bluuebunny/crossref_metadata_2025_split huggingface dataset:
Improving Facility Maintenance through Inspection with the Latest Ultrasonic Imaging Technology
Authors: Pörtzgen Niels 
Published: October 2022

Abstract
For a safe, reliable and efficient operation of facilities, knowledge and information of the condition and integrity of the assets are essential. Therefore, assets must be inspected on a regular base, preferably with non-destructive methods. Many of such methods are known in the industry (e.g. radiographic, magnetic, ultrasonic testing etc.), however their application is often restricted and the resulting data from the inspection is not adequate and/or sufficient for the asset integrity management.
Due to advances in ultrasonic hardware and sensor technology, a novel approach for non-destructive testing based on ultrasonic imaging, IWEX, has been introduced. With this approach, an ultrasonic ‘fingerprint’ called a Full Matrix Capture (FMC) data set, can be measured. With the FMC data set representative 2D and 3D images from the interior of metal components (e.g. welds) can be re-constructed. For the inspection of such components, real time scans can be made in order to reveal possible flaws.
The IWEX technology has already successfully been applied for the inspection of girth welds in newly constructed pipelines ([1], [2]). Furthermore, the method has recently been recognized by international codes and standards (ISO [3], ASME [4]), which opens the door for both new construction and in-service inspection applications in industrial facilities. In this paper, we will present the principles of ultrasonic imaging with the IWEX method. An example will be presented of an actual use case, concerning the inspection of welds in stainless steel pipes in an industrial facility. The welds contained cracks which were in some cases detectable with radiography, but height sizing was not possible. With the IWEX method, the cracks could reliably be detected, and it was possible to determine the crack heights. The height sizing accuracy was confirmed with known artificial targets in a representative reference block. Based on the accurate sizing results, it was possible to perform a fitness for service analysis which allowed a safe continuation of production in the facility.
The use case of the IWEX method illustrates how this new technology facilitates improved maintenance strategies for the industry by enabling the possibility to obtain sufficient and accurate information during inspection. As a next steppingstone the 2D and 3D images can be stored digitally such that they can be used to monitor and predict the condition of the asset over time using novel analyses concepts such as pattern recognition by neural networks.
Monitoring Mechanical Equipment on an Offshore Rig with Contrastive Learning on Acoustic Features
Authors: Kumar Ankit , Priyadarshan Amit , Sekar Pragadeesh K 
Published: October 2022

Abstract
Mechanical equipment, operating in the oil and gas industry produces a wide range of sounds, often culminating in noise. A functional equipment creates a standard sound, while malfunctioning equipment creates an anomalous sound. In this paper, we propose an application of a contrastive learning algorithm to detect anomalous sound amidst industrial noise, which in turn helps in the identification of the malfunctioning equipment on an offshore rig.
The recent success of contrastive learning algorithms to detect novelty in visual representation has inspired our work. We hypothesise that for most of its lifetime, equipment operates normally. Instances of malfunction that produce anomalous sound and hazardous consequences are rare in their defined lifetime. To this end, we implement a contrastive learning algorithm to identify similarities between audio clips using 10-second length normal-audio clips collected from 6 different equipment for training purposes. It extracts local and global features from the augmented audio clips and presents them in latent space, where the loss function differentiates between normal and anomalous sound. Finally, we input normal audio and anomalous audio into the algorithm.
The performance of the proposed algorithm is measured using Receiver-Operating-Curve - Area-Under-Curve (ROC-AUC) (Koizumi et al., 2020; Nunes, 2021)[5]. It successfully differentiates normal and anomalous audio to achieve an average ROC-AUC of 0.77 on the scale (0-1). 26092 audio clips of normal equipment sound and 6065 audio clips of anomalous sound from Valve, pump, fan, slide-rail etc., are used to perform the proposed experiment. Diverse industrial anomalies produced by unbalanced-voltage change, clogging, leaking, contamination, loose belt, no grease etc., are present in the dataset. The proposed methodology of contrastive learning consists of a data-augmentation module followed by an encoder and a Neural Network (NN). Data augmentation transforms the input audio clip to ensure robust performance. The encoder extracts the features from audio clips and projects them in the space of contrastive loss function with the help of NN. Automatic failure detection using Artificial Intelligence is essential for Industry 4.0. Prompt decision by monitoring sound produced by mechanical equipment holds immense potential for asset maintenance in the petroleum industry.
This paper is one of the first instances wherein the application of contrastive learning for the maintenance of mechanical equipment has been demonstrated. The proposed method is a unique approach towards anomaly detection using acoustic-features and it significantly reduces human intervention in hazardous and hard-to-reach environments.
Field Study Application of Ensemble Based Assisted History Matching and Optimization for Reservoir Management
Authors: Forouzanfar Fahim , Verma Santosh 
Published: October 2022

AbstractDesign of an optimal reservoir management strategy requires reliable reservoir performance prediction by the simulation models as well as implementation of robust and efficient mathematical optimization techniques to optimize the operating production and injection well controls. This paper describes an overall reservoir management workflow which consists of (1) the application of a novel data assimilation method for assisted history matching, and (2) the optimization of water-alternating-gas (WAG) injection cycles to maximize the life-cycle estimated ultimate oil recovery (EUR) of the reservoir.In this field study, the permeability field of the reservoir model is tuned to match the historical data. An iterative ensemble smoother (iES) optimization algorithm is used for the tuning, with a parameterization method based on coarsening of the property fields. The observed data includes the production and injection streams, the well bottom-hole pressures and static pressure measurements. The proposed parameterization combined with the iES algorithm resulted in a near-perfect match of the historical data. The property field tuning was within the acceptable and prior uncertainty ranges.For the WAG injection optimization study, the field management strategy and the list of WAG wells were given and fixed. The strategy included target average reservoir pressure, voidage replacement ratio and fluid material balance, in addition to the individual well limits and targets. The study parameters were water and gas injection cycle length. A design of experiment was done to understand the range of EUR for the field and understand its sensitivity to the optimization parameters. Next, a stochastic optimization algorithm was utilized to optimize the life-cycle EUR of the field by adjusting the injection cycle's length. The optimization was successful in improving the EUR of the field - the optimized ultimate recovery outperformed the best engineering design case and the ensemble of Latin Hypercube design cases. That said, the underlying field management strategy seemed to moderate the input controls, hence, the ranges of EUR improvements were relatively small.This study presents a successful application of novel ensemble based algorithms for field management and optimization, where the workflows outperformed the manual solutions both in terms of quality as well as the amount of effort required to do the study. The presented ensemble methods were efficient for estimating the sensitivities required for optimization of large scale problems - a critical aspect for field applications with large and computationally expensive simulation models.
Solving the Problem of Sulphur Deposit in Gas Injection Compressors
Authors: Yadav Sudhanshu Kumar , Al Muhairi Mohammad Ahmad , Al Khanbouli Mohamed Saeed 
Published: October 2022

Abstract
Paper debunks the theory of JT effect being responsible for Sulphur deposits and provides explanation on source of the problem of elemental Sulphur in dry gas seals of Gas Injection Compressors. The paper provides a practical and economical way forward to solve the problem of Sulphur deposit. This solves the problem of frequent failure of dry gas seals in Gas Injection Compressors. It also simplifies the Dry Gas Seal conditioning system design and improves Dry Gas Seal performance and reliability at compressor starts and stop (including ESD and CSD).
Digital Wellhead Integrated System for Production Management
Authors: Avila Reyes Ricardo , Machado Miguel , Torre Mario , Sharma Jitender , Gey Gian Marcio , Koa Johnson , Edwards Thomas 
Published: October 2022

Abstract
The oil and gas industry is lagging behind many industries in the adoption of technology and digital transformation. Our industry must transform at speed and scale to reap the available benefits of such technologies. Managing well integrity through personnel physical presence at the wellsite to take recordings increases the time to react to issues, the time to collect data, and the time to implement actions, and the high field trip rates introduce an increased risk to employees.
A digital wellhead integrated system (DWIS) provides information on wellhead parameters from smart instrumentation and achieves operational intelligence via edge computing. This solution improves the response time between production data and field actions to optimize and improve operational performance following workflow parameters. DWIS provides real-time monitoring, data collection, and management of well conditions, which minimizes the effects of human interactions, while providing consistent information that can be used for future health maintenance activities thus mitigating the risk of unexpected events. Cost efficient remote monitoring and control of well activities prolongs the life of the well and promotes savings.
The DWIS has been deployed and successfully tested in North America, South America, and the Middle East. A single DWIS implemented with a client resulted in production gains of 400,000 bbl./yr. and reduced 10,000 miles of driving—reducing the carbon footprint in a harsh environment. By providing human intelligence at the wellsite through edge computing and smart instrumentation, data can be generated, and actions taken to reduce expensive unplanned maintenance, offline outages, resource-intensive costs, and carbon footprint. Furthermore, this infrastructure at the wellsite enables ideas for new workflow implementation such as site monitoring via computer vision and more elaborate production and optimization workflows, creating a truly intelligent asset. DWIS maintain well integrity and provide continuous monitoring to optimize oil and gas production.
This DWIS supports well integrity maintenance strategies providing real-time control and well optimization enabled by smart instrumentation, surveillance systems, and intelligent workflows powered by artificial intelligence/machine learning algorithms executed at the edge and, in some instances, at the cloud level.
Achieving Productivity and Operational Efficiency, and High-Quality Data Through Automation in Well Log Data Quality Control and Acceptance Process Using AI/ML Techniques
Authors: Akbar Bibi Hussain , Al-Aradi Huda Jassim , Achmad Pajar Rachman , Khan Waqar Ullah 
Published: October 2022

Abstract
Well log data and reports volume generated by oilfield operations in Kuwait Oil Company (KOC) have increased tremendously over the last decade. Currently, KOC receives around 100 to 200 well log packages every week. Each package may contain hundreds of well log files. Mostly, the processes to receive, manage and load this amount of data into the Corporate Database were manual, involving 7 to 9 resources. Logging companies may have to wait a minimum of a week to receive the decision if their data delivery is approved or rejected. The Exploration and Production Information Management (E&P IM) team started an initiative to accelerate and automate these processes by developing and implementing a solution using Artificial Intelligence and Machine Learning techniques to increase productivity and operational efficiency and achieve high-quality data. The main results are high-quality data that meet E&P IM Well Log Standard and Specification, the processing time is reduced by 80%, and the operational efficiency is improved by 66%, allowing the resources assignment to more valuable activities.
Orifice Meter Condition Based Monitoring-Tracing the Ten Technical Developments to Standards Board Acceptance
Authors: Steven Richard Neil , MacGillivray Alick 
Published: October 2022

Abstract
Orifice meters can have advanced diagnostic capabilities which facilitate Condition Base Monitoring (CBM). These diagnostic methods have developed over time and have now reached the maturity to be included in the latest API, AGA, ASME, and ISO standards. This paper describes their development.
An Innovative Method of Water Management by Desalinating the Produced Water Using Thermal Renewable Energy
Authors: Al Ruheili Sharifa Mohammed , Tiefenbacher Felix , Al Mahrami Khansaa Hamed , Shahin Nazir Othman , Al Jaaidi Omar Salim 
Published: October 2022

Abstract
Produced water, which in many cases shows a high degree of salinity, is currently managed using water disposal wells and evaporation ponds. Both methods are conventional and have negative environmental impacts. ARA is adopting a unique and innovative method to manage high salinity produced water in an environmentally sustainable way by extracting potable water from produced water and reducing discharge water volume by at least 50%. For desalination of the produced water an innovative combination of forward and direct osmosis technology is used. This process is driven mostly by thermal energy which is provided to 100% solar thermal collectors.
The structure of the plant is as follows: A solar thermal power plant consisting of 2 desert- and oilfield-proof solar thermal collectors ("HELIOtubes") with thermal energy storage (TES) using pressurized water for nighttime operations (24/7). The solar thermal plant powers an advanced forward osmosis desalination (FO) and brine concentration Direct Osmosis (DO) unit with pre-treatment unit to cope with the challenging produced water.
This project is in the pipeline for implementation. The engineering and the planning design were successfully completed. Two main results are expected from this project, which is: (i) low-cost and low-maintenance thermal power and (ii) desalinated water from the produced water meeting the regulatory specifications.
It is a state-of-the-art system integration engineering based on the simulation of the innovative desert-proof solar thermal plant including TES using 10-year historical satellite-based irradiation data to show optimal use of renewable energy. Lab studies show how low energy consumption pre-treatment of the challenging produced water with its difficult chemical composition can be achieved, allowing membrane-based forward osmosis to be applied even at a salinity level that is 3-times higher than seawater. That leads to high energy efficiency for desalination also at nighttime based on thermal power from cost-effective TES instead of expensive batteries. This system could be considered a game-changer in the oil and gas industry because it introduces a new water management methodology.
Online Robotic Inspection of Above Ground Storage Tanks without Outage
Authors: Al Shamsi Ali Humaid , Castelino Shan Mohan 
Published: October 2022

Abstract
Above Storage tanks are one of the critical equipment's in the Oil & Gas facility that handle large volume of fluids. Integrity Management of Above ground storage tanks is of utmost importance in oil and gas facilities such as Processing plants and Storage terminals. Periodic Internal inspection is a key element on Above ground storage tank Integrity management strategies. Oil and Gas operating companies are faced with applicable regulations and competing economics of expensive and potentially dangerous inspections. To minimize the amount and cost of inspections needed there is need for a technology that performs inspection and assure Integrity while the tank is Inservice and keeping costs, downtime, safety risks, and environmental impacts to a minimum.
Robotic inspection of Above ground storage Tanks can be deployed to conduct detailed investigation and assess integrity of tank while it is in online which is in compliance with Internationally recognized industry codes and standards. Tank internal Online inspection by utilizing the Robotic technique, eliminates high cost of tank downtime, need for temporary product storage, avoid cleaning and handling of sludge, increase personnel safety by avoiding confined space entry
API Pipeline SMS Assessment Program: Key Changes and Learnings from Initial Assessment Outputs
Authors: Frazier Colin M. , Toth S. David , Burr Alexa S. 
Published: October 2022

Abstract
The American Petroleum Institute (API) represents all segments of the natural gas and oil industry, aiming to accelerate safety and environmental progress across operations while meeting global demand for affordable, reliable, and cleaner energy. Through API and partnership with the U.S. Pipeline and Hazardous Materials Safety Administration (PHMSA), state pipeline regulators, and other interested stakeholders, pipeline operators developed API Recommended Practice (RP) 1173: Pipeline Safety Management Systems. API RP 1173 is designed to help operators create a management system structure to systematically manage pipeline safety and continuously measure progress to improve overall pipeline safety performance. The core principle of API RP 1173 is the "Plan-Do-Check-Act" cycle, and it requires an operator to periodically review their pipeline safety management system (PSMS) to the requirements of RP 1173 on a triennial cycle.
In collaboration with industry partners, API developed the not-for-profit Pipeline SMS Assessment Program in 2019 and fully launched the offering in January 2020. In the three years since its inception, API has facilitated 14 assessments of pipeline operators’ safety management systems against the requirements of RP 1173. In doing these 14 assessments, the process for completing assessments has been tested by operators of vastly different sizes, carrying a wide array of products, from very small steam utility operators to large liquids operators with operating regions across North America.
The industry has also identified specific trends relevant to API RP 1173 implementation in conducting these assessments. Specifically, it has created awareness around some areas pipeline operators continue to struggle with in creating a holistic PSMS: 1) ensuring that field-level personnel understands how their roles relate to safety performance relative to the company's PSMS; 2) incorporating learnings from external events into their PSMS so that similar incidents do not occur; 3) incorporating relevant and standardized PSMS requirements into agreements with service companies, and; 4) delineating what RP 1173 requirements mandate programmatic governance or policy within their PSMS (an industry learning).
In this manuscript, API will provide an overview of the PSMS Assessment Program and changes made since its inception, an examination of the key learnings from assessment outputs to date, and a description of how the program is providing a feedback mechanism to improve industry initiatives to help operators implement RP 1173.
Tackling Mixed Scales Issue in Oilfield Using a Novel Robust Scale Dissolver
Authors: Ramly Nora Aida , Hendraningrat Luky , Riyanto Latief Riyanto , Rodzali M Azmeer , Baharuddin Salina 
Published: October 2022

Abstract
The scale problem is turning into a fluid-related flow assurance concern in oil and gas wells at various locations in the Malaysian basin. Flow assurance issues can have a severe influence on the total fluid production trip from subsurface to surface. Oilfield scale comes in different varieties, including organic, inorganic, gas, soap, and mixed scales. In the past, there was only one type of scale dissolver chemical that could be used, making it less efficient to dissolve multiple types of scales at once. The evaluation of a recently developed, so-called robust solid dissolver (RSD), from the lab to a pilot deployment, is presented in this work.
The RSD technology was developed based on a stable micro-emulsion solution that comprises acid and solvent components. Near the wellbore and along the tubing string of an oil well, it can concurrently dissolve organic and inorganic scales. After extensive visual analysis and laboratory experimentation, it was determined that the mixed scales issue was the cause of the oil production's rapid decline. This conclusion was supported by laboratory analysis and observations made from the wellsite throughout the tubing string and near wellbore casing.
Based on laboratory tests, RSD can dissolve mixed scales 100% in 24 hours at well temperature and no incompatibility issue with production chemicals and all the pumping/wireline components. The RSD was developed based on the total organic system that can prevent corrosion, is compatible with hydrocarbon. The RSD was piloted at oil well "1" at Field PN and executed successfully to remove mixed scales and the well was revived. Based on result from the first Pilot, it revived oil well, increased production at a certain level.
By increasing oil production rate and having value creation as a single chemical treatment to address mixed scales concerns, the new RSD has unlocked its potential to revive the well due to mixed scale issues.