A carbon neutrality intelligent analysis method based on artificial intelligence

Abstract

The present invention discloses a carbon neutrality intelligent analysis method based on artificial intelligence, which belongs to the field of carbon emission processing technology and includes the following steps: S1, collecting carbon emission data of the area to be analyzed during the drilling stage; S2, determining the fuel energy consumption weight of the drilling stage; S3, generating the remaining estimated carbon emissions of the drilling stage according to the fuel energy consumption weight of the drilling stage; S4, taking the sum of the existing carbon emissions and the remaining estimated carbon emissions of the drilling stage as the total estimated carbon emissions of the drilling stage, and adjusting the carbon emissions of the fracturing stage and the completion stage according to the total estimated carbon emissions of the drilling stage. This method accurately predicts the carbon emissions of 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 to formulate more targeted emission reduction strategies in the fracturing and completion stages.

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 oil and gas and other energy extraction industries, drilling operations are a critical link in the energy production process, but they are also a major source of energy consumption and carbon emissions. Existing methods for managing carbon emissions from drilling operations rely on empirical estimation, which lacks scientific and systematic principles. This makes it difficult to accurately assess carbon emissions at different drilling stages and under different operating conditions, and therefore fails to provide a precise basis for developing effective emission reduction measures.

[0003] With the rapid development of artificial intelligence (AI), it has demonstrated significant advantages in data processing and predictive analysis. AI can rapidly process vast amounts of carbon emissions data, uncover underlying patterns, and accurately predict future carbon emissions trends. However, research on the application of AI to carbon neutrality analysis in drilling operations is relatively limited, and a mature and comprehensive analytical methodology has yet to be established. 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 includes the following steps:

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

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

[0008] S3. Generate the remaining estimated carbon emissions during the drilling phase based on the fuel energy consumption weight during the drilling phase;

[0009] S4. The sum of the existing carbon emissions and the remaining estimated carbon emissions during the drilling stage is taken as the total estimated carbon emissions during the drilling stage, and the carbon emissions during the fracturing stage and the completion stage are adjusted based on the total estimated carbon emissions during 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] Carbon emissions during the drilling phase are primarily due to diesel consumption and equipment operation. Drilling equipment typically relies on fossil fuels such as diesel for power, which release large amounts of carbon dioxide during combustion. If the total estimated carbon emissions during the drilling phase exceed the preset carbon emissions for 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, such as reducing the emission factor to 1.2 kg CO2 / m3; (2) Use high-efficiency pumping equipment, such as reducing power consumption to 4000 kWh; (3) Optimize the completion process: reduce equipment operating time and reduce power consumption to 2500 kWh; (4) Use environmentally friendly materials, such as reducing carbon emissions from material transportation to 800 kg CO2.

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

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

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

[0015] 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;

[0016] S24. Determine the fuel energy consumption weight of the drilling stage according to the carbon emissions of each working equipment and the performance impact index of the drilling stage.

[0017] The beneficial effect of this further solution is that, during drilling operations, fuel combustion varies depending on various factors, including drilling depth, geological conditions, and equipment load. The average combustion rate reflects the fuel's combustion behavior under drilling conditions, while the actual maximum combustion rate reflects the combustion limit under those conditions. By comprehensively considering these two indicators, the fuel's combustion characteristics throughout the drilling phase are extracted, avoiding the potential bias inherent in a single metric. Furthermore, drilling operations are a dynamic process, and the use of fuel energy consumption weights to reflect these dynamic changes allows the performance impact index to 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 The calculation formula is:

[0019] ;

[0020] Where, 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, Indicates the average burning rate of fuel during the drilling phase.

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

[0022] S241. Generate 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 The sum of carbon emissions from all working equipment using fuel that generates carbon emissions at each drilling time point;

[0023] S242, extracting the variance of the characteristic time series up to each drilling time point;

[0024] 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;

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

[0026] The beneficial effect of this further solution is that, in this invention, the construction of characteristic time series ensures that carbon emission data during the drilling phase has temporal continuity. The variance reflects the degree of data dispersion, and the fluctuation characteristic parameter further characterizes the fluctuation of carbon emissions at different time points, providing an important indicator for assessing the stability of fuel energy consumption. The fuel energy consumption weight tends to reflect the fluctuation of the data at the current time point relative to the changes in the data at adjacent time points.

[0027] Furthermore, in S244, the fuel energy consumption weight during the drilling phase is The calculation formula is:

[0028] ;

[0029] Where, Indicates the performance impact index of drilling equipment, Indicates the Fluctuation characteristic parameters at each drilling time point, Indicates the The sum of carbon emissions from all working equipment using fuel at each drilling time point, Indicates the The sum of carbon emissions from all working equipment using fuel at each drilling time point, Indicates the The sum of carbon emissions from all working equipment using fuel at each drilling time point, Indicates the number of existing drilling time points in the drilling phase.

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

[0031] S31, obtaining the strata corresponding to each drilling time point in the area to be analyzed during the drilling phase;

[0032] S32. Constructing an environmental characteristic coefficient for each drilling time point based on the stratum corresponding to each drilling time point, the sum of carbon emissions from all working equipment using fuel to generate carbon emissions at each drilling time point, and the fuel energy consumption weight during the drilling phase;

[0033] S33. Multiplying the maximum environmental characteristic coefficient by the sum of carbon emissions of all working equipment using the fuel that generated carbon emissions at the last time point as the estimated carbon emissions at the next time point;

[0034] S34, repeating S35 until the estimated drilling end time point is reached, adding up all the estimated carbon emissions obtained to obtain the remaining estimated carbon emissions for the drilling stage.

[0035] The beneficial effect of this further solution is that, in this invention, the fuel consumption of drilling equipment (such as diesel generators) is directly correlated with the drilling difficulty. The geological characteristics of different formations can affect the difficulty of drilling operations, the power requirements of the drilling rig, and the degree of drill bit wear, which in turn affects carbon emissions. By taking into account formation factors, such as rig power, drilling depth, and drilling time, and dynamically adjusting the calculated environmental characteristic coefficient as the drilling process changes, it can reflect the changes in carbon emissions during the drilling phase in real time. By analyzing the environmental characteristic coefficient, it is possible to understand carbon emissions at different drilling time points and identify the environmental characteristic coefficient with the greatest impact on carbon emissions.

[0036] Further, in S32, Environmental characteristic coefficients at each drilling time point The calculation formula is:

[0037] ;

[0038] Where, Indicates the The drilling rig power at each drilling time point, Indicates the The drilling depth reached at each drilling time point, Indicates the 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 replacements at each drilling time point, Indicates the energy consumption each time the drill bit is replaced. Indicates the 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.

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

[0040] (1) This method extracts the fuels used and carbon emissions during the drilling phase in the analyzed area, calculates the performance impact index of the drilling phase, determines the fuel energy consumption weight, combines the carbon emissions during the drilling phase with the specific fuel usage, and calculates the energy consumption weight. This method can accurately quantify the impact of fuel on carbon emissions during the drilling phase.

[0041] (2) This method takes into account that the well conditions will continue to change as the drilling process progresses. Therefore, the environmental characteristic coefficient is determined by combining the real-time well environmental parameters and fuel energy consumption weights, and the estimated carbon emission function at the next time point is dynamically adjusted;

[0042] (3) This method accurately predicts carbon emissions during the drilling phase. By estimating the remaining carbon emissions, the overall trend of carbon emissions during the drilling phase can be understood in advance, providing forward-looking information for the overall carbon emission management of the project and helping to formulate more targeted emission reduction strategies during the fracturing and completion phases. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 This is a flowchart of the carbon neutrality intelligent analysis method based on artificial intelligence. DETAILED DESCRIPTION

[0044] The embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0045] like Figure 1 As shown, the present invention provides a carbon neutrality intelligent analysis method based on artificial intelligence, comprising the following steps:

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

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

[0048] S3. Generate the remaining estimated carbon emissions during the drilling phase based on the fuel energy consumption weight during the drilling phase;

[0049] S4. The sum of the existing carbon emissions and the remaining estimated carbon emissions during the drilling stage is taken as the total estimated carbon emissions during the drilling stage, and the carbon emissions during the fracturing stage and the completion stage are adjusted based on the total estimated carbon emissions during the drilling stage.

[0050] 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.

[0051] Carbon emissions during the drilling phase are primarily due to diesel consumption and equipment operation. Drilling equipment typically relies on fossil fuels such as diesel for power, which release large amounts of carbon dioxide during combustion. If the total estimated carbon emissions during the drilling phase exceed the preset carbon emissions for 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, such as reducing the emission factor to 1.2 kg CO2 / m3; (2) Use high-efficiency pumping equipment, such as reducing power consumption to 4000 kWh; (3) Optimize the completion process: reduce equipment operating time and reduce power consumption to 2500 kWh; (4) Use environmentally friendly materials, such as reducing carbon emissions from material transportation to 800 kg CO2.

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

[0053] S21. Extracting the fuel used in the drilling phase of the area to be analyzed;

[0054] S22. Calculate the performance impact index of the drilling stage based on the average combustion rate and the actual maximum combustion rate of the fuel during the drilling stage;

[0055] 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;

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

[0057] In this invention, fuel combustion during drilling operations varies depending on a variety of factors, including drilling depth, geological conditions, and equipment load. The average combustion rate reflects the fuel's combustion behavior under drilling conditions, while the actual maximum combustion rate reflects the combustion limit under those conditions. By comprehensively considering these two metrics, we can extract the fuel's combustion characteristics throughout the drilling phase, avoiding the potential bias inherent in a single metric. Furthermore, drilling is a dynamic process, and using fuel energy consumption weights to reflect these dynamic changes allows the performance impact index to more accurately reflect the actual impact of fuel combustion on carbon emissions during the drilling phase.

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

[0059] ;

[0060] Where, 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, Indicates the average burning rate of fuel during the drilling phase.

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

[0062] S241. Generate 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 The sum of carbon emissions from all working equipment using fuel that generates carbon emissions at each drilling time point;

[0063] S242, extracting the variance of the characteristic time series up to each drilling time point;

[0064] 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;

[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 this paper, the construction of a characteristic time series ensures that carbon emission data during the drilling phase has temporal continuity. The variance reflects the degree of data dispersion, so the fluctuation characteristic parameter further characterizes the fluctuation of carbon emissions at different time points, providing an important indicator for assessing the stability of fuel energy consumption. The fuel energy consumption weight tends to reflect the fluctuation of the data at the current time point relative to adjacent time points.

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

[0068] ;

[0069] Where, Indicates the performance impact index of drilling equipment, Indicates the Fluctuation characteristic parameters at each drilling time point, Indicates the The sum of carbon emissions from all working equipment using fuel at each drilling time point, Indicates the The sum of carbon emissions from all working equipment using fuel at each drilling time point, Indicates the The sum of carbon emissions from all working equipment using fuel at each drilling time point, Indicates the number of existing drilling time points in the drilling phase.

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

[0071] S31, obtaining the strata corresponding to each drilling time point in the area to be analyzed during the drilling phase;

[0072] S32. Constructing an environmental characteristic coefficient for each drilling time point based on the stratum corresponding to each drilling time point, the sum of carbon emissions from all working equipment using fuel to generate carbon emissions at each drilling time point, and the fuel energy consumption weight during the drilling phase;

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

[0074] S34, repeating S35 until the estimated drilling end time point is reached, adding up all the estimated carbon emissions obtained to obtain the remaining estimated carbon emissions for the drilling stage.

[0075] In this invention, the fuel consumption of drilling equipment (such as diesel generators) is directly related to the difficulty of drilling. The geological characteristics of different formations can affect the difficulty of drilling operations, the power requirements of the drilling rig, and the degree of drill bit wear, which in turn affects carbon emissions. By taking into account formation factors, such as rig power, drilling depth, and drilling time, and dynamically adjusting the calculated environmental characteristic coefficient as the drilling process changes, it can reflect the changes in carbon emissions during the drilling phase in real time. By analyzing the environmental characteristic coefficient, carbon emissions at different drilling time points can be understood, and the environmental characteristic coefficient with the greatest impact on carbon emissions can be identified.

[0076] In the embodiment of the present invention, in S32, Environmental characteristic coefficients at each drilling time point The calculation formula is:

[0077] ;

[0078] Where, Indicates the The drilling rig power at each drilling time point, Indicates the The drilling depth reached at each drilling time point, Indicates the 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 replacements at each drilling time point, Indicates the energy consumption each time the drill bit is replaced. Indicates the 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.

[0079] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand 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 descriptions and embodiments. Those skilled in the art can make various other specific variations and combinations based on the technical teachings disclosed in the present invention without departing from the essence of the present invention, and such variations 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 phase; S2. Extract the fuels used and carbon emissions during the drilling phase in the analyzed area, calculate the performance impact index of the drilling phase, and determine the fuel energy consumption weight during the drilling phase; S3. Generate the remaining estimated carbon emissions during the drilling phase based on the fuel energy consumption weight during the drilling phase; S4. The sum of the existing carbon emissions and the remaining estimated carbon emissions during the drilling phase is taken as the total estimated carbon emissions during the drilling phase, and the carbon emissions during the fracturing phase and the completion phase are adjusted based on the total estimated carbon emissions during the drilling phase; The S3 includes the following sub-steps: S31, obtaining the strata corresponding to each drilling time point in the area to be analyzed during the drilling phase; S32. Constructing an environmental characteristic coefficient for each drilling time point based on the stratum corresponding to each drilling time point, the sum of carbon emissions from all working equipment using fuel to generate carbon emissions at each drilling time point, and the fuel energy consumption weight during the drilling phase; S33. Multiplying the maximum environmental characteristic coefficient by the sum of carbon emissions of all working equipment using the fuel that generated carbon emissions at the last time point as the estimated carbon emissions at the next time point; S34, repeating S33 until the estimated drilling end time is reached, adding up all the estimated carbon emissions obtained to obtain the remaining estimated carbon emissions for the drilling stage; In the S32, Environmental characteristic coefficients at each drilling time point The calculation formula is: ； Where, Indicates the The drilling rig power at each drilling time point, Indicates the The drilling depth reached at each drilling time point, Indicates the 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 replacements at each drilling time point, Indicates the energy consumption each time the drill bit is replaced. Indicates the 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 Drilling time at each drilling time point; The S2 includes the following sub-steps: S21. Extracting the fuel used in the drilling phase of the area to be analyzed; S22. Calculate the performance impact index of the drilling stage based on 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 emissions of each working equipment and the performance impact index of the drilling stage.

2. The carbon neutrality intelligent analysis method based on artificial intelligence according to claim 1 is characterized in that: In S22, the performance impact index of the drilling stage The calculation formula is: ； Where, 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, Indicates the average burning rate of fuel during the drilling phase.

3. The carbon neutrality intelligent analysis method based on artificial intelligence according to claim 1 is characterized in that: The S24 includes the following sub-steps: S241. Generate 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 The sum of carbon emissions from all working equipment using fuel 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 change characteristic parameters at each drilling time point.

4. The carbon neutrality intelligent analysis method based on artificial intelligence according to claim 3 is characterized in that: In S244, the fuel energy consumption weight of the drilling stage The calculation formula is: ； Where, represents the performance impact index of the drilling stage, Indicates the Fluctuation characteristic parameters at each drilling time point, Indicates the The sum of carbon emissions from all working equipment using fuel at each drilling time point, Indicates the The sum of carbon emissions from all working equipment using fuel at each drilling time point, Indicates the The sum of carbon emissions from all working equipment using fuel at each drilling time point, Indicates the number of existing drilling time points in the drilling phase.

Images