bluuebunny/crossref_metadata_2025 huggingface dataset:
Mud Loss Prediction in Realtime Through Hydromechanical Efficiency
Authors: Saihood Tariq , Samuel Rebello
Published: October 2022

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

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

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

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

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

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

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 > relationships > 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

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

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.
Expediting Chemical Enhanced Oil Recovery Processes with Prediction of Chemical Reaction Yield Using Machine Learning
Authors: Kumar Ashutosh , Rajendran Robin Marlar
Published: October 2022

Chemical enhanced oil recovery involves enormous combinations of chemicals, surfactants, etc. The reservoir properties such as temperature, capillary pressure, permeability keeps changing, making the process of identification of suitable chemicals even more challenging. Data driven modelling holds solutions for the complexity involved in identification of most suitable parameters for chemical enhanced oil recovery. Over the last decade, Artificial Intelligence has found its numerous applications in different branches of chemistry. From the selection of chemicals to the operating conditions during synthesis all can be estimated by the use of deep learning models. This paper presents yield prediction which is of high economic significance for chemical enhanced oil recovery, because they enable calculation of investment versus return.

These models give us the conversion of reaction into products before performing the lab experiment. This will help chemists in selecting high performance chemicals for specific reservoirs without spending time on costly iterative chemical processes. These models require application of deep learning architectures like transformers and natural language processing techniques like tokenization for the prediction task. Encoder models like BERT are used for receiving the information on chemical reactions in text-based form for a reaction which is then combined with a regression extension layer to give us the desired reaction yield.

We demonstrate our model on a HTE dataset with an excellent prediction score. Efforts are also made on the USPTO patent dataset which covers a wide variety of chemical reaction space. The USPTO patent dataset consists of almost every chemical reaction published since late 1970s till 2006. Diverse techniques starting with Multi Layer Perceptrons, Sequence to sequence modelling, Long short term memory models and finally transformers are employed for the improvement of accuracy of patent reactions. The paper presents detailed comparative results of predicting chemical reaction yield, and the enhancements that it will bring to Chemical Enhanced Oil Recovery.

Reaction yield prediction models receive very little attention in spite of their enormous potential of determining the reaction conversion rates and its contribution to chemical enhanced oil recovery processes . The paper introduces a novel approach of modelling chemical reaction yield with deep learning models to the petroleum community. Unprecedented result of accuracy beyond 90% in predicting chemical reactions yield and its significance in chemical enhanced oil recovery has been proposed in the paper.
Semi-Supervised Learning for Geotechnical Soil Property Estimation in Offshore Windfarm Sites
Authors: Di Haibin , Abubakar Aria
Published: October 2022

Site characterization and monitoring of the subsurface formations around wind turbine locations are crucial for reliable wind farm construction, operation and maintenance. In order to extract relevant information about subsurface soils, ultrahigh-resolution (UHR) seismic survey and geotechnical cone- penetration testing (CPT) is often acquired, processed, interpreted and integrated, which could be repeated over time for site monitoring purposes. Due to the size of the area to be investigated and the manual efforts to complete multiple steps in the traditional workflow, the turnaround time for soil property estimation in a wind farm site can be quite long. In this study we implement a semi-supervised learning workflow to automate the task, which integrates URH seismic and CPT logs through two convolutional neural networks (CNNs), with one for seismic denoising and feature engineering (SDFE) and the other for seismic-CPT integration (SCI), which reduces the difficulties in CNN training due to poor data quality and small data quantity. The two components are connected by implementing the encoder of the pretrained SDFE-CNN as part of the SCI-CNN encoder. As tested on a public wind farm site, the use of deep learning leads to promising results in terms of both quality and efficiency. The proposed workflow is also extensible to include additional information, such as structure and velocity models, for further constraining the SCI-CNN.

Highlights: A semi-supervised learning workflow is proposed for soil property estimation from UHR seismic and CPT tests in a wind farm site,allows estimating the essential soil properties such as cone-tip resistance from post-stack UHR seismic as tested on a real windfarm site HKZ, andreduces the turnaround time of windfarm site characterization compared to traditional workflows.
Making Under Uncertainty - Edge Intelligent Decision for Drilling: From Data to Actions in Real Time Well Engineering
Authors: Samuel Robello , Agrawal Abhishek
Published: October 2022

Planning and learning are two primary approaches to intelligent decision making at the edge when the well is constructed. Planning enables us to take immediate actions far into the future, but it requires accurate well engineering models, which are often difficult to acquire in practice. The models are built on engineering assumptions which may not be valid all the time. But the data will provide additional support to suppress the assumption and improve the models. This paper presents the results that can be used under uncertainty through planning, through learning, most importantly by integrating planning and learning.

Planning and learning are two primary approaches to intelligent decisions. When it comes to the edge decision whether it is manual or semi-automated or fully automated (Dupre, 2013) it requires tighter coupling of engineering models through microservices and reinforced learning from the data and feedback from the driller. Five major types of uncertainties are considered in calculating the drilling operational parameters are measurement uncertainty, data uncertainty, engineering model uncertainty, computational or algorithmic uncertainty and decision uncertainty. Several examples are presented as the well is steered and navigated with interactive tasks.

Surface hookload and torque values serve as good indicators for some undesirable scenarios or anomalies during drilling, such as stuck pipe, buckling, and inadequate hole cleaning. However, to detect these risks, it requires drilling engineers to perform engineering model calibration manually and regularly, which costs more efforts and poses significant uncertainties on the detection. This paper describes how these problems are circumvented in addition by providing project ahead paths based on various constraints in real-time with predicted uncertainty zone. This option at the edge makes it possible for drilling engineers to monitor live drilling wells anywhere and anytime while enabling the rig personnel to make significant improvement to operations. Hence, the optimization is no longer static and becomes a dynamic function of depth. These conditions result in constantly varying constraints and, thereby, constantly varying optimized operating parameters to maximize the rate of penetration or minimize specific energy or well cost. This study presents a real-time optimization technique for rate of penetration with energy-based models. Based on the place and position of the bit, the updated data has been used to modify the proposed well design on the fly. The above underpinning methodology have been supported with practical field examples.
Case Study for the Deployment of Private LPWA (Low Power Wide Area) Network in ADNOC Onshore for Remote Monitoring and Operations
Authors: Ahmed Muhammad Shahzad , Al Bloushi Eisa Yousef , Al Bloushi Mahdi Abdula , Ali Asad
Published: October 2022

Remote monitoring and operation are a key aspect in oil and gas facilities due to remoteness of producing and gathering facilities. Traditionally, SCADA system based on wired Fiber Optics or wireless radio communication is used to gather this remote data and perform supervisory actions. During last decade IoT (Internet of things) solutions are being adopted in oil and gas industries because of their cost efficiency and ease in deployment.

Most of industrial internet of things solutions (IIoT) available in market are utilizing LPWAN (Low Power Wide Area Network) wireless technologies e.g., LoRaWAN, SigFox, NB-IOT with backbone network infrastructures based on GSM/Satellite/ public cellular network and cloud-based application/network servers. Such solutions, due to the involvement of 3rd party/public networks pose a concern related to data security and complete network ownership.

This paper explains the PoC trials conducted in Giant Oil and Gas Production Field (X Field) in ADNOC Onshore for the assessment of following attributes of LPWA networks. Deployment private LPWAN based on existing telecom backhaul consisting of FoC and WiMAX network.Centralized monitoring of existing active instrumentation by utilizing Class C LoRa/LoRaWAN end nodes through Lora/LoRaWAN private network.Effectiveness of Lora/LoRaWAN Class C devices for bidirectional communication. Thus, utilization of LPWAN for monitoring as well as remote operations.

At present, in ADNOC Onshore there are multiple remote facilities e.g., oil wells, water injection wells, CP system solar stations etc. which are without centralized remote monitoring; at the same instance, the field has a widespread SCADA network based on WiMAX and FoC. This situation is equally applicable for most of oil and gas brownfield facilities. Private LPWA networks built upon these existing SCADA networks are cost effective solutions to cover the gap for remote monitoring of the facilities where near real-time data monitoring is required.

Below LPWA technologies were tested during these PoC trials. LoRaWAN (LoRaWAN protocol over Physical layer of Lora)Private Lora (Modbus RTU over physical layer of Lora)
Remote Monitoring of Equipment Performance Through a Customized Real-Time Performance Solution
Authors: Kalhoro Muhammad Uddin , Al-Saedi Khaled Abdulla , Al-Awadhi Hesham Khalid , Nadeem Muhammed
Published: October 2022

ADNOC Onshore team embarked on a digitalization project contributing towards ADNOC's digitalization and smart growth strategy to maximize the asset efficiency and integrity. Accordingly, to achieve this objective, a tailor-made real-time asset condition monitoring system (ACMS) mainly for rotating equipment was implemented in ADNOC Onshore BuHasa field. Compared to conventionally available solutions, the ACMS is a customized digital solution integrating the multiple online and offline data on a one platform used to ensure asset integrity and optimum performance. This digital solution provides advanced predictive diagnostics and real-time performance monitoring one a single platform connected to IT network to enable remote desktop access to multiple users. The ACMS is integrated to different tools used for condition and performance monitoring. The use of globally recognized performance standards like ISO, ASME PTC and digital performance curves are used to provide actual real-time condition and performance taking into consideration the varying operating conditions. ACMS is connected with different data sources like process operating parameters through DCS and SCADA, gas turbine control system, machinery health manager for vibration monitoring, oil analysis and provides a complete health assessment of rotating equipment in real-time. ACMS calculates thermodynamic performance in real-time in accordance with ASME PTC standards, eliminating any human intervention or manipulation. Through ACMS, we monitor polytropic efficiency, BEP deviation, head loss and power consumption against expected target within allowable variance. Whereas AMS Machinery Manager along with ACMS predictive feature is used for component condition assessment, which includes vibration analysis and temperature trends. This system is primarily covering all the rotating equipment; however, it has capability to monitor other type of plant equipment as well. Through effective utilization of this condition monitoring system, we transformed the maintenance overhauling of rotating equipment from meter based to condition-based overhaul, contributing to efficiency improvement, and reduced operating cost.
Total Business Transformation to Future Proof the Business, Build Change Agility, and Drive Sustainability and Cost Optimization
Authors: Seth Vivek , Goyal Deepak
Published: October 2022

Energy industry is at the crossroads and a major transition is in progress. This puts tremendous expectations on the supporting Logistics industries.

As the industry balances significant challenges and uncertainties, Future Proofing the Business and driving Sustainability was a top priority for us. To future proof the business, both revenue and cost were important. With this premise we embarked on an ambitious journey to drive total business transformation covering the Operating Model, Operations, Commercial, Organization Structure, People, Performance, Data, Technology. Operating Model defines the Business Moat and drives Customer Solutions and to achieve sustainable successes building change agility was also important. This was also aligned with Corporate Values and 3 Key Pillars – People, Profitability and Sustainability.

While the right Processes, Systems, etc. are critical to any business, importance of People can't be underestimated. Without the right mindset, any transformation will end up being a paper exercise on the surface, while the story underneath the waves will be very different. A successful transformation, though often a management prerogative, needs to involve a larger employee population. And while one can be tempted to change everything, it's vital to understand the high impact areas that will deliver the maximum results. This also helps in ensuring smooth business continuity and not disrupt the day to day to operations, while keeping the cost in control and building success stories that help fuel the Change Mindset and appetite for Transformation.

Our transformation journey, Transform 1.0 had 3 focus areas with high impact, and changing the very DNA of the business. People & Mindset. Building Change AgilityFuture Proofing the Business, impacting top and bottom line. Profitability Cost Optimization Talent Asset OptimizationDriving Sustainability
Development and Utilization of Analytical Methods for Rapid GM Detection in Processed Food Products: A Case Study for Regulatory Requirement
Authors: Singh Monika , Paliwal Aparna , Kaur Kushaldeep , Palit Paramita , Randhawa Gurinderjit
Published: October 2022

Genetically modified (GM) food crops for desired traits have been approved in some of the countries. The approval status of a GM event varies from country to country. In India, Bt cotton is the only approved GM crop. So far, no GM food crop has got regulatory approval in the country, which may be considered as unauthorized GM (UGM) event in the Indian context. The entry of UGM events of food crops needs to be checked in the imported consignments as well as in the marketplace. In the present study, screening elements were identified based on the genetically modified organism (GMO) matrix developed as decision support system for 22 GM food crops approved globally. Three sets of multiplex PCR assays were developed and validated for GM detection in food crops and products: triplex PCR targeting control elements [Cauliflower Mosaic Virus 35S promoter (P-35S), Figwort Mosaic Virus promoter (P-FMV), nopaline synthase terminator (T-nos)], triplex PCR targeting marker genes [aminoglycoside-3’-adenyltransferase (aadA), neomycin phosphotransferase (nptII), phosphinothricin-N-acetyltransferase (pat)] and duplex PCR targeting Bt genes (cry1Ab/Ac and cry2Ab2). Limit of detection (LOD) ranged from 0.5 − 0.05% for different targets, which is in compliance with the labelling threshold of many countries. The developed assays were utilized to check the GM status of apple and maize products along with an additional test for ctp2-cp4epsps for herbicide tolerance in maize. These procedures could be efficiently employed as a part of GMO testing to trace GM contamination, if any, in both the imported as well as domestic food products.
El Niño impacts on the ozone column over Mato Grosso do Sul
Authors: Souza Amaury , Dumka Umesh C. , Pobocikova Ivana , Oliveira-Júnior José Francisco , Abreu Marcel Carvalho , Ihaddadene Razika , Cavazzana Guilherme Henrique , Reis Carlos José , Aristone Flavio , Gul Sajid , Ihaddadene Nabila , Shah Munawar , Fernandes Widinei A
Published: October 2022

The El Niño-South Oscillation (ENSO), a dominant factor in interannual climate variations worldwide, is characterized in the Pacific by anomalous sea surface heating during the El Niño phase and cooling during the La Niña phase. Although ENSO strongly affects atmospheric circulation, its effects on tropospheric ozone are not fully explored. We used satellite measurements of the tropospheric column of ozone to assess the effects of atmospheric circulations driven by ENSO on tropospheric column ozone levels in Mato Grosso do Sul (MS). The objective of this work is to analyze the annual variation and the effects of the El Niño atmospheric variability mode in the Total Ozone Column (TCO) on MS between 2005 and 2020 using data from the AUREA satellite and the \Ozone Monitoring Instrument (OMI) sensor. We found that observed ozone tends to increase in the troposphere after the La Niña peak, corresponding to anomalous downward motions and suppressed convection. The model also reveals that the La Niña-related ozone increase in MS is largely due to intensified transport of ozone-rich air from higher latitudes. This suggests that ENSO should be considered for estimating the ozone concentration in MS, requiring close attention to the properties of ENSO in a changing climate.
Infarction of cerebellum vermis attributed to a right-to-left shunt by pulmonary arteriovenous malformation: a case report.
Authors: Chen Guojuan , Zhang Weitao , Kan Fengjie , Cao Yibin
Published: October 2022

Objectives: We reported this case to remind neurologists that pulmonary right-to-left shunt is one of the causes of paradoxical embolism.
Methods: A 47-year-old woman was diagnosed with infarction of cerebellum vermis by MRI. Atheromatous, immunological, infectious and hypercoagulable abnormality was negated. A doubtful pulmonary arteriovenous malformation (PAVM) was accidentally found in the process of CT angiography of subclavian arteries.
Results: Then the pulmonary angiography confirmed the existence of PAVM in her left pulmonary lobe. Meanwhile, the bubble contrast study with transcranial Doppler found bubble signals passing her middle cerebral artery.
Discussion: Other than intracardiac, but pulmonary right-to-left shunt can induce embolic stroke.
Frequency of risk factors of coronary heart diseases in patients with acute coronary syndrome and its comparison in male and female
Authors: Mazhar Naqash , Khan Usman Hassan , Mand Bakhat , Rehman Abdur , Mubeen Maryam , Malik Mohammad Hamza Bin Abdul , Hussain Syed Anas
Published: October 2022

Introduction: Cardiovascular disease (CVD) is a group of diseases that include both the heart and blood vessels, thereby including coronary heart disease (CHD) and coronary artery disease (CAD), and acute coronary syndrome (ACS) among several other conditions.
Objectives: The main objective of the study is to find the frequency of risk factors of coronary heart diseases in patients with acute coronary syndrome and its comparison in male and female.
Material and methods: This cross-sectional study was conducted in Services Institute of medical sciences during June 2021 to June 2022. The data was collected with the permission of ethical committee of hospital.
Results: The data was collected from 100 male and female patients. The mean age was 45.67±2.89 years for males and 49.89±5.67 years for females. The major risk factors for diseases is hypertension, hyperlipidemia, smoking and diabetes. According to data all the patients must contain one of these major risk factors and many of the patients had more then one risk factors.
Conclusion: It is concluded that all the patients must have one of the major risk factors as a cause of diseases. Women with acute coronary syndrome, as compared to men, have more prevalence of diabetes and hypertension, and less prevalence of smoking.
Tsc2 mutation rather than Tsc1 mutation dominantly causes a social deficit in a mouse model of tuberous sclerosis complex
Authors: Kashii Hirofumi , Kasai Shinya , Sato Atsushi , Hagino Yoko , Nishito Yasumasa , Kobayashi Toshiyuki , Hino Okio , Mizuguchi Masashi , Ikeda Kazutaka
Published: October 2022

Background: Tuberous sclerosis complex (TSC) is an autosomal dominant disorder that is associated with neurological symptoms, including autism spectrum disorder. Tuberous sclerosis complex is caused by pathogenic germline mutations of either the TSC1 or TSC2 gene, but somatic mutations were identified in both genes, and the combined effects of TSC1 and TSC2 mutations have been unknown.
Methods: The present study investigated social behaviors by the social interaction test and three-chambered sociability tests, effects of rapamycin treatment, and gene expression profiles with a gene expression microarray in Tsc1 and Tsc2 double heterozygous mutant (TscD+/-) mice.
Results: TscD+/- mice exhibited impairments in social behaviors, and the severity of impairments was similar to Tsc2+/- mice rather than Tsc1+/- mice. Impairments in social behaviors were rescued by rapamycin treatment in all mutant mice. Gene expression profiles in the brain were greatly altered in TscD+/- mice more than in Tsc1+/- and Tsc2+/- mice. The gene expression changes compared with WT mice were similar between TscD+/- and Tsc2+/- mice, and the overlapping genes whose expression was altered in mutant mice compared with wild type (WT) mice were enriched in the neoplasm- and inflammation-related canonical pathways. The “signal transducer and activator of transcription 3, interferon regulatory factor 1, interferon regulatory factor 4, interleukin-2R a chain, and interferon-γ” signaling pathway, which is initiated from signal transducer and activator of transcription 4 and PDZ and LIM domain protein 2, was associated with impairments in social behaviors in all mutant mice. Limitations: It is unclear whether the signaling pathway also plays a critical role in autism spectrum disorders not caused by Tsc1 and Tsc2 mutations.
Conclusions: These findings suggest that TSC1 and TSC2 double mutations cause autistic behaviors similarly to TSC2 mutations, although significant changes in gene expression were attributable to the double mutations. These findings contribute to the knowledge of genotype-phenotype correlations in TSC and suggest that mutations in both the TSC1 and TSC2 genes act in concert to cause neurological symptoms, including autism spectrum disorder.
Systemic BCG administration stimulates NK and T cell-mediated antitumor lung immunity and overcomes tumor resistance to immune checkpoint inhibition
Authors: AGUILO NACHO , Moreo Eduardo , Robles Iñaqui , Guerrero Claudia , Jarit Aitor , Felgueres María José , Esteso Gloria , Uranga-Murillo Iratxe , Uranga Santiago , Gomez Ana Belen , Pardo Julian , Martin Carlos , Vales-Gomez Mar , Fresno Carlos del , Sancho David
Published: October 2022

Intravesical administration of BCG was the first immunotherapy approved by the FDA and it is still nowadays the treatment of choice for a subset of non-muscle invasive bladder cancer patients. Considering this precedent and the strong capabilities of BCG to trigger cellular responses, we hypothesized that systemic administration of BCG by the intravenous route (IV BCG), a vaccination strategy that has been found well-tolerated, highly immunogenic and effective at preventing tuberculosis infection in non-human primates, could result effective against lung tumors. Our data revealed that IV BCG, administered in a therapeutic scenario, slowed tumor growth and extended mouse survival in models of B16-F10 lung metastasis and in an orthotopic LLC lung cancer model, by enhancing both antitumoral innate and adaptive immune responses. IV BCG administration induced tumor-specific CD8+ T cell responses with enhanced cytotoxic function, in a process dependent on conventional type 1 Dendritic Cells (cDC1s). In addition, we found that BCG strongly activated both human and mouse NK cells. Remarkably, NK cell depletion prevented BCG-induced cDC1 recruitment to the tumor-bearing lung and reduced cDC1 upload with tumor-derived antigens, as well as the subsequent induction of tumor-specific CD8+ T cell responses and tumor control. Of note, IV BCG strongly synergized with the immune checkpoint inhibitor (ICI) anti-PD-L1 in lung tumors which were otherwise resistant to immune checkpoint blockade given as monotherapy, suggesting the IV BCG could represent a promising ICI-resensitizing cancer immunotherapy.