Shale reservoir TOC prediction method and device, electronic equipment and computer readable storage medium

Abstract

The invention belongs to the field of oil exploration, and relates to a TOC prediction method and device for a shale reservoir, electronic equipment and a computer readable storage medium. Comprising the following steps: 1, collecting logging data, and constructing a rock physical model; 2, carrying out intersection analysis on different elastic parameters and TOC, and screening out the elastic parameter with the best linear relation with the TOC as a sensitive elastic parameter; 3, decomposing the cross plot of the sensitive elastic parameters and the TOC, dividing the range of the characteristic logging data, obtaining the cross plot of the sensitive elastic parameters and the TOC in the range of the specific characteristic logging data, and establishing a linear relation; step 4, based on the pre-stack seismic data, performing inversion on the pre-stack three parameters to obtain an elastic parameter body; and step 5, calculating a TOC value based on the linear relation and the elastic parameter body. According to the method, the problem that the linear relation between the elastic parameters and TOC intersection analysis is not obvious can be effectively solved, and a calculation model better conforming to the actual situation is constructed.

Description

Technical Field

[0001] The present invention belongs to the field of oil exploration. Specifically, it relates to a method and device for predicting TOC of shale reservoirs, an electronic device, and a computer-readable storage medium. Background Art

[0002] Shale gas is a very important unconventional oil and gas resource with huge resource potential. Reservoir research and evaluation are the main research tasks in the initial stage of shale gas exploration. Reservoir fluid identification refers to using seismic data to identify and describe the fluid-bearing characteristics of the reservoir. Pre-stack seismic inversion is an effective way for reservoir fluid identification and evaluation, which has important practical significance for accurately finding favorable areas. The main factors affecting shale gas accumulation include demonstration mineral composition, adsorbed gas content, total organic carbon content, gas content, permeability, organic matter maturity, burial depth, effective thickness, porosity, formation pressure, temperature, etc.

[0003] The TOC content of shale gas reservoirs is an important indicator for evaluating the organic matter characteristics of reservoirs, which has important guiding significance for the optimization of sweet spots in shale gas reservoirs and also has a positive effect on the reservoir space of the reservoir.

[0004] Currently, there are three main methods for predicting shale TOC (organic matter content): ① directly establishing a linear relationship between elastic parameters and TOC based on crossplot analysis. This method has high calculation efficiency but low accuracy; ② establishing a relationship between elastic parameters and TOC based on the neural network method. This method has high requirements for input elastic parameters, and when the linear relationship between elastic parameters and TOC is weak, the calculation accuracy is limited; ③ based on the rock physics modeling method to establish a relationship between elastic parameters and TOC. This method is currently the best method for calculating TOC. The disadvantage is that when the TOC value is too small, the TOC distribution does not conform to the assumed distribution conditions of rock physics. Therefore, the calculation accuracy of this method is limited in the case of low TOC. Summary of the Invention

[0005] The purpose of the present invention is to complete the TOC prediction of low-TOC shale reservoirs by using a rock physics model to complement well logging data and then conducting crossplot analysis of different lithologies and physical properties based on the complemented well logging data to establish the relationship between TOC and elastic parameters under different lithology and physical property conditions.

[0006] To achieve the above object, the first aspect of the present invention provides a method for predicting TOC of shale reservoirs, including the following steps:

[0007] Step 1: Collect well logging data and construct a rock physics model;

[0008] Step 2: Based on the rock physics model, conduct cross-plot analysis of different elastic parameters and TOC, and screen out the elastic parameter with the best linear relationship with TOC as the sensitive elastic parameter;

[0009] Step 3: Based on the logging data information, decompose the cross-plot of the sensitive elastic parameter and TOC, divide the range of the characteristic logging data, obtain the cross-plot of the sensitive elastic parameter and TOC within the range of the specific characteristic logging data, and establish a linear relationship;

[0010] Step 4: Based on the pre-stack seismic data, invert the three pre-stack parameters to obtain the elastic parameter volume;

[0011] Step 5: Calculate the TOC value based on the linear relationship established in Step 3 and the elastic parameter volume obtained in Step 4.

[0012] Optionally, in Step 1, the logging data includes the P-wave velocity, S-wave velocity, density, porosity, clay content, and saturation curves.

[0013] Optionally, in Step 3, the characteristic logging data is the clay content.

[0014] Optionally, the range of the clay content is divided into: clay content > 35%, clay content 30 - 35%, clay content 25 - 30%, clay content < 25%.

[0015] Optionally, in Step 3, when there are few data points on the cross-plot of the sensitive elastic parameter and TOC, new characteristic logging data is added, and lithology and physical property substitution and increase are completed for the logging curves through the rock physics model, data points within a specific range are increased, and a stable linear relationship is obtained.

[0016] Optionally, the new characteristic logging data is the porosity; the range of the porosity is divided into: porosity < 2.5%, porosity > 2.5%.

[0017] Optionally, the linear relationship is: TOC = TOC(porosity > 2.5%) + TOC(porosity < 2.5%); where TOC(porosity > 2.5%) = TOC(Vclay 20 - 25%) + TOC(Vclay 25 - 30%);

[0018] TOC(porosity < 2.5%) = TOC(Vclay < 25%) + TOC(Vclay 25 - 30%) + TOC(Vclay 30 - 35%) + TOC(Vclay > 35%).

[0019] The second aspect of the present invention provides a TOC prediction device for a shale reservoir, including:

[0020] A rock physics model construction module, configured to collect logging data and construct a rock physics model;

[0021] A sensitive elastic parameter acquisition module, configured to perform crossplot analysis of different elastic parameters and TOC based on the rock physics model, and screen out the elastic parameter with the best linear relationship with TOC as the sensitive elastic parameter;

[0022] A linear relationship establishment module, configured to decompose the crossplot of the sensitive elastic parameter and TOC based on the logging data information, divide the range of characteristic logging data, obtain the crossplot of the sensitive elastic parameter and TOC within the range of specific characteristic logging data, and establish a linear relationship;

[0023] An elastic parameter volume acquisition module, configured to perform inversion on three pre-stack parameters based on pre-stack seismic data to obtain an elastic parameter volume;

[0024] A TOC value calculation module, configured to calculate the TOC value based on the linear relationship and the elastic parameter volume.

[0025] A third aspect of the present invention provides an electronic device, where the electronic device includes:

[0026] A memory, storing executable instructions;

[0027] A processor, where the processor runs the executable instructions in the memory to implement the TOC prediction method for shale reservoirs.

[0028] A fourth aspect of the present invention provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the TOC prediction method for shale reservoirs is implemented.

[0029] The present invention has the following advantages compared with the existing technologies:

[0030] 1. The present invention can effectively solve the problem of unclear linear relationship in the crossplot analysis of elastic parameters and TOC, and construct a calculation model that better conforms to the actual situation.

[0031] 2. The present invention enriches the data points participating in the crossplot by means of replacing lithology and physical properties in rock physics, and can obtain a more stable linear relationship.

[0032] 3. The present invention has better calculation effect compared with other methods when TOC is small.

[0033] Other features and advantages of the present invention will be described in detail in the subsequent specific implementation part. Description of the Drawings

[0034] The above and other objects, features, and advantages of the present invention will become more apparent by describing the exemplary embodiments of the present invention in more detail with reference to the accompanying drawings, wherein, in the exemplary embodiments of the present invention, the same reference numerals generally represent the same components.

[0035] Figure 1 The flowchart of the TOC prediction method for shale reservoirs is shown.

[0036] Figure 2 The crossplot of TOC and density is shown.

[0037] Figure 3 The crossplot of TOC and density when the clay content > 35 is shown.

[0038] Figure 4 The crossplot of TOC and density when the clay content is 30 - 35% is shown.

[0039] Figure 5 The crossplot of TOC and density when the clay content is 25 - 30% is shown.

[0040] Figure 6 The crossplot of TOC and density when the clay content < 25% is shown. Detailed implementation mode

[0041] The preferred embodiments of the present invention will be described in more detail below. Although the preferred embodiments of the present invention are described below, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein.

[0042] To achieve the above object, the first aspect of the present invention provides a TOC prediction method for shale reservoirs, including the following steps:

[0043] Step 1: Collect logging data and build a rock physics model;

[0044] Step 2: Based on the rock physics model, conduct crossplot analysis of different elastic parameters and TOC, and screen out the elastic parameter with the best linear relationship with TOC as the sensitive elastic parameter;

[0045] Step 3: Based on the logging data information, decompose the crossplot of the sensitive elastic parameter and TOC, divide the range of characteristic logging data, obtain the crossplot of the sensitive elastic parameter and TOC within the range of specific characteristic logging data, and establish a linear relationship;

[0046] Step 4: Based on the pre-stack seismic data, invert the three pre-stack parameters to obtain an elastic parameter volume;

[0047] Step 5: Calculate the TOC value based on the linear relationship established in Step 3 and the elastic parameter volume obtained in Step 4.

[0048] Optionally, in step 1, the logging data includes P-wave and S-wave velocities, density, porosity, clay content, and saturation curves.

[0049] Optionally, in step 3, the characteristic logging data is the clay content.

[0050] Optionally, the range of the clay content is divided into: clay content > 35%, clay content 30 - 35%, clay content 25 - 30%, clay content < 25%.

[0051] Optionally, in step 3, when there are few data points on the crossplot of sensitive elastic parameters and TOC, new characteristic logging data is added, and lithology and physical property substitution and increase are completed for the logging curves through a rock physics model, data points within a specific range are increased, and a stable linear relationship is obtained.

[0052] In the present invention, if there are few data points within a specific range and a stable linear relationship cannot be obtained, lithology and physical property substitution and increase are completed for the logging curves through a rock physics model, and data points within the specific range are obtained thereby to obtain a stable linear relationship.

[0053] Optionally, the new characteristic logging data is porosity; the range of the porosity is divided into: porosity less than 2.5% and porosity greater than 2.5%.

[0054] Optionally, the linear relationship is: TOC = TOC(porosity > 2.5%) + TOC(porosity < 2.5%); where TOC(porosity > 2.5%) = TOC(Vclay 20 - 25%) + TOC(Vclay 25 - 30%);

[0055] TOC(porosity < 2.5%) = TOC(Vclay < 25%) + TOC(Vclay 25 - 30%) + TOC(Vclay 30 - 35%) + TOC(Vclay > 35%).

[0056] Currently, the three mainstream methods for predicting shale TOC, whether linear fitting, neural network learning, or rock physics, are all aimed at establishing the relationship between elastic parameters and TOC. When the linear relationship between elastic parameters and TOC in the crossplot is not ideal, the effects of these three methods are very limited. For example Figure 2As shown in the crossplot of density vs. TOC, the linear relationship between density and TOC is not obvious, and the TOC value is very small (basically <1%). Generally, the correlation between TOC and density is very high. Due to the special lithology in this area, the clay content in the shale section is relatively low (basically <30%), while the clay content in conventional shale reservoirs is relatively high (basically >50%), and the corresponding TOC is generally between 2 - 6%. Therefore, it is analyzed that when the clay content is high, the correlation between TOC and density is good, and as the clay content decreases, the correlation between TOC and density gradually decreases.

[0057] Therefore, for Figure 2 the crossplot, the clay content is divided in the well, and the cases of clay content >35%, 30 - 35%, 25 - 30%, and clay content <25% are displayed respectively, obtaining Figures 3 - 6 a total of four crossplots. In Figure 3 and Figure 4 the linear relationship between density and TOC is more obvious. Figure 5 and Figure 6 There seem to be two linear relationships. At this time, the influence of porosity is taken into account. Figure 5 and Figure 6 are divided according to the threshold value of porosity = 2.5%. Porosity less than 2.5% and porosity greater than 2.5% respectively correspond to two sets of linear relationships.

[0058] In the present invention, according to this classification method, for shale reservoirs with relatively small TOC, the porosity and clay content are distinguished respectively to obtain the linear relationship, and finally the TOC prediction result can be calculated based on the elastic parameter inversion result.

[0059] The second aspect of the present invention provides a TOC prediction device for shale reservoirs, including:

[0060] A rock physics model construction module for collecting logging data and constructing a rock physics model;

[0061] A sensitive elastic parameter acquisition module for conducting crossplot analysis of different elastic parameters and TOC based on the rock physics model, and screening out the elastic parameter with the best linear relationship with TOC as the sensitive elastic parameter;

[0062] A linear relationship establishment module for decomposing the crossplot of the sensitive elastic parameter and TOC based on the logging data information, dividing the range of specific logging data, obtaining the crossplot of the sensitive elastic parameter and TOC within the range of specific logging data, and establishing a linear relationship;

[0063] An elastic parameter volume acquisition module for inverting the pre-stack three parameters based on the pre-stack seismic data to obtain an elastic parameter volume;

[0064] TOC value calculation module, configured to calculate the TOC value based on a linear relationship and an elastic parameter volume.

[0065] A third aspect of the present invention provides an electronic device, which includes:

[0066] A memory storing executable instructions;

[0067] A processor that runs the executable instructions in the memory to implement the TOC prediction method for shale reservoirs described above.

[0068] A fourth aspect of the present invention provides a computer-readable storage medium, which stores a computer program that, when executed by a processor, implements the TOC prediction method for shale reservoirs described above.

[0069] Embodiment 1

[0070] This embodiment provides a TOC prediction method for shale reservoirs, which adopts the prediction method flow chart as shown in Figure 1 and includes the following steps:

[0071] Step 1: Collect logging data and construct a rock physics model;

[0072] Step 2: Based on the rock physics model, conduct crossplot analysis of different elastic parameters and TOC, and screen out the elastic parameter with the best linear relationship with TOC as the sensitive elastic parameter;

[0073] Step 3: Based on the logging data information, decompose the crossplot of the sensitive elastic parameter and TOC, divide the range of specific logging data, obtain the crossplot of the sensitive elastic parameter and TOC within the range of specific logging data, and establish a linear relationship;

[0074] Step 4: Based on the prestack seismic data, invert the three prestack parameters to obtain an elastic parameter volume;

[0075] Step 5: Based on the linear relationship established in Step 3 and the elastic parameter volume obtained in Step 4, calculate the TOC value.

[0076] According to the embodiment of the present invention, in Step 1, the logging data includes compressional and shear wave velocities, density, porosity, clay content, and saturation curves.

[0077] According to the embodiment of the present invention, in Step 2, the elastic parameter with the best linear relationship with TOC is density.

[0078] As Figure 1As shown, in the crossplot of density vs. TOC, the linear relationship between density and TOC is not obvious, and the TOC value is very small (basically <1%). Generally, the correlation between TOC and density is very high. Due to the special lithology in this area, the clay content in the shale section is relatively low (basically <30%), while the clay content in conventional shale reservoirs is relatively high (basically >50%), and the corresponding TOC is generally between 2-6%. Therefore, it is analyzed that when the clay content is high, the correlation between TOC and density is good, and as the clay content decreases, the correlation between TOC and density also gradually decreases.

[0079] According to the embodiment of the present invention, in step 3, the characteristic logging data is the clay content, and the range of the clay content is divided into: clay content > 35%, clay content 30 - 35%, clay content 25 - 30%, clay content < 25%, and they are respectively displayed to obtain Figures 2 - 5 A total of four crossplots. In Figure 2 、 Figure 3 the linear relationship between density and TOC is more obvious, Figure 4 、 Figure 5 and there seem to be two linear relationships. At this time, considering the influence of porosity, Figure 4 、 Figure 5 are divided according to the threshold value of porosity = 2.5%. Porosity less than 2.5% and porosity greater than 2.5% respectively correspond to two sets of linear relationships. Therefore, finally TOC can be expressed as the sum of several linear relationships:

[0080] TOC = TOC(porosity > 2.5%) + TOC(porosity < 2.5%)

[0081] TOC(porosity > 2.5%) = TOC(Vclay 20 - 25%) + TOC(Vclay 25 - 30%)

[0082] TOC(porosity < 2.5%) = TOC(Vclay < 25%) + TOC(Vclay 25 - 30%) + TOC(Vclay 30 - 35%) + TOC(Vclay > 35%).

[0083] According to the embodiment of the present invention, through the rock physics model, lithology and physical property substitution and addition are completed for the logging curves, and data points within a specific range are added to obtain a stable linear relationship:

[0084] TOC(Vclay > 35%) = (2698.6 - den) / 55.116;

[0085] TOC(Vclay 30 - 35%) = (2685 - den) / 45;

[0086] TOC (Vclay 25 - 30%, porosity > 2.5%) = (2630 - den) / 45;

[0087] TOC (Vclay 25 - 30%, porosity < 2.5%) = (2680 - den) / 45;

[0088] TOC (Vclay < 25%, porosity > 2.5%) = (2630 - den) / 45;

[0089] TOC (Vclay < 25%, porosity < 2.5%) = (2625 - den) / 45.

[0090] According to the method of this embodiment, for the crossplot of sensitive elastic parameters and TOC, it is divided by the characteristic porosity range and characteristic clay content range, and a more stable fitting relationship is obtained within different characteristic ranges. When the number of data points is insufficient, the petrophysical lithology and physical properties can be used to replace and increase the number of data points to ensure the stability of the fitting relationship. Finally, the predicted result of TOC is obtained through the fitting relationship and elastic parameter inversion results within different characteristic ranges.

[0091] Embodiment 2

[0092] This embodiment provides a TOC prediction device for shale reservoirs, including:

[0093] A petrophysical model construction module for collecting logging data and constructing a petrophysical model;

[0094] A sensitive elastic parameter acquisition module for conducting crossplot analysis of different elastic parameters and TOC based on the petrophysical model, and screening out the elastic parameter with the best linear relationship with TOC as the sensitive elastic parameter;

[0095] A linear relationship establishment module for decomposing the crossplot of sensitive elastic parameters and TOC based on logging data information, dividing the range of characteristic logging data, obtaining the crossplot of sensitive elastic parameters and TOC within the range of specific characteristic logging data, and establishing a linear relationship;

[0096] An elastic parameter volume acquisition module for inverting the pre-stack three parameters based on pre-stack seismic data to obtain an elastic parameter volume;

[0097] A TOC value calculation module for calculating the TOC value based on the linear relationship and the elastic parameter volume.

[0098] In some embodiments, the logging data includes P-wave and S-wave velocities, density, porosity, clay content, and saturation curves.

[0099] In some embodiments, the characteristic logging data is the clay content.

[0100] In some embodiments, the range of the clay content is divided into: clay content > 35%, clay content 30 - 35%, clay content 25 - 30%, clay content < 25%.

[0101] In some embodiments, when there are few data points on the crossplot of sensitive elastic parameters and TOC, new characteristic logging data are added. Through a rock physics model, lithology and physical property substitution of logging curves are increased, data points within a specific range are increased, and a stable linear relationship is obtained.

[0102] In some embodiments, the new characteristic logging data is porosity; the range of the porosity is divided into: porosity less than 2.5% and porosity greater than 2.5%.

[0103] In some embodiments, the linear relationship is: TOC = TOC(porosity > 2.5%) + TOC(porosity < 2.5%); where TOC(porosity > 2.5%) = TOC(Vclay 20 - 25%) + TOC(Vclay 25 - 30%);

[0104] In some embodiments, TOC(porosity < 2.5%) = TOC(Vclay < 25%) + TOC(Vclay 25 - 30%) + TOC(Vclay 30 - 35%) + TOC(Vclay > 35%).

[0105] For the device according to this embodiment, the crossplot of sensitive elastic parameters and TOC is divided by a characteristic porosity range and a characteristic clay content range, a more stable fitting relationship is obtained within different characteristic ranges, and when the number of data points is insufficient, the number of data points can be increased through rock physics lithology and physical property substitution to ensure the stability of the fitting relationship. Finally, the prediction result of TOC is obtained through the fitting relationship and elastic parameter inversion results within different characteristic ranges.

[0106] For other detailed descriptions and advantages related to this embodiment, reference can be made to the corresponding descriptions in the foregoing embodiments, which will not be elaborated herein.

[0107] Embodiment 3

[0108] This embodiment provides an electronic device, including a memory and a processor,

[0109] The memory stores executable instructions;

[0110] The processor runs the executable instructions in the memory to implement the TOC prediction method for shale reservoirs.

[0111] The TOC prediction method for shale reservoirs includes the following steps:

[0112] Step 1: Collect logging data and construct a petrophysical model;

[0113] Step 2: Based on the petrophysical model, conduct cross-plot analysis of different elastic parameters and TOC, and screen out the elastic parameter with the best linear relationship with TOC as the sensitive elastic parameter;

[0114] Step 3: Based on the logging data information, decompose the cross-plot of the sensitive elastic parameter and TOC, divide the range of characteristic logging data, obtain the cross-plot of the sensitive elastic parameter and TOC within the range of specific characteristic logging data, and establish a linear relationship;

[0115] Step 4: Based on the pre-stack seismic data, invert the three pre-stack parameters to obtain an elastic parameter volume;

[0116] Step 5: Calculate the TOC value based on the linear relationship established in Step 3 and the elastic parameter volume obtained in Step 4.

[0117] In some embodiments, the logging data includes P-wave and S-wave velocities, density, porosity, clay content, and saturation curves.

[0118] In some embodiments, the characteristic logging data is the clay content.

[0119] In some embodiments, the range of the clay content is divided into: clay content > 35%, clay content 30 - 35%, clay content 25 - 30%, clay content < 25%.

[0120] In some embodiments, when there are few data points on the cross-plot of the sensitive elastic parameter and TOC, new characteristic logging data is added, and lithology and physical property replacement and increase are completed for the logging curves through the petrophysical model, data points within a specific range are increased, and a stable linear relationship is obtained.

[0121] In some embodiments, the new characteristic logging data is the porosity; the range of the porosity is divided into: porosity less than 2.5% and porosity greater than 2.5%.

[0122] In some embodiments, the linear relationship is: TOC = TOC(porosity > 2.5%) + TOC(porosity < 2.5%); where TOC(porosity > 2.5%) = TOC(Vclay 20 - 25%) + TOC(Vclay 25 - 30%);

[0123] In some embodiments, TOC(porosity < 2.5%) = TOC(Vclay < 25%) + TOC(Vclay 25 - 30%) + TOC(Vclay 30 - 35%) + TOC(Vclay > 35%).

[0124] Specifically, the memory may include one or more computer program products, and the computer program products may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory, etc. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0125] The processor may be a central processing unit (CPU) or other forms of processing units with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In an embodiment of the present invention, the processor is used to run the computer-readable instructions stored in the memory.

[0126] For the crossplot of sensitive elastic parameters and TOC of the electronic device according to this embodiment, it is divided by a characteristic porosity range and a characteristic clay content range, and a more stable fitting relationship is obtained within different characteristic ranges. When the number of data points is insufficient, the number of data points can be increased by replacing rock physics lithology and physical properties, ensuring the stability of the fitting relationship. Finally, the prediction result of TOC is obtained through the fitting relationship within different characteristic ranges and the elastic parameter inversion result.

[0127] For other detailed descriptions and advantages of this embodiment, reference can be made to the corresponding descriptions in the foregoing embodiments, which will not be elaborated herein.

[0128] Embodiment 4

[0129] This embodiment provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, it implements the TOC prediction method for shale reservoirs described above.

[0130] The TOC prediction method for shale reservoirs includes the following steps:

[0131] Step 1: Collect logging data and construct a rock physics model;

[0132] Step 2: Based on the rock physics model, conduct crossplot analysis of different elastic parameters and TOC, and screen out the elastic parameter with the best linear relationship with TOC as the sensitive elastic parameter;

[0133] Step 3: Based on the logging data information, decompose the crossplot of the sensitive elastic parameter and TOC, divide the range of characteristic logging data, obtain the crossplot of the sensitive elastic parameter and TOC within the range of specific characteristic logging data, and establish a linear relationship;

[0134] Step 4: Based on the pre-stack seismic data, invert the three pre-stack parameters to obtain an elastic parameter volume;

[0135] Step 5: Calculate the TOC value based on the linear relationship established in Step 3 and the elastic parameter body obtained in Step 4.

[0136] In some embodiments, the logging data includes compressional and shear wave velocities, density, porosity, clay content, and saturation curves.

[0137] In some embodiments, the characteristic logging data is the clay content.

[0138] In some embodiments, the range of the clay content is divided into: clay content > 35%, clay content 30 - 35%, clay content 25 - 30%, clay content < 25%.

[0139] In some embodiments, when there are few data points on the crossplot of sensitive elastic parameters and TOC, new characteristic logging data is added, and lithology and physical property substitution of the logging curves are completed through a rock physics model to increase data points within a specific range and obtain a stable linear relationship.

[0140] In some embodiments, the new characteristic logging data is porosity; the range of the porosity is divided into: porosity less than 2.5% and porosity greater than 2.5%.

[0141] In some embodiments, the linear relationship is: TOC = TOC(porosity > 2.5%) + TOC(porosity < 2.5%); where TOC(porosity > 2.5%) = TOC(Vclay 20 - 25%) + TOC(Vclay 25 - 30%);

[0142] In some embodiments, TOC(porosity < 2.5%) = TOC(Vclay < 25%) + TOC(Vclay 25 - 30%) + TOC(Vclay 30 - 35%) + TOC(Vclay > 35%).

[0143] The above computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or removable hard disk), media with built-in rewritable non-volatile memory (e.g., memory card), and media with built-in ROM (e.g., ROM cartridge).

[0144] Those skilled in the art should understand that, to solve the technical problem of how to obtain good user experience effects, this embodiment may also include well-known structures such as communication buses and interfaces, and these well-known structures should also be included in the protection scope of the present invention.

[0145] For the computer-readable storage medium according to this embodiment, for the crossplot of sensitive elastic parameters and TOC, it is divided by a specific porosity range and a specific clay content range, and a more stable fitting relationship is obtained within different specific ranges. When the number of data points is insufficient, the lithology and physical properties of rock physics can be used to replace and increase the number of data points to ensure the stability of the fitting relationship. Finally, the prediction result of TOC is obtained through the fitting relationship within different specific ranges and the inversion result of elastic parameters.

[0146] For other detailed descriptions and advantages related to this embodiment, reference can be made to the corresponding descriptions in the foregoing embodiments, which will not be elaborated here.

[0147] Embodiment 5

[0148] In order to verify the effect of the TOC prediction method for shale reservoirs according to the present invention, a low-TOC shale reservoir is selected for verification in this embodiment.

[0149] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0150] Step 1: Collect logging data, including P-wave and S-wave velocities, density, porosity, clay content, saturation curve, and complete rock physics modeling;

[0151] Step 2: Conduct crossplot analysis of different elastic parameters and TOC, and screen out the elastic parameter with the best linear relationship with TOC as density ( Figure 2 );

[0152] Step 3: Based on information such as porosity and clay content, decompose the crossplot to obtain crossplots of density and TOC within the ranges of porosity > 2.5%, porosity < 2.5%, clay content < 25%, clay content 30 - 35%, clay content 30 - 35%, and clay content > 35% ( Figures 3 - 6 ), and complete lithology and physical property replacement and increase for the logging curves through the rock physics model to increase data points within specific ranges, thereby obtaining a stable linear relationship:

[0153] TOC(Vclay > 35%) = (2698.6 - den) / 55.116;

[0154] TOC(Vclay 30 - 35%) = (2685 - den) / 45;

[0155] TOC(Vclay 25 - 30%, porosity > 2.5%) = (2630 - den) / 45;

[0156] TOC(Vclay 25 - 30%, porosity < 2.5%) = (2680 - den) / 45;

[0157] TOC(Vclay < 25%, porosity > 2.5%) = (2630 - den) / 45;

[0158] TOC(Vclay < 25%, porosity < 2.5%) = (2625 - den) / 45;

[0159] Step 4: According to the linear relationship between density and TOC obtained in Step 3, based on the inversion results of elastic parameters, calculate the TOC prediction results.

[0160] For other detailed descriptions of this exemplary embodiment, reference may be made to the corresponding descriptions in the foregoing embodiments, which will not be elaborated herein.

[0161] The embodiments of the present invention have been described above. The above description is exemplary and not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations are obvious to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The choice of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements to the technologies in the market, or to enable other ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A method for predicting TOC of shale reservoirs, characterized in that, it includes the following steps: Step 1: Collect logging data and construct a petrophysical model; Step 2: Based on the petrophysical model, conduct crossplot analysis of different elastic parameters and TOC, and screen out the elastic parameter with the best linear relationship with TOC as the sensitive elastic parameter; Step 3: Based on the logging data information, decompose the crossplot of the sensitive elastic parameter and TOC, divide the range of characteristic logging data, obtain the crossplot of the sensitive elastic parameter and TOC within the range of specific characteristic logging data, and establish a linear relationship; Step 4: Based on the pre-stack seismic data, invert the three pre-stack parameters to obtain an elastic parameter volume; Step 5: Calculate the TOC value based on the linear relationship established in Step 3 and the elastic parameter volume obtained in Step 4.

2. The method for predicting TOC of shale reservoirs according to claim 1, wherein, in Step 1, the logging data includes P-wave velocity, S-wave velocity, density, porosity, clay content and saturation curves.

3. The method for predicting TOC of shale reservoirs according to claim 1, wherein, in Step 3, the characteristic logging data is clay content.

4. The method for predicting TOC of shale reservoirs according to claim 3, wherein, the range of the clay content is divided into: clay content > 35%, clay content 30 - 35%, clay content 25 - 30%, clay content < 25%.

5. The method for predicting TOC of shale reservoirs according to any one of claims 1 - 4, wherein, in Step 3, when there are few data points on the crossplot of the sensitive elastic parameter and TOC, add new characteristic logging data, complete lithology and physical property replacement and increase of the logging curve through the petrophysical model, increase data points within a specific range, and obtain a stable linear relationship.

6. The method for predicting TOC of shale reservoirs according to claim 5, wherein, the new characteristic logging data is porosity; the range of the porosity is divided into: porosity < 2.5%, porosity > 2.5%.

7. The method for predicting TOC of shale reservoirs according to claim 5, wherein, the linear relationship is: TOC = TOC(porosity > 2.5%) + TOC(porosity < 2.5%); wherein, TOC(porosity > 2.5%) = TOC(Vclay 20 - 25%) + TOC(Vclay 25 - 30%); TOC(porosity < 2.5%) = TOC(Vclay < 25%) + TOC(Vclay 25 - 30%) + TOC(Vclay 30 - 35%) + TOC(Vclay > 35%).

8. A device for predicting TOC of shale reservoirs, characterized in that, it includes: a petrophysical model construction module for collecting logging data and constructing a petrophysical model; a sensitive elastic parameter acquisition module for conducting crossplot analysis of different elastic parameters and TOC based on the petrophysical model, and screening out the elastic parameter with the best linear relationship with TOC as the sensitive elastic parameter; A linear relationship establishment module, configured to decompose the sensitive elastic parameter and TOC cross plot based on well logging data information, divide the range of characteristic well logging data, obtain the sensitive elastic parameter and TOC cross plot within the range of specific characteristic well logging data, and establish a linear relationship; An elastic parameter volume acquisition module, configured to perform inversion on three pre-stack parameters based on pre-stack seismic data to obtain an elastic parameter volume; A TOC value calculation module, configured to calculate the TOC value based on the linear relationship and the elastic parameter volume.

9. An electronic device, characterized in that, the electronic device includes: a memory storing executable instructions; a processor that runs the executable instructions in the memory to implement the TOC prediction method for shale reservoirs according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the TOC prediction method for shale reservoirs according to any one of claims 1-7.

Images