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.

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

A Novel Methodology for Optimal Design of Compressor Plants Using Probabilistic Plant Design

2013 ◽  
Vol 135 (11) ◽  
Author(s):  
Rainer Kurz ◽  
J. Michael Thorp ◽  
Erik G. Zentmyer ◽  
Klaus Brun
Keyword(s):  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Operating Conditions ◽  
Process Conditions ◽  
Plant Design ◽  
Well Performance ◽  
Worst Case ◽  
Gas Industry ◽  
Machine Performance ◽  
Equipment Sizing

Equipment sizing decisions in the oil and gas industry often have to be made based on incomplete data. Often, the exact process conditions are based on numerous assumptions about well performance, market conditions, environmental conditions, and others. Since the ultimate goal is to meet production commitments, the traditional method of addressing this is to use worst case conditions and often adding margins onto these. This will invariably lead to plants that are oversized, in some instances, by large margins. In reality, the operating conditions are very rarely the assumed worst case conditions, however, they are usually more benign most of the time. Plants designed based on worst case conditions, once in operation, will, therefore, usually not operate under optimum conditions, have reduced flexibility, and therefore cause both higher capital and operating expenses. The authors outline a new probabilistic methodology that provides a framework for more intelligent process-machine designs. A standardized framework using a Monte Carlo simulation and risk analysis is presented that more accurately defines process uncertainty and its impact on machine performance. Case studies are presented that highlight the methodology as applied to critical turbomachinery.

scholarly journals Digital Twin for the Oil and Gas Industry: Overview, Research Trends, Opportunities, and Challenges

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 104175-104197 ◽  
Author(s):  
Thumeera R. Wanasinghe ◽  
Leah Wroblewski ◽  
Bui K. Petersen ◽  
Raymond G. Gosine ◽  
Lesley Anne James ◽  
...  
Keyword(s):  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Research Trends ◽  
Gas Industry ◽  
Digital Twin

Digital Transformation in Oil and Gas Industry: Developing an OSDU Third-Party Application

2021 ◽  
Author(s):  
Imran Harith Bin Azmy ◽  
Azri Bin Azmi ◽  
Mohd Suffian Sulaiman ◽  
Othman Bin Mohd Yusop
Keyword(s):  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Digital Transformation ◽  
Third Party ◽  
Gas Industry

The Role of Digitalization in Decarbonizing the Oil and Gas Industry

2021 ◽  
Author(s):  
Peace Bello
Keyword(s):  
Oil And Gas ◽  
Enhanced Recovery ◽  
Oil And Gas Industry ◽  
Gas Industry ◽  
Long Run ◽  
Digital Twins ◽  
Operations And Maintenance ◽  
Share Data ◽  
Real Time Visualization ◽  
Visualization Of Data

Abstract As the Oil & Gas industry journeys towards net zero carbon emissions, a lot needs to be done, one of which is the adoption of digital transformation across companies. Decarbonization requires a transformational shift in the way companies operate, how they source, use, consume and think about energy and feedstocks. If the Oil & Gas sector will continue to exist, it must carry out its activities in the safest possible way and digitalizing it will help in achieving this. A survey by Newsweek shows that areas where transformative technologies are having the biggest impact are production-related, operations and maintenance, enhanced recovery, fracking/tight reservoirs, and exploitation at greater depths. Luis Abril of Minsait opined that digital technology enables companies to extract more value from data, using new platforms to share data with the entire organization, suppliers, contractors, and partners. The real-time visualization of data helps optimize decision making. Big data can be analyzed to find answers to questions such as: What piece of equipment is showing signs of wear and should be replaced? What sort of predictive maintenance can be leveraged? What is the most effective fracking approach for this well? AI helps to reduce routine flaring, employ methane capture, optimize production and reservoir management using digital tools such as IoT sensors, digital twins, and virtual reality to model scenarios, monitor operations, track emissions, energy usage and proactively maintain equipment, produce lower-emission products by moving from one hydrocarbon to another (e.g., from coal to natural gas) or creating another product (such as biofuels or syngas). Transformative technologies, particularly IoT, mobility and cloud applications are going to have a profound effect on the future of the oil and gas sector. Investment in these technologies cost a lot which might be difficult for private companies, but it is worth the money in the long run.

Estimating the Lengthy Missing Log Interval Using Group Method of Data Handling (GMDH) Technique

2014 ◽  
Vol 695 ◽  
pp. 850-853 ◽  
Author(s):  
Mohammed Abdallah Ayoub ◽  
Ahmed Abdelhafeez Mohamed
Keyword(s):  
Oil And Gas ◽  
Model Performance ◽  
Oil And Gas Industry ◽  
Group Method ◽  
Generic Model ◽  
Data Sets ◽  
Data Handling ◽  
Gas Industry ◽  
Information Sets ◽  
Code Group

Estimation of well log properties is crucial in identifying the trends and the properties in oil and gas industry, which will enable firms to avoid problems during operation procedures. In this paper, Group Method of Data Handling (GMDH) technique is utilized to generate a generic model with superior prediction capabilities. NeuraLogTM software is used in order to convert the scanned image logs into digit one that can be used by MATLAB code. Group Method of Data Handling is utilized to predict the same missing interval. An aggregate of 601 field data sets were utilized to create the model. These information sets were separated into training, cross validation and testing sets in the degree of 2:1:1. Trend analyses as well as graphical and statistical tools have been utilized in order to assess the model performance.

scholarly journals FROM REQUIREMENTS TO PROTOTYPING: APPLICATION OF HUMAN-SYSTEM INTEGRATION METHODOLOGY TO DIGITAL TWIN DESIGN

2021 ◽  
Vol 1 ◽  
pp. 1617-1626
Author(s):  
Stélian Camara Dit Pinto ◽  
Dimitri Masson ◽  
Eric Villeneuve ◽  
Guy Boy ◽  
Laetitia Urfels
Keyword(s):  
System Integration ◽  
Oil And Gas ◽  
Function Analysis ◽  
Human Perception ◽  
Oil And Gas Industry ◽  
Twin Design ◽  
Gas Industry ◽  
Human System ◽  
Digital Twin ◽  
New Process

AbstractIndustrial digital transformation is bringing a need for new tools and concepts. However, designing such complex tools and concept requires methods to be correctly implemented. These methods are studied as part of system engineering to satisfy various identified goals, and more specifically human-system integration, which is the topic of this paper. This article introduces the method used to define key elements of human perception of reality called reality anchors to design scenarios to be tested in a digital twin prototype. This method goes from regulation study to user cognitive function analysis on the specific case of digital twin designing in oil-and-gas industry. This method highlighted the differences between theoretical process and the followed process as well as tools and competencies used to identify reality anchors. This knowledge will then be used to implement a new process to be implemented with a digital twin and scenarios to test the prototype using realistic simulation.

scholarly journals Verification of the protective steam curtain model by means of experiment’s results

2018 ◽  
Vol 224 ◽  
pp. 04022 ◽  
Author(s):  
Rail Nasibullin ◽  
Sergey Valeyev ◽  
Ainur Galeyev
Keyword(s):  
Mathematical Model ◽  
Combustion Zone ◽  
Laboratory Experiments ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Third Party ◽  
Gas Industry ◽  
Combustible Gases ◽  
Gas Cloud ◽  
The Mathematical Model

To protect the technological furnaces of the oil and gas industry from the penetration into their combustion zone combustible gases that are accidentally released at the production site, steam curtains are used. In the open press, there are practically no methods that allow to evaluate the efficiency of steam curtains, so the solution of this issue seems to be topical. In this paper, we checked the adequacy of the mathematical model developed by the authors of this article. This model describes the operation of the curtain, the movement of the vapor-gas cloud in space, and the scattering of the cloud by the curtain. The verification was carried out by comparing the results of the simulation with the results of laboratory experiments of third-party authors.

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