Method, device, computer equipment and storage medium for determining reservoir type

Abstract

The present application provides a method, device, computer equipment and storage medium for determining reservoir types, and belongs to the field of oil and gas exploration technology. The method includes: determining a rock sample of a target reservoir; obtaining a capillary pressure curve corresponding to the rock sample, the capillary pressure curve is used to represent the corresponding relationship between the mercury injection pressure and the mercury injection volume of the rock sample; fitting the capillary pressure curve through a first objective function to obtain a first fitting parameter, the first fitting parameter includes a first displacement parameter and a first pore morphology parameter; according to the first displacement parameter and the first pore morphology parameter, through a second objective function, determining a first classification parameter of the rock sample; according to the parameter interval where the first classification parameter is located, determining the reservoir type of the reservoir. Since the first fitting parameter is obtained by fitting the capillary pressure curve, the method for determining the reservoir type will not be limited by the pore structure of the rock sample, thereby improving the accuracy of determining the reservoir type of the reservoir.

Description

Technical Field

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

[0002] In the process of oil and gas exploration, the permeability of the reservoir is a key factor affecting the production capacity and recovery rate of the reservoir. The permeability of the reservoir is directly related to the reservoir type of the reservoir; therefore, before determining the permeability of the reservoir, it is necessary to first determine the reservoir type of the reservoir.

[0003] In the related art, the reservoir type of a reservoir is determined by the flow unit index method. The method includes: extracting rock samples from the reservoir; testing the rock samples to obtain the porosity and permeability of the rock samples, determining the ratio of permeability to porosity as the flow unit index; and determining the reservoir type of the reservoir according to the flow unit index of the rock samples.

[0004] However, for rock samples with complex pore structures, the porosity of multiple regions in the rock sample is different, but the permeability of multiple regions is the same, so multiple flow unit indices will be obtained for the same rock sample, and multiple flow unit indices may correspond to multiple reservoir types. It can be seen that the above method cannot accurately determine the reservoir type of the reservoir, so the accuracy of the reservoir type determined by the above method is low. Summary of the invention

[0005] The embodiment of the present application provides a method, device, computer equipment and storage medium for determining reservoir types, which can improve the accuracy of the determined reservoir types. The technical solution is as follows:

[0006] In one aspect, the present application provides a method for determining a reservoir type, the method comprising:

[0007] Identify rock samples of target reservoirs;

[0008] Obtaining a capillary pressure curve corresponding to the rock sample, wherein the capillary pressure curve is used to indicate a corresponding relationship between a mercury injection pressure and a mercury injection volume of the rock sample;

[0009] Fitting the capillary pressure curve by a first objective function to obtain first fitting parameters, wherein the first fitting parameters include a first displacement parameter and a first pore morphology parameter;

[0010] Determining a first classification parameter of the rock sample through a second objective function according to the first displacement parameter and the first pore morphology parameter;

[0011] The reservoir type of the reservoir is determined according to the parameter interval in which the first classification parameter is located.

[0012] In a possible implementation, fitting the capillary pressure curve by using a first objective function to obtain a first fitting parameter includes:

[0013] Determining a plurality of first mercury injection pressures and a plurality of first mercury injection volumes corresponding to the plurality of first mercury injection pressures from the capillary pressure curve;

[0014] Obtaining a pore throat radius distribution curve of the rock sample, and determining the number of pore throat distribution peaks in the pore throat distribution curve according to the pore throat radius distribution curve;

[0015] For each first mercury injection pressure, the first mercury injection pressure, the first mercury injection volume corresponding to the first mercury injection pressure, and the quantity are substituted into the following formula 1 to obtain a fitting overdetermined equation corresponding to the capillary pressure curve;

[0016] Formula 1:

[0017] in, represents the first mercury injection volume, P c represents the first mercury injection pressure, represents the second mercury injection volume corresponding to the i-th pore throat distribution peak, represents the second displacement parameter corresponding to the i-th pore throat distribution peak value, G i represents the second pore morphology parameter corresponding to the i-th pore throat distribution peak, and n represents the number;

[0018] Determine a second fitting parameter and a second mercury injection volume corresponding to at least one pore throat distribution peak value through a plurality of fitting overdetermined equations determined by the plurality of first mercury injection pressures, wherein the second fitting parameter includes a second displacement parameter and a second pore morphology parameter;

[0019] A first fitting parameter is selected from the second fitting parameters corresponding to the at least one pore throat distribution peak value.

[0020] In another possible implementation, the selecting the first fitting parameter from the second fitting parameters corresponding to the at least one pore-throat distribution peak value includes:

[0021] In response to the number of the pore-throat distribution peaks being one, determining the second fitting parameter corresponding to the pore-throat distribution peak as the first fitting parameter; or,

[0022] In response to the number of the pore-throat distribution peaks being multiple, a target pore-throat distribution peak with the largest pore-throat radius is selected from the multiple pore-throat distribution peaks, and a second fitting parameter corresponding to the target pore-throat distribution peak is determined as the first fitting parameter.

[0023] In another possible implementation, determining the first classification parameter of the rock sample through a second objective function according to the first displacement parameter and the first pore morphology parameter includes:

[0024] According to the first displacement parameter and the first pore morphology parameter, a first classification parameter of the rock sample is determined by the following formula 2;

[0025] Formula 2:

[0026] Wherein, Mode represents the first classification parameter, represents the first displacement parameter, and G1 represents the first pore morphology parameter.

[0027] In another possible implementation, determining the reservoir type of the reservoir according to the first classification parameter includes:

[0028] Determining, according to the first classification parameter, a target parameter interval in which the first classification parameter lies;

[0029] According to the target parameter interval, the reservoir type corresponding to the target parameter interval is determined from the corresponding relationship between the parameter interval and the reservoir type.

[0030] In another possible implementation manner, before determining, according to the first classification parameter, the target parameter interval in which the first classification parameter is located, the method further includes:

[0031] Acquire a plurality of first classification parameters corresponding to a plurality of coal and rock samples, wherein the plurality of coal and rock samples are coal and rock samples from a plurality of different regions;

[0032] For each first classification parameter, determining the number of coal and rock samples corresponding to the first classification parameter;

[0033] Determining a plurality of parameter intervals according to the number of coal and rock samples corresponding to the plurality of first classification parameters;

[0034] The reservoir type corresponding to each parameter interval is determined, and the corresponding relationship between the parameter interval and the reservoir type is obtained.

[0035] In another possible implementation, determining multiple parameter intervals according to the number of coal and rock samples corresponding to the multiple first classification parameters includes:

[0036] Sorting the plurality of first classification parameters according to the numerical values ​​of the classification parameters;

[0037] For each first classification parameter after sorting, in response to the difference between the number of coal and rock samples corresponding to the first classification parameter and the number of coal and rock samples corresponding to the second classification parameter being greater than a preset threshold, determining that the first classification parameter is a partition line between two adjacent parameter intervals, and the second classification parameter is a classification parameter preceding the first classification parameter;

[0038] According to the partition lines, the plurality of first classification parameters are divided into a plurality of parameter intervals.

[0039] On the other hand, the present application provides a device for determining a reservoir type, the device comprising:

[0040] A first determination module is used to determine the rock sample of the target reservoir;

[0041] A first acquisition module is used to acquire a capillary pressure curve corresponding to the rock sample, wherein the capillary pressure curve is used to represent a corresponding relationship between a mercury injection pressure and a mercury injection volume of the rock sample;

[0042] A fitting module, used for fitting the capillary pressure curve by a first objective function to obtain a first fitting parameter, wherein the first fitting parameter includes a first displacement parameter and a first pore morphology parameter;

[0043] A second determination module is used to determine a first classification parameter of the rock sample through a second objective function according to the first displacement parameter and the first pore morphology parameter;

[0044] The third determination module is used to determine the reservoir type of the reservoir according to the parameter interval where the first classification parameter is located.

[0045] In a possible implementation, the fitting module is used to determine a plurality of first mercury injection pressures and a plurality of first mercury injection volumes corresponding to the plurality of first mercury injection pressures from the capillary pressure curve; obtain a pore throat radius distribution curve of the rock sample, and determine the number of pore throat distribution peaks in the pore throat distribution curve according to the pore throat radius distribution curve; for each first mercury injection pressure, substitute the first mercury injection pressure, the first mercury injection volume corresponding to the first mercury injection pressure, and the number into the following formula 1 to obtain a fitting overdetermined equation corresponding to the capillary pressure curve;

[0046] Formula 1:

[0047] in, represents the first mercury injection volume, P c represents the first mercury injection pressure, represents the second mercury injection volume corresponding to the i-th pore throat distribution peak, represents the second displacement parameter corresponding to the i-th pore throat distribution peak value, G i represents the second pore morphology parameter corresponding to the i-th pore throat distribution peak, and n represents the number;

[0048] A second fitting parameter and a second mercury injection volume corresponding to at least one pore throat distribution peak are determined through a plurality of fitting overdetermined equations determined by the plurality of first mercury injection pressures, wherein the second fitting parameter includes a second displacement parameter and a second pore morphology parameter; and a first fitting parameter is selected from the second fitting parameters corresponding to the at least one pore throat distribution peak.

[0049] In another possible implementation, the fitting module is used to, in response to the number of the pore-throat distribution peaks being one, determine that the second fitting parameter corresponding to the pore-throat distribution peak is the first fitting parameter; or, in response to the number of the pore-throat distribution peaks being multiple, select a target pore-throat distribution peak with the largest pore throat radius from the multiple pore-throat distribution peaks, and determine that the second fitting parameter corresponding to the target pore-throat distribution peak is the first fitting parameter.

[0050] In another possible implementation, the second determination module is used to determine the first classification parameter of the rock sample according to the first displacement parameter and the first pore morphology parameter by using the following formula 2;

[0051] Formula 2:

[0052] Wherein, Mode represents the first classification parameter, represents the first displacement parameter, and G1 represents the first pore morphology parameter.

[0053] In another possible implementation, the third determination module is used to determine the target parameter interval in which the first classification parameter is located based on the first classification parameter; and determine the reservoir type corresponding to the target parameter interval from the correspondence between the parameter interval and the reservoir type based on the target parameter interval.

[0054] In another possible implementation, the device further includes:

[0055] A second acquisition module is used to acquire a plurality of first classification parameters corresponding to a plurality of coal and rock samples, wherein the plurality of coal and rock samples are coal and rock samples from a plurality of different regions;

[0056] A fourth determination module, used for determining, for each first classification parameter, the number of coal and rock samples corresponding to the first classification parameter;

[0057] A fifth determination module, used to determine a plurality of parameter intervals according to the number of coal and rock samples corresponding to the plurality of first classification parameters;

[0058] The sixth determination module is used to determine the reservoir type corresponding to each parameter interval, and obtain the corresponding relationship between the parameter interval and the reservoir type.

[0059] In another possible implementation, the fifth determination module is used to sort the multiple first classification parameters according to the numerical values ​​of the classification parameters; for each sorted first classification parameter, in response to the difference between the number of coal and rock samples corresponding to the first classification parameter and the number of coal and rock samples corresponding to the second classification parameter being greater than a preset threshold, the first classification parameter is determined to be a partition line between two adjacent parameter intervals, and the second classification parameter is the previous classification parameter of the first classification parameter; according to the partition line, the multiple first classification parameters are divided into multiple parameter intervals.

[0060] 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 reservoir type determination method described in any possible implementation method mentioned above.

[0061] On the other hand, an embodiment of the present application provides a computer-readable storage medium, in which at least one program code is stored. The at least one program code is loaded and executed by a processor to implement the operations performed in the reservoir type determination method described in any possible implementation method mentioned above.

[0062] The beneficial effects of the technical solution provided by the embodiments of the present application include at least:

[0063] The embodiment of the present application provides a method for determining the reservoir type. Since the first displacement parameter and the first pore morphology parameter are obtained by fitting the capillary pressure curve corresponding to the rock sample, and the capillary pressure curve belongs to the overall characteristic of the rock sample and will not be limited by the pore structure of the rock sample, the first classification parameter determined by the first displacement parameter and the first pore morphology parameter will not be limited by the pore structure of the rock sample. It can be seen that even if the pore structure of the rock sample of the reservoir is complex, the reservoir type of the reservoir can be accurately determined by the first classification parameter, so the accuracy of determining the reservoir type of the reservoir is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. 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 creative work.

[0065] Figure 1 is a flow chart of a method for determining a reservoir type provided according to an embodiment of the present application;

[0066] Figure 2 is a schematic diagram of a capillary pressure curve corresponding to a rock sample provided in an embodiment of the present application;

[0067] Figure 3 is a schematic diagram of a capillary pressure curve corresponding to another rock sample provided in an embodiment of the present application;

[0068] Figure 4 is a schematic diagram of determining a target pore throat distribution peak value provided according to an embodiment of the present application;

[0069] Figure 5 is another schematic diagram of determining the target pore throat distribution peak value provided according to an embodiment of the present application;

[0070] Figure 6 is a schematic diagram of a fitting curve corresponding to a rock sample provided in an embodiment of the present application;

[0071] Figure 7 is a schematic diagram of a fitting curve corresponding to another rock sample provided in an embodiment of the present application;

[0072] Figure 8 is a schematic diagram of a functional relationship between a permeability and a first classification parameter provided according to an embodiment of the present application;

[0073] Fig. 9 is a block diagram of a reservoir type determination device provided according to an embodiment of the present application;

[0074] Fig.10 It is a structural block diagram of a computer device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0075] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0076] Figure 1 is a flow chart of a method for determining a reservoir type according to an embodiment of the present application. Figure 1 , the method comprising:

[0077] 101. Computer equipment determines the rock samples of the target reservoir.

[0078] The target reservoir may be a reservoir of a reservoir type to be determined selected by a computer device. The rock sample may be a rock sample with a single pore structure or a rock sample with a complex pore structure.

[0079] 102. The computer device obtains the capillary pressure curve corresponding to the rock sample. The capillary pressure curve is used to represent the corresponding relationship between the mercury injection pressure and the mercury injection volume of the rock sample.

[0080] In a possible implementation, the computer device obtains a capillary pressure curve corresponding to the rock sample through a high-pressure mercury injection experiment. Accordingly, this step is: the computer device sends a test instruction to a test device for the high-pressure mercury injection experiment. The test device receives the instruction, tests the rock sample, and obtains test data of the mercury injection pressure and mercury injection volume of the rock sample. The test device returns the test data to the computer device, and the computer device generates a capillary pressure curve corresponding to the rock sample based on the test data.

[0081] It should be noted that the mercury injection volume is the saturated mercury injection volume corresponding to the mercury injection pressure; for different types of rock samples, the saturated mercury injection volume corresponding to the same mercury injection pressure is different. Among them, the higher the saturated mercury injection volume corresponding to the same mercury injection pressure, the stronger the seepage capacity of the rock sample.

[0082] For example, taking a rock sample with a single pore structure as an example, the computer equipment generates a capillary pressure curve corresponding to the rock sample based on the test data, such as Figure 2 As shown, the X-axis represents the mercury injection volume, and the Y-axis represents the mercury injection pressure. Figure 2 The left curve in the middle is the mercury desorption curve of the rock sample. Figure 2 The curve on the right side of the middle is the mercury injection curve of the rock sample.

[0083] For example, taking a rock sample with a complex pore structure as an example, the computer equipment generates a capillary pressure curve corresponding to the rock sample based on the test data, such as Figure 3 As shown, the X-axis represents the mercury injection volume, and the Y-axis represents the mercury injection pressure. Figure 3 The left curve in the middle is the mercury desorption curve of the rock sample. Figure 3 The curve on the right side of the middle is the mercury injection curve of the rock sample.

[0084] 103. The computer device fits the capillary pressure curve through the first objective function to obtain first fitting parameters, where the first fitting parameters include first displacement parameters and first pore morphology parameters.

[0085] In a possible implementation, the displacement parameter is the displacement pressure of the rock sample. The pore morphology parameter is the pore morphology factor of the rock sample.

[0086] In a possible implementation, the first objective function is a Thommeer function. The capillary pressure curve is fitted by the Thommeer function. Accordingly, this step is: the computer device determines multiple first mercury injection pressures and multiple first mercury injection volumes corresponding to the multiple first mercury injection pressures from the capillary pressure curve; obtains the pore throat radius distribution curve of the rock sample, and determines the number of pore throat distribution peaks in the pore throat distribution curve according to the pore throat radius distribution curve; for each first mercury injection pressure, the first mercury injection pressure, the first mercury injection volume corresponding to the first mercury injection pressure, and the number of pore throat distribution peaks are substituted into the following formula 1 to obtain the fitting overdetermined equation corresponding to the capillary pressure curve;

[0087] Formula 1:

[0088] in, represents the first mercury injection volume, P c represents the first mercury injection pressure, represents the second mercury injection volume corresponding to the i-th pore throat distribution peak, represents the second displacement parameter corresponding to the i-th pore throat distribution peak value, G i represents the second pore morphology parameter corresponding to the i-th pore throat distribution peak, and n represents the number; a second fitting parameter and a second mercury injection volume corresponding to at least one pore throat distribution peak are determined by multiple fitting overdetermined equations determined by multiple first mercury injection pressures, and the second fitting parameters include a second displacement parameter and a second pore morphology parameter; a first fitting parameter is selected from the second fitting parameters corresponding to at least one pore throat distribution peak.

[0089] It should be noted that Formula 1 is the fitting formula of the Thommeer function, and the multiple fitting overdetermined equations are the fitting equations corresponding to the fitting curve obtained by fitting the capillary pressure curve through the Thommeer function. The second mercury injection volume is the mercury injection volume corresponding to the coal rock sample when the mercury injection pressure is the maximum.

[0090] In one possible implementation, the computer device determines the second fitting parameter and the second mercury injection volume corresponding to at least one pore throat distribution peak value by fitting an overdetermined equation group and using a Newton iteration algorithm; or, the computer device determines the second fitting parameter and the second mercury injection volume corresponding to at least one pore throat distribution peak value by fitting an overdetermined equation group and using a radial basis function algorithm.

[0091] One point that needs to be explained is that different pore throat distribution peaks correspond to different pore throat radii. Optionally, the computer device determines that the second fitting parameter of the pore throat distribution peak corresponding to the maximum pore throat radius is the first fitting parameter. Accordingly, the step of the computer device selecting the first fitting parameter from the second fitting parameters corresponding to at least one pore throat distribution peak is: the computer device responds to the number of pore throat distribution peaks being one, and determines that the second fitting parameter corresponding to the pore throat distribution peak is the first fitting parameter; or, in response to the number of pore throat distribution peaks being multiple, selects the target pore throat distribution peak with the largest pore throat radius from the multiple pore throat distribution peaks, and determines that the second fitting parameter corresponding to the target pore throat distribution peak is the first fitting parameter.

[0092] For a rock sample with a single pore structure, the number of pore throat distribution peaks is one. For a rock with a complex pore structure, the number of pore throat distribution peaks is multiple. In a possible implementation, the computer device determines the target pore throat distribution peak through the horizontal coordinate of the pore throat distribution peak; accordingly, the step of the computer device selecting the target pore throat distribution peak with the largest pore throat radius from the multiple pore throat distribution peaks is: the computer device determines the horizontal coordinate of each pore throat distribution peak, and selects the target pore throat distribution peak with the largest horizontal coordinate value from the multiple pore throat distribution peaks.

[0093] For example, see Figure 4 , taking a rock sample with a single pore structure as an example, the number of pore throat distribution peaks is one, and the pore throat distribution peak is determined as the target pore throat distribution peak.

[0094] For example, see Figure 5 , taking a rock sample with a complex pore structure as an example, the number of pore throat distribution peaks is two, the computer device determines the horizontal coordinates of the two pore throat distribution peaks, and selects the target pore throat distribution peak with the largest horizontal coordinate value from the two pore throat distribution peaks.

[0095] In a possible implementation, the computer device verifies the fitting accuracy of the Thommeer function. Accordingly, the second fitting parameter determined by the computer device through the fitting formula also includes the second mercury injection volume. The computer device generates a fitting curve according to the second displacement parameter, the second pore morphology parameter and the second mercury injection volume. The computer device determines the overlap between the fitting curve and the capillary pressure curve, and in response to the overlap exceeding the preset overlap, determines that the fitting accuracy of the Thommeer function is qualified.

[0096] For example, taking a rock sample with a single pore structure as an example, the computer device generates a fitting curve according to the second displacement parameter, the second pore morphology parameter and the second mercury injection volume. Figure 6 As shown in FIG. 1 , the X-axis represents the mercury injection volume, and the Y-axis represents the mercury injection pressure.

[0097] For another example, taking a rock sample with a complex pore structure as an example, the computer device generates a fitting curve according to the second displacement parameter, the second pore morphology parameter and the second mercury injection volume. Figure 7 As shown in FIG. 1 , the X-axis represents the mercury injection volume, and the Y-axis represents the mercury injection pressure.

[0098] It should be noted that the higher the overlap, the higher the accuracy of the second fitting parameter determined by the fitting formula. The preset overlap can be any value between 60% and 100%. In the embodiment of the present application, the value of the preset overlap is not specifically limited and can be set and changed as needed.

[0099] In one possible implementation, the step in which a computer device determines the degree of overlap between a fitting curve and a capillary pressure curve is as follows: the computer device determines a first position of a plurality of pixel points of the fitting curve and determines a second position of a plurality of pixel points of the capillary pressure curve, and determines the degree of overlap between the fitting curve and the capillary pressure curve based on the degree of overlap between the first position and the second position.

[0100] 104. The computer device determines a first classification parameter of the rock sample according to the first displacement parameter and the first pore morphology parameter through a second objective function.

[0101] In a possible implementation, the second objective function is a functional relationship between the first classification parameter and the first displacement parameter and the first pore morphology parameter. Accordingly, this step is: the computer device determines the first classification parameter of the rock sample according to the first displacement parameter and the first pore morphology parameter by the following formula 2;

[0102] Formula 2:

[0103] Among them, Mode represents the first classification parameter, represents the first displacement parameter, and G1 represents the first pore morphology parameter.

[0104] It should be noted that the first classification parameter obtained by the second objective function is the pore throat radius value corresponding to the target pore throat distribution peak value.

[0105] 105. The computer device determines the reservoir type of the reservoir according to the parameter interval of the first classification parameter.

[0106] In a possible implementation, different parameter intervals correspond to different reservoir types. Accordingly, this step is: the computer device determines the target parameter interval where the first classification parameter is located according to the first classification parameter; and determines the reservoir type corresponding to the target parameter interval from the corresponding relationship between the parameter interval and the reservoir type according to the target parameter interval.

[0107] Optionally, a relationship list between parameter intervals and reservoir types is stored in the computer device. The relationship list between parameter intervals and reservoir types is shown in Table 1.

[0108] Table 1 List of relationships between parameter intervals and reservoir types

[0109] Reservoir type RT1 RT2 RT3 RT4 RT5 RT6 Parameter range >7 3.0-7 1.5-3.0 0.6-1.5 0.2-0.6 <0.2

[0110] As shown in Table 1, the parameter intervals include [less than 0.2], [0.2-0.6], [0.6-1.5], [1.5-3.0], [3.0-7.0] and [greater than 7.0]. The reservoir types corresponding to the parameter intervals include RT1, RT2, RT3, RT4, RT5 and RT6. Among them, RT1 represents the reservoir with the best physical quality, and so on, RT6 represents the reservoir with the worst physical quality.

[0111] In a possible implementation, before the computer device determines the target parameter interval where the first classification parameter is located based on the first classification parameter, it is necessary to determine the correspondence between the parameter interval and the reservoir type. The steps for the computer device to determine the correspondence between the parameter interval and the reservoir type are as follows: the computer device obtains multiple first classification parameters corresponding to multiple coal rock samples, and the multiple coal rock samples are coal rock samples from multiple different regions; for each first classification parameter, the number of coal rock samples corresponding to the first classification parameter is determined; based on the number of coal rock samples corresponding to the multiple first classification parameters, multiple parameter intervals are determined; the reservoir type corresponding to each parameter interval is determined, and the correspondence between the parameter interval and the reservoir type is obtained.

[0112] It should be noted that, in one possible implementation, the computer device determines multiple first classification parameters corresponding to multiple coal rock samples through the first objective function and the second objective function. In another possible implementation, the computer device determines the permeability of multiple coal rock samples, and determines multiple first classification parameters corresponding to the multiple coal rock samples through the third objective function based on the permeability of the multiple coal rock samples.

[0113] Optionally, the third objective function is Formula 3: Mode = 0.0073x 3 +0.0246x 2 +0.423x-0.0258; where Mode represents the first classification parameter, and x represents the permeability corresponding to the coal rock sample.

[0114] For example, see Figure 8 The computer device determines multiple first classification parameters corresponding to the multiple coal rock samples according to the permeability of the multiple coal rock samples through the third objective function, and obtains a relationship diagram between the permeability and the first classification parameters. Among them, the X-axis represents the permeability and the Y-axis represents the value of the first classification parameter.

[0115] In a possible implementation, the computer device determines the permeability of the coal rock sample according to the KNN (K-Nearest Neighbor) algorithm. Accordingly, the steps of determining the permeability of the coal rock sample by the computer device are as follows: the computer device obtains a 4-dimensional vector of the coal rock sample, and the 4-dimensional vector includes neutron, density, acoustic wave time difference and natural gamma logging curves; finds a set of k vectors closest to the input vector in the coal rock sample training data set, and determines the permeability of the coal rock sample by distance weighted regression.

[0116] In an embodiment of the present application, a computer device determines the permeability of a coal rock sample according to a KNN algorithm. Since the algorithm is not affected by the pore structure of the coal rock sample, for coal rock samples with complex pore structure, the permeability of the coal rock sample determined by the KNN algorithm is highly accurate.

[0117] In a possible implementation, the steps of determining multiple parameter intervals by a computer device according to the number of coal and rock samples corresponding to multiple first classification parameters are as follows: the computer device sorts the multiple first classification parameters according to the numerical values ​​of the classification parameters; for each sorted first classification parameter, in response to the difference between the number of coal and rock samples corresponding to the first classification parameter and the number of coal and rock samples corresponding to the second classification parameter being greater than a preset threshold, determining that the first classification parameter is a partition line between two adjacent parameter intervals, and the second classification parameter is the previous classification parameter of the first classification parameter; and dividing the multiple first classification parameters into multiple parameter intervals according to the partition lines.

[0118] In a possible implementation, the partition line is a mutation point in the first classification parameter after sorting. Accordingly, the preset threshold is a preset multiple of the average value of multiple differences between the first classification parameter and the second classification parameter. The preset multiple can be any value between 2 and 5 times. In the embodiment of the present application, the value of the preset multiple is not specifically limited and can be set and changed as needed.

[0119] The embodiment of the present application provides a method for determining the reservoir type. Since the first displacement parameter and the first pore morphology parameter are obtained by fitting the capillary pressure curve corresponding to the rock sample, and the capillary pressure curve belongs to the overall characteristic of the rock sample and will not be limited by the pore structure of the rock sample, the first classification parameter determined by the first displacement parameter and the first pore morphology parameter will not be limited by the pore structure of the rock sample. It can be seen that even if the pore structure of the rock sample of the reservoir is complex, the reservoir type of the reservoir can be accurately determined by the first classification parameter, so the accuracy of determining the reservoir type of the reservoir is improved.

[0120] Fig. 9is a block diagram of a reservoir type determination device provided according to an embodiment of the present application. Fig. 9 , the device comprises:

[0121] The first determination module 901 is used to determine the rock sample of the target reservoir;

[0122] A first acquisition module 902 is used to acquire a capillary pressure curve corresponding to the rock sample, where the capillary pressure curve is used to represent the corresponding relationship between the mercury injection pressure and the mercury injection volume of the rock sample;

[0123] A fitting module 903 is used to fit the capillary pressure curve by using a first objective function to obtain first fitting parameters, where the first fitting parameters include a first displacement parameter and a first pore morphology parameter;

[0124] A second determination module 904 is used to determine a first classification parameter of the rock sample according to the first displacement parameter and the first pore morphology parameter through a second objective function;

[0125] The third determination module 905 is used to determine the reservoir type of the reservoir according to the parameter interval where the first classification parameter is located.

[0126] In a possible implementation, the fitting module 903 is used to determine a plurality of first mercury injection pressures and a plurality of first mercury injection volumes corresponding to the plurality of first mercury injection pressures from the capillary pressure curve; obtain a pore throat radius distribution curve of the rock sample, and determine the number of pore throat distribution peaks in the pore throat distribution curve according to the pore throat radius distribution curve; determine a second fitting parameter corresponding to at least one pore throat distribution peak according to the plurality of first mercury injection pressures, the plurality of first mercury injection volumes and the number through the following formula 1, wherein the second fitting parameter includes a second displacement parameter and a second pore morphology parameter;

[0127] Formula 1:

[0128] in, represents the first mercury injection volume, P c represents the first mercury injection pressure, represents the second mercury injection volume corresponding to the i-th pore throat distribution peak, represents the second displacement parameter corresponding to the i-th pore throat distribution peak value, G i represents the second pore morphology parameter corresponding to the i-th pore throat distribution peak, and n represents the number; the first fitting parameter is selected from the second fitting parameters corresponding to at least one pore throat distribution peak.

[0129] In another possible implementation, the fitting module 903 is used to determine that the second fitting parameter corresponding to the pore throat distribution peak is the first fitting parameter in response to the number of pore throat distribution peaks being one; or, in response to the number of pore throat distribution peaks being multiple, a target pore throat distribution peak with the largest pore throat radius is selected from the multiple pore throat distribution peaks, and the second fitting parameter corresponding to the target pore throat distribution peak is determined to be the first fitting parameter.

[0130] In another possible implementation, the second determination module 904 is used to determine the first classification parameter of the rock sample according to the first displacement parameter and the first pore morphology parameter by using the following formula 2;

[0131] Formula 2:

[0132] Among them, Mode represents the first classification parameter, represents the first displacement parameter, and G1 represents the first pore morphology parameter.

[0133] In another possible implementation, the third determination module 905 is used to determine the target parameter interval where the first classification parameter is located according to the first classification parameter; and determine the reservoir type corresponding to the target parameter interval from the correspondence between the parameter interval and the reservoir type according to the target parameter interval.

[0134] In another possible implementation, the device further includes:

[0135] A second acquisition module is used to acquire a plurality of first classification parameters corresponding to a plurality of coal and rock samples, where the plurality of coal and rock samples are coal and rock samples from a plurality of different regions;

[0136] A fourth determination module, used for determining, for each first classification parameter, the number of coal and rock samples corresponding to the first classification parameter;

[0137] A fifth determination module, used to determine a plurality of parameter intervals according to the number of coal and rock samples corresponding to the plurality of first classification parameters;

[0138] The sixth determination module is used to determine the reservoir type corresponding to each parameter interval and obtain the corresponding relationship between the parameter interval and the reservoir type.

[0139] In another possible implementation, the fifth determination module is used to sort multiple first classification parameters according to the numerical values ​​of the classification parameters; for each sorted first classification parameter, in response to the difference between the number of coal and rock samples corresponding to the first classification parameter and the number of coal and rock samples corresponding to the second classification parameter being greater than a preset threshold, the first classification parameter is determined to be a partition line between two adjacent parameter intervals, and the second classification parameter is the previous classification parameter of the first classification parameter; according to the partition line, the multiple first classification parameters are divided into multiple parameter intervals.

[0140] The embodiment of the present application provides a device for determining the reservoir type. Since the first displacement parameter and the first pore morphology parameter are obtained by fitting the capillary pressure curve corresponding to the rock sample, and the capillary pressure curve belongs to the overall characteristic of the rock sample and will not be limited by the pore structure of the rock sample, the first classification parameter determined by the first displacement parameter and the first pore morphology parameter will not be limited by the pore structure of the rock sample. It can be seen that even if the pore structure of the rock sample of the reservoir is complex, the reservoir type of the reservoir can be accurately determined by the first classification parameter, so the accuracy of determining the reservoir type of the reservoir is improved.

[0141] Fig.10 The block diagram of the structure of a computer device 1000 provided by an exemplary embodiment of the present invention is shown. The computer device 1000 may be: a smart phone, a tablet computer, an MP3 player (Moving Picture Experts Group Audio Layer III, Moving Picture Experts Group Audio Layer IV, Moving Picture Experts Group Audio Layer IV) player, a laptop computer or a desktop computer. The computer device 1000 may also be called a user device, a portable computer device, a laptop computer device, a desktop computer device, or other names.

[0142] Typically, the computer device 1000 includes a processor 1001 and a memory 1002 .

[0143] The processor 1001 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 1001 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). The processor 1001 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 1001 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 1001 may also include an AI (Artificial Intelligence) processor, which is used to process computing operations related to machine learning.

[0144] The memory 1002 may include one or more computer-readable storage media, which may be non-transitory. The memory 1002 may also include a high-speed random access memory, and a non-volatile memory, such as one or more disk storage devices, flash memory storage devices. In some embodiments, the non-transitory computer-readable storage medium in the memory 1002 is used to store at least one instruction, which is used to be executed by the processor 1001 to implement the method for determining the reservoir type provided in the method embodiment of the present application.

[0145] In some embodiments, the computer device 1000 may also optionally include: a peripheral device interface 1003 and at least one peripheral device. The processor 1001, the memory 1002 and the peripheral device interface 1003 may be connected via a bus or a signal line. Each peripheral device may be connected to the peripheral device interface 1003 via a bus, a signal line or a circuit board. Specifically, the peripheral device includes: at least one of a radio frequency circuit 1004, a display screen 1005, a camera 1006, an audio circuit 1007, a positioning component 1008 and a power supply 1009.

[0146] The peripheral device interface 1003 may be used to connect at least one peripheral device related to I / O (Input / Output) to the processor 1001 and the memory 1002. In some embodiments, the processor 1001, the memory 1002, and the peripheral device interface 1003 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 1001, the memory 1002, and the peripheral device interface 1003 may be implemented on a separate chip or circuit board, which is not limited in this embodiment.

[0147] The radio frequency circuit 1004 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The radio frequency circuit 1004 communicates with communication networks and other communication devices through electromagnetic signals. The radio frequency circuit 1004 converts electrical signals into electromagnetic signals for transmission, or converts received electromagnetic signals into electrical signals. Optionally, the radio frequency circuit 1004 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 radio frequency circuit 1004 can communicate with other computer devices through at least one wireless communication protocol. The wireless communication protocol includes, but is not limited to: a metropolitan area network, various generations of mobile communication networks (2G, 3G, 4G and 5G), a wireless local area network and / or a WiFi (Wireless Fidelity) network. In some embodiments, the radio frequency circuit 1004 may also include circuits related to NFC (Near Field Communication), which is not limited in this application.

[0148] The display screen 1005 is used to display a UI (User Interface). The UI may include graphics, text, icons, videos, and any combination thereof. When the display screen 1005 is a touch display screen, the display screen 1005 also has the ability to collect touch signals on the surface or above the surface of the display screen 1005. The touch signal can be input to the processor 1001 as a control signal for processing. At this time, the display screen 1005 can also be used to provide virtual buttons and / or virtual keyboards, also known as soft buttons and / or soft keyboards. In some embodiments, the display screen 1005 can be one, and the front panel of the computer device 1000 is set; in other embodiments, the display screen 1005 can be at least two, which are respectively set on different surfaces of the computer device 1000 or are folded; in some other embodiments, the display screen 1005 can be a flexible display screen, which is set on the curved surface or folded surface of the computer device 1000. Even, the display screen 1005 can also be set to a non-rectangular irregular shape, that is, a special-shaped screen. The display screen 1005 can be made of materials such as LCD (Liquid Crystal Display) and OLED (Organic Light-Emitting Diode).

[0149] The camera assembly 1006 is used to capture images or videos. Optionally, the camera assembly 1006 includes a front camera and a rear camera. Typically, the front camera is arranged on the front panel of the computer device, and the rear camera is arranged 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 the panoramic shooting and VR (Virtual Reality) shooting function or other fusion shooting functions. In some embodiments, the camera assembly 1006 may also include a flash. The flash can be a monochrome 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 at different color temperatures.

[0150] The audio circuit 1007 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 and input them into the processor 1001 for processing, or input them into the radio frequency circuit 1004 to achieve voice communication. For the purpose of stereo acquisition or noise reduction, there may be multiple microphones, which are respectively arranged at different parts of the computer device 1000. The microphone may also be an array microphone or an omnidirectional acquisition microphone. The speaker is used to convert the electrical signal from the processor 1001 or the radio frequency circuit 1004 into sound waves. The speaker may be a traditional 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 ranging. In some embodiments, the audio circuit 1007 may also include a headphone jack.

[0151] The positioning component 1008 is used to locate the current geographic location of the computer device 1000 to implement navigation or LBS (Location Based Service). The positioning component 1008 can be a positioning component based on the US GPS (Global Positioning System), China's Beidou system, Russia's Grenas system, or the European Union's Galileo system.

[0152] The power supply 1009 is used to power various components in the computer device 1000. The power supply 1009 can be an alternating current, a direct current, a disposable battery, or a rechargeable battery. When the power supply 1009 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.

[0153] In some embodiments, the computer device 1000 further includes one or more sensors 1010 , including but not limited to: an acceleration sensor 1011 , a gyroscope sensor 1012 , a pressure sensor 1013 , a fingerprint sensor 1014 , an optical sensor 1015 , and a proximity sensor 1016 .

[0154] The acceleration sensor 1011 can detect the magnitude of acceleration on the three coordinate axes of the coordinate system established by the computer device 1000. For example, the acceleration sensor 1011 can be used to detect the components of gravity acceleration on the three coordinate axes. The processor 1001 can control the display screen 1005 to display the user interface in a horizontal view or a vertical view according to the gravity acceleration signal collected by the acceleration sensor 1011. The acceleration sensor 1011 can also be used to collect game or user motion data.

[0155] The gyro sensor 1012 can detect the body direction and rotation angle of the computer device 1000, and the gyro sensor 1012 can cooperate with the acceleration sensor 1011 to collect the user's 3D actions on the computer device 1000. The processor 1001 can implement the following functions based on the data collected by the gyro sensor 1012: motion sensing (such as changing the UI according to the user's tilt operation), image stabilization during shooting, game control, and inertial navigation.

[0156] The pressure sensor 1013 can be set on the side frame of the computer device 1000 and / or the lower layer of the display screen 1005. When the pressure sensor 1013 is set on the side frame of the computer device 1000, it can detect the user's grip signal of the computer device 1000, and the processor 1001 performs left and right hand recognition or shortcut operations according to the grip signal collected by the pressure sensor 1013. When the pressure sensor 1013 is set on the lower layer of the display screen 1005, the processor 1001 controls the operability controls on the UI interface according to the user's pressure operation on the display screen 1005. The operability controls include at least one of a button control, a scroll bar control, an icon control, and a menu control.

[0157] The fingerprint sensor 1014 is used to collect the user's fingerprint, and the processor 1001 identifies the user's identity based on the fingerprint collected by the fingerprint sensor 1014, or the fingerprint sensor 1014 identifies the user's identity based on the collected fingerprint. When the user's identity is identified as a trusted identity, the processor 1001 authorizes the user to perform relevant sensitive operations, which include unlocking the screen, viewing encrypted information, downloading software, paying, and changing settings. The fingerprint sensor 1014 can be set on the front, back, or side of the computer device 1000. When a physical button or a manufacturer logo is set on the computer device 1000, the fingerprint sensor 1014 can be integrated with the physical button or the manufacturer logo.

[0158] The optical sensor 1015 is used to collect the ambient light intensity. In one embodiment, the processor 1001 can control the display brightness of the display screen 1005 according to the ambient light intensity collected by the optical sensor 1015. Specifically, when the ambient light intensity is high, the display brightness of the display screen 1005 is increased; when the ambient light intensity is low, the display brightness of the display screen 1005 is reduced. In another embodiment, the processor 1001 can also dynamically adjust the shooting parameters of the camera assembly 1006 according to the ambient light intensity collected by the optical sensor 1015.

[0159] The proximity sensor 1016, also called a distance sensor, is usually disposed on the front panel of the computer device 1000. The proximity sensor 1016 is used to collect the distance between the user and the front of the computer device 1000. In one embodiment, when the proximity sensor 1016 detects that the distance between the user and the front of the computer device 1000 is gradually decreasing, the processor 1001 controls the display screen 1005 to switch from the screen-on state to the screen-off state; when the proximity sensor 1016 detects that the distance between the user and the front of the computer device 1000 is gradually increasing, the processor 1001 controls the display screen 1005 to switch from the screen-off state to the screen-on state.

[0160] Those skilled in the art will understand that Fig.10 The structure shown in the figure does not constitute a limitation on the computer device 1000, and the computer device 1000 may include more or less components than those shown in the figure, or combine some components, or adopt a different component arrangement.

[0161] An embodiment of the present application also provides a computer-readable storage medium, in which at least one program code is stored. The at least one program code is loaded and executed by a processor to implement the operations performed by the reservoir type determination method in any of the above possible implementations.

[0162] The above description is only an optional embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for determining a reservoir type, characterized in that: The method comprises: Identify rock samples of target reservoirs; Obtaining a capillary pressure curve corresponding to the rock sample, wherein the capillary pressure curve is used to indicate a corresponding relationship between a mercury injection pressure and a mercury injection volume of the rock sample; determining a plurality of first mercury injection pressures and a plurality of first mercury injection volumes corresponding to the plurality of first mercury injection pressures from the capillary pressure curve; Obtaining a pore throat radius distribution curve of the rock sample, and determining the number of pore throat distribution peaks in the pore throat radius distribution curve according to the pore throat radius distribution curve; For each first mercury injection pressure, the first mercury injection pressure, the first mercury injection volume corresponding to the first mercury injection pressure, and the quantity are substituted into the following formula 1 to obtain a fitting overdetermined equation corresponding to the capillary pressure curve; Formula 1: in, represents the first mercury injection volume, represents the first mercury injection pressure, Indicates The second mercury injection volume corresponding to the pore throat distribution peak value is Indicates The second displacement parameter corresponding to the peak value of pore throat distribution is Indicates The second pore morphology parameter corresponding to the pore throat distribution peak, indicates the stated quantity; Determine a second fitting parameter and a second mercury injection volume corresponding to at least one pore throat distribution peak value through a plurality of fitting overdetermined equations determined by the plurality of first mercury injection pressures, wherein the second fitting parameter includes a second displacement parameter and a second pore morphology parameter; In response to the number of the pore throat distribution peaks being one, determining the second fitting parameter corresponding to the pore throat distribution peak as the first fitting parameter; or, in response to the number of the pore throat distribution peaks being multiple, selecting a target pore throat distribution peak with the largest pore throat radius from the multiple pore throat distribution peaks, and determining the second fitting parameter corresponding to the target pore throat distribution peak as the first fitting parameter; the first fitting parameter includes a first displacement parameter and a first pore morphology parameter; According to the first displacement parameter and the first pore morphology parameter, a first classification parameter of the rock sample is determined by the following formula 2, wherein the first classification parameter is a pore throat radius value corresponding to a target pore throat distribution peak value; Formula 2: in, represents the first classification parameter, represents the first displacement parameter, Indicates the first pore morphology parameter; obtains a plurality of first classification parameters corresponding to a plurality of coal rock samples, wherein the plurality of coal rock samples are coal rock samples from a plurality of different regions; For each first classification parameter, determining the number of coal and rock samples corresponding to the first classification parameter; Sorting the plurality of first classification parameters according to the numerical values ​​of the classification parameters; For each first classification parameter after sorting, in response to the difference between the number of coal and rock samples corresponding to the first classification parameter and the number of coal and rock samples corresponding to the second classification parameter being greater than a preset threshold, the first classification parameter is determined to be a partition line between two adjacent parameter intervals, and the second classification parameter is a previous classification parameter of the first classification parameter, wherein the partition line is a mutation point in the first classification parameter after sorting, and the preset threshold is a preset multiple of an average value of multiple differences between the first classification parameter and the second classification parameter, and the preset multiple is any value between 2 and 5 times; According to the partition line, the plurality of first classification parameters are divided into a plurality of parameter intervals; Determine the reservoir type corresponding to each parameter interval, and obtain the corresponding relationship between the parameter interval and the reservoir type; Determining, according to the first classification parameter, a target parameter interval in which the first classification parameter lies; According to the target parameter interval, the reservoir type corresponding to the target parameter interval is determined from the corresponding relationship between the parameter interval and the reservoir type. The parameter intervals include [less than 0.2], [0.2-0.6], (0.6-1.5], (1.5-3.0], (3.0-7.0] and [greater than 7.0]. The reservoir types corresponding to the parameter intervals include RT1, RT2, RT3, RT4, RT5 and RT6, wherein RT1 represents a type of reservoir with the best physical property quality, and RT6 represents a type of reservoir with the worst physical property quality.

2. A device for determining reservoir type, characterized in that: The reservoir type determination device is applied to the reservoir type determination method according to claim 1, and the device comprises: A first determination module is used to determine the rock sample of the target reservoir; An acquisition module, used for acquiring a capillary pressure curve corresponding to the rock sample, wherein the capillary pressure curve is used for indicating a corresponding relationship between a mercury injection pressure and a mercury injection volume of the rock sample; A fitting module, used for fitting the capillary pressure curve by a first objective function to obtain a first fitting parameter, wherein the first fitting parameter includes a first displacement parameter and a first pore morphology parameter; A second determination module is used to determine a first classification parameter of the rock sample through a second objective function according to the first displacement parameter and the first pore morphology parameter; The third determination module is used to determine the reservoir type of the reservoir according to the parameter interval where the first classification parameter is located.

3. A computer device, characterized in that: The computer device comprises: A processor and a memory, wherein at least one program code is stored in the memory, and the at least one program code is loaded and executed by the processor to implement the operations performed in the reservoir type determination method according to claim 1.

4. 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 reservoir type determination method according to claim 1.

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