Method, device, computer equipment and storage medium for determining white cloud parameters

Abstract

The present application provides a method, device, equipment, and storage medium for determining dolomization parameters, belonging to the field of oil and gas exploration technology. The method includes: obtaining initial logging data of a sandstone reservoir to be determined, the initial logging data including the resistivity corresponding to multiple depth values of the sandstone reservoir; smoothing the resistivity corresponding to the multiple depth values to obtain smoothed logging data, the smoothed logging data including the smoothed resistivity corresponding to the multiple depth values, the smoothed resistivity being determined based on multiple resistivity corresponding to a preset depth range; determining the formation coefficient of the sandstone reservoir; and determining the dolomization parameters of the sandstone reservoir based on the resistivity and smoothed resistivity corresponding to the multiple depth values and the formation coefficient. Since the dolomization rates corresponding to the multiple depth values of the sandstone reservoir are determined by the resistivity and smoothed resistivity corresponding to the multiple depth values, the influence of the depth value on the dolomization rate is taken into account, thereby improving the accuracy of determining the dolomization parameters of the entire sandstone reservoir.

Description

Technical Field

[0001] The present application relates to the field of oil and gas exploration technology, and in particular to a method, device, equipment and storage medium for determining dolomization parameters. Background Art

[0002] Currently, hydraulic fracturing is an important technology for increasing reservoir productivity. For sandstone reservoirs, the higher the dolomite rate, the greater the productivity increase achieved through hydraulic fracturing. Therefore, to determine the productivity of sandstone reservoirs after hydraulic fracturing, it is necessary to determine the dolomite parameter, which represents the dolomite rate of sandstone reservoirs.

[0003] In related art, the dolomization parameters of the sandstone reservoir are determined by taking core samples from the sandstone reservoir; the rock composition of the core samples is analyzed to determine the percentage of dolomite in the core samples, thereby obtaining the dolomization rate of the core samples; and the dolomization rate is determined as the dolomization parameter of the sandstone reservoir.

[0004] However, since the dolomization rates of sandstone reservoirs may vary at different depths, the dolomization parameters determined by the above method can only represent the dolomization parameters at the coring location and cannot accurately represent the dolomization parameters of the entire sandstone reservoir. Therefore, the accuracy of determining the dolomization parameters of the entire sandstone reservoir using the above method is low. Summary of the Invention

[0005] The embodiments of the present application provide a method, apparatus, computer device, and storage medium for determining dolomite parameters, which can improve the accuracy of determining dolomite parameters for an entire sandstone reservoir. The technical solution is as follows:

[0006] In one aspect, the present application provides a method for determining white cloud parameters, the method comprising:

[0007] Acquiring initial logging data of a sandstone reservoir to be determined, wherein the initial logging data includes resistivity corresponding to multiple depth values of the sandstone reservoir;

[0008] Smoothing the resistivities corresponding to the multiple depth values to obtain smoothed logging data, the smoothed logging data including smoothed resistivities corresponding to the multiple depth values, the smoothed resistivities being determined based on multiple resistivities corresponding to a preset depth range;

[0009] determining a formation coefficient of the sandstone reservoir;

[0010] Based on the resistivity and smoothed resistivity corresponding to the multiple depth values and the formation coefficient, a dolomization parameter of the sandstone reservoir is determined, where the dolomization parameter is used to represent the dolomization rate corresponding to the multiple depth values of the sandstone reservoir.

[0011] In a possible implementation, smoothing the resistivities corresponding to the multiple depth values to obtain smoothed logging data includes:

[0012] For each depth value, determining a target depth range within which the depth value lies;

[0013] determining a plurality of target resistivities corresponding to the target depth range;

[0014] The median value of the multiple target resistivities is used as the smoothed resistivity corresponding to the depth value to obtain the smoothed logging data.

[0015] In another possible implementation, determining the dolomization parameter of the sandstone reservoir based on the resistivity and smoothed resistivity corresponding to the multiple depth values and the formation coefficient includes:

[0016] For each depth value, determining the difference between the resistivity corresponding to the depth value and the smoothed resistivity;

[0017] determining a dolomite rate corresponding to the depth value based on the difference, the smoothed resistivity, and the formation coefficient, to obtain dolomite rates corresponding to the multiple depth values;

[0018] The dolomization rates corresponding to the multiple depth values are used as dolomization parameters of the sandstone reservoir.

[0019] In another possible implementation, determining the dolomite rate corresponding to the depth value based on the difference, the smoothed resistivity, and the formation coefficient includes:

[0020] Based on the difference, the smoothed resistivity and the formation coefficient, the dolomite rate corresponding to the depth value is determined by the following formula 1;

[0021] Formula 1:

[0022]

[0023] Wherein, Dol represents the dolomite rate corresponding to the depth value, A represents the formation coefficient, RT smo -RT represents the difference, RT represents the resistivity, RT smo represents the smoothed resistivity.

[0024] In another possible implementation, the method further includes:

[0025] determining a plurality of actual dolomitization rates corresponding to core samples at a plurality of target depth values, and determining a plurality of dolomitization rates corresponding to the plurality of target depth values from the dolomitization parameters;

[0026] determining correlation coefficients between the plurality of white clouding rates and the plurality of actual white clouding rates;

[0027] When the correlation coefficient is greater than a preset threshold, it is determined that the accuracy of the dolomite parameter of the sandstone reservoir meets the standard.

[0028] In another possible implementation, determining the correlation coefficients between the multiple white clouding rates and the multiple actual white clouding rates includes:

[0029] determining a first variance of the plurality of dolomite rates, a second variance of the plurality of actual dolomite rates, and a covariance between the plurality of dolomite rates and the plurality of actual dolomite rates;

[0030] Correlation coefficients between the plurality of clouding rates and the plurality of actual clouding rates are determined based on the first variance, the second variance, and the covariance.

[0031] In another possible implementation, determining the correlation coefficient between the multiple actual white cloud rates based on the first variance, the second variance, and the covariance includes:

[0032] Determine, based on the first variance, the second variance, and the covariance, a correlation coefficient between the multiple white clouding rates and the multiple actual white clouding rates using the following formula 2;

[0033] Formula 2:

[0034]

[0035] Among them, r(Dol x ,Dol y ) represents the correlation coefficient, cov(Dol x ,Dol y ) represents the covariance, Var[Dol x ] represents the first variance, Var[Dol y ] represents the second variance.

[0036] In another aspect, the present application provides a device for determining white cloud parameters, the device comprising:

[0037] an acquisition module, configured to acquire initial logging data of the sandstone reservoir to be determined, wherein the initial logging data includes resistivity corresponding to a plurality of depth values of the sandstone reservoir;

[0038] a smoothing processing module, configured to smooth the resistivity corresponding to each depth value to obtain smoothed logging data, wherein the smoothed logging data includes smoothed resistivities corresponding to the plurality of depth values, and the smoothed resistivity is a median value of a plurality of resistivities corresponding to a preset depth range;

[0039] A first determining module is used to determine the formation coefficient of the sandstone reservoir;

[0040] The second determination module is configured to determine a dolomization parameter of the sandstone reservoir based on the resistivity and smoothed resistivity corresponding to the multiple depth values and the formation coefficient, wherein the dolomization parameter is used to represent the dolomization rate corresponding to the multiple depth values of the sandstone reservoir.

[0041] In one possible implementation, the smoothing processing module is configured to determine, for each depth value, a target depth range in which the depth value lies; determine multiple target resistivities corresponding to the target depth range; and use a median value of the multiple target resistivities as the smoothed resistivity corresponding to the depth value to obtain the smoothed logging data.

[0042] In another possible implementation, the second determination module is configured to determine, for each depth value, a difference between the resistivity corresponding to the depth value and the smoothed resistivity; determine a dolomite ratio corresponding to the depth value based on the difference, the smoothed resistivity, and the formation coefficient, to obtain dolomite ratios corresponding to the multiple depth values; and use the dolomite ratios corresponding to the multiple depth values as dolomite parameters of the sandstone reservoir.

[0043] In another possible implementation, the second determining module is configured to determine the dolomite rate corresponding to the depth value by using the following formula 1 based on the difference, the smoothed resistivity, and the formation coefficient;

[0044] Formula 1:

[0045]

[0046] Wherein, Dol represents the dolomite rate corresponding to the depth value, A represents the formation coefficient, RT smo -RT represents the difference, RT represents the resistivity, RT smo represents the smoothed resistivity.

[0047] In another possible implementation, the apparatus further includes:

[0048] a third determining module, configured to determine a plurality of actual dolomitization rates corresponding to the core samples at a plurality of target depth values, and to determine a plurality of dolomitization rates corresponding to the plurality of target depth values from the dolomitization parameters;

[0049] a fourth determining module, configured to determine correlation coefficients between the plurality of white clouding rates and the plurality of actual white clouding rates;

[0050] The fifth determining module is configured to determine that the accuracy of the dolomite parameter of the sandstone reservoir meets the standard when the correlation coefficient is greater than a preset threshold.

[0051] In another possible implementation, the fourth determination module is used to determine a first variance of the multiple white cloud rates, a second variance of the multiple actual white cloud rates, and a covariance between the multiple white cloud rates and the multiple actual white cloud rates; and based on the first variance, the second variance, and the covariance, determine a correlation coefficient between the multiple white cloud rates and the multiple actual white cloud rates.

[0052] In another possible implementation, the fourth determining module is configured to determine, based on the first variance, the second variance, and the covariance, a correlation coefficient between the multiple white clouding rates and the multiple actual white clouding rates by using the following formula 2;

[0053] Formula 2:

[0054]

[0055] Among them, r(Dol x ,Dol y ) represents the correlation coefficient, cov(Dol x ,Dol y ) represents the covariance, Var[Dol x ] represents the first variance, Var[Dol y ] represents the second variance.

[0056] On the other hand, an embodiment of the present application provides a computer device, comprising: a processor and a memory, wherein the memory stores at least one program code, and the at least one program code is loaded and executed by the processor to implement the operations performed in the method for determining white cloud parameters described in any of the above possible implementation methods.

[0057] On the other hand, an embodiment of the present application provides a computer-readable storage medium, which stores at least one program code, and the at least one program code is loaded and executed by a processor to implement the operations performed in the method for determining white cloud parameters described in any of the above possible implementation methods.

[0058] On the other hand, an embodiment of the present application provides a computer program product, which includes at least one program code, and the at least one program code is loaded and executed by a processor to implement the operations performed in the method for determining white cloud parameters described in any of the above possible implementation methods.

[0059] The beneficial effects of the technical solutions provided by the embodiments of the present application include at least:

[0060] An embodiment of the present application provides a method for determining dolomization parameters. By using the resistivity and smoothed resistivity corresponding to multiple depth values of a sandstone reservoir, the dolomization rate corresponding to multiple depth values of the sandstone reservoir can be determined. Furthermore, the dolomization rate corresponding to multiple depth values can be used to represent the dolomization parameters of the sandstone reservoir. Compared with representing the dolomization parameters of a sandstone reservoir using only the dolomization parameters of a certain coring position, this method takes into account the influence of the depth value on the dolomization rate of the sandstone reservoir, thereby improving the accuracy of determining the dolomization parameters of the entire sandstone reservoir. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0062] Figure 1 is a flow chart showing a method for determining white cloud parameters according to an exemplary embodiment;

[0063] Figure 2 is a schematic diagram showing a deep resistance curve according to an exemplary embodiment;

[0064] Figure 3 is a schematic diagram showing a peak jump of a deep resistance curve according to an exemplary embodiment;

[0065] Figure 4 is a schematic diagram showing a smooth resistance curve according to an exemplary embodiment;

[0066] Figure 5 is a flow chart showing a method for determining white cloud parameters according to an exemplary embodiment;

[0067] Figure 6 is a schematic diagram showing a method for determining a correlation coefficient according to an exemplary embodiment;

[0068] Figure 7 is a block diagram of a device for determining white cloud parameters according to an exemplary embodiment;

[0069] Figure 8 is a block diagram of a device for determining white cloud parameters according to an exemplary embodiment;

[0070] Figure 9 The figure is a structural block diagram of a computer device according to an exemplary embodiment. DETAILED DESCRIPTION

[0071] In order to make the objectives, technical solutions and advantages of this application clearer, the implementation methods of this application will be further described in detail below with reference to the accompanying drawings.

[0072] Figure 1 FIG1 is a flow chart showing a method for determining white cloud parameters according to an exemplary embodiment. Figure 1 , the method comprising:

[0073] 101. A computer device obtains initial logging data of a sandstone reservoir to be determined, where the initial logging data includes resistivities corresponding to multiple depth values of the sandstone reservoir.

[0074] In one possible implementation, a computer device determines initial well logging data for a sandstone reservoir from a stored correspondence between reservoir identifiers and well logging data. The well logging data for a drilled well includes well logging data for a drilled well within the sandstone reservoir, and the well logging data includes resistivity values corresponding to multiple depth values. Accordingly, this step involves: the computer device determines a target reservoir identifier for the sandstone reservoir to be determined; based on the target reservoir identifier, determines target well logging data corresponding to the target reservoir identifier from the stored correspondence between the reservoir identifier and the well logging data; and determines the resistivity values corresponding to the multiple depth values as the initial well logging data for the sandstone reservoir.

[0075] In one possible implementation, when drilling a sandstone reservoir, a worker measures the resistivity of multiple depth values of the sandstone reservoir to obtain logging data; the worker uploads the logging data to a computer device; the computer device associates and stores the logging data with the reservoir identifier of the sandstone reservoir where the well has been drilled, and obtains a corresponding relationship between the reservoir identifier and the logging data. In an embodiment of the present application, the number of multiple depth values is not specifically limited and can be set and changed as needed. In one possible implementation, the worker measures a resistivity at each preset distance, and the number of multiple depth values is related to the depth of the drilling. Optionally, the preset distance can be any value between 0.1m and 0.5m; for example, if the preset distance is 0.125m, a resistivity is measured at every 0.125m interval.

[0076] In one possible implementation, see Figure 2, the logging data is a deep resistivity curve, which includes the resistivity corresponding to multiple depth values of the sandstone reservoir. Among them, the horizontal axis represents the resistivity, and the range of the resistivity is 0Ω·m to 1000Ω·m; the vertical axis represents the depth value, and the range of the depth value is 4960m to 4970m. It should be noted that after the sandstone reservoir is dolomitized, the deep resistivity curve will show a series of sharp jumps, so that the dolomitization rate of the sandstone reservoir can be determined based on the deep resistivity curve. That is, the deep resistivity curve is a sensitive curve related to the dolomitization of the sandstone reservoir. For example, see Figure 3 , deep resistivity curves of Well A and Well B. Among them, the resistivity corresponding to the cloudification section has a sharp jump, while the resistivity corresponding to the non-cloudification section does not have a sharp jump.

[0077] In the embodiment of the present application, the initial logging data of the sandstone reservoir is determined by the logging data of the drilled well, and the drilled well is a completion well in the sandstone reservoir, which can reflect the actual resistivity of multiple depth values of the sandstone reservoir.

[0078] 102. The computer device smoothes the resistivities corresponding to the multiple depth values to obtain smoothed logging data. The smoothed logging data includes smoothed resistivities corresponding to the multiple depth values of the sandstone reservoir. The smoothed resistivities are determined based on the multiple resistivities corresponding to a preset depth range.

[0079] In one possible implementation, for each depth value, the depth value is smoothed using multiple resistivities corresponding to multiple depth values within a preset depth range corresponding to the depth value. The computer device determines the preset depth range corresponding to the depth value by, for each depth value, determining the difference between the depth value and the preset value as the left endpoint of the preset depth range, determining the sum of the depth value and the preset value as the right endpoint of the preset depth range, and determining the preset depth range using the left and right endpoints. In this embodiment of the present application, the numerical value of the preset value is not specifically limited and can be set and modified as needed. Optionally, the preset difference value is any value between 0.5m and 5m, for example, 0.5m, 1m, 1.5m, etc.

[0080] In one possible implementation, the preset depth range includes multiple depth values, and the resistivity corresponding to each depth value is smoothed using the median of the multiple resistivities corresponding to the multiple depth values. Accordingly, this step involves: for each depth value, the computer device determines a target depth range within which the depth value falls; determines multiple target resistivities corresponding to the target depth range; and uses the median of the multiple target resistivities as the smoothed resistivity corresponding to the depth value to obtain smoothed logging data.

[0081] Optional, see Figure 4The smoothed logging data includes smoothed resistivity data corresponding to multiple depth values of the sandstone reservoir. The peak position of the smoothed logging data, obtained by smoothing the resistivity corresponding to each depth value, is the same as the peak position of the initial logging data. In other words, the smoothed logging data and the initial logging data have the same changing trend.

[0082] In the embodiment of the present application, by smoothing the resistivity corresponding to each depth value, it is possible to avoid situations where the resistivity in the initial logging data is too high or too low, reduce the interference of measurement noise, and improve the correlation between the determined smoothed logging data and the dolomization rate, thereby improving the accuracy of the dolomization parameters determined based on the smoothed logging data.

[0083] 103. Computer equipment is used to determine the formation coefficient of sandstone reservoirs.

[0084] In an embodiment of the present application, corresponding to the same section of the sandstone reservoir, the formation coefficient of the sandstone reservoir is basically similar at multiple depth values and can be approximated as a fixed parameter. In one possible implementation, the formation coefficient of the sandstone reservoir is determined by taking a core sample from the sandstone reservoir. Accordingly, the steps for the computer device to determine the formation coefficient of the sandstone reservoir are: analyzing the rock composition of the core sample of the sandstone reservoir to obtain the dolomite content in the core sample and the dolomite rate of the core sample; obtaining the depth value of the core sample, determining the resistivity corresponding to the depth value and the smoothed resistivity corresponding to the depth value; determining the formation coefficient of the sandstone reservoir based on the dolomite rate of the core sample, the resistivity corresponding to the depth value and the smoothed resistivity corresponding to the depth value.

[0085] In a possible implementation, the step of determining the formation coefficient of the sandstone reservoir by a computer device based on the dolomitization rate of the core sample, the resistivity corresponding to the depth value, and the smoothed resistivity corresponding to the depth value is as follows: the computer device determines the formation coefficient of the sandstone reservoir by the following formula 3 based on the dolomitization rate of the core sample, the resistivity corresponding to the depth value, and the smoothed resistivity corresponding to the depth value;

[0086] Formula 3:

[0087]

[0088] Where A represents the formation coefficient of the sandstone reservoir, Dol(1) represents the dolomite ratio of the core sample, RT(1) represents the resistivity of the core sample at its depth, and RT smo (1) The smoothed resistivity of the core sample at its depth.

[0089] In the embodiment of the present application, since the formation coefficients of the sandstone reservoir at multiple depth values corresponding to the same sandstone reservoir are similar, the formation coefficient of the sandstone reservoir can be determined by determining the formation coefficient of the core sample of the sandstone reservoir. In this way, there is no need to calculate the formation coefficients of multiple depth values of the sandstone reservoir one by one, thereby improving the efficiency of determining the formation coefficient of the sandstone reservoir.

[0090] 104. The computer device determines a dolomization parameter of the sandstone reservoir based on the resistivity and smoothed resistivity corresponding to the multiple depth values and the formation coefficient. The dolomization parameter is used to represent the dolomization rate corresponding to the multiple depth values of the sandstone reservoir.

[0091] In one possible implementation, this step is as follows: for each depth value, a computer device determines a difference between the resistivity corresponding to the depth value and the smoothed resistivity; based on the difference, the smoothed resistivity, and the formation coefficient, determines a dolomization rate corresponding to the depth value, thereby obtaining dolomization rates corresponding to multiple depth values; and uses the dolomization rates corresponding to the multiple depth values as dolomization parameters of the sandstone reservoir.

[0092] In the embodiment of the present application, since the resistivity and smoothed resistivity corresponding to the depth value and the formation coefficient are all parameters related to the dolomization rate, by combining the resistivity, smoothed resistivity and formation coefficient, the dolomization parameters are determined from multiple dimensions, thereby improving the accuracy of the determined dolomization parameters.

[0093] In one possible implementation, the step of determining the dolomite rate corresponding to the depth value by the computer device based on the difference, the smoothed resistivity, and the formation coefficient is as follows: the computer device determines the dolomite rate corresponding to the depth value by using the following formula 1 based on the difference, the smoothed resistivity, and the formation coefficient;

[0094] Formula 1:

[0095]

[0096] Among them, Dol represents the dolomite rate corresponding to the depth value, A represents the formation coefficient, RT smo -RT means difference, RT means resistivity, RT smo Indicates smoothed resistivity.

[0097] One thing to note is that, continue to refer to Figure 4 The computer device determines the white cloud rate corresponding to each depth value. After obtaining the white cloud rates corresponding to multiple depth values, the white cloud rate curve can be determined based on the white cloud rates corresponding to the multiple depth values, such as Figure 4 As shown; and, according to the difference values corresponding to the multiple depth values, determine the difference curve, such as Figure 4 shown.

[0098] An embodiment of the present application provides a method for determining dolomization parameters. By using the resistivity and smoothed resistivity corresponding to multiple depth values of a sandstone reservoir, the dolomization rate corresponding to multiple depth values of the sandstone reservoir can be determined. Furthermore, the dolomization rate corresponding to multiple depth values can be used to represent the dolomization parameters of the sandstone reservoir. Compared with representing the dolomization parameters of a sandstone reservoir using only the dolomization parameters of a certain coring position, this method takes into account the influence of the depth value on the dolomization rate of the sandstone reservoir, thereby improving the accuracy of determining the dolomization parameters of the entire sandstone reservoir.

[0099] In the embodiment of the present application, the computer device can also determine whether the accuracy of the dolomite parameters of the sandstone reservoir determined through steps 101 to 104 meets the standard based on the actual dolomite rates of multiple core samples selected from the sandstone reservoir. Figure 5 The method further includes the following steps 105 to 107.

[0100] 105. The computer device determines a plurality of actual dolomitization rates corresponding to the core samples at a plurality of target depth values, and determines a plurality of dolomitization rates corresponding to the plurality of target depth values from the dolomitization parameters.

[0101] In one possible implementation, the steps for a computer device to determine multiple actual dolomite rates corresponding to core samples of multiple target depth values are as follows: for each core sample, the computer device analyzes the rock composition of the core sample, determines the percentage of dolomite in the core sample, and determines the percentage as the actual dolomite rate of the core sample, thereby obtaining multiple actual dolomite rates corresponding to the core samples of multiple target depth values.

[0102] In the embodiment of the present application, the number of multiple target depth values is not specifically limited and can be set and modified as needed. The core samples of multiple target depth values can be core samples from the same well or core samples from different wells. For example, see Figure 6 The core samples at multiple target depths are core samples from Wells A, B, C, D, and E. There are three core samples from Well A, one from Well B, three from Well C, one from Well D, and ten from Well E. The horizontal axis represents the actual dolomization rates of the core samples at the multiple target depths, while the vertical axis represents the dolomization rates determined using the multiple target depths.

[0103] In one possible implementation, the dolomite parameter includes dolomite rates corresponding to multiple depth values of the sandstone reservoir. Accordingly, the computer device determines, based on the multiple target depth values, multiple dolomite rates corresponding to the multiple depth values from the dolomite rates corresponding to the multiple depth values.

[0104] 106. The computer device determines a correlation coefficient between a plurality of clouding rates and a plurality of actual clouding rates.

[0105] In one possible implementation, this step is: a computer device determines a first variance of multiple white clouding rates, a second variance of multiple actual white clouding rates, and a covariance between the multiple white clouding rates and the multiple actual white clouding rates; and based on the first variance, the second variance, and the covariance, determines a correlation coefficient between the multiple white clouding rates and the multiple actual white clouding rates.

[0106] In one possible implementation, a computer device determines a first variance of the plurality of white clouding rates using an average value of the plurality of white clouding rates. Accordingly, the step of determining the first variance of the plurality of white clouding rates by the computer device includes: the computer device determines a first average value of the plurality of white clouding rates and the number of the plurality of white clouding rates; for each white clouding rate, determines a difference between the white clouding rate and the first average value; and, based on the difference and the number of the plurality of white clouding rates, determines the first variance of the plurality of white clouding rates using the following formula 4.

[0107] Formula 4:

[0108]

[0109] Among them, Var[Dol x ] represents the first variance, Dol i represents the i-th white cloud rate, represents a first average value of a plurality of white clouding rates, and n represents the number of the plurality of white clouding rates.

[0110] In one possible implementation, a computer device determines a second variance of the multiple actual white clouding rates using an average value of the multiple actual white clouding rates. Accordingly, the step of the computer device determining the second variance of the multiple actual white clouding rates includes: the computer device determining a second average value of the multiple actual white clouding rates and the number of the multiple actual white clouding rates; for each actual white clouding rate, determining a difference between the actual white clouding rate and the second average value; and determining the second variance of the multiple actual white clouding rates using the following formula 5 based on the difference and the number of the multiple actual white clouding rates.

[0111] Formula 5:

[0112]

[0113] Among them, Var[Dol y ] represents the second variance, Dol j represents the actual dolomitization rate of the jth core sample, represents a second average value of a plurality of actual clouding rates, and m represents the number of the plurality of actual clouding rates.

[0114] In one possible implementation, the step of determining, by the computer device, a correlation coefficient between a plurality of actual white clouding rates based on the first variance, the second variance, and the covariance is as follows: the computer device determines, by using the following formula 2, the correlation coefficient between the plurality of white clouding rates and the plurality of actual white clouding rates based on the first variance, the second variance, and the covariance;

[0115] Formula 2:

[0116]

[0117] Among them, r(Dol x ,Dol y ) represents the correlation coefficient, cov(Dol x ,Dol y ) represents the covariance, Var[Dol x ] represents the first variance, Var[Dol y ] represents the second variance.

[0118] For example, see Figure 6 , there were 3 core samples from Well A, 1 core sample from Well B, 3 core samples from Well C, 1 core sample from Well D, and 10 core samples from Well E. The correlation coefficient between the multiple dolomization rates determined by the computer equipment and the multiple actual dolomization rates was 0.9782.

[0119] 107. When the correlation coefficient is greater than a preset threshold, the computer equipment determines that the accuracy of the dolomite parameters of the sandstone reservoir meets the standard.

[0120] In the embodiment of the present application, the value of the preset threshold is not specifically limited and can be set and modified as needed. Optionally, the data of the preset threshold is any value between 0.8 and 1, for example, the preset threshold is 0.85. Figure 6 The correlation coefficient between the multiple dolomization rates determined by the computer device and the multiple actual dolomization rates is 0.9782, which is greater than the preset threshold value of 0.85. Therefore, the accuracy of the dolomization parameters of the sandstone reservoir determined by the computer device meets the standard.

[0121] In one possible implementation, when the correlation coefficient is not greater than a preset threshold, the computer device determines that the accuracy of the dolomization parameters of the sandstone reservoir does not meet the standard, and then replaces it with other drilled logging data, and determines the dolomization parameters of the sandstone reservoir through steps 101 to 103 until the correlation coefficient is greater than the preset threshold.

[0122] In the embodiment of the present application, the accuracy of the dolomite parameters of the sandstone reservoir is verified by using the correlation coefficients of multiple actual dolomite rates corresponding to core samples of multiple target depth values and multiple calculated dolomite rates, thereby ensuring that the accuracy of the obtained dolomite parameters is greater than the preset threshold, thereby improving the accuracy of the determined dolomite parameters.

[0123] It should be noted that the computer device can also determine a sweet spot reservoir based on multiple dolomization rates corresponding to multiple depth values. Accordingly, this step involves the computer device determining the maximum dolomization rate from the multiple dolomization rates, determining the depth corresponding to the maximum dolomization rate, and identifying the reservoir corresponding to the depth as a high-quality reservoir, i.e., a sweet spot reservoir.

[0124] Figure 7 FIG. 1 is a block diagram of a device for determining white cloud parameters according to an exemplary embodiment. Figure 7 , the device comprises:

[0125] An acquisition module 701 is configured to acquire initial well logging data of a sandstone reservoir to be determined, wherein the initial well logging data includes resistivities corresponding to multiple depth values of the sandstone reservoir;

[0126] A smoothing processing module 702 is configured to smooth the resistivity corresponding to each depth value to obtain smoothed logging data, where the smoothed logging data includes smoothed resistivities corresponding to multiple depth values, and the smoothed resistivity is a median value of multiple resistivities corresponding to a preset depth range;

[0127] The first determination module 703 is used to determine the formation coefficient of the sandstone reservoir;

[0128] The second determining module 704 is configured to determine a dolomization parameter of the sandstone reservoir based on the resistivity and smoothed resistivity corresponding to the multiple depth values and the formation coefficient. The dolomization parameter is used to represent the dolomization rate corresponding to the multiple depth values of the sandstone reservoir.

[0129] In one possible implementation, the smoothing processing module 702 is configured to determine, for each depth value, a target depth range within which the depth value lies; determine multiple target resistivities corresponding to the target depth range; and use a median of the multiple target resistivities as the smoothed resistivity corresponding to the depth value to obtain smoothed logging data.

[0130] In another possible implementation, the second determination module 704 is used to determine, for each depth value, the difference between the resistivity corresponding to the depth value and the smoothed resistivity; determine the dolomization rate corresponding to the depth value based on the difference, the smoothed resistivity, and the formation coefficient, to obtain the dolomization rates corresponding to multiple depth values; and use the dolomization rates corresponding to the multiple depth values as the dolomization parameters of the sandstone reservoir.

[0131] In another possible implementation, the second determining module 704 is configured to determine the dolomite rate corresponding to the depth value by using the following formula 1 based on the difference, the smoothed resistivity, and the formation coefficient;

[0132] Formula 1:

[0133]

[0134] Among them, Dol represents the dolomite rate corresponding to the depth value, A represents the formation coefficient, RT smo -RT means difference, RT means resistivity, RT smo Represents smoothed resistivity.

[0135] In another possible implementation, see Figure 8 , the device further comprises:

[0136] The third determination module 705 is configured to determine a plurality of actual dolomitization rates corresponding to the core samples at a plurality of target depth values, and to determine a plurality of dolomitization rates corresponding to the plurality of target depth values from the dolomitization parameters;

[0137] A fourth determining module 706 is configured to determine correlation coefficients between a plurality of white clouding rates and a plurality of actual white clouding rates;

[0138] The fifth determining module 707 is configured to determine whether the accuracy of the dolomite parameters of the sandstone reservoir meets the requirements when the correlation coefficient is greater than a preset threshold.

[0139] In another possible implementation, the fourth determination module 706 is used to determine a first variance of multiple white cloud rates, a second variance of multiple actual white cloud rates, and a covariance between the multiple white cloud rates and the multiple actual white cloud rates; and based on the first variance, the second variance, and the covariance, determine a correlation coefficient between the multiple white cloud rates and the multiple actual white cloud rates.

[0140] In another possible implementation, the fourth determining module 706 is configured to determine, based on the first variance, the second variance, and the covariance, a correlation coefficient between the plurality of white clouding rates and the plurality of actual white clouding rates using the following formula 2:

[0141] Formula 2:

[0142]

[0143] Among them, r(Dol x ,Dol y ) represents the correlation coefficient, cov(Dol x ,Dol y ) represents the covariance, Var[Dol x ] represents the first variance, Var[Doly ] represents the second variance.

[0144] An embodiment of the present application provides a device for determining dolomite parameters. The device can determine the dolomite ratio corresponding to multiple depth values of a sandstone reservoir using the resistivity and smoothed resistivity corresponding to multiple depth values of the sandstone reservoir. Furthermore, the dolomite ratio corresponding to multiple depth values can be used to represent the dolomite parameters of the sandstone reservoir. Compared with representing the dolomite parameters of a sandstone reservoir using only the dolomite parameters of a coring position at a certain location, the device takes into account the influence of the depth value on the dolomite ratio of the sandstone reservoir, thereby improving the accuracy of the determined dolomite parameters.

[0145] Figure 9 The following is a block diagram of a computer device 900 according to an exemplary embodiment of the present invention. Computer device 900 may be a smartphone, a tablet computer, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 player (Moving Picture Experts Group Audio Layer IV), a laptop computer, or a desktop computer. Computer device 900 may also be referred to as a user device, a portable computer device, a laptop computer device, a desktop computer device, or other similar names.

[0146] Typically, the computer device 900 includes a processor 901 and a memory 902 .

[0147] The processor 901 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 901 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), or PLA (Programmable Logic Array). The processor 901 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 901 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 901 may also include an AI (Artificial Intelligence) processor, which is used to process computing operations related to machine learning.

[0148] The memory 902 may include one or more computer-readable storage media, which may be non-transitory. The memory 902 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices and flash memory storage devices. In some embodiments, the non-transitory computer-readable storage medium in the memory 902 is used to store at least one instruction, which is executed by the processor 901 to implement the method for determining the white cloud parameters provided in the method embodiment of the present application.

[0149] In some embodiments, computer device 900 may optionally include a peripheral device interface 903 and at least one peripheral device. Processor 901, memory 902, and peripheral device interface 903 may be connected via a bus or signal lines. Each peripheral device may be connected to peripheral device interface 903 via a bus, signal lines, or circuit boards. Specifically, the peripheral device may include at least one of a radio frequency circuit 904, a display screen 905, a camera 906, an audio circuit 907, a positioning component 908, and a power supply 909.

[0150] The peripheral device interface 903 can be used to connect at least one I / O (Input / Output)-related peripheral device to the processor 901 and the memory 902. In some embodiments, the processor 901, the memory 902, and the peripheral device interface 903 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 901, the memory 902, and the peripheral device interface 903 can be implemented on separate chips or circuit boards, which is not limited in this embodiment.

[0151] The RF circuit 904 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The RF circuit 904 communicates with communication networks and other communication devices via electromagnetic signals. The RF circuit 904 converts electrical signals into electromagnetic signals for transmission, or converts received electromagnetic signals into electrical signals. Optionally, the RF circuit 904 includes an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a user identity module card, and the like. The RF circuit 904 can communicate with other computer devices via at least one wireless communication protocol. Such wireless communication protocols include, but are not limited to, metropolitan area networks (MANs), various generations of mobile communication networks (2G, 3G, 4G, and 5G), wireless local area networks (WLANs), and / or WiFi (Wireless Fidelity) networks. In some embodiments, the RF circuit 904 may also include circuits related to NFC (Near Field Communication), which is not limited in this application.

[0152] Display screen 905 is used to display a user interface (UI). This UI can include graphics, text, icons, videos, or any combination thereof. When display screen 905 is a touchscreen display, it can also capture touch signals on or above the surface of display screen 905. These touch signals can be input as control signals to processor 901 for processing. Display screen 905 can also be used to provide virtual buttons and / or a virtual keyboard, also known as soft buttons and / or a soft keyboard. In some embodiments, there can be a single display screen 905, located on the front panel of computer device 900. In other embodiments, there can be at least two display screens 905, located on different surfaces of computer device 900 or in a foldable design. In still other embodiments, display screen 905 can be a flexible display screen, located on a curved or foldable surface of computer device 900. Display screen 905 can also be configured as a non-rectangular, irregular shape, also known as a special-shaped screen. Display screen 905 can be made of materials such as LCD (Liquid Crystal Display) and OLED (Organic Light-Emitting Diode).

[0153] The camera assembly 906 is used to capture images or videos. Optionally, the camera assembly 906 includes a front camera and a rear camera. Typically, the front camera is set on the front panel of the computer device, and the rear camera is set on the back of the computer device. In some embodiments, there are at least two rear cameras, which are any one of a main camera, a depth of field camera, a wide-angle camera, and a telephoto camera, so as to realize the fusion of the main camera and the depth of field camera to realize the background blur function, the fusion of the main camera and the wide-angle camera to realize panoramic shooting and VR (Virtual Reality) shooting function or other fusion shooting functions. In some embodiments, the camera assembly 906 may also include a flash. The flash can be a single-color temperature flash or a dual-color temperature flash. A dual-color temperature flash refers to a combination of a warm light flash and a cold light flash, which can be used for light compensation under different color temperatures.

[0154] The audio circuit 907 may include a microphone and a speaker. The microphone is used to collect sound waves from the user and the environment, and convert the sound waves into electrical signals to be input into the processor 901 for processing, or input into the radio frequency circuit 904 to achieve voice communication. For the purpose of stereo sound collection or noise reduction, there can be multiple microphones, each located in different parts of the computer device 900. The microphone can also be an array microphone or an omnidirectional collection microphone. The speaker is used to convert the electrical signal from the processor 901 or the radio frequency circuit 904 into sound waves. The speaker can be a traditional thin film speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can not only convert the electrical signal into sound waves audible to humans, but also convert the electrical signal into sound waves inaudible to humans for purposes such as distance measurement. In some embodiments, the audio circuit 907 may also include a headphone jack.

[0155] Positioning component 908 is used to locate the current geographic location of computer device 900 to implement navigation or LBS (Location Based Service). Positioning component 908 can be a positioning component based on the US GPS (Global Positioning System), China's Beidou system, Russia's Greninja system, or the European Union's Galileo system.

[0156] Power supply 909 is used to power the various components of computer device 900. Power supply 909 can be AC power, DC power, disposable batteries, or rechargeable batteries. When power supply 909 includes a rechargeable battery, the rechargeable battery can support wired charging or wireless charging. The rechargeable battery can also be used to support fast charging technology.

[0157] In some embodiments, the computer device 900 further includes one or more sensors 910 , including but not limited to: an acceleration sensor 911 , a gyroscope sensor 912 , a pressure sensor 913 , a fingerprint sensor 914 , an optical sensor 915 , and a proximity sensor 916 .

[0158] The accelerometer 911 can detect the magnitude of acceleration along the three coordinate axes of the coordinate system established by the computer device 900. For example, the accelerometer 911 can be used to detect the components of gravity acceleration along the three coordinate axes. The processor 901 can control the display screen 905 to display the user interface in a landscape or portrait view based on the gravity acceleration signal collected by the accelerometer 911. The accelerometer 911 can also be used to collect game or user motion data.

[0159] The gyroscope sensor 912 can detect the orientation and rotation angle of the computer device 900. It can also work with the accelerometer 911 to collect 3D motions of the user on the computer device 900. Based on the data collected by the gyroscope sensor 912, the processor 901 can implement the following functions: motion sensing (e.g., changing the UI based on the user's tilt), image stabilization during shooting, game control, and inertial navigation.

[0160] The pressure sensor 913 can be installed on the side frame of the computer device 900 and / or below the display screen 905. When the pressure sensor 913 is installed on the side frame of the computer device 900, it can detect the user's grip signal of the computer device 900, and the processor 901 can perform left and right hand recognition or shortcut operations based on the grip signal collected by the pressure sensor 913. When the pressure sensor 913 is installed below the display screen 905, the processor 901 controls the operational controls on the UI interface based on the user's pressure operation on the display screen 905. The operational controls include at least one of a button control, a scroll bar control, an icon control, and a menu control.

[0161] The fingerprint sensor 914 is used to collect the user's fingerprint. The processor 901 identifies the user's identity based on the fingerprint collected by the fingerprint sensor 914, or the fingerprint sensor 914 identifies the user's identity based on the collected fingerprint. When the user's identity is identified as a trusted identity, the processor 901 authorizes the user to perform relevant sensitive operations, such as unlocking the screen, viewing encrypted information, downloading software, making payments, and changing settings. The fingerprint sensor 914 can be set on the front, back, or side of the computer device 900. When a physical button or manufacturer logo is set on the computer device 900, the fingerprint sensor 914 can be integrated with the physical button or manufacturer logo.

[0162] The optical sensor 915 is used to detect ambient light intensity. In one embodiment, the processor 901 can control the display brightness of the display screen 905 based on the ambient light intensity detected by the optical sensor 915. Specifically, when the ambient light intensity is high, the display brightness of the display screen 905 is increased; when the ambient light intensity is low, the display brightness of the display screen 905 is decreased. In another embodiment, the processor 901 can also dynamically adjust the shooting parameters of the camera assembly 906 based on the ambient light intensity detected by the optical sensor 915.

[0163] Proximity sensor 916, also known as a distance sensor, is typically located on the front panel of computer device 900. Proximity sensor 916 is used to detect the distance between the user and the front of computer device 900. In one embodiment, when proximity sensor 916 detects that the distance between the user and the front of computer device 900 is gradually decreasing, processor 901 controls display screen 905 to switch from the screen-on state to the screen-off state. When proximity sensor 916 detects that the distance between the user and the front of computer device 900 is gradually increasing, processor 901 controls display screen 905 to switch from the screen-off state to the screen-on state.

[0164] Those skilled in the art will understand that Figure 9 The structure shown in the figure does not constitute a limitation on the computer device 900, and the computer device 900 may include more or fewer components than shown in the figure, or combine some components, or adopt a different component arrangement.

[0165] An embodiment of the present application further provides a computer-readable storage medium, which stores at least one program code, and the at least one program code is loaded and executed by a processor to implement the method for determining white cloud parameters in any of the above possible implementations.

[0166] An embodiment of the present application further provides a computer program product, which includes at least one program code, and the at least one program code is loaded and executed by a processor to implement the method for determining white cloud parameters in any of the possible implementations described above.

[0167] Other embodiments of the present disclosure will readily occur to those skilled in the art after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the present disclosure being indicated by the following claims.

[0168] The above description is merely an optional embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A method for determining dolomite parameters, characterized in that: The method comprises: Acquiring initial logging data of a sandstone reservoir to be determined, wherein the initial logging data includes resistivity corresponding to multiple depth values of the sandstone reservoir; Smoothing the resistivities corresponding to the multiple depth values to obtain smoothed logging data, the smoothed logging data including smoothed resistivities corresponding to the multiple depth values, the smoothed resistivities being determined based on multiple resistivities corresponding to a preset depth range; determining a formation coefficient of the sandstone reservoir; For each depth value, determining the difference between the resistivity corresponding to the depth value and the smoothed resistivity; Based on the difference, the smoothed resistivity and the formation coefficient, the dolomite rate corresponding to the depth value is determined by the following formula 1; Formula 1: Wherein, Dol represents the dolomite rate corresponding to the depth value, A represents the formation coefficient, RT smo -RT represents the difference, RT represents the resistivity, RT smo represents the smoothed resistivity; The dolomization rates corresponding to the multiple depth values are used as dolomization parameters of the sandstone reservoir, and the dolomization parameters are used to represent the dolomization rates corresponding to the multiple depth values of the sandstone reservoir.

2. The method according to claim 1, characterized in that The step of smoothing the resistivities corresponding to the plurality of depth values to obtain smoothed logging data includes: For each depth value, determining a target depth range within which the depth value lies; determining a plurality of target resistivities corresponding to the target depth range; The median value of the multiple target resistivities is used as the smoothed resistivity corresponding to the depth value to obtain the smoothed logging data.

3. The method according to claim 1, characterized in that The method further comprises: determining a plurality of actual dolomitization rates corresponding to core samples at a plurality of target depth values, and determining a plurality of dolomitization rates corresponding to the plurality of target depth values from the dolomitization parameters; determining correlation coefficients between the plurality of white clouding rates and the plurality of actual white clouding rates; When the correlation coefficient is greater than a preset threshold, it is determined that the accuracy of the dolomite parameter of the sandstone reservoir meets the standard.

4. The method according to claim 3, characterized in that Determining the correlation coefficients between the plurality of white clouding rates and the plurality of actual white clouding rates includes: determining a first variance of the plurality of dolomite rates, a second variance of the plurality of actual dolomite rates, and a covariance between the plurality of dolomite rates and the plurality of actual dolomite rates; Correlation coefficients between the plurality of clouding rates and the plurality of actual clouding rates are determined based on the first variance, the second variance, and the covariance.

5. The method according to claim 4, characterized in that The determining, based on the first variance, the second variance, and the covariance, correlation coefficients between the plurality of white clouding rates and the plurality of actual white clouding rates includes: Determine, based on the first variance, the second variance, and the covariance, a correlation coefficient between the multiple white clouding rates and the multiple actual white clouding rates using the following formula 2; Formula 2: Among them, r(Dol x ,Dol y ) represents the correlation coefficient, cov(Dol x ,Dol y ) represents the covariance, Var[Dol x ] represents the first variance, Var[Dol y ] represents the second variance.

6. A device for determining white cloud parameters, characterized in that: The device comprises: an acquisition module, configured to acquire initial logging data of the sandstone reservoir to be determined, wherein the initial logging data includes resistivity corresponding to a plurality of depth values of the sandstone reservoir; a smoothing processing module, configured to smooth the resistivity corresponding to each depth value to obtain smoothed logging data, wherein the smoothed logging data includes smoothed resistivities corresponding to the plurality of depth values, and the smoothed resistivity is a median value of a plurality of resistivities corresponding to a preset depth range; A first determining module is used to determine the formation coefficient of the sandstone reservoir; A second determining module is configured to determine, for each depth value, a difference between the resistivity corresponding to the depth value and the smoothed resistivity; and determine a dolomite rate corresponding to the depth value using the following formula 1 based on the difference, the smoothed resistivity, and the formation coefficient; Formula 1: Wherein, Dol represents the dolomite rate corresponding to the depth value, A represents the formation coefficient, RT smo -RT represents the difference, RT represents the resistivity, RT smo represents the smoothed resistivity; and uses the dolomite ratio corresponding to the multiple depth values as the dolomite parameter of the sandstone reservoir, wherein the dolomite parameter is used to represent the dolomite ratio corresponding to the multiple depth values of the sandstone reservoir.

7. A computer device, characterized in that: The computer device comprises: A processor and a memory, wherein the memory stores at least one program code, and the at least one program code is loaded and executed by the processor to implement the operations performed in the method for determining dolomite parameters according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores at least one program code, and the at least one program code is loaded and executed by a processor to implement the operations performed in the method for determining white cloud parameters according to any one of claims 1 to 5.

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