Ullah , Ibrahim Awaji Ayoub , Alhussain Abdulhakim , Garwan Ahmed 
Published: February 2022

Abstract
Halliburton uses the term "local content" to describe programs to develop and use local resources in providing our services in that host country. Local content requirements vary by country, but commonly include providing employment opportunities to local citizens, procurement of goods and services from within the country, manufacturing and value addition through partnerships with and development of local entities, training programs to develop the technical skills of local individuals and businesses, and carry out research and development for finding indigenous alternates of imported materials. In almost all cases, local content requirements are mandated by the laws of the countries where we operate.
Adhering to and improving local content is an important part of Halliburton's commitment to support the countries in which it operates and it also brings benefits for both Halliburton and local communities. In this paper, we share a case study of how Halliburton carried out the process of localizing chemical manufacturing in Saudi Arabia, the steps taken, and support extended in developing the local suppliers. Meeting local content requirements requires precise collaboration and communication between regional and country management teams, compliance with host country laws and regulations, adherence to Halliburton company policies, and meeting the quality standards the National Operating Company which is the end user in most cases. The methodology for achieving effective localization results started with setting the right strategy and putting challenging but achievable targets.
This localization initiative exemplifies company efforts to create value in every aspect of the company's business through the In-Kingdom Total Value Add (iktva) program mandated by the national operating company in the country.
Having a local source of manufacturing and supply chain mitigates any disruptions like the one we saw during COVID 19 whereby the movement across borders was partially closed and supply chain globally was disrupted. Any local souring effectively diminishes the impact of any such disruptions.
This initiative considered more than 50 Halliburton commercial chemical products and resulted in more than 10 successful replacements. Halliburton was able to export three products to company's operations outside Saudi Arabia. Partnering with Saudi Aramco, chamber of commerce and local manufacturers and suppliers in this program will drive additional domestic value creation to support a rapidly changing economic environment and foster future prosperity.
Out of Distribution Detection of Well Logs for AI-Assisted Formation Evaluation
Authors: Stuov Nikolay , Zaytsev Alexey , Ismailova Leyla , Kovalev Dmitry , Katterbauer Klemens , Marsala Alberto 
Published: February 2022

Abstract
In formation evaluation, we assess and predict the structure of rock formations and saturating hydrocarbon distribution by comparing and determining the similarity between different well properties. For similar wells, in a specific field and geological context, there is an expectation that the subsurface reservoir properties around the wells are similar or share similar characteristics.
In many cases, it is more important to look not directly at similarity, but at how anomalous a well or a well interval is. We develop a data-based model for novelty and outlier detection procedures for oil&gas well intervals. The model constructs scores for measuring a similarity between wells using well logs data. Then, it uses these scores to identify anomalous wells and well intervals. Technical, our approach consists of two steps. In the first step, we construct a representation of well intervals using a self-supervised autoregressive approach. In the second step, we calculate similarity scores for outlier detection based on these representations. The representation of the complete well interval is a mixture of the representations for the various intervals.
The results demonstrate the strong performance of the proposed framework. We evaluated different machine learning models in terms of their performance and analyzed their estimates in a self-supervised learning problem on predicting well log measurement ranges. For out-of-distribution detection based on multiple wells log data, deep neural network-based methods provide significantly better performance than simpler models, such as linear regression. Analysis of the neural network algorithms shows that they can represent the well logs whose embeddings adequately represent the wells.
The developed similarity learning framework presents a first and innovative approach to determine the specific distribution for subsurface reservoir properties. It will help petrophysicists with well logs correlation and interpretation and serve as a recommendation engine for assisted formation evaluation.
Application of Xgboost Algorithm in Rate of Penetration Prediction with Accuracy
Authors: Zhou Fei , Fan Honghai , Liu Yuhan , Ye Yuguang , Diao Haoyu , Wang Zhifa , Rached Rached , Tu Yulin , Davio Eduardo 
Published: February 2022

Abstract
In oil and gas exploration, rate of penetration (ROP) is one of the most important parameters that affect drilling costs and drilling performance. Therefore, prediction of rate of penetration (ROP) with accuracy has become an important direction of current drilling work. In order to solve the problem that the existing models are not accurate enough to predict the ROP, this paper proposes an ensemble learning approach called extreme gradient boosting (XGBoost) model.
In this paper, in addition to the XGBoost model, different models were constructed through various approaches, such as traditional theoretical/empirical models (Hareland model and Motahhari model) and machine learning models (support vector regression (SVR), artificial neural network (ANN) and k-nearest neighbor (KNN)). Then, the accuracy of the constructed models was compared with each other. Aiming at the problem of ROP prediction, the historical data of a specific field that has been collected is mined. The input data for training these models includes mud logging parameters and geomechanical parameters. Before data is entered into the model, preprocessing is required. After normalizing the input data, ategorical variables, and use One-hot encoding method for converting categorical variables into a form which can be used in machine learning models. These data were used as input variables, and ROP is the output variable for modeling. Useing the GridSearchCV tool in scikit-learn to perform grid search and cross-validation to find the optimal parameters of the model.
Comparison of accuracy and computational performance between above models for rate of penetration prediction, the results show that the XGBoost model with prediction accuracy of 85%, the accuracy of other models is 48% (Hareland model), 45% (Motahhari model),51% (KNN), 52% (ANN)and SVR (55%), respectively. It is obviously that XGBoost model has higher accuracy than traditional theoretical/empirical methods and other machine learning models, indicating that XGBoost model has stronger ability to deal with high-dimensional and non-linear problems. XGBoost model can be used to predict the ROP at the specific field reliably and accurately provided the model be developed with enough data, which can provide a scientific and reliable reference for reducing drilling costs and improving drilling performance.
The Contribution of Virtual Reality in Awareness and Preparedness of Oil and Gas Professionals
Authors: Kokkinos Nikolaos , Mitropoulos Athanassios 
Published: February 2022

Abstract
Human factor played a dominant role in the majority of the past technological accidents. Ignorance, carelessness or even worse negligence by operators were only some reasons of those accidents. Chemical industry keens on new techniques for optimizing human and environmental safety. The main scope of this study is to examine the contribution of virtual reality in awareness and preparedness of oil and gas industry professionals in emergencies. The methodology based on an in-situ threefold assessment process of the chemical industry staff on virtual reality high tech facilities at the Department of Chemistry of International Hellenic University. Firstly, the control room operations of the examinees were assessed in a state of the art Virtual Control Room (VCR) environment. Then, participants’ interaction with faults, incidents and accidents was examined in a Virtual Reality Oilfield (VRO) using wearable technology. Finally, examinees’ skills in crisis management were evaluated in a Virtual Dispatch Center (VDC). Various emergency what-if scenarios were investigated which were not hypothetical or fiction products, but rather the result of long-term interviews with industry executives, field operators and control room operators in order to reproduce accurately, efficiently and effectively near-misses, dangerous incidents and accidents. Useful results were drawn regarding the behavior of the participants during emergencies, their teamwork performance and their interaction with the virtual system.
Digital Twin of Expensive Multiphase Flow Loop Test to Develop Next Generation of Production Technologies
Authors: Karimi Muhammad Akram , Arsalan Muhammad , Shamim Atif 
Published: February 2022

Abstract
Multiphase flow is frequently encountered in upstream O&G industry that has significant impact on the development of numerous production technologies such as multiphase flowmeter. Before the deployment of these technologies in an oil/gas field, the technologies are tested in a multiphase industrial flow loop test that emulates multiphase test conditions. This paper presents a digital twin of 2-phase flow (oil & water) as a low cost alternative to expensive multiphase flow test.
We have adopted backward strategy to design the digital twin of multiphase flow. At first, we characterized our proprietary microwave water-cut (WC) meter in an industrial flow loop in variable test conditions. Then, multiple digital models of the flow regimes were built and tested on our microwave WC meter. One of those models (rotated zigzag) was able to accurately predict WC sensor response over full WC range in oil continuous as well as water continuous flow conditions under varying salinity levels.
Two sets of responses have been recorded and compared – first obtained from the industrial flow loop trials and second from our EM simulation model. Key microwave resonator parameters such as resonant frequency (f0) and quality (Q) factor have been compared under varying conditions. The comparison suggests that f0 & Q-factor give higher sensitivity against WC in oil continuous and water continuous flow conditions respectively. Moreover, WC sensor performance was also compared under varying salinity conditions in the range of 20,000 ppm to 80,000 ppm and digital twin is able to successfully predict the sensor response in these conditions as well.
Significant amount of resources are spent on setting desired flow condition such as flow regime, WC and required salinity level. Our proposed digital twin model is able to emulate all of these multiphase flow conditions at negligible cost. It can help develop & test new production technologies without requiring to spend huge amount of money on lengthy, complex and expensive multiphase flow loop tests.
Segmentation of X-Ray Images of Rocks Using Supervoxels Over-Segmentation
Authors: Alqahtani Hussain , Alqahtani Naif , Armstrong Ryan T. , Mostaghimi Peyman 
Published: February 2022

Abstract
Digital core analysis has gained the interest of many scientific communities because of its impact on our understanding of flow in porous media. A typical workflow in digital core analysis includes scanning, reconstruction, denoising, segmentation, and modeling. Image analysis and modeling highly depend on the quality of the segmentation step. In this regard, conventional image segmentation methods often require user input/interference. This results in user bias and may produce a range of possible segmentation outcomes. To address this, we propose an unsupervised machine learning framework that offers multiple functionalities including improved mineral and micro-porosity identification. Superpixel (2D) and (3D) work by over-segmenting greyscale images using a family of over-segmentation algorithms. Simple Linear Iterative Clustering (SLIC) is one of these algorithms that is recognized for its speed and memory efficiency. The proposed framework utilizes SLIC and unsupervised clustering methods for segmenting greyscale images. SLIC divides the 2D and 3D images into segments having pixels (or voxels) with similar features (i.e., intensity range). Statistical features of each segment are computed and used for identifying the segment label through unsupervised clustering techniques. The unsupervised voting clustering implements a majority voting policy from multiple clustering algorithms including Hierarchical clustering and k-means clustering. A North Sea sandstone 2D X-ray image along with its SEM image were used to validate this framework. Different metrics were used to measure the accuracy of the X-ray segmentation with SEM segmentation. Our results show a mean Jaccard index of 70% and a mean Dice index of 81%. The same workflow is applied using supervoxels on a high-resolution 3D Indiana Limestone image and the results show similar accuracy margins compared to watershed segmentation. Comparison with other segmentation methods shows an average Jaccard score of 74% and an average Dice index score of 83%. To the best of our knowledge, this is the first application of superpixels over-segmentation algorithms in semantic segmentation of X-ray micro-CT images of porous media. The findings of this study highlighted the advantage of these algorithms in detecting sub-resolution porosity regions in greyscale images and obtaining accurate multi-label segmentation.
Utilizing Immersive Technology to Enhance Plant Operations and Speed Up Knowledge Transfer
Authors: Balhareth Hamad 
Published: February 2022

Abstract
The objective of this paper is to share current operational challenges concerning Oil and Gas (O&G) operators, and to suggest certain solutions/applications from the IR4.0 technology toolbox. Augmented Reality/Virtual Reality (AR/VR) will be discussed to address the operational challenges detailed in this paper, while simultaneously showing how AR/VR can be leveraged to speed up knowledge transfer process, especially at the current alarming rates of attritions.
The proposed approach is to first provide clear and concise explanation of the current challenges from an O&G operational point of view. Next, AR/VR will be explained and clearly defined to show its general benefits, before zooming in to innovate, and capitalize on specific features to address the challenges at hand. AR/VR can play a key role in managing other installed technologies (i.e. artificial intelligence, mobility, etc.) to transfer essential data to proper personnel at the right time and location. In other words, AR/VR can link up to all other applications and create one central hub to organize, streamline, and create actionable data in any facility. While showing the operational benefits, AR/VR will also be discussed to show how it can speed up knowledge transfer and reduce training costs, especially when software developers work together with O&G technologists to create meaningful product, rather than a generic one that truly serves no one.
The result is showing that AR/VR can clearly solve and enhance O&G operations by delivering important prescriptive messages with knowledge-based actions to concerned parties at the proper location, so they can take timely, proactive actions. Simultaneously, acting ahead of time translates into reduced plant upsets, while increasing safety and well-being of all plant personnel.
Most O&G companies employ stat-of-the-art technologies to address challenges affecting various parts of their operating facilities. Integrating these technologies and allowing operators to organize and strategize where and how to start, and showing potential links is itself a challenge. AR/VR can address this predicament, if employed properly. This paper will address AR/VR implicit and explicit capabilities in more details.
The novelty of this paper is not in presenting AR/VR technology, as it has been around for several years, but rather in showing how to design an agile system to take advantage of the technology. The newly designed system can incorporate several scattered software applications across a facility; therefore AR/VR will act as an orchestrator by managing the flow of data and ensuring they reach the right personnel at the right time.
Halliburton Saudi Arabia Crisis Management and Lessons Learn During COVID-19 Pandemic in the Kingdom of Saudi Arabia
Authors: Albish Roa'a , Al-Qarni Fahad , Al-Zaidy Khalid 
Published: February 2022

Abstract
During the first quarter of 2020, the world encountered a crucial and unprecedented health crisis. The global transmission of COVID-19 poses a significant challenging situation for Oil and Gas industry, particularly in the absence of standardized procedures and recognized methods. Like many other countries worldwide, Saudi Arabia implemented the lockdown for utmost public and private services and controlled population movement through curfew. With the execution of these tight mitigation requirements, Halliburton Saudi Arabia has been able to maintain business continuity by looking at the basic approach of health, safety, and environmental (HSE) processes through crisis management decision making and utilizing digital solutions.
The purpose of this paper is to showcase how Halliburton Saudi Arabia developed sustainable adjustable process and methods that reduced exposure and the pandemic-related potential risks associated with working in offices, rig sites, workshops, and laboratories while maintaining business continuity in operation, manufacturing, and technology.
Halliburton Saudi Arabia preformed risk analysis, tracking systems, exposure modification methodologies, communication strategies and management decisions that helped the company overcome challenges during the pandemic.
The implementation of risk assessments, adaptable safety procedures and utilizing more than 5 digital platforms, served Halliburton employees and its work force throughout 2021 and into 2032. In this paper, we share lessons learned during the pandemic, how we overcame the unprecedented health crisis and how we continue to deal with the pandemic impact.