bluuebunny/crossref_metadata_2025_split huggingface dataset:
Mud Loss Prediction in Realtime Through Hydromechanical Efficiency
Authors: Saihood Tariq , Samuel Rebello 
Published: October 2022

Abstract
Improving drilling efficiency is more of a concern as we prepare for fully autonomous drilling. It is evident that increasing the rate of penetration for minimum cost is not always the best solution, as the drilling problems encountered may negate the benefits. One of the significant drilling problems to be considered is mud loss. This study presents a method to evaluate and quantify circulation loss with a specific energy concept.
The methodology is used to compare different wells with and without mud loss conditions using real-time data from two wells. Mechanical specific energy and hydraulic mechanical specific energy (HMSE) using the pressure component were calculated. The unconfined compressive strength pressure, UCS, is also calculated from log data to calculate drilling efficiency. The Realtime loss circulation depths are pointed as the digit [1] for no loss circulation event and digit [0] for loss circulation event. An equation is suggested to relate the loss circulation event to the amount of energy applied to the loss zones.
For the present study, several cases with Realtime data were analyzed. The drillability is calculated and modified to account for the differential pressure effect on the ROP. The drilling efficiency, defined by using UCS to HMSE, is modified to reflect the differential pressure effect showed the mud loss conditions in the wells where the mud loss was observed. Also, the impact of the differential pressure between the bottom hole pressure and the formation pressure is presented in this study. The results show a big difference between the amount of energy applied in the loss formation and the required energy to excavate the formation in the analyzed wells. The calculated loss circulation severity was fingerprinted to estimate the amount of mud loss from daily drilling reports. The method developed also predicted the severity of loss circulation using the HMSE and UCS. The effect of the differential pressure can be estimated in real-time and quantified between the high-pressure depth interval and the low-pressure zones. The proposed workflow was tested on historical wells; the results are compared against the proposed analytical model. The results were also verified with the daily drilling reports from the field to confirm the predictions of the algorithm presented. The results predicted were synchronous.
A new modified approach of using pressure in the mechanical specific energy is conducted. The study suggests monitoring the ratio of the ROP over the modified drilling efficiency. The drilling crew now has the whole picture about all contributed drilling parameters to manage the drilling parameters to improve the drilling efficiency.
Machine Learning Vs. Statistical Models Approach: A Case for Probabilistic AFE Cost in Drilling
Authors: Muhammad Yunus Muhammad Nasir , Wan Ibrahim Wan Mohd Syazwan , Mahadi Ahmad Afiq , Zainalabidin Muhammad Arief Fikry , Aminuddin Muhammad Faizuan , Pratama Edo , Abdul Rahman Mohd Redzuan , Yusoff Ikhwan 
Published: October 2022

Abstract
Traditionally, statistical AFE (Authorization For Expenditure) cost considers the probabilistic variance of time which is can be obtained by carefully using historical data. However, the data pattern may not be well captured to represent a realistic variance of drilling duration due to the nature of different operations complexity. This paper describes the development and comparison of machine learning and statistical models as well as their integration to estimate the drilling duration for probabilistic AFE cost prediction.
Exploratory data analysis and data pattern recognition are performed to model the well's parameters that affect the well drilling duration. Subsequently, the random forest (RF) and multiple linear regression (MLR) models are developed to estimate the well duration for any well complexity. To capture the variation of the input data, Monte Carlo (MC) simulation is then employed to construct 100 simulation cases prepared using the developed RF and MLR models. Finally, the probabilistic variance of drilling duration from these two models are fed into the data-driven models to predict AFE cost.
Based on blind-test results, it is observed that the combination of RF and MC simulation models provides a more realistic variance of drilling duration compared to other models. It can reduce the uncertainty provided by a purely statistical model which is a combination of MLR and MC simulation models. In addition, it also provides a better accuracy against the standalone RF model. Incorporating the probabilistic variance of time from a combined RF and MC models into the random forest structure for well cost prediction resulted in better model performance compared to other data-driven-based well cost models developed using multiple linear regression, decision tree, and artificial neural network algorithms. In terms of the R-square value between predicted and actual costs, the AFE accuracy can be improved from 0.74 to 0.91 using the proposed model. The cumulative density function of the simulated time and cost provides insight to evaluate the drilling duration and AFE cost based on P10, P50, and P90 values. Tens of cases are performed and the support vector machine (SVM) classification plot of the probabilistic AFE cost against historical wells data is generated indicating the range of possible cost clusters.
The proposed methodology enables drilling industry personnel to quickly estimate the probabilistic time and cost of the project during well planning and during drilling using widely available drilling variables. This novel approach provides a realistic and efficient alternative to explore hundreds of scenarios encompassing the range of drilling variables uncertainties in predicting the probabilistic AFE cost.
How to Digitalize Green LNG and Value Carbon-Neutral LNG Cargos with Blockchain Technology and Digital Assets
Authors: Finidori Jean-Christophe JC 
Published: October 2022

Abstract
In this paper we present our Blockchain-based solution to digitalize green LNG (liquefied natural gas); how with our digital asset, we create the differentiated-product carbon-neutrality LNG, leveraging innovation carbon-neutral business models in the natural gas eco-system.
We design digital twins to represent a certain volume of non-fungible gas. We track the digital twin of a molecule, tagged with metadata. The purpose of our technical development is to create a "digital asset" (DA); a virtual representation of a volume of LNG associated with a specific LNG cargo, reflecting specific green attributes. The DA can be split into phases, corresponding to LNG supply chain steps, to represent a specific volume of GHG emitted. Our model incorporates the possibility for LNG stakeholders to offset and compensate GHG emitted they are directly/indirectly responsible for and/or accountable for. Additionally, LNG Stakeholders can interact on a specific volume (parceled), differentiating the same molecules of gas (fungible) with specific green attributes (non-fungible).
We developed innovative digital assets’ features:
– Fractionable (dividable digital asset, for a specific volume of LNG); – Transferable (change of ownership, for traceability along all value chain); – Transactional & Valuable (a Premium offers with evidence of low emission); and, Stackable (rewards mechanisms).
This permits to provide a life-time value oriented service in a dynamic multi-actors model, to incentivize sustainable LNG with a premium, and value non-tangible assets.
Lastly, we programmed a redistribution mechanism, governed by smart contracts, to transfer collected fees (value pool) to local communities, triggering positive impacts; contributing to UN SDGs achievement.
Our Blockchain solution aims to establish a single origin of truth to provide LNG buyers and sellers with a decentralized interoperable view of carbon-neutral LNG cargoes; offering new capabilities to tackle issues LNG players are dealing with: clear delineation, silos approach (vs. collaborative), authoritative single source of information, data transparency, identified legitimate users, added-value generation; premium (attached / detached) in case of re-export, co-purchasing, borrowing & lending; and attribution of benefits of auditable emission intensities, including to big industrial gas consumers.
LNG players can help entities meet their net-zero commitments by sourcing green LNG; supporting companies for their green/carbon neutral initiatives throughout the whole supply chain.
Near-And Remote-Field Eddy Current Data Fusion: Wellbore Casing Inspection with Hybrid Neural Networks
Authors: Ooi Guang An , Mostafa Tarek M. , Khater Moutazbellah , Ozakin Mehmet Burak , Bagci Hakan , Ahmed Shehab 
Published: October 2022

Abstract
The structural stability of wellbores depends on the concentric steel casings that are lowered into the wells and cemented in place. Such casings are often subjected to intense forces and high pressure, as well as being exposed to corrosive elements. As a result, defects such as pits, cracks, and other forms of metal loss inevitably occur on the casings. The presence of defects poses a threat to wellbore integrity that increases overtime as the metal losses increase in both depth of penetration and surface area, which may result in severe environmental and financial damage if left unchecked. Hence, many acoustic, visual, and electromagnetic (EM) inspection methods have been developed to assess the health of casings to facilitate risk management decisions.
EM inspection methods are widely used because of their ability to detect metal loss on multiple concentric casings while being largely unaffected by the cement between the casings. While visual and acoustic methods generally produce results that are readily interpretable, EM measurements are often more difficult to utilize due to their high nonlinearity. This research investigates the EM inspection of wellbore casings using the near- and remote-field eddy current (NFEC and RFEC) methods. Cross-sectional images are reconstructed by a hybrid neural network (HNN) with two parallel modules that map EM measurements to the pixels of the images. A specialized neural network module is designed for each of these methods. Both modules include convolutional and recurrent layers in their structures to extract spatial and sequential attributes from EM data. Using this approach, the physical locations of metal loss and casing material are inherently represented by the coordinates of the pixels on the reconstructed image, while the values of the pixels represent the probability of metal loss at their location. In addition, in-depth analyses show that this approach is generalizable to metal loss scenarios that are different in terms of shape and location from the training data.
Multi-Frequency Data Acquisition Model and Hybrid Neural Network for Precise Electromagnetic Wellbore Casing Inspection
Authors: Ooi Guang An , Khater Moutazbellah , Ozakin Mehmet Burak , Mostafa Tarek M. , Bagci Hakan , Ahmed Shehab 
Published: October 2022

Abstract
Casing integrity inspection tools are indispensable in identifying defects that threaten the structural integrity of oil wells. In particular, electromagnetics-based (EM-based) inspection tools are commonly used for multi-casing corrosion imaging. These tools measure the scattered EM fields inside the inspected casings and generate estimations of metal loss properties. However, the interpretation of EM measurements is difficult due to their intrinsic nonlinearity with respect to defect characteristics. In this paper, a new machine learning-based inspection framework is developed to generate accurate cross-sectional images of casings to characterize metal loss location and shape. A hybrid neural network (HNN) consisting of a main structure that integrates both convolutional and recurrent layers, as well as a parallel cross-frequency module with convolutional filters predicts the cross-sectional images of the inspected casings. Metal losses on the inner surface of the inspected casing, as well as fully-penetrating losses, are detected using high-frequency signals. On the other hand, low-frequency signals enable the detection of metal losses on the outer surface, in addition to the two previous kinds of losses. The resulting inspection scheme requires only four receiver (RX) coils for each frequency of signals to accurately predict both the azimuthal location and size of defects.
Reservoir Characterization of the Middle/Upper Jurassic Sargelu and Najmah Formations, West Kuwait: Key Workflows and Findings for Future Development of Unconventionals
Authors: Al-Morakhi Rasha , Verma Naveen , Long Nguyen Kim , Nelson Nicholas , Hayes Dawn , Rowe Harry , Reed Jules , Ghiniwa Ibrahim , Arrington Sean , Stemberger Dario , Narasimhan Santhosh , Sutton David 
Published: October 2022

Abstract
Whole cores provide important and high-quality information about mineralogy, rock fabric and texture, porosity, saturations, geomechanical properties, facies stacking patterns, depositional environment, and fracture networks that often cannot be discerned with much certainty using other subsurface data sets. Here we present results from the detailed digital sedimentologic descriptions of three whole cores in West Kuwait that capture portions of the Middle/Upper Jurassic Najmah Formation, an important source rock and unconventional reservoir. The integration of detailed digital core descriptions with high-resolution XRF scanning of the cores, high-density routine core analysis and petrographic (thin section, epifluorescent, fracture-focused, and SEM) data, and a combination of XRD mineralogy and organic petrology provide the opportunity to evaluate multiple reservoir quality indicators across several depositional and diagenetic facies. Linking diagnostic indicators to specific facies (and providing constraints on the likely variation within) rather than simply associating these properties with broader stratigraphic intervals should enhance our ability to predict naturally inherent variations in the formation which will improve lateral well placement and completions strategies. It is anticipated that the data gathered, and the interpretations made from it will be used to design and alter the development and completion strategies across the asset to increase production and reduce risk.
Assets Information and Data Management for a Mature Digital Twin
Authors: Almadhoun Wael 
Published: October 2022

Abstract
The Company assets average life span have reached 50 years. Which over such a long period creates challenges in maintaining the information changes during the plant life cycle, the change in terminology, formats, standards, and systems. According to today’s digital twin and operational excellence mandates, it is required to manage and represent the physical asset information. For that, the company implemented state of the art technology, which is intelligent, open and scalable data-model. This technology is based on web ontology, and it defines the formal naming, types, properties, and interrelationships of entities that fundamentally exist to represent the physical asset information during the asset life cycle from concept and design to operate stage. The objective of this paper is to share ADNOC Offshore experience, challenges and the benefits gained by implementing asset information Class Library, and the Reference Data Library as aligned with ISO15926 that is known as Data-Model, which describes the engineering Tags classification networks and ultimately represents the physical assets intelligent data and attributes. The data-model fundamentally is based on entities &gt; relationships &gt; values formula, where entities can lead to various values with unlimited relationships allowing any solution to quickly identify all related data in real-time. The formulas allow automatic extraction and identification of entities to facilitate the recognition and associations. This data management approach depends on Ontologies that are a formal way to describe taxonomies, essentially defining the structure of data, where it can be fully utilized to build the digital assets knowledge. Thus, the solution is designed to be flexible and allow reading from various data sources, which enriched the digital assets information and provided business, operations and integrity values. The challenges came from the rapidly growing Data, the newly engaged young engineers, and the demand for new digital technologies such as artificial intelligence, which is adding another layer of complexity.
In the recent years, it has proven that this data model is expandable, agile and able to accommodate both small and large variances from projects and changes in standards into one consolidated master model. This in turn acts as the core of enterprise Digital Twin, which provide end users with a unified experience in accessing engineering and operation data. Ontologies are meant to represent information widely and are expected to be evolving frequently based on technical and business requirements in flexible manner and easy to maintain. Unlike the conventional data-models, which is based on database and class hierarchy representing structured data that evolve slowly with complicated constraints. Whereas, with the implemented data-model we were able to add new data sources and classes instantly to represent information for an operation business case.
Self-Supervised Learning for Automated Seismic Wavelet Extraction
Authors: Di Haibin , Abubakar Aria 
Published: October 2022

Abstract
Seismic wavelet extraction is a fundamental but crucial component in seismic data analysis, which aims at estimating the source wavelet for better decoding the subsurface reflectivities from seismic signals and calibrating the measurements between logging and seismic. Due to the rock heterogeneity, however, seismic energy attenuates during its propagation, and correspondingly the seismic wavelet is considered varying from shallow to deep as well as from near to far offsets, causing the wavelet time-variant. While machine learning (ML) appears feasible for assisting the challenge, without a comprehensive understanding of the seismic propagation, representative training labels cannot be prepared, and supervised learning appears less applicable. This study proposes implementing self-supervised learning into time-variant seismic wavelet extraction. Specifically, the proposed network is in the architecture of a dual-task auto-encoder (DTAE). Starting from 1-D seismic amplitude, the DTAE first uses an encoder to extract a set of features at multiple scales, which are then split into two flows, with one through a decoder that aims at reconstructing the input 1D seismic signal and the other through a few convolutional layers that aim at matching the spectrum between seismic trace and extracted wavelet. Correspondingly, the objective function of training such DTAE consists of two parts, the mean-square-error of the amplitude reconstruction and the mean-square-error of the spectrum matching. The proposed workflow is tested on various field datasets, and compared to the statistical wavelets, the extracted wavelets are of better match with the spectrum variation of seismic from shallow to deep and from near offset to far offset.
Use of Virtual and Augmented Reality to Digitize and Optimize Turbomachinery Assembly Operation
Authors: Fiore Nicoletta , Chiari Francescosaverio , Riyaz Mahamad 
Published: October 2022

Abstract
This paper describes an innovative digital approach to optimize turbomachinery manufacturing processes. By adopting virtual and augmented reality technology it is possible to build a digital assembly sequence and make it available to the shop-floor. These digital technologies are changing the way operators interact with 3D digital content and by access to the work instructions having all the info they need in their hands.
Using the engineering 3D CAD structure, the design is validated through simulation in 1:1 scale immersive environment applying virtual reality features such as gravity, kinematics, part collision, surface sliding, parts flexibility. Then manufacturing routing is built defining a 3D Bill of Process and relevant assembly sequence operation by operation, including assembling tooling, work areas and ergonomics validation. Finally, this digital content flows to the shopfloor where the operator can access the digital work instructions through unique interface using virtual or augmented technology and can interact with component 3D model visualizing metadata and BOM codes.
Virtual and Augmented Reality have been successfully applied to produce an innovative aeroderivative gas turbine prototype. Having the opportunity to validate the design in a very detailed way and to define a pre-validated assembly sequence, the assembly process has been speeded up ensuring the on-time delivery and the scraps and rework reduced. The team has been quickly able to re-structure the sequence whenever it was needed to make sure to not stop the production. More than 8000 components have been organized and supplied to the operators exactly per sequence just in time being able to complete the first-time assembly in 8 weeks, as per schedule. The possibility to immediately locate the 3D virtual component using both VR and AR interfaces has enabled fast accessibility check, possible errors detection and improved quality. Component codes embedded in the 3D model have allowed an immediate identification of the real parts needed for the assembly while navigating the digital structure ensuring the product completeness. This digital approach has also changed the way of training the operators (10+) by reproducing complex operation sequence in a consequence free virtual environment improving safety of our operators and products.
By focusing on the importance of 3D native data (attributes and product manufacturing information), the new approach of leveraging virtual and augmented reality applications will make possible to access information whenever and wherever needed, empowering the workforce, and democratizing the knowledge. This new approach is also enabling remote solutions, playing a key role in the energy transition challenge and being a game changer for Oil & Gas and Energy industries.