Carbon neutralization intelligent analysis method based on artificial intelligence

Abstract

The invention discloses a carbon neutralization intelligent analysis method based on artificial intelligence, and belongs to the technical field of carbon emission processing, and the method comprises the following steps: S1, collecting carbon emission data of a to-be-analyzed region in a drilling stage; s2, determining the fuel energy consumption weight of the drilling stage; s3, according to the fuel energy consumption weight of the drilling stage, generating residual estimated carbon emission of the drilling stage; and S4, the sum of the existing carbon emission amount and the residual estimated carbon emission amount in the well drilling stage serves as the total estimated carbon emission amount in the well drilling stage, and carbon emission amount adjustment is conducted on the fracturing stage and the well completion stage according to the total estimated carbon emission amount in the well drilling stage. According to the method, the carbon emission condition in the well drilling stage is accurately predicted, the overall trend of carbon emission in the well drilling stage can be known in advance by estimating the residual carbon emission, prospective information is provided for overall carbon emission management of a project, and making of a more targeted emission reduction strategy in fracturing and well completion stages is facilitated.

Description

Technical Field

[0001] The present invention belongs to the technical field of carbon emission treatment, and specifically relates to a carbon neutrality intelligent analysis method based on artificial intelligence. Background Art

[0002] In the energy extraction industry such as oil and gas, drilling operations are a key link in the energy production process, but they are also a major source of energy consumption and carbon emissions. The existing carbon emission management method for drilling operations uses empirical estimation methods, which lack scientificity and systematicity. It is difficult to accurately assess carbon emissions at different drilling stages and under different operating conditions, and cannot provide an accurate basis for formulating effective emission reduction measures.

[0003] With the rapid development of artificial intelligence technology, it has shown strong advantages in data processing and predictive analysis. Artificial intelligence technology can quickly process massive amounts of carbon emission data, explore the potential patterns behind the data, and accurately predict future carbon emission trends. However, there are relatively few studies on the application of artificial intelligence technology in the field of carbon neutrality analysis of drilling operations, and a mature and complete analysis method has not yet been formed. Summary of the invention

[0004] In order to solve the above problems, the present invention proposes a carbon neutrality intelligent analysis method based on artificial intelligence.

[0005] The technical solution of the present invention is: a carbon neutrality intelligent analysis method based on artificial intelligence comprises the following steps:

[0006] S1. Collect carbon emission data of the area to be analyzed during the drilling stage;

[0007] S2. Extract the fuels and carbon emissions used in the drilling stage in the area to be analyzed, calculate the performance impact index of the drilling stage, and determine the fuel energy consumption weight of the drilling stage;

[0008] S3. Generate the remaining estimated carbon emissions in the drilling stage according to the fuel energy consumption weight in the drilling stage;

[0009] S4. The sum of the existing carbon emissions and the remaining estimated carbon emissions in the drilling stage is taken as the total estimated carbon emissions in the drilling stage, and the carbon emissions in the fracturing stage and the completion stage are adjusted according to the total estimated carbon emissions in the drilling stage.

[0010] Drilling refers to the project of using specialized equipment to establish a channel between the ground and underground oil wells, so that the underground oil and gas can be collected along the oil pipeline to the ground for use.

[0011] The carbon emissions generated during the drilling phase mainly stem from diesel consumption and equipment operation. Drilling equipment typically relies on fossil fuels such as diesel to provide power, and a large amount of carbon dioxide is released during the combustion process. If the total estimated carbon emissions during the drilling phase exceed the preset carbon emissions of the drilling project, the following methods can be used to adjust the carbon emissions: (1) Optimize the fracturing fluid formula: Reduce the use of chemical additives, for example, the emission factor is reduced to 1.2 kg CO 2 / m³; (2) Adopt high-efficiency pumping equipment, for example, the power consumption is reduced to 4000 kWh; (3) Optimize the well completion process: Reduce the equipment operation time, and the power consumption is reduced to 2500 kWh; (4) Adopt environmentally friendly materials, for example, the carbon emissions from material transportation are reduced to 800 kg CO 2 .

[0012] Furthermore, S2 includes the following sub-steps:

[0013] S21. Extract the fuel used in the drilling phase in the area to be analyzed;

[0014] S22. Calculate the performance impact index during the drilling phase based on the average combustion rate and the actual maximum combustion rate of the fuel during the drilling phase;

[0015] S23. Extract the working equipment that generates carbon emissions from the fuel used in the drilling phase in the area to be analyzed, and determine the carbon emissions of each working equipment;

[0016] S24. Determine the fuel energy consumption weight during the drilling phase based on the carbon emissions of each working equipment and the performance impact index during the drilling phase.

[0017] The beneficial effects of the above further solution are as follows: In the present invention, during the drilling operation, the fuel combustion situation will change due to various factors such as drilling depth, geological conditions, and equipment load. The average combustion rate reflects the fuel combustion situation under the drilling working conditions, and the actual maximum combustion rate reflects the combustion limit under the working conditions. By comprehensively considering these two indicators, the combustion characteristics of the fuel throughout the drilling phase are extracted, avoiding the one-sidedness that may be brought by a single indicator. Moreover, the drilling operation is a dynamic process, and this dynamic change can be reflected by the fuel energy consumption weight, making the performance impact index more accurately reflect the actual impact of fuel combustion on carbon emissions during the drilling phase.

[0018] Furthermore, in S22, the performance impact index of the drilling equipment is calculated by the following formula:

[0019] ;

[0020] In the formula, represents the lowest temperature at which the fuel can continue to burn, represents the maximum combustion temperature of the fuel during the drilling phase, represents the actual maximum combustion rate of the fuel during the drilling stage, represents the ideal maximum combustion rate of the fuel, represents the burnout temperature of the fuel, represents the average combustion rate of the fuel during the drilling stage.

[0021] Further, S24 includes the following sub-steps:

[0022] S241. Generate a characteristic time series based on the carbon emissions of each working device in the drilling stage in the area to be analyzed, where, represents the total carbon emissions of all working devices that use fuel at the first drilling time point, represents the total carbon emissions of all working devices that use fuel at the second drilling time point, represents the total carbon emissions of all working devices that use fuel at the

[0023] S242. Extract the variance up to each drilling time point in the characteristic time series;

[0024] S243. Take the ratio between the sum of the variances corresponding to the previous drilling time point and the next drilling time point in the characteristic time series and the variance corresponding to the current drilling time point as the fluctuation change characteristic parameter at the current drilling time point;

[0025] S244. Determine the fuel energy consumption weight in the drilling stage according to the fluctuation change characteristic parameters at each drilling time point.

[0026] The beneficial effects of the above further solution are as follows: In the present invention, the construction of the characteristic time series makes the carbon emission data in the drilling stage have continuity in the time dimension. The variance can reflect the degree of data dispersion, so the fluctuation change characteristic parameter further depicts the fluctuation of carbon emissions between different time points, providing an important index for evaluating the stability of fuel energy consumption. The fuel energy consumption weight tends to reflect the change of the fluctuation degree of the data at the current time point relative to its adjacent time points.

[0027] Further, in S244, the fuel energy consumption weight in the drilling stage has the following calculation formula:

[0028] ;

[0029] In the formula, represents the performance influence index of the drilling equipment, represents the fluctuation change characteristic parameter at the represents the total carbon emissions of all working equipment that generates carbon emissions using fuel at the th drilling time point, represents the total carbon emissions of all working equipment that generates carbon emissions using fuel at the th drilling time point, represents the total carbon emissions of all working equipment that generates carbon emissions using fuel at the th drilling time point, represents the number of existing drilling time points in the drilling stage.

[0030] Furthermore, S3 includes the following sub-steps:

[0031] S31. Obtain the formations corresponding to each drilling time point in the drilling stage of the area to be analyzed;

[0032] S32. Construct the environmental characteristic coefficient of each drilling time point according to the formations corresponding to each drilling time point, the total carbon emissions of all working equipment that generates carbon emissions using fuel at each drilling time point, and the fuel energy consumption weight in the drilling stage;

[0033] S33. Take the product of the maximum environmental characteristic coefficient and the total carbon emissions of all working equipment that generates carbon emissions using fuel at the last time point as the estimated carbon emissions at the next time point;

[0034] S34. Repeat S35 until the expected drilling end time point is reached, and add up all the obtained estimated carbon emissions to get the remaining estimated carbon emissions in the drilling stage.

[0035] The beneficial effects of the above further solution are as follows: In the present invention, the fuel consumption of drilling equipment (such as a diesel generator) is directly related to the drilling difficulty. The geological characteristics of different formations will affect the drilling operation difficulty, the power demand of the drilling rig, and the bit wear degree, etc., and thus affect carbon emissions. By considering the formation factors, as the drilling process changes continuously, such as the drilling rig power, drilling depth, and drilling duration, etc., the dynamically adjusted calculated environmental characteristic coefficient can reflect the change of carbon emissions in the drilling stage in real time. By analyzing the environmental characteristic coefficient, the carbon emissions at different drilling time points can be understood, and the maximum environmental characteristic coefficient affecting carbon emissions can be found.

[0036] Furthermore, in S32, the environmental characteristic coefficient at the th drilling time point is calculated by the following formula:

[0037] ;

[0038] In the formula, represents the The rig power at each drilling time point, represents the drilling depth reached at the th drilling time point, represents the total carbon emissions of all working equipment that generate carbon emissions using fuel at the th drilling time point, represents taking a random number between 0 and 1, represents up to the th drilling time point, the number of times the drill bit of the rig is replaced, represents the energy consumption generated each time the drill bit of the rig is replaced, represents the th drilling time point, the carbon emission factor, represents up to the th drilling time point, the maximum carbon emission factor, represents the expected well depth in the drilling stage, represents up to the th drilling time point, the drilling duration.

[0039] The beneficial effects of the present invention are:

[0040] (1) This method extracts the fuel and carbon emissions used in the drilling stage of the area to be analyzed, calculates the performance impact index in the drilling stage, determines the fuel energy consumption weight, combines the carbon emissions in the drilling stage with the specific fuel usage situation, and calculates the energy consumption weight, which can accurately quantify the impact degree of fuel on carbon emissions in the drilling stage;

[0041] (2) This method takes into account that as the drilling process progresses, the well conditions will change continuously. Therefore, it combines the real-time environmental parameters in the well during drilling and the fuel energy consumption weight to determine the environmental characteristic coefficient, and dynamically adjusts the estimated carbon emission function for the next time point;

[0042] (3) This method accurately predicts the carbon emission situation in the drilling stage. By estimating the remaining carbon emissions, it can understand the overall trend of carbon emissions in the drilling stage in advance, provide forward-looking information for the overall carbon emission management of the project, and help formulate more targeted emission reduction strategies in the fracturing and completion stages. Description of the Drawings

[0043] Figure 1 is a flow chart of an artificial intelligence-based carbon neutral intelligent analysis method. Detailed Embodiments

[0044] The following further describes the embodiments of the present invention with reference to the drawings.

[0045] As Figure 1As shown in the figure, the present invention provides an artificial intelligence-based intelligent carbon neutral analysis method, which includes the following steps:

[0046] S1. Collect carbon emission data in the drilling stage of the area to be analyzed;

[0047] S2. Extract the fuels used in the drilling stage of the area to be analyzed and their carbon emissions, calculate the performance impact index in the drilling stage, and determine the fuel energy consumption weight in the drilling stage;

[0048] S3. Generate the remaining estimated carbon emissions in the drilling stage according to the fuel energy consumption weight in the drilling stage;

[0049] S4. Take the sum of the existing carbon emissions and the remaining estimated carbon emissions in the drilling stage as the total estimated carbon emissions in the drilling stage, and adjust the carbon emissions in the fracturing stage and the completion stage according to the total estimated carbon emissions in the drilling stage.

[0050] Drilling refers to the project of using special equipment to establish a channel between the ground and the underground oil well, so as to collect the underground oil and gas along the oil pipeline to the ground for utilization.

[0051] The carbon emissions generated in the drilling stage mainly come from diesel consumption and equipment operation. Drilling equipment usually relies on fossil fuels such as diesel to provide power, and a large amount of carbon dioxide will be released during the combustion process. If the total estimated carbon emissions in the drilling stage exceed the preset carbon emissions of the drilling project, the following methods can be used to adjust the carbon emissions: (1) Optimize the fracturing fluid formula: reduce the use of chemical additives, for example, the emission factor is reduced to 1.2 kg CO 2 / m³; (2) Adopt high-efficiency pumping equipment, for example, the power consumption is reduced to 4000 kWh; (3) Optimize the completion process: reduce the equipment operation time, and the power consumption is reduced to 2500 kWh; (4) Adopt environmentally friendly materials, for example, the carbon emissions from material transportation are reduced to 800 kg CO 2 .

[0052] In the embodiment of the present invention, S2 includes the following sub-steps:

[0053] S21. Extract the fuels used in the drilling stage of the area to be analyzed;

[0054] S22. Calculate the performance impact index in the drilling stage according to the average combustion rate and the actual maximum combustion rate of the fuel in the drilling stage;

[0055] S23. Extract the working equipment that generates carbon emissions from the fuels used in the drilling stage of the area to be analyzed, and determine the carbon emissions of each working equipment;

[0056] S24. Determine the fuel energy consumption weight in the drilling stage according to the carbon emissions of each working equipment and the performance impact index in the drilling stage.

[0057] In the present invention, during the drilling operation, the fuel combustion situation will vary due to various factors such as drilling depth, geological conditions, and equipment load. The average combustion rate reflects the fuel combustion situation under the drilling conditions, and the actual maximum combustion rate reflects the combustion limit under the working conditions. By comprehensively considering these two indicators, the combustion characteristics of the fuel during the entire drilling stage are extracted, avoiding the one-sidedness that may be brought about by a single indicator. Moreover, the drilling operation is a dynamic process. By using the fuel energy consumption weight, this dynamic change can be reflected, enabling the performance impact index to more accurately reflect the actual impact of fuel combustion on carbon emissions during the drilling stage.

[0058] In the embodiment of the present invention, in S22, the performance impact index of the drilling equipment has the following calculation formula:

[0059] ;

[0060] In the formula, represents the lowest temperature at which the fuel can continuously burn, represents the maximum combustion temperature of the fuel during the drilling stage, represents the actual maximum combustion rate of the fuel during the drilling stage, represents the ideal maximum combustion rate of the fuel, represents the burnout temperature of the fuel, represents the average combustion rate of the fuel during the drilling stage.

[0061] In the embodiment of the present invention, S24 includes the following sub-steps:

[0062] S241. Generate a characteristic time series , where represents the total carbon emissions of all working equipment that uses fuel to generate carbon emissions at the first drilling time point, represents the total carbon emissions of all working equipment that uses fuel to generate carbon emissions at the second drilling time point, represents the total carbon emissions of all working equipment that uses fuel to generate carbon emissions at the th drilling time point;

[0063] S242. Extract the variance up to each drilling time point in the characteristic time series;

[0064] S243. Use the ratio between the sum of the variances corresponding to the previous drilling time point and the next drilling time point in the characteristic time series and the variance corresponding to the current drilling time point as the fluctuation change characteristic parameter at the current drilling time point;

[0065] S244. Determine the fuel energy consumption weight of the drilling stage according to the fluctuation change characteristic parameters at each drilling time point.

[0066] In the present invention, the construction of the characteristic time series makes the carbon emission data in the drilling stage have continuity in the time dimension, and the variance can reflect the degree of dispersion of the data. Therefore, the fluctuation change characteristic parameters further depict the fluctuation of carbon emissions between different time points, providing an important index for evaluating the stability of fuel energy consumption. The fuel energy consumption weight tends to reflect the change in the degree of fluctuation of the data at the current time point relative to its adjacent time points.

[0067] In the embodiment of the present invention, in S244, the fuel energy consumption weight of the drilling stage The calculation formula is:

[0068] ;

[0069] In the formula, represents the performance influence index of the drilling equipment, represents the fluctuation change characteristic parameter of the th drilling time point, represents the sum of the carbon emissions of all working equipment that generates carbon emissions using fuel at the th drilling time point, represents the sum of the carbon emissions of all working equipment that generates carbon emissions using fuel at the th drilling time point, represents the sum of the carbon emissions of all working equipment that generates carbon emissions using fuel at the th drilling time point, represents the number of existing drilling time points in the drilling stage.

[0070] In the embodiment of the present invention, S3 includes the following sub-steps:

[0071] S31. Obtain the formations corresponding to each drilling time point in the drilling stage of the area to be analyzed;

[0072] S32. Construct the environmental characteristic coefficient of each drilling time point according to the formations corresponding to each drilling time point, the sum of the carbon emissions of all working equipment that generates carbon emissions using fuel at each drilling time point, and the fuel energy consumption weight of the drilling stage;

[0073] S33. Use the product of the maximum environmental characteristic coefficient and the sum of the carbon emissions of all working equipment that generates carbon emissions using fuel at the last time point as the estimated carbon emissions at the next time point;

[0074] S34. Repeat S35 until the expected drilling end time point is reached, and add up all the estimated carbon emissions obtained to get the remaining estimated carbon emissions during the drilling stage.

[0075] In the present invention, the fuel consumption of drilling equipment (such as a diesel generator) is directly related to the drilling difficulty. The geological characteristics of different formations will affect the drilling operation difficulty, the rig power demand, the bit wear degree, etc., and thus affect carbon emissions. By considering the formation factors, which change continuously during the drilling process, such as the rig power, the drilling depth, and the drilling duration, the dynamically adjusted environmental characteristic coefficient can reflect the changes in carbon emissions during the drilling stage in real time. By analyzing the environmental characteristic coefficient, the carbon emissions at different drilling time points can be understood, and the maximum environmental characteristic coefficient affecting carbon emissions can be found.

[0076] In the embodiment of the present invention, in S32, the environmental characteristic coefficient at the th drilling time point

[0077] is calculated by the formula:

[0078] In the formula, represents the rig power at the th drilling time point, represents the drilling depth reached at the th drilling time point, represents the total carbon emissions of all working equipment that generates carbon emissions from fuel at the th drilling time point, represents the fuel energy consumption weight during the drilling stage, represents a random number taken between 0 and 1, represents the number of times the rig bit has been replaced up to the th drilling time point, represents the energy consumption generated each time the rig bit is replaced, represents the carbon emission factor at the th drilling time point, represents the maximum carbon emission factor up to the th drilling time point, represents the expected well depth during the drilling stage, represents the drilling duration up to the th drilling time point.

[0079] Those of ordinary skill in the art will realize that the embodiments described herein are provided to assist the reader in understanding the principles of the present invention, and it should be understood that the scope of protection of the present invention is not limited to such specific statements and embodiments. Those of ordinary skill in the art can make various other specific deformations and combinations that do not depart from the essence of the present invention based on these technical revelations disclosed in the present invention, and these deformations and combinations are still within the scope of protection of the present invention.

Claims

1. A carbon neutrality intelligent analysis method based on artificial intelligence, characterized in that: The following steps are involved: S1. Collect carbon emission data of the area to be analyzed during the drilling stage; S2. Extract the fuels and carbon emissions used in the drilling stage in the area to be analyzed, calculate the performance impact index of the drilling stage, and determine the fuel energy consumption weight of the drilling stage; S3. Generate the remaining estimated carbon emissions in the drilling stage according to the fuel energy consumption weight in the drilling stage; S4. The sum of the existing carbon emissions and the remaining estimated carbon emissions in the drilling stage is taken as the total estimated carbon emissions in the drilling stage, and the carbon emissions in the fracturing stage and the completion stage are adjusted according to the total estimated carbon emissions in the drilling stage.

2. The carbon neutrality intelligent analysis method based on artificial intelligence according to claim 1 is characterized in that: The S2 comprises the following sub-steps: S21, extracting the fuel used in the drilling stage in the area to be analyzed; S22, calculating the performance impact index of the drilling stage according to the average combustion rate and the actual maximum combustion rate of the fuel during the drilling stage; S23, extracting the working equipment that uses fuel to generate carbon emissions during the drilling phase in the area to be analyzed, and determining the carbon emissions of each working equipment; S24. Determine the fuel energy consumption weight of the drilling stage according to the carbon emission of each working equipment and the performance impact index of the drilling stage.

3. The carbon neutrality intelligent analysis method based on artificial intelligence according to claim 2 is characterized in that: In S22, the performance impact index of the drilling equipment The calculation formula is: ； In the formula, The lowest temperature at which the fuel can continue to burn. Indicates the maximum combustion temperature of the fuel during the drilling phase, Indicates the actual maximum burning rate of the fuel during the drilling phase, represents the ideal maximum combustion rate of the fuel, The combustion temperature of the fuel. Represents the average burning rate of fuel during the drilling phase.

4. The carbon neutrality intelligent analysis method based on artificial intelligence according to claim 2 is characterized in that: The S24 comprises the following sub-steps: S241. Generate a characteristic time series based on the carbon emissions of each working equipment in the area to be analyzed during the drilling phase. ,in, It represents the sum of carbon emissions of all working equipment using fuel at the first drilling time point. It represents the sum of carbon emissions of all working equipment using fuel at the second drilling time point. Indicates The sum of carbon emissions from all working equipment using fuel to generate carbon emissions at each drilling time point; S242, extracting the variance of the characteristic time series up to each drilling time point; S243, taking the ratio of the sum of the variance corresponding to the previous drilling time point and the variance corresponding to the next drilling time point in the characteristic time series to the variance corresponding to the current drilling time point as the fluctuation change characteristic parameter of the current drilling time point; S244. Determine the fuel energy consumption weight of the drilling stage according to the fluctuation characteristic parameters at each drilling time point.

5. The carbon neutrality intelligent analysis method based on artificial intelligence according to claim 4 is characterized in that: In S244, the fuel energy consumption weight of the drilling stage The calculation formula is: ； In the formula, Indicates the performance impact index of drilling equipment, Indicates The fluctuation characteristic parameters of each drilling time point, Indicates The sum of carbon emissions from all working equipment using fuel to generate carbon emissions at each drilling time point, Indicates The sum of carbon emissions from all working equipment using fuel to generate carbon emissions at each drilling time point, Indicates The sum of carbon emissions from all working equipment using fuel to generate carbon emissions at each drilling time point, Indicates the number of existing drilling time points in the drilling phase.

6. The carbon neutrality intelligent analysis method based on artificial intelligence according to claim 1 is characterized in that: The S3 comprises the following sub-steps: S31, obtaining the strata corresponding to each drilling time point in the drilling stage of the area to be analyzed; S32, constructing the environmental characteristic coefficient of each drilling time point according to the stratum corresponding to each drilling time point, the sum of carbon emissions of all working equipment using fuel to generate carbon emissions at each drilling time point, and the fuel energy consumption weight of the drilling stage; S33, taking the product of the maximum environmental characteristic coefficient and the sum of carbon emissions of all working equipment using the fuel to generate carbon emissions at the last time point as the estimated carbon emissions at the next time point; S34, repeat S35 until the estimated drilling end time point is reached, and all the estimated carbon emissions obtained are added together to obtain the remaining estimated carbon emissions in the drilling stage.

7. The carbon neutrality intelligent analysis method based on artificial intelligence according to claim 6 is characterized in that: In the S32, Environmental characteristic coefficients at each drilling time point The calculation formula is: ； In the formula, Indicates The drilling rig power at each drilling time point, Indicates The drilling depth reached at each drilling time point, Indicates The sum of carbon emissions from all working equipment using fuel to generate carbon emissions at each drilling time point, represents the fuel energy consumption weight during the drilling phase, Indicates a random number between 0 and 1. Indicates that the Number of drill bit changes at each drilling time point, Indicates the energy consumption generated each time the drill bit is replaced. Indicates Carbon emission factor at each drilling time point, Indicates that the The maximum carbon emission factor at each drilling time point, represents the expected well depth during the drilling phase, Indicates that the The drilling time at each drilling time point.

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