Digital modeling

All three Russian hydrocarbons – oil, gas and coal – will be in demand on world markets for decades to come. This conclusion suggests itself based on Russia’s energy strategy, which is currently being developed up to 2050.
Not only China, but also Europe is buying more Russian gas this year.
The task of improving the exploration of deposits, developing their infrastructure, production, processing, transportation, and supplying customers with contractual volumes requires specialists to implement the most modern technologies at all stages of design and support of existing production complexes. Where such technologies do not exist, they have to be created practically from scratch independently. It is very important to use developments in the field of digital modeling of objects, all processes, including field management as a whole.

The purpose of the publication is primarily educational, informative, popularization of science, as well as the desire to attract new young (and not so young) minds to the ranks of researchers, to science, to arouse in such minds the desire to search for answers to emerging questions. The scale of the topic requires the introduction of reasonable restrictions on the presented material after a brief panoramic review.

Modeling. General Provisions

Digital modeling is a method of studying real objects, phenomena, processes, systems, based on the study of their mathematical models using a digital computer. This is modeling implemented using computer technology.
A special role in modern production and its modeling is played by digital twins (Digital twin) – virtual copies of physical devices that simulate their behavior in real conditions. A digital twin (DT) is a digital (virtual) model of any objects, systems, processes or people. It accurately reproduces the form and actions of the original and is synchronized with it.

With their help, it is possible to develop new technological production schemes, select the optimal mode and composition of raw materials, evaluate the economic effect, “playing out” the operation of the object in advance. They help to make optimal management decisions in real time in the conditions of complex multi-parameter processes, and sometimes completely eliminate the human factor.
A digital twin is needed to simulate what will happen to the original under certain conditions. This helps, firstly, to save time and money (for example, if we are talking about complex and expensive equipment), and secondly, to avoid harm to people and the environment.

When using the CD, consider:
– A physical product in real space.
– A virtual product in virtual space.
– Data and information that connects virtual and physical products.

Seismic exploration in the search for oil and gas deposits

The basic principle of conducting seismological research in oil and gas exploration is the study of vibrations in the rock mass by generating a sound wave propagated deep into the earth's interior.

Sound waves can be generated in seismic exploration in a variety of ways, from creating vibrations and explosions to blows with compressed air and dropping a heavy load onto the ground.

Smart manufacturing is the automated organization of production processes of an enterprise and the industry as a whole using robotics, machine vision, 3D printing, images, virtual, augmented and extended reality, big data, the Internet of Things, cloud services and other modern technologies to increase productivity and reduce costs.
Internet of Things (IoT) technology is a technology for transmitting data between physical objects (“things”) equipped with built-in tools and technologies for interacting with each other or with the external environment.

What are digital twins?
– prototype (Digital Twin Prototype (DTP)) – is a virtual analogue of a real object, which contains all the data for producing the original;
– instance (Digital Twin InstanceDTI) – contains data on all the characteristics and operation of a physical object, including a three-dimensional model, and operates in parallel with the original;
– aggregated twin (Digital Twin Aggregate (DTA)) is a computing system of digital twins and real objects that can be controlled from a single center and exchange data within the system.

Modeling tasks

What tasks do digital twins solve:
– Conducting a test run of a process or production chain quickly and without significant investment.
– Detecting problems or vulnerabilities before production starts or a facility goes into operation.
– Improving the efficiency of processes or systems, tracking all failures before they start.
– Reducing risks, including financial ones, as well as those related to safety for the life and health of personnel.
– Increasing competitiveness and profitability of business.
– Making long-term forecasts and planning the development of a company or product for years to come.
– Increasing customer loyalty through accurate forecasting of demand and consumer qualities of the product.

Types of Realities in Models
Virtual Reality (VR) — technology of full immersion in the virtual world due to devices: VR glasses, headphones, gloves. For example, in simulators for training pilots, VR is used to create a visualization of real places, landscapes and weather conditions.

Augmented Reality (AR) — a technology for superimposing digital objects onto real-world objects. For example, when a user points a smartphone camera at a real building façade and sees an animation of its history on it.

Mixed Reality (MR) — technology that allows the user to interact with virtual objects in reality. For example, when a student sees a 3D model of a hovering globe through special glasses and can rotate it, click on continents and countries.

Extended Reality (XR) — a general term for all virtual technologies. XR is the basis of metaverses — online spaces in which physical, augmented and virtual realities are intertwined.

Methods of creating a CD:
– 3D graphic model;
– a model based on the Internet of Things;
– integrated mathematical models – such as CAE systems (Computer-aided engineering, solutions for engineering analysis, calculations and simulations) for engineering calculations;
– various visualization technologies – including holograms, physical AR for augmented reality and computer AR for virtual reality VR.
A hologram is a method of reproducing information that recreates three-dimensional images of objects. There are two methods of creating holograms: physical and computer

Research of the object
This stage precedes development only if the digital twin has a real prototype – for example, a working enterprise or a communications system. Then the developers create a detailed map of the prototype, reproduce all processes and characteristics. It is important to study the object in different conditions.

Modeling a digital copy of an object
This stage may be the first one if there is no real prototype yet and the creation of a digital twin precedes it. For example, in construction or design, when a digital 3D model is created first, and only then the original of a building or other object.
To build a complex model, mathematical methods of calculation and analysis are used:

– Finite Element Analysis (FEA), which allows calculating the operating load. It is used, for example, to calculate the mechanics of a deformable solid, heat transfer, hydrodynamics and electrodynamics.
– FMEA models (Failure Mode and Effects Analysis) are necessary for analyzing the reliability of systems and identifying the most critical steps in production processes.
– CAD (computer-aided design/drafting) models are used to calculate the external characteristics and structure of objects, materials and processes.

Embodiment of the model
Then the previously calculated digital twin architecture is transferred to special platforms, such as Siemens or Dassault Systems. They combine mathematical models, data, and an interface for managing the digital twin, turning it into a dynamic system. This stage can be compared to the transformation of program code into a program or application with a visual interface that is understandable to any user.

Testing the main work processes on a digital twin
The main goal of this stage is to predict how an object or system will behave in normal mode and in emergency situations in order to avoid breakdowns and overloads after launch. To do this, technical analysts are involved in the process, who collect a large array of data during testing in order to calculate algorithms for any possible conditions and situations.

Start-up and adjustment
If the previous stage is carried out correctly, up to 90% of failures and breakdowns can be avoided during the operation of a real prototype. However, some situations still cannot be predicted, and then they are tracked already at the stage of launching and adjusting the digital twin
.
Adjustment and development of the original object or system
Engineers then continue to work with the digital twin as with a real physical object until all systems and processes are debugged. Based on the results of this work, changes are made to the original object to achieve its maximum efficiency.

The Prospects of Digital Twins

The digital twin market will soon reach $16 billion. According to Gartner, by 2021, up to 50% of all large industrial companies will use digital twins. In industry, the technology already helps to increase efficiency by at least 10%, and in the oil industry, it can save from 5% to 20% of capital investments..

Digital Twin (DT)

For example, in the Middle East, digital twin technology has enabled ADNOC to consolidate 20 oil refining and production facilities into a single control room and standardize all processes.
On the territory of a state, geological exploration establishes an oil and/or gas (O&G) deposit. Usually, such a deposit is located under the surface of the earth and is a huge tangled labyrinth (network) of cavities, connected by channels like pipelines of different cross-sections, and partially hidden in sand, rocks or water. A person cannot correctly determine what and where is located underground.

The CD allows this to be done at a qualitatively new level. The CD is a virtual copy of a real field, its digital analogue. The application and use of the CD makes it possible to obtain data on a real field, how it is structured, what rocks are present in it, what are the chemical and physical properties of these rocks, where and how much of the N&G is located. Thanks to the CD, oil and gas industry workers simulate various scenarios for the development and operation of a field in real time, choose the most optimal production methods, predict the occurrence of problems and prepare solutions.

It is clear that such an approach helps to optimize the processes of extraction, transportation, reduce risks and costs, minimize damage and harm to the environment. Such use of CD fields in the oil and gas industry makes it more efficient and safer, both for the population and the natural environment of the planet as a whole.

Oil industry is an industry where the latest achievements of science and technology are applied. The industry is characterized by hundreds of thousands of production facilities, including drilling rigs, wells and communications (in particular, the satellite system “Gonets”) with them oil and gas pipeline networks, power lines, pumping stations, etc. objects

Every production facility generates a huge amount of technological information every day. This huge array of data requires continuous processing over time.

The strategic activities of Rosneft Oil Company include the integration of information technologies into all production processes and aspects of activity – from exploration and production of oil and gas to transportation (Fig. D, E, G) and processing (Fig. G), including processing and analysis ofmore dataabout mechanisms for increasing efficiency from the point of view of economics and ecology. Here it is necessary to mention geological and hydrodynamic models of the deposit, about the use of neural networks for data analysis and about the creation of integrated models of operational management of deposits based on the digital database.

The Digital Field project is known for its significant growth in production indicators and significant economic effect. The project includes many components with their own tasks. For example, 3D field models and 3D visualization technologies are necessary for highly accurate assessment of oil and gas reserves and understanding where exactly they are located under the earth's crust.

Wells are becoming intelligent with automatic production control. Such wells provide remote control of the process in real time. The presence of a neural network provides forecasting of the behavior of the field and identification of patterns among a huge number of indicators at a speed inaccessible to traditional approaches.

A separate component of the field's central data is the digital model data storage. Information is not only a blessing, but also a heavy, ever-growing burden. A person cannot initially master big data, and until recently, computers did not have enough computing power. But technologies capable of handling the data exchange traffic that has increased several times have come to the rescue. One of them is the Internet of Things, designed to make life on the planet smart, and therefore more convenient and comfortable for people.

Not all data that takes up a lot of space is called Big Data. The term is applied only to data that meets the VVV principle, which stands for Volume, Velocity, Variety.

Big data are arrays of any heterogeneous data, both valuable and worthless, the faster the volume grows, the more data there is. At the same time, the number of packets of such data is large, and the size of one packet is small. What does, for example, 1 terabyte of data contain? This could be 250 thousand photos taken with a 12-megapixel camera, or 250 films, or 6.5 million pages of text documents. Is this a lot or a little? To accommodate a paper analogue of such an amount of information, more than 1000 cabinets with documents would be required.

Rosneft uses and operates the Seismic Information Storage Center. The storage contains more than 5 petabytes of information. This is the largest volume among all oil and gas companies in the country. More than 40 Rosneft enterprises and all corporate scientific institutes are connected to the system, and the information is available online.

Also, data in the model of the field's CD are collected from 120 area objects from 12 thousand wells. Analysis of this data allows implementing scenarios for issuing recommendations for field management. A human operator is not able to cover such a huge flow of information without an assistant, without special software with elements of artificial intelligence (AI).

It is these programs that promptly identify incorrectly installed settings, predict equipment failure moments, and track parameters coming from sensors and smart devices. Thus, a single information system helps to obtain up-to-date and complete information about all stages of the production process chain.

Cybersecurity is a set of conditions, technologies, methods and processes for protecting the integrity of computers, networks, operating systems, applications and other programmable configurable components from cyber threats, impacts that cause unwanted damage, and deliberate cyber attacks.

After the qualitative and quantitative complication of the digital infrastructure, as well as the creation of regular cyber units, the first salvos of digital wars were fired. The second decade of the 21st century is marked by a whole scattering of high-profile hacker “achievements”.
Among the main technological directions of modern cybersecurity, we can highlight:
– security services;
– infrastructure protection;
– network protection;
– managing access to identification data;
– consumer security software.

Conclusion

Digital modeling is a powerful tool for studying the properties of existing or designed objects. If the processes in the existing model proceed faster than in the real object, then, by equipping the model with interface means by which the modeled object interacts with the external environment, we obtain a digital device with properties adequate to the modeled object.

Such a model can be embedded into a real system and is called digital twinIf the performance of the digital twin is higher than the performance of the modeled object, then it becomes possible to predict the processes occurring in the object and use the results of the prediction in automatic control systems for these processes.

A more expanded understanding of the digital twin is embedded in the production process modeling environment. The said environment should contain, in addition to direct modeling tools, information security (software protection), tools for analyzing the results of impact on the control object, implementing feedback.

1. A digital twin is a virtual model of an object, process, or even a person.

2. Digital twins can be used in a wide variety of business areas, but they are most justified in large-scale production and complex projects.

3. Businesses can increase profits, efficiency, and improve work safety with the help of digital twins. Thus, experiments can be conducted in a digital environment where there is no risk of doing something wrong.

4. The time frame for creating and the cost of a digital twin depend on the tasks it must solve. The degree of digitalization of the business is also important.

5. Before creating a digital twin, you need to calculate the benefits it will bring to the business. The effect of implementation should significantly exceed the cost of development, otherwise there is no point in it.

Literature

1. Moy, Torbjorn; Chibichik, Andrey; Relvåg, Terje (2020-05-01). “Health Monitoring of a Swing Boom Crane Based on a Digital Twin: An Experimental Study”. Engineering Failure Analysis. 112: 104517. doi:10.1016/j.engfailanal.2020.104517. hdl:11250/2650461. ISSN 1350-6307.

2. Haag, Sebastian; Anderl, Rainer (01.01.2018). “Digital Twin – Proof of Concept”. Manufacturing Letters. Industry 4.0 and Smart Manufacturing. 15: 64–66. doi:10.1016/j.mfglet.2018.02.006. ISSN 2213-8463.

3. Boschert, Stefan; Rosen, Roland (2016), Hechenberger, Peter; Bradley, David (eds.), “Digital Twin – An Aspect of Modeling”The Future of Mechatronics: Challenges and Solutions for Mechatronic Systems and Their Designers, Sham: Springer International Publishing, pp. 59–74, doi: 10.1007/978-3-319-32156-1_5, ISBN 978-3-319-32156-1access date 2024-03-16

4. Greaves, Michael (October 31, 2002). “Closing the Loop: Leveraging PLM Information in Sales and Service”. Source: November 19, 2022.

5. Greaves, Michael (October 5, 2015). “Can Digital Twins Change Manufacturing?”. World Economic Forum. New Technologies. Source: November 19, 2022.