Predictive Maintenance Using the Executable Digital Twin xDT

2021 ◽  
Author(s):  
Kevin Goodheart ◽  
Peter Mas ◽  
Maged Ismail ◽  
Umberto Badiali ◽  
Wim Hendicx
Keyword(s):  
Real Time ◽  
Oil And Gas ◽  
Order Reduction ◽  
Oil And Gas Industry ◽  
Operating Conditions ◽  
Predictive Maintenance ◽  
Field Equipment ◽  
Loop Control ◽  
Digital Twin ◽  
Digital Twins

Abstract Through the introduction of programmable logic controller (PLCs), Dynamic Process & Controls modeling, integrating with Multiphysics Mechatronics & 3D equipment simulation modeling, companies can work in the online real-time environment. This modeling of equipment or processes builds the foundation for digital transformation of subsea, topside, onshore and plant environments. In the design and operation of field equipment, the physics based Digital Twin is getting more and more traction to develop virtually the equipment because of recent prediction accuracy improvement and faster calculation times. Such digital twins allow to find the optimal operating conditions and predictive maintenance schedules for operation. In this timeslot we will explain, based on few industrial examples, a new set of capabilities that allow companies to get the maximum out of digital twins to be able to use them on their equipment. By applying a structured process using Digital Twins to be able to convert the existing knowledge & data at Companies into solution to be more predictive on their equipment. This will deliver substantial return on investment (ROI) for the Oil and Gas Industry. An AI based methodology to perform Model Order Reduction on the digital twin to be able to get real time response in connection to online unit information An AI based methodology to convert the reduced model into a virtual sensor for online quality predictions or predictive maintenance scheduling as well as to use it for creating an optimal controller of the unit based on the product requirements Fast edge computing hardware that can collect data from sensors and, in real time, run the Executable Digital Twin (xDT) and suggest corrective action to the operator or run in closed loop control

Will This Be the Decade of Full Digital Twins for Well Construction?

2021 ◽  
Vol 73 (03) ◽  
pp. 34-37
Author(s):  
Judy Feder
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Real World ◽  
Data Analytics ◽  
Oil And Gas ◽  
Real Life ◽  
Physical World ◽  
Sensor Technology ◽  
Digital Twin ◽  
Digital Twins

The time needed to eliminate complications and accidents accounts for 20–25% of total well construction time, according to a 2020 SPE paper (SPE 200740). The same paper notes that digital twins have proven to be a key enabler in improving sustainability during well construction, shrinking the carbon footprint by reducing overall drilling time and encouraging and bringing confidence to contactless advisory and collaboration. The paper also points out the potential application of digital twins to activities such as geothermal drilling. Advanced data analytics and machine learning (ML) potentially can reduce engineering hours up to 70% during field development, according to Boston Consulting Group. Increased field automation, remote operations, sensor costs, digital twins, machine learning, and improved computational speed are responsible. It is no surprise, then, that digital twins are taking on a greater sense of urgency for operators, service companies, and drilling contractors working to improve asset and enterprise safety, productivity, and performance management. For 2021, digital twins appear among the oil and gas industry’s top 10 digital spending priorities. DNV GL said in its Technology Outlook 2030 that this could be the decade when cloud computing and advanced simulation see virtual system testing, virtual/augmented reality, and machine learning progressively merge into full digital twins that combine data analytics, real-time, and near-real-time data for installations, subsurface geology, and reservoirs to bring about significant advancements in upstream asset performance, safety, and profitability. The biggest challenges to these advancements, according to the firm, will be establishing confidence in the data and computational models that a digital twin uses and user organizations’ readiness to work with and evolve alongside the digital twin. JPT looked at publications from inside and outside the upstream industry and at several recent SPE papers to get a snapshot of where the industry stands regarding uptake of digital twins in well construction and how the technology is affecting operations and outcomes. Why Digital Twins Gartner Information defines a digital twin as a digital representation of a real-world entity or system. “The implementation of a digital twin,” Gartner writes, “is an encapsulated software object or model that mirrors a unique physical object, process, organization, person or other abstraction.” Data from multiple digital twins can be aggregated for a composite view across several real-world entities and their related processes. In upstream oil and gas, digital twins focus on the well—and, ultimately, the field—and its lifecycle. Unlike a digital simulation, which produces scenarios based on what could happen in the physical world but whose scenarios may not be actionable, a digital twin represents actual events from the physical world, making it possible to visualize and understand real-life scenarios to make better decisions. Digital well construction twins can pertain to single assets or processes and to the reservoir/subsurface or the surface. Ultimately, when process and asset sub-twins are connected, the result is an integrated digital twin of the entire asset or well. Massive sensor technology and the ability to store and handle huge amounts of data from the asset will enable the full digital twin to age throughout the life-cycle of the asset, along with the asset itself (Fig. 1).

Probabilistic Digital Twins for Transmission Pipelines

2020 ◽  
Author(s):  
Francois Ayello ◽  
Guanlan Liu ◽  
Yonghe Yang ◽  
Ning Cui
Keyword(s):  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Third Party ◽  
Weather Data ◽  
Data Sets ◽  
Worst Case ◽  
Gas Industry ◽  
Digital Twin ◽  
Digital Twins ◽  
Threat Modelling

Abstract Digitalization in the oil and gas industry has led to the formation of digital twins. Digital twins bring closer the physical and virtual world as data is transmitted seamlessly between real time sensors, databases and models. The strength of the digital twin concept is the interconnectivity of data and models. Any model can use any combination of inputs (e.g. operator owned data sets and sensors, third-party databases such as soil composition or weather data, results from other models such as flow assurance, threat modelling or risk modelling). Consequently, the result of one model may become the input of another. This strength is also a weakness, as uncertain (or missing data) will lead to a great source of uncertainty and may lead to wrong results. Worst case scenarios have been used to solve this issue without success. This paper presents a new concept: probabilistic digital twins for pipelines. Probabilistic digital twins do not lose uncertainty as results pass from one model to another, thus providing greater confidence in the final results. This publication reviews the probabilistic digital twin concept and demonstrates how it can be implemented using gas pipeline data from West Pipeline Company, CNPC.

scholarly journals Development of Real-Time Time Gated Digital (TGD) OFDR Method and Its Performance Verification

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4865
Author(s):  
Kinzo Kishida ◽  
Artur Guzik ◽  
Ken’ichi Nishiguchi ◽  
Che-Hsien Li ◽  
Daiji Azuma ◽  
...  
Keyword(s):  
Real Time ◽  
Optical Fibers ◽  
High Speed ◽  
Oil And Gas ◽  
Scattered Light ◽  
Oil And Gas Industry ◽  
High Signal ◽  
Chirp Pulse ◽  
Sound Waves ◽  
Industry Applications

Distributed acoustic sensing (DAS) in optical fibers detect dynamic strains or sound waves by measuring the phase or amplitude changes of the scattered light. This contrasts with other distributed (and more conventional) methods, such as distributed temperature (DTS) or strain (DSS), which measure quasi-static physical quantities, such as intensity spectrum of the scattered light. DAS is attracting considerable attention as it complements the conventional distributed measurements. To implement DAS in commercial applications, it is necessary to ensure a sufficiently high signal-noise ratio (SNR) for scattered light detection, suppress its deterioration along the sensing fiber, achieve lower noise floor for weak signals and, moreover, perform high-speed processing within milliseconds (or sometimes even less). In this paper, we present a new, real-time DAS, realized by using the time gated digital-optical frequency domain reflectometry (TGD-OFDR) method, in which the chirp pulse is divided into overlapping bands and assembled after digital decoding. The developed prototype NBX-S4000 generates a chirp signal with a pulse duration of 2 μs and uses a frequency sweep of 100 MHz at a repeating frequency of up to 5 kHz. It allows one to detect sound waves at an 80 km fiber distance range with spatial resolution better than a theoretically calculated value of 2.8 m in real time. The developed prototype was tested in the field in various applications, from earthquake detection and submarine cable sensing to oil and gas industry applications. All obtained results confirmed effectiveness of the method and performance, surpassing, in conventional SM fiber, other commercially available interrogators.

A Transient 3D CFD Model of a Progressive Cavity Pump

2016 ◽  
Author(s):  
K. R. Mrinal ◽  
Md. Hamid Siddique ◽  
Abdus Samad
Keyword(s):  
Oil And Gas ◽  
Complex Geometry ◽  
Transient Behavior ◽  
Oil And Gas Industry ◽  
High Viscosity ◽  
Solid Content ◽  
Operating Conditions ◽  
Cfd Model ◽  
Ansys Fluent ◽  
Flow Variables

A progressive cavity pump (PCP) is a positive displacement pump and has been used as an artificial lift method in the oil and gas industry for pumping fluid with solid content and high viscosity. In a PCP, a single-lobe rotor rotates inside a double-lobe stator. Articles on computational works for flows through a PCP are limited because of transient behavior of flow, complex geometry and moving boundaries. In this paper, a 3D CFD model has been developed to predict the flow variables at different operating conditions. The flow is considered as incompressible, single phase, transient, and turbulent. The dynamic mesh model in Ansys-Fluent for the rotor mesh movement is used, and a user defined function (UDF) written in C language defines the rotor’s hypocycloid path. The mesh deformation is done with spring based smoothing and local remeshing technique. The computational results are compared with the experiment results available in the literature. Thepump gives maximum flowrate at zero differential pressure.

Machine learning-based digital twins reduce seasonal remapping in aeroderivative gas turbines

2021 ◽  
pp. 1-7
Author(s):  
Nick Petro ◽  
Felipe Lopez
Keyword(s):  
Machine Learning ◽  
Ambient Temperature ◽  
Gas Turbines ◽  
Operating Conditions ◽  
Pilot Site ◽  
Digital Twin ◽  
Digital Twins ◽  
Machine Learning Model ◽  
Unacceptable Behavior ◽  
External Conditions

Abstract Aeroderivative gas turbines have their combustion set points adjusted periodically in a process known as remapping. Even turbines that perform well after remapping may produce unacceptable behavior when external conditions change. This article introduces a digital twin that uses real-time measurements of combustor acoustics and emissions in a machine learning model that tracks recent operating conditions. The digital twin is leveraged by an optimizer that select adjustments that allow the unit to maintain combustor dynamics and emissions in compliance without seasonal remapping. Results from a pilot site demonstrate that the proposed approach can allow a GE LM6000PD unit to operate for ten months without seasonal remapping while adjusting to changes in ambient temperature (4 - 38 °C) and to different fuel compositions.

Study the effect of casting temperature on details from thermo-plastic materials

2020 ◽  
pp. 42-45
Author(s):  
J.A. Kerimov ◽  
Keyword(s):  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Mold Temperature ◽  
Quality Parameters ◽  
Oil Field ◽  
Casting Temperature ◽  
Gas Industry ◽  
Field Equipment ◽  
Wide Range ◽  
The Impact

The implementation of plastic details in various constructions enables to reduce the prime cost and labor intensity of machine and device manufacturing, decrease the weight of design and improve their quality and reliability at the same time. The studies were carried out with the aim of labor productivity increase and substitution of colored and black metals with plastic masses. For this purpose, the details with certain characteristics were selected for further implementation of developed technological process in oil-gas industry. The paper investigates the impact of cylinder and compression mold temperature on the quality parameters (shrinkage and hardness) of plastic details in oil-field equipment. The accessible boundaries of quality indicators of the details operated in the equipment of exploration, drilling and exploitation of oil and gas industry are studied in a wide range of mode parameters. The mathematic dependences between quality parameters (shrinkage and hardness) of the details on casting temperature are specified.

A Novel Corrosion Monitoring and Prediction System Utilizing Advanced Artificial Intelligence

2021 ◽  
Author(s):  
Klemens Katterbauer ◽  
Waleed Dokhon ◽  
Fahmi Aulia ◽  
Mohanad Fahmi
Keyword(s):  
Real Time ◽  
Oil And Gas ◽  
Salt Water ◽  
Oil And Gas Industry ◽  
Training Dataset ◽  
Metal Loss ◽  
Corrosion Monitoring ◽  
Prediction System ◽  
Well Performance ◽  
Data Driven Approach

Abstract Corrosion in pipes is a major challenge for the oil and gas industry as the metal loss of the pipe, as well as solid buildup in the pipe, may lead to an impediment of flow assurance or may lead to hindering well performance. Therefore, managing well integrity by stringent monitoring and predicting corrosion of the well is quintessential for maximizing the productive life of the wells and minimizing the risk of well control issues, which subsequently minimizing cost related to corrosion log allocation and workovers. We present a novel supervised learning method for a corrosion monitoring and prediction system in real time. The system analyzes in real time various parameters of major causes of corrosion such as salt water, hydrogen sulfide, CO2, well age, fluid rate, metal losses, and other parameters. The data are preprocessed with a filter to remove outliers and inconsistencies in the data. The filter cross-correlates the various parameters to determine the input weights for the deep learning classification techniques. The wells are classified in terms of their need for a workover, then by the framework based on the data, utilizing a two-dimensional segmentation approach for the severity as well as risk for each well. The framework was trialed on a probabilistically determined large dataset of a group of wells with an assumed metal loss. The framework was first trained on the training dataset, and then subsequently evaluated on a different test well set. The training results were robust with a strong ability to estimate metal losses and corrosion classification. Segmentation on the test wells outlined strong segmentation capabilities, while facing challenges in the segmentation when the quantified risk for a well is medium. The novel framework presents a data-driven approach to the fast and efficient characterization of wells as potential candidates for corrosion logs and workover. The framework can be easily expanded with new well data for improving classification.

A Survey on the Use of Digital Twins for Maintenance and Safety in the Offshore Oil and Gas Industry

2021 ◽  
Author(s):  
Tom Ivar Pedersen ◽  
Håkon Grøtt Størdal ◽  
Håvard Holm Bjørnebekk ◽  
Jørn Vatn
Keyword(s):  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Gas Industry ◽  
Digital Twins ◽  
Offshore Oil And Gas ◽  
Offshore Oil

Technology Focus: Offshore Drilling and Completion (October 2021)

2021 ◽  
Vol 73 (10) ◽  
pp. 45-45
Author(s):  
Martin Rylance
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Oil And Gas ◽  
Hybrid Approach ◽  
Data Communication ◽  
Oil And Gas Industry ◽  
Oil Field ◽  
Real Time Processing ◽  
National University ◽  
Seoul National University

Communication and prediction are symmetrical. Communication, in effect, is prediction about what has happened. And prediction is communication about what is going to happen. Few industries contain as many phases, steps, and levels of interface between the start and end product as the oil and gas industry—field, office, offshore, plant, subsea, downhole, not to mention the disciplinary, functional, managerial, logistics handovers, and boundaries that exist. It therefore is hardly surprising that communication, in all its varied forms, is at the very heart of our business. The papers selected this month demonstrate how improved communication can deliver the prediction required for a variety of reasons, including safety, efficiency, and informational purposes. The application of new and exciting ways of working, partially accelerated by recent events, is leading to breakthrough improvements on all levels. Real-time processing, improved visualization, and predictive and machine-learning methods, as well as improvements in all forms of data communication, are all contributing to incremental enhancements across the board. This month, I encourage the reader to review the selected articles and determine where and how the communication and prediction are occurring and what they are delivering. Then perhaps consider performing an exercise wherein your own day-to-day roles—your own areas of communication, interfacing, and cooperation—are reviewed to see what enhancements you can make as an individual. You may be pleasantly surprised that some simple tweaks to your communication style, frequency, and format can deliver quick wins. In an era of remote working for many individuals, it is an exercise that has some value. Recommended additional reading at OnePetro: www.onepetro.org. OTC 30184 - Augmented Machine-Learning Approach of Rate-of-Penetration Prediction for North Sea Oil Field by Youngjun Hong, Seoul National University, et al. OTC 31278 - A Digital Twin for Real-Time Drilling Hydraulics Simulation Using a Hybrid Approach of Physics and Machine Learning by Prasanna Amur Varadarajan, Schlumberger, et al. OTC 31092 - Integrated Underreamer Technology With Real-Time Communication Helped Eliminate Rathole in Exploratory Operation Offshore Nigeria by Raphael Chidiogo Ozioko, Baker Hughes, et al.

Digital Oil Field; The NPDC Experience

2021 ◽  
Author(s):  
Henry Ijomanta ◽  
Lukman Lawal ◽  
Onyekachi Ike ◽  
Raymond Olugbade ◽  
Fanen Gbuku ◽  
...  
Keyword(s):  
Real Time ◽  
Oil And Gas ◽  
Data Gathering ◽  
Oil And Gas Industry ◽  
Oil Field ◽  
Battery Life ◽  
Gas Industry ◽  
Field Management ◽  
Digital Oilfield ◽  
Data Analytic

Abstract This paper presents an overview of the implementation of a Digital Oilfield (DOF) system for the real-time management of the Oredo field in OML 111. The Oredo field is predominantly a retrograde condensate field with a few relatively small oil reservoirs. The field operating philosophy involves the dual objective of maximizing condensate production and meeting the daily contractual gas quantities which requires wells to be controlled and routed such that the dual objectives are met. An Integrated Asset Model (IAM) (or an Integrated Production System Model) was built with the objective of providing a mathematical basis for meeting the field's objective. The IAM, combined with a Model Management and version control tool, a workflow orchestration and automation engine, A robust data-management module, an advanced visualization and collaboration environment and an analytics library and engine created the Oredo Digital Oil Field (DOF). The Digital Oilfield is a real-time digital representation of a field on a computer which replicates the behavior of the field. This virtual field gives the engineer all the information required to make quick, sound and rational field management decisions with models, workflows, and intelligently filtered data within a multi-disciplinary organization of diverse capabilities and engineering skill sets. The creation of the DOF involved 4 major steps; DATA GATHERING considered as the most critical in such engineering projects as it helps to set the limits of what the model can achieve and cut expectations. ENGINEERING MODEL REVIEW, UPDATE AND BENCHMARKING; Majorly involved engineering models review and update, real-time data historian deployment etc. SYSTEM PRECONFIGURATION AND DEPLOYMENT; Developed the DOF system architecture and the engineering workflow setup. POST DEPLOYMENT REVIEW AND UPDATE; Currently ongoing till date, this involves after action reviews, updates and resolution of challenges of the DOF, capability development by the operator and optimizing the system for improved performance. The DOF system in the Oredo field has made it possible to integrate, automate and streamline the execution of field management tasks and has significantly reduced the decision-making turnaround time. Operational and field management decisions can now be made within minutes rather than weeks or months. The gains and benefits cuts across the entire production value chain from improved operational safety to operational efficiency and cost savings, real-time production surveillance, optimized production, early problem detection, improved Safety, Organizational/Cross-discipline collaboration, data Centralization and Efficiency. The DOF system did not come without its peculiar challenges observed both at the planning, execution and post evaluation stages which includes selection of an appropriate Data Gathering & acquisition system, Parts interchangeability and device integration with existing field devices, high data latency due to bandwidth, signal strength etc., damage of sensors and transmitters on wellheads during operations such as slickline & WHM activities, short battery life, maintenance, and replacement frequency etc. The challenges impacted on the project schedule and cost but created great lessons learnt and improved the DOF learning curve for the company. The Oredo Digital Oil Field represents a future of the oil and gas industry in tandem with the industry 4.0 attributes of using digital technology to drive efficiency, reduce operating expenses and apply surveillance best practices which is required for the survival of the Oil and Gas industry. The advent of the 5G technology with its attendant influence on data transmission, latency and bandwidth has the potential to drive down the cost of automated data transmission and improve the performance of data gathering further increasing the efficiency of the DOF system. Improvements in digital integration technologies, computing power, cloud computing and sensing technologies will further strengthen the future of the DOF. There is need for synergy between the engineering team, IT, and instrumentation engineers to fully manage the system to avoid failures that may arise from interface management issues. Battery life status should always be monitored to ensure continuous streaming of real field data. New set of competencies which revolves around a marriage of traditional Petro-technical skills with data analytic skills is required to further maximize benefit from the DOF system. NPDC needs to groom and encourage staff to venture into these data analytic skill pools to develop knowledge-intelligence required to maximize benefit for the Oredo Digital Oil Field and transfer this knowledge to other NPDC Asset.

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