This article analyzes the construction ideas, methods and key points of digital twins in oil and gas exploration and development.
Construction ideas
Through in-depth analysis of a large number of typical application cases of digital twins at home and abroad, it is not difficult to find that the construction of digital twin systems has a very clear and clear goal orientation. The digital twin system aims to break the barriers between the physical world and the virtual world and achieve deep integration and interaction between the two. In the specific implementation process, strictly following the core elements of digital twins is the key to the success of the project.
Physical environment modeling: Use precise technical means such as laser scanning, photogrammetry, and sensor networks to collect all-round and high-precision data on the physical environment in reality. After that, use advanced 3D modeling software and algorithms to convert the collected data into a virtual model to fully replicate the physical environment in the virtual world. Not only should the appearance of the physical entity be presented, but also its physical characteristics and movement laws should be simulated to build a highly realistic virtual space to achieve a one-to-one correspondence between reality and virtuality. For example, in the digital twin project of smart cities, accurate modeling of urban buildings, roads, bridges, underground pipe networks, etc. is carried out, providing a reliable virtual platform for urban planning, traffic management, emergency response, etc.
Business data flow: It is crucial to build a complete data integration system. First, it is necessary to integrate data from different data sources, including data generated by production equipment sensors, enterprise information systems, and IoT terminals. Through data cleaning, transformation, and loading (ETL) technology, these multi-source heterogeneous data are standardized to ensure that the data can be smoothly integrated into the digital twin application scenario. Based on the business process logic, real-time data processing technology and big data analysis platform are used to achieve real-time monitoring of the production process. For example, in industrial production, through real-time data collection and analysis of production line equipment, abnormal equipment status can be discovered in time, faults can be warned in advance, and data can be efficiently circulated throughout the business process, providing strong support for decision-making and execution in all links.
Professional software integration: Integrate various advanced and mature professional software, such as CAD (computer-aided design), CAE (computer-aided engineering), CAM (computer-aided manufacturing) software, and professional simulation software. These software have powerful simulation algorithms and can simulate and analyze the production process in multiple dimensions. Through data interfaces and middleware technologies, these professional software are integrated into the digital twin system to deeply explore the value of data. For example, in automobile manufacturing, CAE software is used to simulate and analyze the structural strength and aerodynamic performance of automobiles, accurately diagnose, scientifically predict and comprehensively optimize the production process, thereby significantly improving production efficiency and ensuring product quality.
Construction method
Comprehensively analyzing classic cases at home and abroad, we can summarize the following key methods:
Virtual-real mapping of application scenarios: In virtual space, with the help of advanced graphics rendering technology, virtual reality (VR) and augmented reality (AR) technology, physical products are mapped in all directions. Not only should the appearance and structure of the product be presented, but also its operating status under different working conditions should be simulated. By establishing a real-time data transmission channel, the sensor data of the physical equipment in the production process is transmitted to the virtual model in real time, accurately presenting the dynamic changes and potential failures of the physical equipment in the production process. For example, in the digital twin application of aircraft engines, through the real-time collection and analysis of engine operation data, the changes in parameters such as engine temperature, pressure, and speed are displayed in real time in the virtual model. Once an abnormality occurs, the system can issue an alarm in time, allowing managers to detect and respond in time.
Project construction process: The construction process of digital twin projects is similar to that of traditional software projects. Both projects need to go through key stages such as demand analysis, technical design, process modeling, data integration, and professional software integration. In the demand analysis stage, we need to deeply understand user needs and business pain points, and clarify the functions and performance requirements of the digital twin system. In the technical design stage, we select appropriate technical architecture and technical solutions based on the results of demand analysis. In the process modeling stage, we sort out and model business processes to optimize business processes. In the data integration stage, we integrate multi-source data, establish data warehouses and data lakes. In the professional software integration stage, we integrate various professional software into the system to realize the functions of the system. Each stage is closely linked and interlocked, and together promotes the steady development of the project.
Focus
From many successful typical application scenarios, we have extracted the following key points of attention:
Virtual construction based on real business scenarios: A successful digital twin project will first carefully and clearly define the boundaries and specific contents of the application scenarios to ensure that these scenarios can give full play to the unique advantages of digital twin technology. In the process of virtually constructing the real physical world, the scope and sophistication of physical entities are clearly defined according to business needs and application scenarios. For example, in the application of digital twins in the medical field, the accuracy and scope of modeling of human organs should be determined according to different medical diagnosis and treatment needs. Relying on complete data resources and efficient business algorithms, a solid guarantee is provided for the stable operation and accurate decision-making of the digital twin system. By establishing a data governance mechanism, the accuracy, completeness and consistency of the data are ensured. Artificial intelligence and machine learning algorithms are used to deeply mine and analyze data to provide intelligent decision-making support for the digital twin system.