Well cementation engineering digitization process method and device

Abstract

The invention discloses a well cementation engineering digital process method and device, and the method comprises the steps: forming well cementation design basic data through employing a large model and an intelligent agent of a cloud end, and generating a preliminary well cementation design scheme; performing analogue simulation on the well cementation design scheme based on a mechanism model and an AI model, analyzing and checking the well cementation design scheme and a simulation result, performing iterative optimization and analogue simulation again on the well cementation design scheme according to a checking result until the well cementation design scheme passes analysis and checking, and pushing the well cementation design scheme to a field edge server; a construction operation system of the field edge server formulates an operation procedure and a flow instruction according to the operation procedure and the flow instruction and sends target well cementation operation parameters to a well cementation equipment control system for well cementation construction automatic control; and the construction operation system analyzes the field operation data and the potential operation risk by adopting an AI model, and dynamically optimizes the target well cementation operation parameters according to the analysis result. Intelligent design of the well cementation design scheme is achieved, and well cementation operation parameters can be dynamically optimized.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of marine oil and gas exploration technology, and specifically to a digital process method and device for cementing engineering. Background Art

[0002] As offshore oil and gas exploration and development shifts from shallow to deepwater, offshore cementing is evolving toward a "double-depth, double-high, and narrow pressure window" approach. This means that increasing water depth, increasing well depth, higher bottomhole temperatures, higher bottomhole pressures, and a narrower pressure window are increasing cementing operations and presenting significant challenges to the cementing industry. Improving cementing quality and reducing cementing costs are key challenges facing the industry going forward.

[0003] Traditional optimization solutions focused solely on materials are no longer sufficient to meet future demands for cementing technology advancement, necessitating digital transformation. However, current cementing operation design and data processing suffer from numerous deficiencies. Data flow during these operations is discontinuous, relying primarily on file transfers, resulting in low efficiency. Furthermore, field operations are characterized by a low level of automation and a relatively rigid process, making automatic adjustment of operational data impossible. Summary of the Invention

[0004] In view of the above problems, the present invention is proposed to provide a digital process method and device for cementing engineering that overcomes the above problems or at least partially solves the above problems.

[0005] According to one aspect of an embodiment of the present application, a digital process method for cementing engineering is provided, comprising:

[0006] Using cloud-based large models and intelligent agents, the cementing design document data is processed, the cementing design document data is parsed to form cementing design basic data, similar design schemes are retrieved based on the cementing design basic data, and a preliminary cementing design scheme is generated; the cementing design scheme is simulated based on the mechanism model and AI model, and the scheme verification module is used to analyze and review the cementing design scheme and simulation results to form an audit result. Based on the audit result, the cementing design scheme is iteratively optimized and simulated again until it passes the analysis and review;

[0007] After approval, the cementing design plan is pushed to the on-site edge server;

[0008] The construction operation system of the on-site edge server formulates operation procedures and process instructions according to the cementing design plan, and sends target cementing operation parameters to the cementing equipment control system. The cementing equipment control system automatically controls the cementing construction of related equipment according to the target cementing operation parameters;

[0009] The construction operation system receives the field operation data collected by various sensors, uses an AI model to analyze the field operation data and potential operation risks, dynamically optimizes the target cementing operation parameters based on the analysis results, and feeds the optimized target cementing operation parameters back to the cementing equipment control system.

[0010] Furthermore, the use of the cloud-based large model and intelligent agent to process the cementing design document data and analyze the cementing design document data to form cementing design basic data further includes:

[0011] Using the large model, the cementing design document data including the drilling design and the geological design is processed and analyzed to extract the cementing design basic data involved in the cementing design;

[0012] The basic cementing design data includes at least: basic oil well information, wellbore structure design data, drilling fluid design data, wellbore trajectory design data, formation pressure prediction data, layer prediction data and formation static temperature prediction data.

[0013] Furthermore, the retrieving similar design solutions based on the cementing design basic data and generating a preliminary cementing design solution further includes:

[0014] By using the large model, by studying the cementing well history data set and performing analysis and intelligent retrieval based on the cementing design basic data, a similar design scheme with the highest degree of proximity to the cementing design basic data is found, and a preliminary cementing design scheme is generated based on the similar design scheme; wherein the cementing design scheme includes cement slurry formula design, slurry column structure design and pumping program design.

[0015] Furthermore, the cementing design scheme is simulated based on the mechanism model and the AI ​​model, the cementing design scheme and the simulation results are analyzed and reviewed by the scheme verification module to form a review result, the cementing design scheme is iteratively optimized according to the review result, and the simulation is performed again until the analysis and review are passed. The process further includes:

[0016] Utilizing the large model and intelligent agent, calling the mechanism model interface and the AI ​​model interface, analyzing and calculating the cementing design plan, and using the large model to compare the analysis and calculation results and the cementing design plan with the required data to determine whether the cementing design plan meets various requirements; the required data includes standard requirements, specification requirements, enterprise requirements, and expert experience data;

[0017] If all requirements are met, a cementing design report is generated based on the cementing design plan and analysis and calculation results, and the cementing design report is sent to the expert end for review;

[0018] If all requirements are not met, the cementing design plan is readjusted using the large model and the above review is conducted again until it passes the analysis and review.

[0019] Furthermore, the mechanism model includes: a wellbore hydraulic model, a temperature field calculation model, a displacement efficiency calculation model, a cement sheath integrity calculation model, an anti-channeling pressure stabilization model and a tubing mechanics model.

[0020] Furthermore, the AI ​​model includes: a formula intelligent recommendation model and a cementing quality prediction model.

[0021] Furthermore, the construction operation system of the on-site edge server formulates operation procedures and process instructions according to the cementing design plan, and sends target cementing operation parameters to the cementing equipment control system, and the cementing equipment control system automatically controls the cementing construction of related equipment according to the target cementing operation parameters, further including:

[0022] The construction operation system parses the cementing design and construction program into time series data including displacement and density according to the cementing design plan, and sends the latest target cementing operation parameters to the cementing equipment control system at set time intervals; the cementing equipment control system controls the cementing automatic pump skid system, automatic ash supply system and automatic liquid addition system to complete the control of the target cementing operation parameters.

[0023] Furthermore, the field operation data includes: equipment operation data, wellhead entry operation data and pressure-controlled cementing operation data;

[0024] Among them, the equipment operation data includes: equipment oil temperature, equipment pressure, equipment speed and equipment health status; the wellhead inlet operation data includes: wellhead inlet pressure, flow, density and temperature; the pressure-controlled cementing operation data includes: outlet back pressure, throttle valve opening and pressure compensation value of the pressure compensation device.

[0025] Furthermore, the use of AI models to analyze on-site operation data and potential operation risks further includes:

[0026] Based on the field operation data, a real-time analysis algorithm is used to calculate wellbore parameters including the wellbore pressure profile, temperature profile, and displacement profile. The AI ​​model analyzes the operation status based on the wellbore parameters to determine whether there are potential operation risks at present and / or in the future, and dynamically adjusts and optimizes the target cementing operation parameters based on the analysis results.

[0027] Furthermore, the real-time analysis algorithm includes: a cementing hydraulics real-time analysis algorithm and a cementing temperature field real-time analysis algorithm.

[0028] Furthermore, the method further comprises:

[0029] The on-site edge server and / or land end uses digital twin technology to dynamically analyze and display the cementing operation conditions; the operation AI analysis model is applied to monitor whether any abnormal conditions occur during the cementing operation, and the type of abnormality is determined based on the abnormal conditions that occur, and the corresponding solution is determined.

[0030] Furthermore, applying the operation AI analysis model to monitor whether abnormal conditions occur during cementing operations further includes: applying the operation AI analysis model to analyze the health status of cementing equipment tools, fault types, downhole abnormal working condition types, and uphole abnormal working condition types.

[0031] Furthermore, the operation AI analysis model includes: an intelligent analysis and decision-making model for cementing operations and an intelligent monitoring model for the status of cementing equipment.

[0032] According to another aspect of the present application, a digital device for cementing engineering is provided, comprising:

[0033] The design and review module is suitable for using large models and intelligent agents in the cloud to process cementing design document data, analyze the cementing design document data to form cementing design basic data, retrieve similar design schemes based on the cementing design basic data, and generate a preliminary cementing design scheme; simulate the cementing design scheme based on the mechanism model and AI model, apply the scheme verification module to analyze and review the cementing design scheme and simulation results to form an audit result, iteratively optimize the cementing design scheme based on the audit result, and simulate again until it passes the analysis and review;

[0034] A push module, adapted to push the cementing design plan to an on-site edge server after approval;

[0035] The construction operation module is suitable for the construction operation system of the on-site edge server to formulate operation procedures and process instructions according to the cementing design plan, and send the target cementing operation parameters to the cementing equipment control system, and the cementing equipment control system automatically controls the cementing construction of related equipment according to the target cementing operation parameters; the construction operation system receives the on-site operation data collected by various sensors, uses the AI ​​model to analyze the on-site operation data and potential operation risks, dynamically optimizes the target cementing operation parameters based on the analysis results, and feeds back the optimized target cementing operation parameters to the cementing equipment control system.

[0036] According to another aspect of an embodiment of the present application, a computing device is provided, comprising: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other via the communication bus;

[0037] The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute operations corresponding to the above-mentioned digital process method for cementing engineering.

[0038] According to another aspect of the embodiments of the present application, a computer storage medium is provided, in which at least one executable instruction is stored. The executable instruction enables a processor to perform operations corresponding to the above-mentioned digital process method for cementing engineering.

[0039] According to another aspect of the embodiments of the present application, a computer program product is provided, comprising at least one executable instruction, wherein the executable instruction enables a processor to execute operations corresponding to the above-mentioned digital process method for cementing engineering.

[0040] According to another aspect of the embodiment of the present application, a cementing equipment is provided, including: an automatic cementing pump skid system, an automatic ash supply system, and an automatic liquid addition system; the cementing equipment is controlled by a cementing equipment control system in the above-mentioned digital process method for cementing engineering.

[0041] According to another aspect of the embodiment of the present application, a cementing intelligent tool is provided, including: a wellhead inlet parameter acquisition tool, an automatic cementing head, a downhole parameter acquisition tool, and an annulus outlet parameter acquisition tool; the cementing intelligent tool is controlled by a cementing equipment control system in the above-mentioned cementing engineering digital process method.

[0042] According to another aspect of the embodiments of the present application, a cementing device is provided, including a cementing operation control center and an edge server; the cementing device provides hardware support for the above-mentioned digital process method for cementing engineering.

[0043] According to the technical solution provided in the embodiment of the present application, by utilizing the large model in the cloud and combining it with the mechanism model, factors such as cementing quality, economic benefits and carbon emissions are comprehensively considered to automatically design and recommend a better cementing design scheme, thereby realizing intelligent recommendation of cementing design schemes and greatly improving cementing design efficiency. After the cementing design scheme is reviewed and approved, it is pushed to the on-site edge server, and the construction operation system of the on-site edge server formulates the operation process and process instructions according to the cementing design scheme, and sends the target cementing operation parameters to the cementing equipment control system, which completes the joint control of the equipment and improves the degree of automation of on-site operations. The construction operation system analyzes the on-site operation data collected by various sensors and Dynamically optimize the target cementing operation parameters, and feed the optimized target cementing operation parameters back to the cementing equipment control system, realizing dynamic update and automatic adjustment of cementing operation parameters, effectively improving cementing quality and efficiency, and creating a digital collaborative ecosystem between equipment, realizing precise equipment control and collaborative operation of multiple equipment, which helps to reduce on-site labor and reduce cementing costs; applying digital twin technology and AR technology, cementing visualization is realized, improving the understanding of real-time downhole working conditions, and enabling remote operation technical support and real-time optimization of cementing operations; in addition, it also has the intelligent recognition and decision-making function of complex cementing events, automatically identifying complex downhole cementing events and providing corresponding solutions, effectively reducing downhole risks.

[0044] The above description is only an overview of the technical solution of the embodiment of the present application. In order to more clearly understand the technical means of the embodiment of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the embodiment of the present application more obvious and easy to understand, the specific implementation method of the embodiment of the present application is specifically listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the embodiments of the present application. The same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0046] Figure 1 Shows a schematic diagram of the implementation of the cementing operation stage;

[0047] Figure 2a A schematic diagram of a digital process method for cementing engineering according to an embodiment of the present application is shown;

[0048] Figure 2b A schematic diagram showing the technical principle of the digital process method for cementing engineering according to an embodiment of the present application is shown;

[0049] Figure 3 The following is a structural block diagram of a digital device for cementing engineering according to an embodiment of the present application;

[0050] Figure 4 A schematic structural diagram of a computing device according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0051] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0052] In order to facilitate understanding of this application plan, the cementing project is first introduced below.

[0053] Cementing is a crucial step in the drilling process. Its primary purpose is to reinforce the wellbore, ensure continued safe drilling, isolate oil, gas, and water zones, and ensure stratified testing during exploration and efficient oil and gas production throughout the entire drilling process. This involves running high-quality steel pipes and filling the wellbore and annulus with cement to prevent leaks and seepage. This process is known as cementing. Cementing operations are divided into two main phases: cementing design and simulation, and cementing and injection operations.

[0054] (1) Cementing design simulation stage:

[0055] During this phase, engineers utilize engineering simulation software to analyze and calculate the design plan, primarily including wellbore hydraulic models, temperature field calculation models, displacement efficiency calculation models, rock mechanics models, and tubing string mechanics models. The wellbore hydraulics model primarily analyzes wellbore pressure changes during cementing, including wellbore circulating equivalent density, hydrostatic column pressure equivalent density, U-tube effect, and inlet or pump pressure. This ensures that the design plan will not fall below the formation pore pressure or exceed the formation fracture pressure during execution, ensuring operational safety and preventing leaks and blowouts. The temperature field calculation model primarily predicts bottomhole circulating temperatures during cementing, providing temperature data for cement slurry formulation design. This ensures the designed cement slurry has a more accurate pumpable time and avoids premature or slow solidification during pumping. This includes analyzing the changes in cement slurry front temperature and wellbore temperature profile over time. The displacement efficiency calculation model primarily predicts cement slurry displacement and ensures proper filling of the wellbore, thereby ensuring cementing quality. This includes analyzing the concentration distribution of various fluids within the wellbore at different time intervals and the turbulent contact time within each well section. The rock mechanics model predicts stress changes and fragmentation during production after the cement slurry solidifies into cement paste, ensuring wellbore integrity during later production periods. This model includes cement sheath stress distribution, damage risk assessment, and cement paste temperature analysis during production. The anti-channeling pressure stability model assesses formation fluid intrusion before and after cement solidification. It primarily includes analysis of the pressure differential inside and outside the casing, analysis of formation fluid channeling resistance and feasibility, and simulation of formation fluid channeling. The tubing string mechanics model analyzes the mechanical behavior during casing running and installation to ensure safe and reliable casing installation. It primarily analyzes friction resistance and installation feasibility during casing running, casing centering after installation, and casing stress and damage risk analysis.

[0056] (2) Cementing operation stage:

[0057] The cementing operation phase is the implementation of the cementing design plan, which mainly uses mud pumps and cementing pumps to inject cement slurry into the annulus formed by the formation and casing. Figure 1 The schematic diagram of the cementing operation stage is shown in FIG. Figure 1As shown, it is mainly divided into two sub-stages. One sub-stage is to use a cementing pump to pump in cementing working fluid, and the other sub-stage is to use a mud pump to displace the pumped cement slurry to a preset position. This process relies mainly on drilling, cementing equipment and tools. The equipment mainly includes cementing pumps and mud pumps, and the tools mainly include cement heads, float collars, float shoes, wellhead collection tools, downhole collection tools, etc. Injection usually refers to the process of injecting cement slurry into the wellbore annulus (that is, the space between the formation and the casing) to replace the drilling fluid (mud) originally present in the area. The main steps of injection include: 1) injecting the designed cementing working fluid, including pre-fluid and cement slurry, from the wellhead in sequence; 2) placing a rubber plug to physically isolate the cement slurry from the drilling fluid; 3) using drilling fluid to displace the cementing cement slurry and pre-fluid to the annulus. Among them, Figure 1 The lead slurry refers to the cement slurry that is first pumped into the wellbore annulus during the cementing process. Its main purpose is to provide a good foundation for the subsequent cement slurry and ensure effective isolation of the entire cementing section; the tail slurry refers to the cement slurry injected after the lead slurry, which is located above the lead slurry to the ground or the next casing shoe; the open hole refers to the well section where no casing or liner is inserted during the drilling process, that is, the part that directly exposes the original formation. In cementing operations, the open hole specifically refers to the section of the formation that is not protected by casing and needs to be cemented.

[0058] Figure 2a FIG. 1 shows a flow chart of a digital process method for cementing engineering according to an embodiment of the present application, as shown in FIG. Figure 2a As shown, the method includes the following steps:

[0059] In step S201, the cementing design document data is processed using the cloud-based big model and intelligent agent, the cementing design document data is analyzed to form cementing design basic data, similar design schemes are retrieved based on the cementing design basic data, and a preliminary cementing design scheme is generated.

[0060] The embodiment of this application is aimed at the entire cementing business, empowering it with digital technology, connecting the relevant data of the cementing business, and applying cloud computing technology, simulation technology, digital twin technology, automation technology, Internet of Things technology and big data analysis technology, etc., to realize cloud-based cementing engineering design, and realize cloud-edge integration during the cementing operation process, which can automatically perform data collection, data analysis, decision-making and execution.

[0061] Among them, the big model was introduced into the field of cementing design, and a high-quality cementing well history data set was constructed. The cementing well history data set contains geological data, drilling data, historical cementing design plans, drilling design reports, construction data, logging results, cementing specification data, etc., and a set of collaborative design software for cloud-based application was constructed. The mechanism model was also integrated. Combined with the mechanism model, the better cementing design plan was automatically designed and recommended based on comprehensive factors such as cementing quality, economic benefits and carbon emissions, realizing intelligent recommendation of cementing design plans.

[0062] Collaborative design software can adopt a browser / server architecture, a network application architecture model that uses a web browser as a client to communicate with a server via the internet or a local area network. This architecture greatly simplifies client maintenance and makes the application widely accessible, requiring only a network connection and a supported browser. Collaborative design software includes functions such as cementing design, cementing simulation, cementing AI, design analysis and evaluation, and design report output, effectively meeting the needs of cloud-based collaborative design optimization.

[0063] The software features intelligent solution recommendation and prediction capabilities. Leveraging a large model, the software can process cementing design documentation data by learning from cementing well history datasets and analyzing it. This data is then analyzed to generate cementing design foundation data. Based on this foundation data, similar design solutions are retrieved and a preliminary cementing design solution is intelligently generated. Cementing design foundation data is a multi-dimensional technical basis that requires the integration of knowledge from multiple disciplines, including geology, engineering, materials, and mechanics. For example, it may include foundational geomechanics, wellbore parameters, cement slurry system design, casing centering optimization, construction parameter design, material performance standards, and integrity verification standards. Using the large model, the software processes and analyzes cementing design foundation data, including drilling and geological designs, to extract the foundational cementing design data required for the cementing design. This foundational cementing design data includes at least basic well information, wellbore structure design data, drilling fluid design data, wellbore trajectory design data, formation pressure prediction data, layer prediction data, and formation static temperature prediction data.

[0064] Leveraging a large model, the system analyzes and intelligently searches cementing design data from historical data sets, identifying similar design solutions that closely match these data. Based on these similar solutions, a preliminary cementing design is generated. This cementing design includes cementing technology, cement slurry formulation, slurry column structure, and pumping program design. Specifically, the large model intelligently recommends cementing designs by studying historical cementing design solutions and interpreting drilling design reports.

[0065] In step S202, the cementing design scheme is simulated based on the mechanism model and the AI ​​model, and the scheme verification module is used to analyze and review the cementing design scheme and the simulation results to form a review result. The cementing design scheme is iteratively optimized based on the review result, and simulation is performed again until it passes the analysis and review.

[0066] To further improve design accuracy, the cementing design can be optimized to produce a final cementing solution. For example, designers can optimize the cementing solution recommended by the large model, thereby improving both design efficiency and accuracy. Furthermore, intelligent cementing quality prediction is possible, integrating mechanistic models such as the wellbore mechanics model, temperature field calculation model, displacement efficiency calculation model, cement sheath integrity calculation model, and string mechanics model. Cementing solutions are simulated based on these mechanistic models, and a solution verification module is used to analyze and review the cementing solution and simulation results. This results in an audit, and the cementing solution is iteratively optimized based on the audit results to ensure that the cementing solution meets design requirements.

[0067] The software also provides an intelligent solution verification function. After the cementing design is completed, the solution verification module is used to analyze and verify the rationality of the cementing design plan. Specifically, the large model and intelligent agent are used to call the mechanism model interface and the AI ​​model interface to analyze and calculate the cementing design plan. The large model is used to compare the analysis and calculation results and the cementing design plan with the required data to determine whether the cementing design plan meets various requirements; the required data includes standard requirements, specification requirements, enterprise requirements, and expert experience data; if all requirements are met, a cementing design report is generated based on the cementing design plan and the analysis and calculation results, and the cementing design report is sent to the expert side for review; if all requirements are not met, the large model is used to readjust the cementing design plan, and the above review is carried out again until it passes the analysis and review. Among them, the AI ​​model includes: a recipe intelligent recommendation model and a cementing quality prediction model. The recipe intelligent recommendation model mainly automatically recommends a set of recipes based on the current design conditions, formation temperature, pumping time, process requirements, and economic efficiency, effectively saving recipe construction time and the number of experiments. The cementing quality prediction model mainly predicts the possible cementing quality of each stage based on the design plan, geological data, drilling data, etc.

[0068] Mechanism models may include: wellbore hydraulics model, temperature field calculation model, displacement efficiency calculation model, rock mechanics model, cement sheath integrity calculation model, anti-channeling pressure stability model, and tubing mechanics model. The wellbore hydraulics model primarily analyzes wellbore pressure changes during cementing, including analysis of wellbore circulating equivalent density, hydrostatic column pressure equivalent density, U-tube effect, and inlet pressure or pump pressure. This ensures that the design plan will not fall below the formation pore pressure or exceed the formation fracture pressure during implementation, thereby ensuring operational safety and avoiding well leakage and blowout accidents. The temperature field calculation model primarily predicts the bottomhole circulating temperature during cementing, providing temperature data for cement slurry formulation design, ensuring a more accurate pumpable time for the designed cement slurry, and avoiding premature or slow solidification during pumping. This includes analyzing the changes in cement slurry front temperature and wellbore temperature profile over time. The displacement efficiency calculation model primarily predicts cement slurry displacement and ensures proper filling of the wellbore, thereby ensuring cementing quality. This includes analyzing the concentration distribution of various fluids within the wellbore at different time intervals and the turbulent contact time within each well section. The rock mechanics model predicts stress changes and fragmentation during production after the cement slurry solidifies into cement paste, ensuring wellbore integrity during later production periods. This model includes cement sheath stress distribution, damage risk assessment, and cement paste temperature analysis during production. The anti-channeling pressure stability model assesses formation fluid intrusion before and after cement solidification. It primarily includes analysis of the pressure differential inside and outside the casing, analysis of formation fluid channeling resistance and feasibility, and simulation of formation fluid channeling. The tubing string mechanics model analyzes the mechanical behavior during casing running and installation to ensure safe and reliable casing installation. It primarily analyzes friction resistance and installation feasibility during casing running, casing centering after installation, and casing stress and damage risk analysis.

[0069] The software provides an intelligent report generation function. When the cementing design plan is verified to meet all requirements, the intelligent report generation function can be applied to generate a complete cementing design report based on the cementing design plan and analysis and calculation results, realizing automatic report generation.

[0070] A cementing design plan is a technical proposal developed based on specific well conditions (including geological conditions, drilling parameters, formation pressure, etc.). It serves as a technical guideline before cementing operations, detailing all the technical measures and steps required to achieve the desired cementing results. A drilling design report is a comprehensive technical document compiled for the entire drilling project. It comprehensively plans and designs every aspect of the drilling process based on exploration or development requirements, combined with geological data and engineering conditions. Its content covers everything from well site selection, geological design, wellbore structure design, drilling process selection, well control design, drilling equipment selection, construction schedule, cost budget, and management measures. It serves as a programmatic document guiding drilling operations. A cementing design report is a detailed explanation and description of the cementing design plan. Building on the cementing design plan, it further analyzes and demonstrates the design basis, design concepts, calculation process, and expected results. It not only includes all the technical details in the cementing design plan, but also includes an analysis of the geological conditions, cementing quality evaluation standards and methods, and analysis of economic and technical indicators.

[0071] In order to effectively ensure the feasibility of the plan, the embodiment of the present application also introduces an expert review function, which sends the cementing design report to the expert side for review, so that the cementing experts on the expert side can review the cementing design report based on their professional knowledge and experience.

[0072] Optionally, the software may also have functions such as intelligent question-and-answer on cementing knowledge and generation of report files (such as ppt) based on cementing design plans.

[0073] Step S203: After passing the review, the cementing design plan is pushed to the on-site edge server.

[0074] After the review is passed, the cementing design plan is pushed to the on-site edge server through the network, and the on-site edge server controls and completes the cementing construction phase according to the cementing design plan.

[0075] In step S204, the construction operation system of the on-site edge server formulates the operation process and process instructions according to the cementing design plan, and sends the target cementing operation parameters to the cementing equipment control system. The cementing equipment control system automatically controls the cementing construction of related equipment according to the target cementing operation parameters.

[0076] After completing the design and review of the cementing plan, the construction phase begins. The on-site edge server includes a construction operation system and a cementing equipment control system (SCADA or DCS). The construction operation system covers all aspects of cementing operations. It is a comprehensive system encompassing all equipment, tools, materials, personnel, and process flow involved in the cementing process. It ensures safe and orderly cementing operations by rationally arranging the construction process and organizing personnel and equipment. The cementing equipment control system is a system used to control and manage all mechanical equipment and tools used in the cementing process. It is primarily responsible for automated or semi-automated operation of these equipment to achieve efficient and accurate cementing operations. The construction operation system and the cementing equipment control system are interdependent and exchange information. The construction operation system relies on the cementing equipment control system, which in turn serves the construction operation system. The cementing equipment control system provides data support to the construction operation system, and the construction operation system issues commands to the cementing equipment control system. The two systems are interdependent, mutually influential, and work together to ensure cementing quality and safe and efficient operations.

[0077] After receiving the design data from the cementing plan, the construction operation system automatically formulates the operation procedures and process instructions based on the cementing design plan and sends the target cementing operation parameters to the cementing equipment control system. The cementing equipment control system then controls the relevant equipment based on the target cementing operation parameters to complete the equipment's joint control. Target cementing operation parameters are an important component of the cementing design plan. Target cementing operation parameters refer to the key performance indicators that need to be achieved during the cementing operation, covering fluid properties, mechanical properties, and construction parameters. Target cementing operation parameters typically include cement slurry density, cement slurry displacement, cementing time, displacement displacement, and impact pressure value. These parameters are key factors in ensuring cementing quality, as they directly affect the flow of cement slurry within the well, displacement efficiency, cement stone strength, and sealing effect.

[0078] Specifically, the construction operation system parses the cementing design and construction procedures into time-series data including displacement and density according to the cementing design plan, and sends the latest target cementing operation parameters to the cementing equipment control system at the set time interval; the cementing equipment control system controls the cementing automatic pump skid system, automatic ash supply system and automatic liquid addition system to complete the control of the target cementing operation parameters.

[0079] In step S205, the construction operation system receives the field operation data collected by various sensors, uses the AI ​​model to analyze the field operation data and potential operation risks, dynamically optimizes the target cementing operation parameters based on the analysis results, and feeds the optimized target cementing operation parameters back to the cementing equipment control system.

[0080] Sensors include surface sensors, equipment sensors, and downhole sensors. These sensors collect real-time field operation data generated at the worksite and transmit it to the construction operation system. Field operation data includes equipment operation data, wellhead entry operation data, and pressure-controlled cementing operation data. Specifically, equipment operation data includes equipment oil temperature, equipment pressure, equipment speed, and equipment health status. Wellhead entry operation data includes wellhead inlet pressure, flow rate, density, and temperature. Pressure-controlled cementing operation data includes outlet backpressure, throttle valve opening, and pressure compensation device compensation value.

[0081] After receiving the field operation data collected by various sensors, the construction operation system can use the AI ​​model to dynamically analyze and calculate the real-time field operation data and potential operation risks, and recommend decision results to determine the optimized target cementing operation parameters. The optimized target cementing operation parameters are then fed back to the cementing equipment control system, thereby realizing dynamic updating and automatic adjustment of the cementing operation parameters.

[0082] Specifically, based on field operation data, a real-time analysis algorithm is used to calculate wellbore parameters including wellbore pressure profile, temperature profile, and displacement profile. The AI ​​model analyzes the operation status based on the wellbore parameters to determine whether there are potential operation risks at present and / or in the future, and dynamically adjusts and optimizes the target cementing operation parameters based on the analysis results.

[0083] Real-time analysis algorithms include cementing hydraulics and temperature field analysis. The latter dynamically calculates key cementing parameters, such as wellbore ECD, ESD wellhead pressure, U-tube effect, vacuum section length, outlet flow rate, and displacement efficiency, based on operational parameters to ensure construction safety and quality. The latter also dynamically analyzes the cementing temperature profile and cement slurry front temperature based on operational parameters.

[0084] The embodiments of this application provide cementing equipment and operation analysis and decision-making capabilities based on edge-cloud integration. These capabilities include data collection and detection, operation analysis and decision-making, and automatic control. These capabilities include intelligent analysis and decision-making based on the cementing operation status. Based on a mechanism model that calculates real-time field operation data, these capabilities identify and warn of cementing operation conditions and risks. Furthermore, these capabilities enable multi-objective control of cementing operations, determine optimized target cementing operation parameters, and dynamically optimize cementing operation parameters. Edge computing technology is employed to enable dynamic edge-side computation of field data and real-time analysis and decision-making of data. Software and equipment are linked, enabling the sending of instructions to the equipment.

[0085] The embodiment of the present application also has intelligent cementing operation functions, and is suitable for intelligent cementing equipment and tools for multiple working conditions, including intelligent cementing pump groups, automated mixing equipment, intelligent wellhead tools, etc.; based on historical equipment operation data and working condition parameters, it realizes a digital collaborative ecology between equipment, precise equipment control and collaborative operation of multiple equipment.

[0086] The method also includes: using digital twin technology by the on-site edge server and / or the land end to dynamically analyze and display the cementing operation conditions. Among them, digital twin technology is a cutting-edge technology that uses digital means to build a virtual mapping model of a physical entity and uses real-time data, algorithms and simulation technology to achieve virtual-real interaction. Its core lies in the deep integration of data-driven and model-driven to achieve dynamic synchronization and intelligent decision-making between the physical world and the digital world. Digital twin technology can use sensor data, historical data, machine learning algorithms and other advanced analytical tools to simulate, predict, and optimize the behavior of physical entities and support the decision-making process.

[0087] Figure 2b The technical principle diagram of the digital process method for cementing engineering in the embodiment of the present application is shown as follows: Figure 2b As shown in the figure, during the design phase, the cementing design plan is completed through collaborative design and engineering simulation. During the construction phase, field operation data can be collected and dynamically calculated and monitored. Digital twin technology is applied on-site to dynamically analyze and display cementing operation conditions, achieving cementing visualization. Field operation data can also be transmitted back to the land end via satellite network. Digital twin technology and AR technology are used to achieve two-way data exchange between the site and the expert end, enabling remote operation technical support and control. Intelligent parameter prediction can also be performed to better achieve pressure-controlled cementing.

[0088] The method also includes: applying an operational AI analysis model to monitor whether abnormal conditions occur during cementing operations, determining the type of abnormality based on the abnormal conditions, and determining corresponding solutions. The operational AI analysis model can be applied to analyze the health status of cementing equipment tools, fault types, downhole abnormal operating conditions, and surface abnormal operating conditions. The operational AI analysis model includes an intelligent cementing operation analysis and decision-making model and an intelligent cementing equipment status monitoring model. The intelligent cementing operation analysis and decision-making model primarily analyzes operating conditions based on parameters collected on-site using the aforementioned real-time analysis algorithm. It also predicts future cementing parameter changes based on the design plan and current status and issues early warnings. It then dynamically optimizes the operation parameters and future operation plans based on current and future parameter changes and early warnings. The intelligent cementing equipment status monitoring model primarily monitors the health of the equipment based on various operating parameters, such as oil temperature, rotational speed, and maintenance status.

[0089] The embodiment of the present application has the function of intelligent identification and decision-making of complex cementing events, and can combine the experience of cementing experts to provide solutions for different types of complex situations, such as abnormal surface equipment and abnormal downhole parameter processing. It also has the function of monitoring and early warning of cementing equipment and tool status, and can monitor, analyze and issue early warnings on the life and abnormal status of equipment and tools based on a monitoring system that integrates multi-source data, such as Figure 2b shown.

[0090] According to the digital process method for cementing engineering provided in the embodiment of the present application, by utilizing the large model in the cloud and combining it with the mechanism model, factors such as cementing quality, economic benefits and carbon emissions are comprehensively considered to automatically design and recommend a better cementing design scheme, thereby realizing intelligent recommendation of cementing design schemes and greatly improving cementing design efficiency. After the cementing design scheme is reviewed and approved, it is pushed to the on-site edge server, and the construction operation system of the on-site edge server formulates the operation process and process instructions according to the cementing design scheme, and sends the target cementing operation parameters to the cementing equipment control system, which completes the joint control of the equipment, thereby improving the degree of automation of on-site operations. The construction operation system analyzes the on-site operation data collected by various sensors and According to the data, the target cementing operation parameters are dynamically optimized, and the optimized target cementing operation parameters are fed back to the cementing equipment control system, realizing dynamic update and automatic adjustment of cementing operation parameters, effectively improving cementing quality and efficiency, and creating a digital collaborative ecosystem between equipment. It realizes precise equipment control and collaborative operation of multiple equipment, which helps to reduce on-site labor and reduce cementing costs; the application of digital twin technology and AR technology realizes cementing visualization, improves the grasp of real-time downhole working conditions, and can provide remote operation technical support and optimize cementing operations in real time; in addition, it also has the intelligent identification and decision-making function of complex cementing events, automatically identifies complex downhole cementing events and provides corresponding solutions, effectively reducing downhole risks.

[0091] Figure 3 FIG. 1 shows a structural block diagram of a digital device for cementing engineering according to an embodiment of the present application. Figure 3 As shown, the device includes: a design and review module 310, a push module 320 and a construction operation module 330.

[0092] The design and review module 310 is suitable for: using the large model and intelligent body in the cloud to process the cementing design based on the document data, analyzing the cementing design based on the document data to form the cementing design basic data, retrieving similar design schemes based on the cementing design basic data, and generating a preliminary cementing design scheme; simulating the cementing design scheme based on the mechanism model and the AI ​​model, applying the scheme verification module to analyze and review the cementing design scheme and simulation results to form an audit result, iteratively optimizing the cementing design scheme based on the audit result, and performing simulation again until it passes the analysis and review.

[0093] The push module 320 is adapted to push the cementing design plan to the on-site edge server after the plan is approved.

[0094] The construction operation module 330 is suitable for: the construction operation system of the on-site edge server formulates the operation procedures and process instructions according to the cementing design plan, and sends the target cementing operation parameters to the cementing equipment control system, and the cementing equipment control system automatically controls the cementing construction of related equipment according to the target cementing operation parameters; the construction operation system receives the on-site operation data collected by each sensor, uses the AI ​​model to analyze the on-site operation data and potential operation risks, dynamically optimizes the target cementing operation parameters based on the analysis results, and feeds back the optimized target cementing operation parameters to the cementing equipment control system.

[0095] Optionally, the design and review module 310 is further suitable for: using a large model to process and analyze the cementing design based on the document data including the drilling design and the geological design, and extracting the cementing design basic data involved in the cementing design; wherein the cementing design basic data includes at least: basic information of the oil well, wellbore structure design data, drilling fluid design data, wellbore trajectory design data, formation pressure prediction data, stratification prediction data and formation static temperature prediction data.

[0096] Optionally, the design and review module 310 is further adapted to: utilizing a large model, by studying cementing well history data sets, and performing analysis and intelligent retrieval based on cementing design basic data, find similar design schemes that are most similar to the cementing design basic data, and generate preliminary cementing design schemes based on the similar design schemes; wherein the cementing design schemes include cement slurry formula design, slurry column structure design, and pumping program design.

[0097] Optionally, the design and review module 310 is further adapted to: utilizing the large model and intelligent agent, calling the mechanism model interface and the AI ​​model interface, analyzing and calculating the cementing design scheme, utilizing the large model to compare the analysis and calculation results and the cementing design scheme with the required data, and determining whether the cementing design scheme meets various requirements; the required data includes standard requirement data, specification requirement data, enterprise requirement data, and expert experience data; if various requirements are met, a cementing design report is generated based on the cementing design scheme and the analysis and calculation results, and the cementing design report is sent to the expert end for review; if various requirements are not met, the large model is utilized to readjust the cementing design scheme, and the above-mentioned review is conducted again until the analysis and review is passed.

[0098] Optionally, the mechanism model includes: a wellbore hydraulic model, a temperature field calculation model, a displacement efficiency calculation model, a cement sheath integrity calculation model, an anti-channeling pressure stabilization model, and a tubing mechanics model.

[0099] Optionally, the AI ​​model includes: a recipe intelligent recommendation model and a cementing quality prediction model.

[0100] Optionally, the construction operation module 330 is further adapted for: the construction operation system parses the cementing design construction procedure into time series data including displacement and density according to the cementing design plan, and sends the latest target cementing operation parameters to the cementing equipment control system at a set time interval; the cementing equipment control system controls the cementing automatic pump skid system, the automatic ash supply system and the automatic liquid addition system to complete the target cementing operation parameter control.

[0101] Optionally, the field operation data includes: equipment operation data, wellhead inlet operation data and pressure-controlled cementing operation data; among which, the equipment operation data includes: equipment oil temperature, equipment pressure, equipment speed and equipment health status; the wellhead inlet operation data includes: wellhead inlet pressure, flow, density and temperature; the pressure-controlled cementing operation data includes: outlet back pressure, throttle valve opening and pressure compensation value of the pressure compensation device.

[0102] Optionally, the construction operation module 330 is further adapted to: calculate wellbore parameters including wellbore pressure profile, temperature profile and displacement profile based on field operation data using a real-time analysis algorithm; the AI ​​model analyzes the operation status based on the wellbore parameters to determine whether there are potential operation risks at present and / or in the future, and dynamically adjusts and optimizes the target cementing operation parameters based on the analysis results.

[0103] Optionally, the construction operation module 330 is further suitable for: using digital twin technology by the on-site edge server and / or the land end to dynamically analyze and display the cementing operation conditions; applying the operation AI analysis model to monitor whether any abnormal conditions occur during the cementing operation, and judging the type of abnormality based on the abnormal conditions that occur, and determining the corresponding solution.

[0104] The description of each module above refers to the corresponding description in the method embodiment and will not be repeated here.

[0105] According to the digital device for cementing engineering provided in the embodiment of the present application, by utilizing a large model in the cloud and combining it with a mechanism model, factors such as cementing quality, economic benefits and carbon emissions are comprehensively considered to automatically design and recommend a better cementing design scheme, thereby realizing intelligent recommendation of cementing design schemes and greatly improving cementing design efficiency. After the cementing design scheme is reviewed and approved, it is pushed to the on-site edge server, and the construction operation system of the on-site edge server formulates the operation process and process instructions according to the cementing design scheme, and sends the target cementing operation parameters to the cementing equipment control system, which completes the joint control of the equipment, thereby improving the degree of automation of on-site operations. The construction operation system analyzes the on-site operation data collected by various sensors , dynamically optimize the target cementing operation parameters, and feed back the optimized target cementing operation parameters to the cementing equipment control system, realizing dynamic update and automatic adjustment of cementing operation parameters, effectively improving cementing quality and efficiency, and creating a digital collaborative ecosystem between equipment, realizing precise equipment control and collaborative operation of multiple equipment, which helps to reduce on-site labor and reduce cementing costs; applying digital twin technology and AR technology, cementing visualization is realized, improving the understanding of real-time downhole working conditions, and can provide remote operation technical support and optimize cementing operations in real time; in addition, it also has the intelligent identification and decision-making function of complex cementing events, automatically identifying complex downhole cementing events and providing corresponding solutions, effectively reducing downhole risks.

[0106] An embodiment of the present application provides a cementing device, including: an automatic cementing pump skid system, an automatic ash supply system, and an automatic liquid addition system; the cementing device is controlled by the cementing equipment control system in the digital process method for cementing engineering in any of the above method embodiments.

[0107] An embodiment of the present application provides an intelligent cementing tool, including: a wellhead inlet parameter acquisition tool, an automatic cementing head, a downhole parameter acquisition tool, and an annulus outlet parameter acquisition tool; the intelligent cementing tool is controlled by the cementing equipment control system in the digital process method for cementing engineering in any of the above method embodiments.

[0108] An embodiment of the present application provides a cementing device, including a cementing operation control center and an edge server; the cementing device provides hardware support for the digital process method of cementing engineering in any of the above method embodiments.

[0109] An embodiment of the present application provides a non-volatile computer storage medium storing at least one executable instruction or computer program, which enables a processor to execute operations corresponding to the digital process method for cementing engineering in any of the above method embodiments.

[0110] An embodiment of the present application provides a computer program product, which includes at least one executable instruction or computer program, which can enable a processor to perform operations corresponding to the digital process method for cementing engineering in any of the above method embodiments.

[0111] Figure 4 A schematic structural diagram of a computing device according to an embodiment of the present application is shown. The specific embodiment of the present application does not limit the specific implementation of the computing device.

[0112] like Figure 4 As shown, the computing device may include: a processor 402 , a communications interface 404 , a memory 406 , and a communication bus 408 .

[0113] Processor 402, communication interface 404, and memory 406 communicate with each other via communication bus 408. Communication interface 404 is used to communicate with other devices, such as client devices or other server network elements. Processor 402 is used to execute program 410, specifically, the steps described in the embodiment of the digital cementing process method for computing devices.

[0114] Specifically, the program 410 may include program codes, which include computer operation instructions.

[0115] Processor 402 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application. The one or more processors included in the computing device may be processors of the same type, such as one or more CPUs, or may be processors of different types, such as one or more CPUs and one or more ASICs.

[0116] The memory 406 is used to store the program 410. The memory 406 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0117] Program 410 can specifically be used to cause processor 402 to execute the digital cementing process method described in any of the aforementioned method embodiments. The specific implementation of each step in program 410 can be found in the corresponding descriptions of the corresponding steps and units in the aforementioned digital cementing process embodiments and will not be repeated here. Those skilled in the art will clearly understand that, for ease and brevity of description, the specific operating processes of the devices and modules described above can refer to the corresponding process descriptions in the aforementioned method embodiments and will not be repeated here.

[0118] The algorithm and display provided herein are not inherently relevant to any particular computer, virtual system or other device. Various general-purpose systems can also be used together with the teachings based on this. According to the above description, it is obvious that the structure required for constructing this type of system. In addition, the embodiments of the present application are not directed to any specific programming language yet. It should be understood that various programming languages ​​can be utilized to realize the content of the embodiments of the present application described herein, and the description of the specific languages ​​above is for the purpose of disclosing the best mode of implementation of the embodiments of the present application.

[0119] In the description provided herein, a large number of specific details are described. However, it is understood that the embodiments of the present application can be practiced without these specific details. In some instances, well-known methods, structures, and techniques are not shown in detail so as not to obscure the understanding of this description.

[0120] Similarly, it should be understood that in order to streamline the present disclosure and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of the present application, the various features of the embodiments of the present application are sometimes grouped together into a single embodiment, figure, or description thereof. However, this disclosed method should not be interpreted as reflecting the following intention: that the claimed embodiments of the present application require more features than the features explicitly recited in each claim. More precisely, as reflected in the claims below, inventive aspects lie in less than all the features of the individual embodiments disclosed above. Therefore, the claims following the detailed description are hereby expressly incorporated into the detailed description, with each claim itself serving as a separate embodiment of the embodiments of the present application.

[0121] Those skilled in the art will appreciate that the modules in the devices in the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments may be combined into one module or unit or component, and in addition may be divided into multiple submodules or subunits or subcomponents. All features disclosed in this specification (including the accompanying claims, abstracts and drawings) and all processes or units of any method or device disclosed herein may be combined in any combination, except that at least some of such features and / or processes or units are mutually exclusive. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstracts and drawings) may be replaced by an alternative feature providing the same, equivalent or similar purpose.

[0122] Furthermore, those skilled in the art will appreciate that although some embodiments described herein include certain features that are included in other embodiments but not other features, combinations of features from different embodiments are intended to be within the scope of the present invention and to form different embodiments. For example, in the claims below, any of the claimed embodiments may be used in any combination.

[0123] The various component embodiments of the embodiments of the present application can be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. It should be understood by those skilled in the art that a microprocessor or digital signal processor (DSP) can be used in practice to implement some or all of the functions of some or all of the components in the embodiments of the present application. The embodiments of the present application can also be implemented as a device or apparatus program (e.g., computer program and computer program product) for performing a part or all of the methods described herein. Such a program implementing the embodiments of the present application can be stored on a computer-readable medium, or can have the form of one or more signals. Such a signal can be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0124] It should be noted that the above embodiments illustrate rather than limit the embodiments of the present application, and that a person skilled in the art may devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference symbols placed between brackets should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The embodiments of the present application may be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In a unit claim enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc. does not indicate any order. These words may be interpreted as names.

Claims

1. A digital process method for cementing engineering, characterized in that: include: Using a large model and intelligent agent in the cloud, the cementing design document data is processed, the cementing design document data is parsed to form cementing design basic data, similar design solutions are retrieved based on the cementing design basic data, and a preliminary cementing design solution is generated; The cementing design scheme is simulated based on the mechanism model and the AI ​​model, and the scheme verification module is used to analyze and review the cementing design scheme and the simulation results to form a review result. The cementing design scheme is iteratively optimized based on the review result, and the simulation is performed again until it passes the analysis and review; After approval, the cementing design plan is pushed to the on-site edge server; The construction operation system of the on-site edge server formulates operation procedures and process instructions according to the cementing design plan, and sends target cementing operation parameters to the cementing equipment control system. The cementing equipment control system automatically controls the cementing construction of related equipment according to the target cementing operation parameters; The construction operation system receives the field operation data collected by various sensors, uses an AI model to analyze the field operation data and potential operation risks, dynamically optimizes the target cementing operation parameters based on the analysis results, and feeds the optimized target cementing operation parameters back to the cementing equipment control system.

2. The method according to claim 1, characterized in that The processing of the cementing design document data by using the cloud-based large model and intelligent agent, and parsing the cementing design document data to form cementing design basic data further includes: Using the large model, the cementing design document data including the drilling design and the geological design is processed and analyzed to extract the cementing design basic data involved in the cementing design; The basic cementing design data includes at least: basic oil well information, wellbore structure design data, drilling fluid design data, wellbore trajectory design data, formation pressure prediction data, layer prediction data and formation static temperature prediction data.

3. The method according to claim 1, characterized in that The retrieving similar design solutions based on the cementing design basic data and generating a preliminary cementing design solution further includes: By using the large model, by studying the cementing well history data set and performing analysis and intelligent retrieval based on the cementing design basic data, a similar design scheme with the highest degree of proximity to the cementing design basic data is found, and a preliminary cementing design scheme is generated based on the similar design scheme; wherein the cementing design scheme includes cement slurry formula design, slurry column structure design and pumping program design.

4. The method according to claim 1, wherein The method further includes: simulating the cementing design scheme based on the mechanism model and the AI ​​model, analyzing and reviewing the cementing design scheme and the simulation results using a scheme verification module to form a review result, iteratively optimizing the cementing design scheme based on the review result, and performing simulation again until the analysis and review are passed. Utilizing the large model and intelligent agent, calling the mechanism model interface and the AI ​​model interface, analyzing and calculating the cementing design plan, and using the large model to compare the analysis and calculation results and the cementing design plan with the required data to determine whether the cementing design plan meets various requirements; the required data includes standard requirements, specification requirements, enterprise requirements, and expert experience data; If all requirements are met, a cementing design report is generated based on the cementing design plan and analysis and calculation results, and the cementing design report is sent to the expert end for review; If all requirements are not met, the cementing design plan is readjusted using the large model and the above review is conducted again until it passes the analysis and review.

5. The method according to claim 1, wherein The mechanism model includes: a wellbore hydraulic model, a temperature field calculation model, a displacement efficiency calculation model, a cement sheath integrity calculation model, an anti-channeling pressure stabilization model and a pipe string mechanics model.

6. The method according to claim 1, characterized in that The AI ​​model includes: a recipe intelligent recommendation model and a cementing quality prediction model.

7. The method according to claim 1, characterized in that The construction operation system of the on-site edge server formulates operation procedures and process instructions according to the cementing design plan, and sends target cementing operation parameters to the cementing equipment control system, and the cementing equipment control system automatically controls the cementing construction of related equipment according to the target cementing operation parameters, further comprising: The construction operation system parses the cementing design and construction program into time series data including displacement and density according to the cementing design plan, and sends the latest target cementing operation parameters to the cementing equipment control system at set time intervals; the cementing equipment control system controls the cementing automatic pump skid system, automatic ash supply system and automatic liquid addition system to complete the control of the target cementing operation parameters.

8. The method according to claim 1, characterized in that The field operation data includes: equipment operation data, wellhead operation data and pressure-controlled cementing operation data; Among them, the equipment operation data includes: equipment oil temperature, equipment pressure, equipment speed and equipment health status; the wellhead inlet operation data includes: wellhead inlet pressure, flow, density and temperature; the pressure-controlled cementing operation data includes: outlet back pressure, throttle valve opening and pressure compensation value of the pressure compensation device.

9. The method according to claim 1, characterized in that The use of AI models to analyze on-site operation data and potential operation risks further includes: Based on the field operation data, a real-time analysis algorithm is used to calculate wellbore parameters including the wellbore pressure profile, temperature profile, and displacement profile. The AI ​​model analyzes the operation status based on the wellbore parameters to determine whether there are potential operation risks at present and / or in the future, and dynamically adjusts and optimizes the target cementing operation parameters based on the analysis results.

10. The method according to claim 1, characterized in that The method further comprises: The on-site edge server and / or land end uses digital twin technology to dynamically analyze and display the cementing operation conditions; the operation AI analysis model is applied to monitor whether any abnormal conditions occur during the cementing operation, and the type of abnormality is determined based on the abnormal conditions that occur, and the corresponding solution is determined.

11. A digital device for cementing engineering, characterized in that: include: The design and review module is suitable for processing cementing design basis document data using a large model and intelligent agent in the cloud, parsing the cementing design basis document data to form cementing design basic data, searching for similar design solutions based on the cementing design basic data, and generating a preliminary cementing design solution; The cementing design scheme is simulated based on the mechanism model and the AI ​​model, and the scheme verification module is used to analyze and review the cementing design scheme and the simulation results to form a review result. The cementing design scheme is iteratively optimized based on the review result, and the simulation is performed again until it passes the analysis and review; A push module, adapted to push the cementing design plan to an on-site edge server after approval; The construction operation module is suitable for the construction operation system of the on-site edge server to formulate operation procedures and process instructions according to the cementing design plan, and send the target cementing operation parameters to the cementing equipment control system, and the cementing equipment control system automatically controls the cementing construction of related equipment according to the target cementing operation parameters; the construction operation system receives the on-site operation data collected by various sensors, uses the AI ​​model to analyze the on-site operation data and potential operation risks, dynamically optimizes the target cementing operation parameters based on the analysis results, and feeds back the optimized target cementing operation parameters to the cementing equipment control system.

12. A cementing device comprising: Cementing automatic pump skid system, automatic ash supply system, automatic liquid addition system; The cementing equipment is controlled by the cementing equipment control system in the digital process method for cementing engineering according to any one of claims 1 to 10.

13. An intelligent cementing tool comprising: Wellhead parameter collection tool, automatic cementing head, downhole parameter collection tool, annulus outlet parameter collection tool; The intelligent cementing tool is controlled by the cementing equipment control system in the digital process method for cementing engineering according to any one of claims 1 to 10.

14. A cementing device comprising a cementing operation control center and an edge server; the cementing device provides hardware support for the digital process method for cementing engineering according to any one of claims 1 to 10.

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