Detailed analysis of the implementation steps and strategies for the construction of digital twins of wellbores in the field of oil and gas exploration and development
1. Construction Difficulties
Wellbore production business covers multiple links from exploration to mining, involving many technical fields and operation processes, and its diversity and complexity are beyond imagination. Different geological conditions, mining stages and production requirements make each wellbore operation and production type have unique characteristics and requirements. Therefore, it is necessary to build a static model that can accurately reflect various working conditions. For example, in deep oil and gas wells with high temperature and pressure, the fluid characteristics and pressure distribution in the wellbore are completely different from those in shallow wells with normal temperature and pressure. The corresponding static model needs to take these special factors into account and accurately simulate the physical processes in the wellbore, including fluid flow, heat transfer and mass transfer, so as to achieve accurate mapping of different working conditions.
In today's digital age, professional manufacturers have invested a lot of R&D power in the field of wellbore lifting and injection, accumulated rich experience, and developed a series of mature data mining models. These models are built based on a large amount of actual production data and professional knowledge, and have extremely high practical value. Integrating them into the wellbore digital twin system can effectively make up for the shortcomings of professional software in the diagnosis algorithm of production well conditions. Take the working condition diagnosis model for plunger gas lift wells developed by a well-known professional manufacturer as an example. Through in-depth analysis of multi-dimensional data such as wellhead pressure, flow, and gas composition, the model can quickly and accurately determine the possible faults such as pump jams and gas locks during the plunger gas lift process, greatly improving the accuracy and comprehensiveness of the diagnosis, and providing strong support for timely and effective maintenance measures.
The business process of wellbore production and operation is extremely complex, involving the collaboration of multiple departments and professional teams. Different business goals, such as improving recovery, reducing production costs, and ensuring safe production, have different requirements for business processes. Therefore, it is necessary to systematically sort out and build a business process system based on different business goals. This includes clarifying the work content, responsible parties, time nodes, and information transmission paths of each link to ensure that each link is closely connected and operates efficiently. For example, when conducting a large-scale fracturing operation, from the early geological assessment and scheme design to the mid-term equipment preparation and construction operation, and then to the later effect evaluation and production adjustment, each link needs to be carefully planned and coordinated. Any mistake in any link may affect the progress and effect of the entire project.
2. Implementation Strategy
Comprehensive Business Scope and Object Type
The business scope involved in wellbore production is extremely wide and the types are very complicated. Drilling operation is the primary link in wellbore construction. The process includes multiple steps such as pre-drilling preparation, drilling, and cementing. Each step requires precise control of parameters to ensure the quality and safety of the wellbore. Completion operation is the key link to connect the wellbore with the oil and gas layer after drilling is completed, including perforation, casing, and installation of wellhead devices. Well logging operations obtain the physical parameters of the formation around the wellbore through various logging instruments, providing an important basis for subsequent mining decisions. Oil testing operations are used to evaluate the production capacity and properties of oil and gas layers, and daily oil and gas production are the process of extracting underground oil and gas resources to the ground. Water injection and steam injection operations are to supplement the energy of the formation and improve the recovery rate. Downhole operation overhaul and minor repairs are for the maintenance of wellbore equipment and pipe strings, and downhole testing is used to monitor various parameters in the wellbore in real time. When combing these business scopes and types, special attention should be paid to specific application scenarios and data sources. For example, in the production operation of offshore oil fields, due to the particularity of the operating environment, it is necessary to consider the impact of factors such as marine climate and waves on production equipment. At the same time, data collection and transmission also face greater challenges, so it is necessary to establish a special offshore data collection and transmission system.
For the visible parts of the wellbore, such as the pipe-rod pump, the entity-based modeling method can truly restore its physical properties and operating status. By modeling the material properties, structural dimensions, movement mode, etc. of the pipe-rod pump in detail, its working conditions under different working conditions can be accurately simulated, and the possibility of wear, fatigue and other failures can be predicted. For parts such as casing and perforation sections, due to their complex internal structures and the interaction with the formation, it is necessary to integrate the design results and the user's analysis habits to achieve accurate model restoration. For example, when modeling the casing, it is necessary to consider the influence of factors such as the material, wall thickness, and connection method of the casing on its mechanical properties. At the same time, combined with the user's habits in analyzing the stress conditions of the casing, select the appropriate modeling method and parameter settings to ensure that the model can accurately reflect the actual situation.
Focus on data-driven artificial intelligence analysis
In terms of the working condition diagnosis of oil wells, data mining-based methods have become quite mature for pumping wells, submersible pump wells and screw pump wells. These methods can establish a complex relationship model between the working conditions of oil wells and various parameters by learning and analyzing a large amount of historical production data. For example, through real-time monitoring and data analysis of parameters such as the suspension point load, motor current, stroke, and stroke frequency of the pumping well, it is possible to accurately determine whether the pumping unit has imbalance, stuck pumps, broken rods and other faults.
Data-driven algorithms are highly adaptive and can be flexibly adjusted according to the actual production conditions of different oil wells. Compared with traditional diagnostic methods based on empirical formulas and rules, data-driven algorithms do not require pre-setting of complex diagnostic rules, but automatically discover fault modes through learning data. At the same time, these algorithms can achieve lightweight and convenient access. By simply adding a data processing module to the existing production data acquisition system, the algorithm can be integrated into the existing production management system. Compared with professional software, it has higher flexibility and operability. For example, an oil field has achieved real-time condition monitoring and diagnosis of thousands of oil wells by introducing data-driven artificial intelligence diagnostic algorithms, greatly improving the timeliness and accuracy of fault detection and reducing the cost and workload of manual inspections.
Collaboration between Party A and Party B based on business processes
Different types of oil wells, such as self-flowing wells, pumping wells, submersible pump wells, etc., have different production principles and process flows, and the corresponding business processes are also different. Clearly distinguishing these types of oil wells and their business processes is crucial to improving production efficiency and management level. At the same time, focus on strengthening the collaboration between the owner and the construction party in the business process. As the initiator and decision maker of the project, the owner needs to clarify the goals and requirements of the project and provide the necessary support and resources for the construction party. As the implementer of the project, the construction party needs to strictly follow the owner's requirements and standards to ensure the quality and progress of the project. For example, in a drilling project, the owner needs to provide detailed geological data and drilling design plans, and the construction party needs to organize professional drilling teams and equipment based on these data and plans to carry out safe and efficient drilling operations.
Relying on business processes such as drilling, oil and gas production, steam injection, overhaul, and downhole testing, an integrated collaborative model based on digital twin technology is constructed. Through the digital twin model, the owner and the construction party can share information such as the progress of the project, equipment status, production data, etc. in real time to achieve all-round monitoring and management of the project. For example, in a water injection operation, the owner can view the water injection pressure, flow, water quality and other parameters in real time through the digital twin model, and the construction party can adjust the operating parameters of the water injection equipment in time according to the owner's feedback to ensure the smooth progress of the water injection operation. This integrated collaborative model can not only promote information sharing, but also improve the scientificity and timeliness of decision-making, achieve efficient collaboration, and ultimately improve the economic and social benefits of the entire wellbore production project.