bluuebunny/crossref_metadata_2025_split huggingface dataset:
Machinery Fault Detection Through Ultrasound Technology
Authors: Alousif Ali , Alali Saad 
Published: December 2021

Abstract
Ultrasound is a versatile advanced technology that is utilized in the oil and gas industry for various mechanical and electrical applications such as bearing's faults detection, pump's cavitation, valve's leakage, steam traps, electrical faults, gearbox's issues, compressed air and gas leak's detection..etc. The technology allows the end-user to measure dynamic data using contact (Structure borne) and non-contact (air borne) sensors and converts the ultrasound waves to an audible range for humans to associate sounds with the measured signal. As a result, the sound of the machine can be heard and recorded as voice clip as well as time wave form, which in turn can be translated into frequency spectrum for analysis. The technology has recently evolved in the industry as an important condition monitoring tool, to increase the reliability of rotating equipment. Moreover, it used as a complementary tool to vibration analysis. As well, it can be used as a tool for troubleshooting and preventive maintenance inspection.

Background
Ultrasound is sound waves with frequencies that are higher than the upper audible limit of human hearing. The human hearing limit varies from person to another, and it is approximated to be around 20Hz to 20 kHz. This is in contrary to the ultrasound range, which is above 20,000 Hz, and hence, it is in audible to human. This range is used widely in various industrial processes, including: cleaning, cutting, forming, testing of materials, and welding. It is characterized by its directional waves, unlike normal sound waves that travel in all directions. This directional characteristic makes ultrasound useful for many applications. Furthermore, ultrasound technology is used in different fields: medical, automotive, etc. and recently in the oil and gas industry as non-destructive-testing tool (NDT).

The ultrasound technology in the oil and gas industry is used primarily in the following area's, for example
Leak detection. Steam traps inspection. Bearing condition monitoring. Bearing lubrication monitoring. Electrical Inspection. Valve condition monitoring. Pump cavitation. Gearbox issues.
Coupled Effect of Imbibition Capillary Pressure and Matrix-Fracture Transfer on Oil Recovery from Dual-Permeability Reservoirs
Authors: AlRamadhan Aymen , Cinar Yildiray , Hussain Arshad , BuKhamseen Nader 
Published: December 2021

Abstract
This paper presents a numerical study to examine how the interplay between the matrix imbibition capillary pressure (Pci) and matrix-fracture transfer affects oil recovery from naturally-fractured reservoirs under waterflooding. We use a dual-porosity, dual-permeability (DPDP) finite difference simulator to investigate the impact of uncertainties in Pci on the waterflood recovery behavior and matrix-fracture transfer. A comprehensive assessment of the factors that control the matrix-fracture transfer, namely Pci, gravity forces, shape factor and fracture-matrix permeabilities is presented. We examine how the use of Pci curves in reservoir simulation can affect the recovery assessment. We present two conceptual scenarios to demonstrate the impact of spontaneous and forced imbibition on the flood-front movement, waterflood recovery processes, and ultimate recovery in the DPDP reservoir systems of varying reservoir quality.
The results demonstrate that the inclusion of Pci in reservoir simulation delays the breakthrough time due to a higher displacement efficiency. The study reveals that the matrix-fracture transfer is mainly controlled by the fracture surface area, fracture permeability, shape factor, and the uncertainty in Pci. We underline a discrepancy among various shape factors proposed in the literature due to three main factors: (1) the variations in matrix-block geometries considered, (2) how the physics of imbibition forces that control the multiphase fluid transfer is captured, and (3) how the assumption of pseudo steady-state flow is addressed.
Human Factors in Domain Adaptation Within the Oil and Gas Industry
Authors: Ershaghi Iraj , Ershaghi Milad A. , Al-Ruwai Fatimah 
Published: December 2021

Abstract
A serious issue facing many oil and gas companies is the uneasiness among the traditional engineering talents to learn and adapt to the changes brought about by digital transformation. The transformation has been expected as the human being is limited in analyzing problems that are multidimensional and there are difficulties in doing analysis on a large scale. But many companies face human factor issues in preparing the traditional staff to realize the potential of adaptation of AI (Artificial Intelligence) based decision making.
As decision-making in oil and gas industry is growing in complexity, acceptance of digital based solutions remains low. One reason can be the lack of adequate interpretability. The data scientist and the end-users should be able to assure that the prediction is based on correct set of assumptions and conform to accepted domain expertise knowledge. A proper set of questions to the experts can include inquiries such as where the information comes from, why certain information is pertinent, what is the relationship of components and also would several experts agree on such an assignment. Among many, one of the main concerns is the trustworthiness of applying AI technologies
There are limitations of current continuing education approaches, and we suggest improvements that can help in such transformation. It takes an intersection of human judgment and the power of computer technology to make a step-change in accepting predictions by (ML) machine learning. A deep understanding of the problem, coupled with an awareness of the key data, is always the starting point. The best solution strategy in petroleum engineering adaptation of digital technologies requires effective participation of the domain experts in algorithmic-based preprocessing of data. Application of various digital solutions and technologies can then be tested to select the best solution strategies. For illustration purposes, we examine a few examples where digital technologies have significant potentials. Yet in all, domain expertise and data preprocessing are essential for quality control purposes
Enhancing Stratigraphic Framework Consistency Using Spectral Gamma-Ray Data
Authors: Nugraha Reza Satria , Tumbarinu Oliver Esteva 
Published: December 2021

Abstract
Stratigraphic correlation is crucial for reservoir characterization; therefore, it requires more advanced methods and techniques to reduce the stratigraphic correlation uncertainty, especially when variation in lateral facies is high. The studied formations from bottom to top consist of fluvial to marginal marine X Formation, shallow marine Y Formation, and fluvial distributary channels to estuarine Z Formation. Spectral gamma-ray logs give additional consistent information on lithological composition that can support identification of boundary between formations within the stratigraphic framework.
Wells with a full section of Y Formation, core, palynology, and spectral gamma-ray were selected as key wells. The top and base of the Y Formation were picked using conventional logs refined by a thorium/potassium (Th/K) ratio log and cross plot with core and palynology data as validations. The internal Y Formation markers were also picked with the aid of the Th/K cross plots. The top picking criteria from the key wells was implemented to the rest of the wells across the field with consistency.
The uniform low Th/K ratio log (&lt;3.5) across the Y Formation indicates illite as the dominant clay type, confirmed by X-ray diffraction (XRD) data with an average of more than 80%. The character is consistent with the interpreted depositional environment. This character makes the Y Formation stand out from the overlying Z and the underlying X formations. The change from X to Y Formation is defined by the decrease of the Th/K ratio log, from high kaolinite content to illite dominated environment. Inversely, the top of the Y Formation (base of Z) is indicated by the increase of the Th/K ratio log moving from shallow marine Y Formation to the fluvial-influenced Z Formation. The Th/K cross plot indicates different clusters amongst the studied formations and the internal Y zonation. The X Formation is located in the high Th and low K area where kaolinite is predominant, related to fluvial environment. The case is similar for the Z Formation but with more influence of mixed-clay type. The Y Formation shows clear clustering along the mixed-clay and illite window. Internal Y zonation displays, from bottom to top, an increasing K value within the clusters. This method provides a semi-quantitative interpretation to define the studied formations boundaries and the Y Formation internal zonation.
This study has increased the consistency of the studied formations’ stratigraphic and structural framework. This consistency has, in turn, fine-tuned the structural framework and aided field development through better geosteering and lateral well placements. These results are a valuable starting point to refine and extend the work to other areas.
Seismic Wave Simulation in Fractured Media
Authors: Zhang Houzhu , Li Jiaxuan , Ali Abdulmohsen 
Published: December 2021

Abstract
Fractured reservoirs, including unconventional fields, are important in global energy supply, particularly for carbonate source rocks. Fractures can influence subsurface fluid flow and the stress state of a reservoir. The knowledge about the existence of fractures, their spatial distributions, and orientations can help us optimize well productivity and reservoir performance. Seismic detection of subsurface fractures provides important measurements to remotely image field-scale fractures. In developing such technology, forward modeling of the seismic response from fractures in the reservoir provides an important alternate tool for imaging subsurface fractures. In this paper, we implement a seismic modeling algorithm which can simulate 3D wave propagation in an arbitrary background media with imbedded fractures. During modeling, the fractures are added to the background medium by linear slip theory. Examples demonstrated the impacts of fractures on the wave propagation patterns for both PP and PS waves. We also investigate the amplitude versus offset (AVO) effects caused by fractures in a layer media and lay out potential applications of forward modeling in the inversion of fracture parameters and the estimation of fluid contents.
Integrated Cloud Computing Environment for Upstream Geoscience Workflows
Authors: Al-Habib Murtadha , Al-Ghamdi Yasser 
Published: December 2021

Abstract
Extensive computing resources are required to leverage todays advanced geoscience workflows that are used to explore and characterize giant petroleum resources. In these cases, high-performance workstations are often unable to adequately handle the scale of computing required. The workflows typically utilize complex and massive data sets, which require advanced computing resources to store, process, manage, and visualize various forms of the data throughout the various lifecycles. This work describes a large-scale geoscience end-to-end interpretation platform customized to run on a cluster-based remote visualization environment.
A team of computing infrastructure and geoscience workflow experts was established to collaborate on the deployment, which was broken down into separate phases. Initially, an evaluation and analysis phase was conducted to analyze computing requirements and assess potential solutions. A testing environment was then designed, implemented and benchmarked. The third phase used the test environment to determine the scale of infrastructure required for the production environment. Finally, the full-scale customized production environment was deployed for end users.
During testing phase, aspects such as connectivity, stability, interactivity, functionality, and performance were investigated using the largest available geoscience datasets. Multiple computing configurations were benchmarked until optimal performance was achieved, under applicable corporate information security guidelines.
It was observed that the customized production environment was able to execute workflows that were unable to run on local user workstations. For example, while conducting connectivity, stability and interactivity benchmarking, the test environment was operated for extended periods to ensure stability for workflows that require multiple days to run.
To estimate the scale of the required production environment, varying categories of users’ portfolio were determined based on data type, scale and workflow. Continuous monitoring of system resources and utilization enabled continuous improvements to the final solution.
The utilization of a fit-for-purpose, customized remote visualization solution may reduce or ultimately eliminate the need to deploy high-end workstations to all end users. Rather, a shared, scalable and reliable cluster-based solution can serve a much larger user community in a highly performant manner.
New Catalyst-Free Polycrystalline Diamond with Industry-Record Wear Resistance
Authors: Zhan Guodong David , Li Bodong , Moellendick Timothy Eric , He Duanwei , Xu Jianhui 
Published: December 2021

Abstract
PDC drill bits are the primary drilling tools for oil and gas in most of formations. In a PDC drill bit, PDC cutters are key cutting components to engage with these formations. However, there is often a big challenge for today's PDC drill bits when drilling very hard and abrasive formation. The main weakness in the PDC cutter is due to the unavoidable use of metallic catalyst which is used to bond the diamond grains in the PDC cutters. The thermal expansion of the metallic catalysts resulting from high frictional heat at the cutter/rock interface during drilling operation is higher than that of diamond grains, causing the thermal stress between the metallic catalyst and diamond grain which can break the PDC cutter. Therefore, development of catalyst-free PDC cutters would be a game-changing technology for drill bit by delivering significant increase in performance, durability, and drilling economics. In this study, an innovative ultra-high pressure and ultra-high temperature technology was developed with ultra-high pressures up to 35 GPa, much higher than current PDC cutter technology. We report a new type of catalyst-free PDC cutting material, synthesized under one of conditions using ultra-high pressure of 16 GPa. The new material breaks all single-crystal-diamond indenters in Vickers hardness testing which sets a new world record as the hardest diamond material as of today. Also, the material shows the highest thermal stability in the family of diamonds in air at 1,200°C, which is about 600 °C higher than current PDC cutters. As a consequence of these superior properties, this new material exhibited industry-recorded wear resistance, which is four times of that of current PDC cutters. All of these achievements demonstrated a breakthrough in PDC cutter technology development and presented a feasibility for the goal of "One-Run-To-TD" game-changing drilling technology.
First-Break Picking Classification Models Using Recurrent Neural Network
Authors: Ayub Mohammed , Kaka SanLinn 
Published: December 2021

Abstract
Manual first-break picking from a large volume of seismic data is extremely tedious and costly. Deployment of machine learning models makes the process fast and cost effective. However, these machine learning models require high representative and effective features for accurate automatic picking. Therefore, First- Break (FB) picking classification model that uses effective minimum number of features and promises performance efficiency is proposed. The variants of Recurrent Neural Networks (RNNs) such as Long ShortTerm Memory (LSTM) and Gated Recurrent Unit (GRU) can retain contextual information from long previous time steps. We deploy this advantage for FB picking as seismic traces are amplitude values of vibration along the time-axis. We use behavioral fluctuation of amplitude as input features for LSTM and GRU. The models are trained on noisy data and tested for generalization on original traces not seen during the training and validation process. In order to analyze the real-time suitability, the performance is benchmarked using accuracy, F1-measure and three other established metrics. We have trained two RNN models and two deep Neural Network models for FB classification using only amplitude values as features. Both LSTM and GRU have the accuracy and F1-measure with a score of 94.20%. With the same features, Convolutional Neural Network (CNN) has an accuracy of 93.58% and F1-score of 93.63%. Again, Deep Neural Network (DNN) model has scores of 92.83% and 92.59% as accuracy and F1-measure, respectively. From the pexperiment results, we see significant superior performance of LSTM and GRU to CNN and DNN when used the same features. For robustness of LSTM and GRU models, the performance is compared with DNN model that is trained using nine features derived from seismic traces and observed that the performance superiority of RNN models. Therefore, it is safe to conclude that RNN models (LSTM and GRU) are capable of classifying the FB events efficiently even by using a minimum number of features that are not computationally expensive. The novelty of our work is the capability of automatic FB classification with the RNN models that incorporate contextual behavioral information without the need for sophisticated feature extraction or engineering techniques that in turn can help in reducing the cost and fostering classification model robust and faster.
Uncertainty Quantification and Optimization of Deep Learning for Fracture Recognition
Authors: Santoso Ryan , He Xupeng , Alsinan Marwa , Kwak Hyung , Hoteit Hussein 
Published: December 2021

Abstract
Automatic fracture recognition from borehole images or outcrops is applicable for the construction of fractured reservoir models. Deep learning for fracture recognition is subject to uncertainty due to sparse and imbalanced training set, and random initialization. We present a new workflow to optimize a deep learning model under uncertainty using U-Net. We consider both epistemic and aleatoric uncertainty of the model. We propose a U-Net architecture by inserting dropout layer after every "weighting" layer. We vary the dropout probability to investigate its impact on the uncertainty response. We build the training set and assign uniform distribution for each training parameter, such as the number of epochs, batch size, and learning rate. We then perform uncertainty quantification by running the model multiple times for each realization, where we capture the aleatoric response. In this approach, which is based on Monte Carlo Dropout, the variance map and F1-scores are utilized to evaluate the need to craft additional augmentations or stop the process. This work demonstrates the existence of uncertainty within the deep learning caused by sparse and imbalanced training sets. This issue leads to unstable predictions. The overall responses are accommodated in the form of aleatoric uncertainty. Our workflow utilizes the uncertainty response (variance map) as a measure to craft additional augmentations in the training set. High variance in certain features denotes the need to add new augmented images containing the features, either through affine transformation (rotation, translation, and scaling) or utilizing similar images. The augmentation improves the accuracy of the prediction, reduces the variance prediction, and stabilizes the output. Architecture, number of epochs, batch size, and learning rate are optimized under a fixed-uncertain training set. We perform the optimization by searching the global maximum of accuracy after running multiple realizations. Besides the quality of the training set, the learning rate is the heavy-hitter in the optimization process. The selected learning rate controls the diffusion of information in the model. Under the imbalanced condition, fast learning rates cause the model to miss the main features. The other challenge in fracture recognition on a real outcrop is to optimally pick the parental images to generate the initial training set. We suggest picking images from multiple sides of the outcrop, which shows significant variations of the features. This technique is needed to avoid long iteration within the workflow. We introduce a new approach to address the uncertainties associated with the training process and with the physical problem. The proposed approach is general in concept and can be applied to various deep-learning problems in geoscience.
Unconventional Engineering Toward Efficient Geosteering and Well Placement - Logging-While-Drilling in an Oil-Based Mud Environment
Authors: Almasmoom Salaheldeen S , Santoso Gagok I , Rubaie Naif M , Lagraba Javier O , Stonestreet David B , Faraj Omar A , Belowi Ali , Alomoush Jamal 
Published: December 2021

Abstract
This paper presents a success story of deploying new technology to improve geosteering operations in an unconventional horizontal well. A new-generation logging-while-drilling (LWD) imaging tool, that provides high resolution resistivity and ultrasonic images in an oil-based mud environment, was tested while drilling a long lateral section of an unconventional horizontal well. In addition to improving the geosteering operations, this tool has proven the ability to eliminate the wireline image log requirements (resistivity and ultrasonic), hence reducing rig time significantly. The LWD bottomhole-assembly (BHA) included the following components: gamma ray (GR), density, neutron, resistivity, sonic, density imager, and the newly deployed dual imager (resistivity and ultrasonic). The dual imager component adds an additional 15-ft sub to the drilling BHA, which includes four ultrasonic sensors orthogonal to each other, and two electromagnetic sensors diametrically opposite to each other (reference figure 1).
This new technology was deployed in an unconventional horizontal well to help geosteer the well in the intended zone, which led to an improvement in well placement, enhanced the evaluation of the lateral facies distribution, and allowed better identification of natural fractures. The dual images provided the necessary information for interpreting geological features, drilling induced features, and other sedimentological features, thus enhancing the multistage hydraulic fracturing stimulation design. In addition, an ultrasonic caliper was acquired while drilling the curve and lateral section, providing a full-coverage image of the borehole walls and cross-sectional borehole size.
The unique BHA was designed to fulfill all the directional drilling, formation evaluation and geosteering requirements. A dynamic simulation was done to confirm the required number of stabilizers, and their respective locations within the BHA, to reduce shock and vibration, borehole tortuosity and drilling related issues, thereby improving over-all performance. Real-time drilling monitoring included torque and drag trending, back-reaming practices and buckling avoidance calculations, which were implemented to support geosteering, and for providing a smooth wellbore for subsequent wireline and completion operations run in this well.
A new generation dual-image oil-based mud environment LWD tool was successfully deployed to show the multifaceted benefits of enhanced geo-steering/well placement, formation evaluation, and hydraulic fracturing design in an unconventional horizontal well. Complexities in the multifunctioning nature of the BHA were strategically optimized to support all requirements without introducing any significant risk in operation.