Well repair digital intelligent decision-making method and device, electronic equipment and storage medium

Abstract

The invention relates to an intelligent decision-making technology, and discloses a well repair digital intelligent decision-making method which comprises the following steps: acquiring data of more than one data source in an oil and gas well, and summarizing the data of more than one data source to obtain a well repair database; extracting more than one data index in the well repair database, and identifying a well repair fault type; matching candidate tools according to the well repair fault type to obtain a candidate tool set, and calling a standard process template according to the well repair fault type; using the candidate tool set and the standard process template to generate a workover process scheme, and using the workover process scheme as a process evaluation factor; and calculating a well repair economic evaluation factor according to a well repair economic evaluation rule, and generating a well repair decision result by using the decision model in combination with the process evaluation factor. In addition, the invention further relates to a block chain technology, and data of more than one data source in the oil and gas well can be stored in nodes of a block chain. The invention further provides a well repair digital intelligent decision-making device and equipment and a storage medium. According to the invention, the timeliness of well repair decision making can be realized and the efficiency of well repair decision making can be improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent decision-making technology, and in particular to a method, device, electronic device, and storage medium for making intelligent decisions about well repair. Background Art

[0002] Well repair operations refer to an important means of handling various downhole accidents during drilling and oil production to ensure the smooth use of oil and gas wells.

[0003] Currently, the level of well repair performance is determined by the experience and capabilities of on-site technicians. Due to the increasing frequency of oil and gas well accidents and the relative shortage of experienced well repair experts and technicians, well repair operations are currently based on a "discover first, treat later" model, driven by the increasing number of oil and gas wells and the frequency of accidents. "Discovery" refers to damage to a well that has resulted in a significant decrease in production or even shutdown. "Treatment" occurs when the oil production plant, upon learning of the situation, contacts the repair company, and both parties agree on the repair project cost before commencing repair operations. However, because data on problematic wells is distributed across various oil production plants, with incomplete formats and content, managing this data is difficult, hindering timely repair decisions for problematic wells. Summary of the Invention

[0004] The present invention provides a digital intelligent decision-making method, device, electronic device and storage medium for well repair, which can achieve the timeliness of well repair decision-making and improve the efficiency of well repair decision-making.

[0005] To achieve the above objectives, the present invention provides a digital intelligent decision-making method for well repair, comprising:

[0006] Acquire data from more than one data source in an oil and gas well, and aggregate the data from more than one data source to obtain a well repair database;

[0007] Extracting one or more data indicators from a well repair database, and identifying a well repair fault type based on the one or more data indicators;

[0008] According to the workover fault type, candidate tools are matched from a preset workover tool library to obtain a candidate tool set, and a standard process template is called according to the workover fault type;

[0009] Generate a workover process plan using the candidate tool set and standard process template, and use the workover process plan as a process evaluation factor;

[0010] The workover economic evaluation factors are calculated according to the preset workover economic evaluation rules, and the workover decision results are generated using the pre-built decision model in combination with the process evaluation factors.

[0011] Optionally, extracting one or more data indicators from the well repair database includes:

[0012] Extract wellbore data of oil and gas fields from the well repair database and calculate the effective stress index of the wellbore based on the pre-built mechanical model;

[0013] The in-well data of the oil and gas field in the well repair database is obtained, and the failure probability of each component in the oil and gas field well is predicted using a pre-built fault prediction model. The failure probability of each component is mapped to a pre-built risk level to obtain the risk level index of each component in the oil and gas field well.

[0014] Optionally, identifying the workover fault type according to one or more data indicators includes: analyzing the workover fault type using a fault tree model according to a wellbore effective stress indicator and a risk level indicator of each component in the oil and gas field well among the one or more data indicators.

[0015] Optionally, matching candidate tools from a preset workover tool library according to the workover fault type to obtain a candidate tool set includes:

[0016] Match the appropriate tools according to the type of workover failure;

[0017] Extract the performance parameters of applicable tools and filter the candidate tool sets based on the performance parameters.

[0018] Optionally, generating a workover process plan using a candidate tool set and a standard process template includes:

[0019] Based on the candidate tool set and the standard process template, each tool in the candidate tool set is associated with the standard process template, and multiple basic solutions are generated using the rule engine tool;

[0020] The risk prediction model is used to predict the risk probabilities corresponding to multiple basic plans. The basic plan with the lowest risk probability is selected according to the risk probability to obtain the well repair process plan.

[0021] Optionally, calculating the well workover economic evaluation factor according to a preset well workover economic evaluation rule includes:

[0022] Extract the main economic indicators and economic influencing factors, generate the weights of economic influencing factors based on the sensitivity analysis matrix, and generate economic impact factors based on the weights of economic influencing factors;

[0023] Obtain the cost of each node in the well workover operation and calculate the well workover cost assessment factor based on the cost of each node;

[0024] The economic impact factors and the workover cost evaluation factors are summarized to obtain the workover economic evaluation factors.

[0025] In order to solve the above problems, the present invention further provides a well repair digital intelligent decision-making device, which includes:

[0026] The decision factor extraction module is used to obtain data from one or more data sources in oil and gas wells and aggregate the data from the one or more data sources to obtain a well workover database; extract one or more data indicators from the well workover database and identify the type of well workover failure based on the one or more data indicators; match candidate tools from a preset well workover tool library based on the well workover failure type to obtain a candidate tool set, and call a standard process template based on the well workover failure type;

[0027] The workover decision module is used to generate a workover process plan using a candidate tool set and a standard process template, and use the workover process plan as a process evaluation factor. The workover economic evaluation factor is calculated according to the preset workover economic evaluation rules, and the workover decision result is generated using a pre-built decision model in combination with the process evaluation factor.

[0028] In order to solve the above problem, the present invention further provides an electronic device, comprising:

[0029] at least one processor; and,

[0030] a memory communicatively connected to the at least one processor; wherein,

[0031] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the above-mentioned well repair digital decision-making method.

[0032] In order to solve the above problems, the present invention also provides a computer-readable storage medium, in which at least one computer program is stored. The at least one computer program is executed by a processor in an electronic device to implement the above-mentioned digital intelligent decision-making method for well repair.

[0033] The present invention obtains data from more than one data source in oil and gas wells and aggregates the data from more than one data source to obtain a well repair database, which can realize data integration to provide a structured data basis for subsequent analysis, effectively avoid data loss, extract more than one data index from the well repair database, and identify the type of well repair fault based on more than one data index, which can reduce the dependence on manual work in fault identification and improve the efficiency and accuracy of fault identification. In addition, the use of candidate tool sets and standard process templates to generate well repair process plans can save time in plan formulation, ensure the timeliness of well repair operations, and provide a well repair plan reference for subsequent well repair decisions. In addition, the well repair economic evaluation factor is calculated according to the preset well repair economic evaluation rules, and the well repair decision result is generated by using a pre-built decision model in combination with the process evaluation factor. The well repair decision result can be made in combination with economic factors and process factors, which can realize the timeliness of well repair decisions and improve the efficiency of well repair decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 A flowchart of a digital intelligent decision-making method for well repair provided by one embodiment of the present invention;

[0035] Figure 2 This is an example flow chart of a digital intelligent decision-making method for well repair provided by one embodiment of the present invention;

[0036] Figure 3 A schematic diagram of data collection for a well repairing digital decision-making method according to an embodiment of the present invention;

[0037] Figure 4 A schematic diagram of a well workover decision process of a well workover digital intelligent decision-making method provided in one embodiment of the present invention;

[0038] Figure 5 A functional module diagram of the digital intelligence cloud platform provided in one embodiment of the present invention;

[0039] Figure 6 A schematic structural diagram of an electronic device for implementing the well workover digital intelligent decision-making method provided in one embodiment of the present invention;

[0040] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0041] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0042] The embodiment of the present application provides a digital intelligent decision-making method for well repair. The execution subject of the digital intelligent decision-making method for well repair includes but is not limited to at least one of the electronic devices such as the server and the terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the digital intelligent decision-making method for well repair can be executed by software or hardware installed on the terminal device or the server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc. The server can be an independent server, or it can be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0043] Reference Figure 1 FIG. 1 is a flow chart of a well repairing digital intelligent decision-making method provided by an embodiment of the present invention.

[0044] In this embodiment, the well repairing digital intelligent decision-making method includes:

[0045] S1. Obtain data from more than one data source in an oil and gas well, and aggregate the data from more than one data source to obtain a well repair database.

[0046] In an embodiment of the present invention, the data source refers to the data source of the collected oil and gas well data, for example, formation pressure, permeability, porosity, oil saturation, lithologic characteristics, etc. are obtained from the geological parameter data source; production (oil / gas / water), production pressure difference, water cut, gas-oil ratio, production cycle, etc. are obtained from the production parameter data source; well depth, casing integrity, cementing quality, well inclination, wellbore trajectory, etc. are obtained from the wellbore parameter data source; and drill wear degree, pump pressure, torque, drilling speed, mud properties (density, viscosity), etc. are obtained from the equipment parameter data source.

[0047] S2. Extract one or more data indicators from the well repair database, and identify the type of the well repair fault based on the one or more data indicators.

[0048] As an embodiment of the present invention, one or more data indicators are extracted from the well repair database, including:

[0049] Extract wellbore data of oil and gas fields from the well repair database and calculate the effective stress index of the wellbore based on the pre-built mechanical model;

[0050] The in-well data of the oil and gas field in the well repair database is obtained, and the failure probability of each component in the oil and gas field well is predicted using a pre-built fault prediction model. The failure probability of each component is mapped to a pre-built risk level to obtain the risk level index of each component in the oil and gas field well.

[0051] In the embodiment of the present invention, the pre-constructed mechanical model refers to a mechanical formula constructed for calculating the effective stress of the wellbore, wherein the pre-constructed mechanical model is:

[0052] σ eff =σ h -P pore

[0053] Among them, σ eff is the effective stress of the wellbore, σ h is the horizontal ground stress, P pore is the pore stress.

[0054] In the embodiment of the present invention, pore stress refers to the force exerted by the fluid pressure (such as groundwater, oil, gas, etc.) existing in the pores of the rock on the rock skeleton.

[0055] In the embodiment of the present invention, the wellbore data includes but is not limited to well depth, casing integrity, cementing quality, well inclination angle, and wellbore trajectory.

[0056] In the embodiment of the present invention, the in-well data includes but is not limited to formation pressure, permeability, water content, and porosity.

[0057] In an embodiment of the present invention, the pre-built fault prediction model refers to a pre-built deep learning model with classification and regression functions. For example, the pre-built fault prediction model may be a random forest algorithm model or an XGBoost algorithm model.

[0058] In the embodiment of the present invention, the pre-constructed risk level refers to a level gradient divided according to the fault threshold corresponding to each risk level.

[0059] Furthermore, identifying the workover fault type according to one or more data indicators includes: analyzing the workover fault type using a fault tree model according to the wellbore effective stress indicator and the risk level indicator of each component in the oil and gas field well among the one or more data indicators.

[0060] In the embodiment of the present invention, the component refers to a component of a well structure for constructing an oil and gas field.

[0061] In the embodiment of the present invention, the fault tree model is a logical graphical tool for system reliability and safety analysis, which identifies the root causes and combination relationships that lead to system failures through a top-down deductive reasoning method.

[0062] In the embodiment of the present invention, the fault type refers to the category of faults occurring in oil and gas wells, such as casing failure or sand plugging in the well.

[0063] S3. Match candidate tools from a preset workover tool library according to the workover fault type to obtain a candidate tool set, and call a standard process template according to the workover fault type.

[0064] As an embodiment of the present invention, candidate tools are matched from a preset workover tool library according to the workover fault type to obtain a candidate tool set, including:

[0065] Match the appropriate tools according to the type of workover failure;

[0066] Extract the performance parameters of applicable tools and filter the candidate tool sets based on the performance parameters.

[0067] In the embodiment of the present invention, the performance parameters of the applicable tool refer to various attribute parameters of the tool itself, such as the compressive strength and applicable depth of the tool.

[0068] In the embodiment of the present invention, the standard process template refers to a general processing process corresponding to the fault type.

[0069] S4. Generate a workover process plan using the candidate tool set and the standard process template, and use the workover process plan as a process evaluation factor.

[0070] As an embodiment of the present invention, a well repair process plan is generated using a candidate tool set and a standard process template, including:

[0071] Based on the candidate tool set and the standard process template, each tool in the candidate tool set is associated with the standard process template, and multiple basic solutions are generated using the rule engine tool;

[0072] The risk prediction model is used to predict the risk probabilities corresponding to multiple basic plans. The basic plan with the lowest risk probability is selected according to the risk probability to obtain the well repair process plan.

[0073] In the embodiment of the present invention, the rule engine tool refers to a tool for defining, managing and automating the execution of business rules, and allows rules to be defined through a specific language or a visual interface.

[0074] S5. Calculate the workover economic evaluation factor according to the preset workover economic evaluation rules, and generate the workover decision result by using the pre-built decision model in combination with the process evaluation factor.

[0075] In the embodiment of the present invention, the preset well workover economic evaluation rules refer to reference standards of the oil and gas field industry. For example, the preset well workover economic evaluation rules may be the "Economic Evaluation Standards for Oilfield Well Workover Operations".

[0076] As an embodiment of the present invention, calculating the well workover economic evaluation factor according to a preset well workover economic evaluation rule includes:

[0077] Extract the main economic indicators and economic influencing factors, generate the weights of economic influencing factors based on the sensitivity analysis matrix, and generate economic impact factors based on the weights of economic influencing factors;

[0078] Obtain the cost of each node in the well workover operation and calculate the well workover cost assessment factor based on the cost of each node;

[0079] The economic impact factors and the workover cost evaluation factors are summarized to obtain the workover economic evaluation factors.

[0080] In the embodiment of the present invention, the economic impact factor includes a net present value factor, an incremental benefit factor, a marginal benefit ratio factor, and an economic influence factor, wherein the net present value factor can be calculated using the following formula: Among them, CF t is the net cash flow in year t, r is the industry benchmark discount rate, and C0 is the initial investment cost; the incremental benefit factor can be calculated using the following formula: ΔNPV=NPV 修后 -NPV 修前 -C 修井 , where NPV 修后 NPV is the net present value after well repair修前 is the net present value before workover, C 修井 is the investment cost of well repair; the marginal benefit ratio factor MBR can be calculated using the following formula: Among them, ΔQ is the increased production after workover, P oil is the crude oil price, C total is the total cost of well repair; the economic impact factor can be obtained based on the sensitivity analysis matrix, as shown in the following table:

[0081] Influencing factors Range of variation NPV Volatility IRR changes Oil price fluctuations ±20％ 15-25％ 2-4pp Repair success rate ±15％ 8-12％ 1.5-3pp Operation cycle ±30％ 5-10％ 1-2pp

[0082] Among them, IRR is the internal rate of return, which is a core financial indicator used to evaluate the profitability of investment projects or financial products. Its core is to reflect the actual annualized rate of return of the investment by calculating the discount rate that makes the net present value (NPV) of the project zero.

[0083] In the embodiment of the present invention, the well repair cost assessment factor can be calculated using the following formula:

[0084] total cost =(rig rate +crew cost )*days*comp factor +material base *depth+μ*(rig rate +crew cost )*days

[0085] Among them, total 印st is the total cost, rig rate The workover rig rate, crew cost is the team cost, days is the number of working days, comp factor is the complexity coefficient, comp factor =1+0.2×(complexity-1), complexity is the complexity of the operation, usually 1, material base is the basic material cost, depth is the well depth, and μ is the additional rate coefficient, which is usually taken as 0.12.

[0086] The well repair economic evaluation factor obtained in the embodiment of the present invention can be calculated using the following formula:

[0087]

[0088] Among them, E norm is a standard economic indicator, α is the net present value standardization coefficient, β is the internal rate of return table conversion coefficient, and γ is the ratio that converts the internal rate of return into a dimensionless value.

[0089] Before obtaining the workover economic evaluation factor, the embodiment of the present invention further includes weighting the workover economic evaluation factor using a technical indicator factor. The technical indicator factor TQ is calculated by obtaining the technical compliance rate of each node in the workover operation, and the calculation formula is as follows:

[0090]

[0091] Among them, m is the total number of technical projects, ω i is the technical weight of the i-th technical project, P i is the technical compliance rate of the i-th technical project.

[0092] In the embodiment of the present invention, the pre-built decision model refers to an expert decision model built using multiple expert evaluation methods.

[0093] The present invention obtains data from more than one data source in oil and gas wells and aggregates the data from more than one data source to obtain a well repair database, which can realize data integration to provide a structured data basis for subsequent analysis, effectively avoid data loss, extract more than one data index from the well repair database, and identify the type of well repair fault based on more than one data index, which can reduce the dependence on manual work in fault identification and improve the efficiency and accuracy of fault identification. In addition, the use of candidate tool sets and standard process templates to generate well repair process plans can save time in plan formulation, ensure the timeliness of well repair operations, and provide a well repair plan reference for subsequent well repair decisions. In addition, the well repair economic evaluation factor is calculated according to the preset well repair economic evaluation rules, and the well repair decision result is generated by using a pre-built decision model in combination with the process evaluation factor. The well repair decision result can be made in combination with economic factors and process factors, which can realize the timeliness of well repair decisions and improve the efficiency of well repair decisions.

[0094] Reference Figure 2 FIG. 1 is a flowchart illustrating an example of a well repairing digital intelligent decision-making method according to an embodiment of the present invention.

[0095] Reference Figure 3 FIG. 1 is a schematic diagram of data collection for a well repairing digital intelligent decision-making method provided by an embodiment of the present invention.

[0096] Reference Figure 4 FIG. 1 is a schematic diagram of a well repair decision process of a well repair digital intelligent decision method provided by an embodiment of the present invention.

[0097] like Figure 5 The figure shows a functional module diagram of the digital cloud platform provided by one embodiment of the present invention.

[0098] The well repair digital intelligent decision-making device 100 of the present invention can be installed in an electronic device. According to the functions to be implemented, the well repair digital intelligent decision-making device 100 can include a decision factor extraction module 101 and a well repair decision module 102.

[0099] The module described in the present invention may also be referred to as a unit, which refers to a series of computer program segments that can be executed by a processor of an electronic device and can complete fixed functions, and is stored in a memory of the electronic device.

[0100] In this embodiment, the functions of each module / unit are as follows:

[0101] The decision factor extraction module 101 is configured to obtain data from one or more data sources in oil and gas wells, aggregate the data from the one or more data sources to obtain a well workover database; extract one or more data indicators from the well workover database, and identify the type of well workover failure based on the one or more data indicators; match candidate tools from a preset well workover tool library based on the well workover failure type to obtain a candidate tool set, and call a standard process template based on the well workover failure type.

[0102] In an embodiment of the present invention, the data source refers to the data source of the collected oil and gas well data, for example, formation pressure, permeability, porosity, oil saturation, lithologic characteristics, etc. are obtained from the geological parameter data source; production (oil / gas / water), production pressure difference, water cut, gas-oil ratio, production cycle, etc. are obtained from the production parameter data source; well depth, casing integrity, cementing quality, well inclination, wellbore trajectory, etc. are obtained from the wellbore parameter data source; and drill wear degree, pump pressure, torque, drilling speed, mud properties (density, viscosity), etc. are obtained from the equipment parameter data source.

[0103] As an embodiment of the present invention, one or more data indicators are extracted from the well repair database, including:

[0104] Extract wellbore data of oil and gas fields from the well repair database and calculate the effective stress index of the wellbore based on the pre-built mechanical model;

[0105] The in-well data of the oil and gas field in the well repair database is obtained, and the failure probability of each component in the oil and gas field well is predicted using a pre-built fault prediction model. The failure probability of each component is mapped to a pre-built risk level to obtain the risk level index of each component in the oil and gas field well.

[0106] In the embodiment of the present invention, the pre-constructed mechanical model refers to a mechanical formula constructed for calculating the effective stress of the wellbore, wherein the pre-constructed mechanical model is:

[0107] σ eff =σ h -P pore

[0108] Among them, σ eff is the effective stress of the wellbore, σ h is the horizontal ground stress, P pore is the pore stress.

[0109] In the embodiment of the present invention, pore stress refers to the force exerted by the fluid pressure (such as groundwater, oil, gas, etc.) existing in the pores of the rock on the rock skeleton.

[0110] In the embodiment of the present invention, the wellbore data includes but is not limited to well depth, casing integrity, cementing quality, well inclination angle, and wellbore trajectory.

[0111] In the embodiment of the present invention, the in-well data includes but is not limited to formation pressure, permeability, water content, and porosity.

[0112] In an embodiment of the present invention, the pre-built fault prediction model refers to a pre-built deep learning model with classification and regression functions. For example, the pre-built fault prediction model may be a random forest algorithm model or an XGBoost algorithm model.

[0113] In the embodiment of the present invention, the pre-constructed risk level refers to a level gradient divided according to the fault threshold corresponding to each risk level.

[0114] Furthermore, identifying the workover fault type according to one or more data indicators includes: analyzing the workover fault type using a fault tree model according to the wellbore effective stress indicator and the risk level indicator of each component in the oil and gas field well among the one or more data indicators.

[0115] In the embodiment of the present invention, the component refers to a component of a well structure for constructing an oil and gas field.

[0116] In the embodiment of the present invention, the fault tree model is a logical graphical tool for system reliability and safety analysis, which identifies the root causes and combination relationships that lead to system failures through a top-down deductive reasoning method.

[0117] In the embodiment of the present invention, the fault type refers to the category of faults occurring in oil and gas wells, such as casing failure or sand plugging in the well.

[0118] As an embodiment of the present invention, candidate tools are matched from a preset workover tool library according to the workover fault type to obtain a candidate tool set, including:

[0119] Match the appropriate tools according to the type of workover failure;

[0120] Extract the performance parameters of applicable tools and filter the candidate tool sets based on the performance parameters.

[0121] In the embodiment of the present invention, the performance parameters of the applicable tool refer to various attribute parameters of the tool itself, such as the compressive strength and applicable depth of the tool.

[0122] In the embodiment of the present invention, the standard process template refers to a general processing process corresponding to the fault type.

[0123] The workover decision module 102 is configured to generate a workover process plan using a candidate tool set and a standard process template, and use the workover process plan as a process evaluation factor; calculate the workover economic evaluation factor according to a preset workover economic evaluation rule, and generate a workover decision result using a pre-built decision model in combination with the process evaluation factor.

[0124] As an embodiment of the present invention, a well repair process plan is generated using a candidate tool set and a standard process template, including:

[0125] Based on the candidate tool set and the standard process template, each tool in the candidate tool set is associated with the standard process template, and multiple basic solutions are generated using the rule engine tool;

[0126] The risk prediction model is used to predict the risk probabilities corresponding to multiple basic plans. The basic plan with the lowest risk probability is selected according to the risk probability to obtain the well repair process plan.

[0127] In the embodiment of the present invention, the rule engine tool refers to a tool for defining, managing and automating the execution of business rules, and allows rules to be defined through a specific language or a visual interface.

[0128] In the embodiment of the present invention, the preset well workover economic evaluation rules refer to reference standards of the oil and gas field industry. For example, the preset well workover economic evaluation rules may be the "Economic Evaluation Standards for Oilfield Well Workover Operations".

[0129] As an embodiment of the present invention, calculating the well workover economic evaluation factor according to a preset well workover economic evaluation rule includes:

[0130] Extract the main economic indicators and economic influencing factors, generate the weights of economic influencing factors based on the sensitivity analysis matrix, and generate economic impact factors based on the weights of economic influencing factors;

[0131] Obtain the cost of each node in the well workover operation and calculate the well workover cost assessment factor based on the cost of each node;

[0132] The economic impact factors and the workover cost evaluation factors are summarized to obtain the workover economic evaluation factors.

[0133] In the embodiment of the present invention, the economic impact factor includes a net present value factor, an incremental benefit factor, a marginal benefit ratio factor, and an economic influence factor, wherein the net present value factor can be calculated using the following formula: Among them, CF t is the net cash flow in year t, r is the industry benchmark discount rate, and C0 is the initial investment cost; the incremental benefit factor can be calculated using the following formula: ΔNPV=NPV 修后 -NPV 修前 -C 修井 , where NPN 修后 NPN is the net present value after well repair 修前 is the net present value before workover, C 修井 is the investment cost of well repair; the marginal benefit ratio factor MBR can be calculated using the following formula: Among them, ΔQ is the increased production after workover, P oil is the crude oil price, C total is the total cost of well repair; the economic impact factor can be obtained based on the sensitivity analysis matrix, as shown in the following table:

[0134] Influencing factors Range of variation NPV Volatility IRR changes Oil price fluctuations ±20％ 15-25％ 2-4pp Repair success rate ±15％ 8-12％ 1.5-3pp Operation cycle ±30％ 5-10％ 1-2pp

[0135] Among them, IRR is the internal rate of return, which is a core financial indicator used to evaluate the profitability of investment projects or financial products. Its core is to reflect the actual annualized rate of return of the investment by calculating the discount rate that makes the net present value (NPV) of the project zero.

[0136] In the embodiment of the present invention, the well repair cost assessment factor can be calculated using the following formula:

[0137] total cost =(rig rate +crew cost )*days*comp factor +material base *depth+μ*(rig rate +crew cost )*days

[0138] Among them, total cost is the total cost, rig rate The workover rig rate, crew cost is the team cost, days is the number of working days, comp factor is the complexity coefficient, comp factor =1+0.2×(complexity-1), complexity is the complexity of the operation, usually 1, material base is the basic material cost, depth is the well depth, and μ is the additional rate coefficient, which is usually taken as 0.12.

[0139] The well repair economic evaluation factor obtained in the embodiment of the present invention can be calculated using the following formula:

[0140]

[0141] Among them, E norm is a standard economic indicator, α is the net present value standardization coefficient, β is the internal rate of return table conversion coefficient, and γ is the ratio that converts the internal rate of return into a dimensionless value.

[0142] Before obtaining the workover economic evaluation factor, the embodiment of the present invention further includes weighting the workover economic evaluation factor using a technical indicator factor. The technical indicator factor TQ is calculated by obtaining the technical compliance rate of each node in the workover operation, and the calculation formula is as follows:

[0143]

[0144] Among them, m is the total number of technical projects, ω i is the technical weight of the i-th technical project, p i is the technical compliance rate of the i-th technical project.

[0145] In the embodiment of the present invention, the pre-built decision model refers to an expert decision model built using multiple expert evaluation methods.

[0146] like Figure 6 FIG. 1 is a schematic diagram of the structure of an electronic device for implementing a digital intelligent decision-making method for well repair provided by one embodiment of the present invention.

[0147] The electronic device may include a processor 10, a memory 11, a communication bus 12, and a communication interface 13, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a well repair digital intelligent decision-making method program.

[0148] In some embodiments, the processor 10 may be composed of an integrated circuit, such as a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and a combination of various control chips. The processor 10 is the control core (Control Unit) of the electronic device, connecting the various components of the entire electronic device using various interfaces and lines, and executing or executing programs or modules stored in the memory 11 (such as executing a well repair digital decision-making method program, etc.), as well as calling data stored in the memory 11, to perform various functions of the electronic device and process data.

[0149] The memory 11 includes at least one type of readable storage medium, and the readable storage medium includes a flash memory, a mobile hard disk, a multimedia card, a card-type memory (for example, an SD or DX memory, etc.), a magnetic memory, a disk, an optical disk, etc. In some embodiments, the memory 11 may be an internal storage unit of an electronic device, such as a mobile hard disk of the electronic device. In other embodiments, the memory 11 may also be an external storage device of an electronic device, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Furthermore, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device. The memory 11 can not only be used to store application software and various types of data installed in the electronic device, such as the code of a well repair digital decision-making method program, but can also be used to temporarily store data that has been output or is to be output.

[0150] The communication bus 12 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is configured to enable communication between the memory 11 and at least one processor 10, etc.

[0151] The communication interface 13 is used for communication between the above-mentioned electronic device and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a display (Display), an input unit (such as a keyboard (Keyboard)), optionally, the user interface may also be a standard wired interface, a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode, organic light-emitting diode) touch device, etc. Among them, the display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device and to display a visual user interface.

[0152] Figure 6 Only the electronic device with components is shown, and it can be understood by those skilled in the art that Figure 6The structure shown does not limit the electronic device, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0153] For example, although not shown, the electronic device may further include a power source (such as a battery) for powering various components. Preferably, the power source may be logically connected to the at least one processor 10 via a power management device, thereby implementing functions such as charge management, discharge management, and power consumption management through the power management device. The power source may further include any components such as one or more DC or AC power sources, a recharging device, a power failure detection circuit, a power converter or inverter, a power status indicator, etc. The electronic device may further include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0154] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0155] The memory 11 in the electronic device stores a program of a well repairing digital intelligent decision-making method, which is a combination of multiple instructions. When running in the processor 10, the program can achieve the following:

[0156] Acquire data from more than one data source in an oil and gas well, and aggregate the data from more than one data source to obtain a well repair database;

[0157] Extracting one or more data indicators from a well repair database, and identifying a well repair fault type based on the one or more data indicators;

[0158] According to the workover fault type, candidate tools are matched from a preset workover tool library to obtain a candidate tool set, and a standard process template is called according to the workover fault type;

[0159] Generate a workover process plan using the candidate tool set and standard process template, and use the workover process plan as a process evaluation factor;

[0160] The workover economic evaluation factors are calculated according to the preset workover economic evaluation rules, and the workover decision results are generated using the pre-built decision model in combination with the process evaluation factors.

[0161] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to the description of the relevant steps in the corresponding embodiment of the drawings, which will not be repeated here.

[0162] Furthermore, if the modules / units integrated into the electronic device 1 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0163] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program. When the computer program is executed by a processor of an electronic device, the computer program can implement:

[0164] Acquire data from more than one data source in an oil and gas well, and aggregate the data from more than one data source to obtain a well repair database;

[0165] Extracting one or more data indicators from a well repair database, and identifying a well repair fault type based on the one or more data indicators;

[0166] According to the workover fault type, candidate tools are matched from a preset workover tool library to obtain a candidate tool set, and a standard process template is called according to the workover fault type;

[0167] Generate a workover process plan using the candidate tool set and standard process template, and use the workover process plan as a process evaluation factor;

[0168] The workover economic evaluation factors are calculated according to the preset workover economic evaluation rules, and the workover decision results are generated using the pre-built decision model in combination with the process evaluation factors.

[0169] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the module division is merely a logical function division, and other division methods may be used in actual implementation.

[0170] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed across multiple network elements. Some or all of the modules may be selected to achieve the purpose of the solution of this embodiment according to actual needs.

[0171] In addition, the functional modules in various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or hardware plus software functional modules.

[0172] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0173] Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the claims are intended to be embraced therein. Any reference to a figure in a claim should not be construed as limiting the claim to which it relates.

[0174] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Artificial Intelligence (AI) is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to achieve optimal results.

[0175] Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices recited in a system claim may also be implemented by a single unit or device through software or hardware. Terms such as "first" and "second" are used to indicate names and do not imply any particular order.

[0176] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A digital intelligent decision-making method for well repair, characterized by: The method comprises: Acquire data from more than one data source in an oil and gas well, and aggregate the data from more than one data source to obtain a well repair database; Extracting one or more data indicators from a well repair database, and identifying a well repair fault type based on the one or more data indicators; According to the workover fault type, candidate tools are matched from a preset workover tool library to obtain a candidate tool set, and a standard process template is called according to the workover fault type; Generate a workover process plan using a candidate tool set and a standard process template, and use the workover process plan as a process evaluation factor; The workover economic evaluation factor is calculated according to the preset workover economic evaluation rules, and the workover economic evaluation factor and process evaluation factor are combined and the pre-built decision model is used to generate the workover decision result.

2. The well repairing digital intelligent decision-making method according to claim 1, characterized in that: The extraction of one or more data indicators from the well repair database includes: Extract wellbore data of oil and gas fields from the well repair database and calculate the effective stress index of the wellbore based on the pre-built mechanical model; The in-well data of the oil and gas field in the well repair database is obtained, and the failure probability of each component in the oil and gas field well is predicted using a pre-built fault prediction model. The failure probability of each component is mapped to a pre-built risk level to obtain the risk level index of each component in the oil and gas field well.

3. The well repairing digital intelligent decision-making method according to claim 1 or 2, characterized in that: Identifying the type of workover failure according to one or more data indicators includes: analyzing the type of workover failure using a fault tree model according to an effective stress indicator of a wellbore and a risk level indicator of each component in an oil and gas field well among the one or more data indicators.

4. The well repairing digital intelligent decision-making method according to claim 1, characterized in that: The method of matching candidate tools from a preset workover tool library according to the workover fault type to obtain a candidate tool set includes: Match the appropriate tools according to the type of workover failure; Extract the performance parameters of applicable tools and filter the candidate tool sets based on the performance parameters.

5. The well repairing digital intelligent decision-making method according to claim 1, characterized in that: The method of generating a workover process plan by using a candidate tool set and a standard process template includes: Based on the candidate tool set and the standard process template, each tool in the candidate tool set is associated with the standard process template, and multiple basic solutions are generated using the rule engine tool; The risk prediction model is used to predict the risk probabilities corresponding to multiple basic plans. The basic plan with the lowest risk probability is selected according to the risk probability to obtain the well repair process plan.

6. The well repairing digital intelligent decision-making method according to claim 1, characterized in that: The calculation of the well workover economic evaluation factor according to the preset well workover economic evaluation rules includes: Extract the main economic indicators and economic influencing factors, generate the weights of economic influencing factors based on the sensitivity analysis matrix, and generate economic impact factors based on the weights of economic influencing factors; Obtain the cost of each node in the well workover operation and calculate the well workover cost assessment factor based on the cost of each node; The economic impact factors and the workover cost evaluation factors are summarized to obtain the workover economic evaluation factors.

7. A digital intelligent decision-making device for well repair, characterized by: The well workover digital intelligent decision-making device is used to implement the well workover digital intelligent decision-making method according to any one of claims 1 to 6, and the well workover digital intelligent decision-making device includes: The decision factor extraction module is used to obtain data from one or more data sources in oil and gas wells and aggregate the data from the one or more data sources to obtain a well workover database; extract one or more data indicators from the well workover database and identify the type of well workover failure based on the one or more data indicators; match candidate tools from a preset well workover tool library based on the well workover failure type to obtain a candidate tool set, and call a standard process template based on the well workover failure type; The workover decision module is used to generate a workover process plan using a candidate tool set and a standard process template, and use the workover process plan as a process evaluation factor. The workover economic evaluation factor is calculated according to the preset workover economic evaluation rules, and the workover decision result is generated using a pre-built decision model in combination with the process evaluation factor.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the well repair digital intelligent decision-making method according to any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the well repair digital intelligent decision-making method according to any one of claims 1 to 6 is implemented.

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