Method and device for predicting geological sweet spot parameters of a continental shale formation

Abstract

The application discloses a method and device for predicting geological dessert parameters of a continental shale series, and the method comprises the following steps: obtaining core indoor test data and logging curve data of a target continental shale series; based on geological parameters of multiple depth points and logging curve data corresponding to multiple logging parameters corresponding to the multiple depth points, establishing a corresponding relationship between each logging parameter data and the core indoor test data; based on the corresponding relationship between each logging parameter data and the core indoor test data, determining a sensitive logging parameter and a target corresponding relationship; based on logging curve data corresponding to the sensitive logging parameter, establishing a geological parameter interpretation model; based on the target corresponding relationship, obtaining a first sensitive logging parameter attribute body by using an inversion technology; and based on the first sensitive logging parameter attribute body and the geological parameter interpretation model, obtaining a geological parameter attribute body of the target continental shale series. The method can provide technical support for oil and gas resource exploration of the continental shale series.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of oil and gas exploration, and in particular to a method and device for predicting geological sweet spot parameters of a continental shale formation. BACKGROUND

[0002] The continental shale formation is an important oil and gas reservoir in China. In order to maximize economic benefits during oil and gas development, it is necessary to accurately explore the oil and gas resources in the reservoir of the continental shale formation, and thus accurate geological sweet spot parameters need to be obtained.

[0003] In related technologies, a method for predicting geological sweet spot parameters of a continental shale formation usually involves fitting logging curve data (such as natural potential curve, resistivity curve or acoustic time difference curve) obtained based on logging data and sweet spot parameter (such as rock type, porosity) data obtained based on core measurement, determining the correspondence between the conventional logging curve and the sweet spot parameters, and then determining the sweet spot parameters of the target continental shale formation based on the correspondence and the logging characteristic curve of the target continental shale formation.

[0004] However, when the logging characteristic curve obtained based on logging data and the sweet spot parameter data obtained based on core measurement are fitted, the feature point distribution is relatively scattered, which makes the sweet spot parameters predicted based on the correspondence between the logging characteristics and the sweet spot parameters inaccurate, and thus unable to provide technical support for the exploration of oil and gas resources in the reservoir of the continental shale formation. SUMMARY

[0005] In view of this, the present application provides a method and device for predicting geological sweet spot parameters of a continental shale formation, which realizes accurate prediction of sweet spot parameters of a continental shale formation and provides technical support for the exploration of oil and gas resources in the reservoir of the continental shale formation.

[0006] Specifically, the technical solutions include the following:

[0007] In a first aspect, the embodiments of the present application provide a method for predicting geological sweet spot parameters of a continental shale formation, which comprises:

[0008] obtaining core laboratory test data and logging curve data of a target continental shale formation, wherein the core laboratory test data comprises geological parameters of a plurality of depth points, and the logging curve data comprises logging curve data corresponding to a plurality of logging parameters;

[0009] based on the geological parameters of the plurality of depth points and the logging curve data corresponding to the plurality of logging parameters corresponding to the plurality of depth points in the logging curve data, establishing a correspondence between each logging parameter data and the core laboratory test data;

[0010] determine a sensitive logging parameter based on the correspondence between each logging parameter data and the core laboratory test data, and determine the correspondence between the sensitive logging parameter data and the core laboratory test data as a target correspondence;

[0011] establish a geological parameter interpretation model based on the logging curve data corresponding to the sensitive logging parameter;

[0012] obtain a first sensitive logging parameter attribute volume based on the target correspondence and using an inversion technique;

[0013] obtain a geological parameter attribute volume of a target continental shale series based on the first sensitive logging parameter attribute volume and the geological parameter interpretation model.

[0014] In some embodiments, the establishing of the correspondence between each logging parameter data and the core laboratory test data based on the logging curve data corresponding to the multiple logging parameters corresponding to the multiple depth points of the geological parameter and the logging curve data of the multiple depth points comprises:

[0015] for the logging curve data corresponding to each logging parameter, a two-dimensional coordinate system is established with the logging parameter as the horizontal axis and the geological parameter as the vertical axis;

[0016] based on the two-dimensional coordinate system, the geological parameters and the corresponding logging parameters of the multiple depth points are fitted to obtain the correspondence between the geological parameters and the logging parameters.

[0017] In some embodiments, the geological parameters include porosity, free hydrocarbon content, and organic carbon content, the target correspondence includes a target correspondence corresponding to the porosity, a target correspondence corresponding to the free hydrocarbon content, and a target correspondence corresponding to the organic carbon content, and the obtaining of the first sensitive logging parameter attribute volume based on the target correspondence and using an inversion technique comprises:

[0018] based on the target correspondence corresponding to the porosity, a first sensitive logging parameter attribute volume corresponding to the porosity is obtained using a pre-stack geostatistical inversion technique;

[0019] based on the target correspondence corresponding to the free hydrocarbon content, a first sensitive logging parameter attribute volume corresponding to the free hydrocarbon content is obtained using an elastic parameter inversion technique;

[0020] based on the target correspondence corresponding to the organic carbon content, a first sensitive logging parameter attribute volume corresponding to the organic carbon content is obtained using an elastic parameter inversion technique.

[0021] In some embodiments, the geological parameters include porosity, free hydrocarbon content, and organic carbon content, the sensitive logging parameters include a sensitive logging parameter corresponding to the porosity, a sensitive logging parameter corresponding to the free hydrocarbon content, and a sensitive logging parameter corresponding to the organic carbon content, the establishing the geological parameter interpretation model based on the logging curve data corresponding to the sensitive logging parameters includes:

[0022] establishing a porosity interpretation model based on the logging curve data corresponding to the sensitive logging parameter corresponding to the porosity;

[0023] establishing a free hydrocarbon content interpretation model based on the logging curve data corresponding to the sensitive logging parameter corresponding to the free hydrocarbon content through multiple linear regression analysis;

[0024] establishing an organic carbon content interpretation model based on the logging curve data corresponding to the sensitive logging parameter corresponding to the organic carbon content through multiple linear regression analysis.

[0025] In some embodiments, the sensitive logging parameter corresponding to the porosity includes a sensitive logging parameter corresponding to shale formation porosity and a sensitive logging parameter corresponding to sandstone porosity, the establishing the porosity interpretation model based on the logging curve data corresponding to the sensitive logging parameter corresponding to the porosity includes:

[0026] establishing a shale formation porosity interpretation model based on the sensitive logging parameter corresponding to the shale formation porosity;

[0027] establishing a sandstone porosity interpretation model based on the sensitive logging parameter corresponding to the sandstone porosity;

[0028] obtaining the porosity interpretation model based on the shale formation porosity interpretation model and the sandstone porosity interpretation model.

[0029] In some embodiments, the geological sweet spot parameter further includes an overmatch effect, and the method further includes:

[0030] obtaining an overmatch effect attribute volume of the target continental shale formation based on the geological parameter attribute volume of the target continental shale formation.

[0031] In some embodiments, the geological parameter attribute volume includes a free hydrocarbon content attribute volume, an organic carbon content attribute volume, and a shale porosity attribute volume, and obtaining the overmatch effect attribute volume of the target continental shale formation based on the geological parameter attribute volume of the target continental shale formation includes:

[0032] obtaining the overmatch effect attribute volume of the target continental shale formation through attribute fusion based on the free hydrocarbon content attribute volume and the organic carbon content attribute volume in the geological parameter attribute volume.

[0033] In some embodiments, the geological dessert parameter further comprises a rock type, and the method further comprises:

[0034] Based on the target correspondence relationship, an attribute volume of a second sensitive logging parameter is obtained;

[0035] The attribute volume of the second sensitive logging parameter is sliced to obtain an attribute volume slice;

[0036] Based on the attribute volume slice, a rock type of a target continental shale formation is determined.

[0037] In some embodiments, the geological parameter attribute volume comprises a free hydrocarbon content attribute volume, an organic carbon content attribute volume, and a shale porosity attribute volume, and the method further comprises:

[0038] The free hydrocarbon content attribute volume, the organic carbon content attribute volume, and the shale porosity attribute volume are respectively sliced to obtain a free hydrocarbon content slice, an organic carbon content slice, and a shale porosity slice.

[0039] In a second aspect, the embodiments of the present application further provide a device for predicting a geological dessert parameter of a continental shale formation, and the device comprises:

[0040] A first obtaining module is configured to obtain core laboratory test data and logging curve data of a target continental shale formation, wherein the core laboratory test data comprises geological parameters of a plurality of depth points, and the logging curve data comprises logging curve data corresponding to a plurality of logging parameters of the plurality of depth points;

[0041] A first establishing module is configured to establish a correspondence relationship between each logging parameter data and the core laboratory test data based on the geological parameters of the plurality of depth points and the logging curve data corresponding to the plurality of logging parameters of the plurality of depth points;

[0042] A determining module is configured to determine a sensitive logging parameter based on the correspondence relationship between each logging parameter data and the core laboratory test data, and determine a target correspondence relationship between the sensitive logging parameter data and the core laboratory test data;

[0043] A second establishing module is configured to establish a geological parameter interpretation model based on logging curve data corresponding to the sensitive logging parameter;

[0044] A second obtaining module is configured to obtain an attribute volume of a first sensitive logging parameter based on the target correspondence relationship and using an inversion technique;

[0045] A obtaining module is configured to obtain a geological parameter attribute volume of the target continental shale formation based on the attribute volume of the first sensitive logging parameter and the geological parameter interpretation model.

[0046] The technical scheme provided by the embodiments of the present application has at least the following beneficial effects:

[0047] The method for predicting the geological sweet spot parameters of the continental shale series provided by the embodiments of the present application uses the core indoor test data and the logging curve data of the target continental shale series, establishes the corresponding relationship between each logging parameter data and the core indoor test data, determines the sensitive logging parameters sensitive to the geological sweet spot parameters based on the corresponding relationship, establishes the geological parameter interpretation model based on the logging curve data corresponding to the sensitive logging parameters, and obtains the geological parameter attribute volume of the target continental shale series based on the geological parameter interpretation model and the sensitive logging parameter attribute volume obtained by using the inversion technology. Here, the rock type and the geological parameter attribute volume of the target continental shale series are the predicted parameters of the geological sweet spot of the target continental shale series. The method predicts the geological sweet spot parameters based on the sensitive logging parameters, realizes the accurate prediction of the geological sweet spot parameters, and provides technical support for the exploration of the oil and gas resources in the reservoir of the continental shale series. BRIEF DESCRIPTION OF DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0049] Figure 1 The flowchart of the method for predicting the geological sweet spot parameters of the continental shale series provided by the embodiments of the present application;

[0050] Figure 2 The flowchart of another method for predicting the geological sweet spot parameters of the continental shale series provided by the embodiments of the present application;

[0051] Figure 3 The flowchart of the method for establishing the corresponding relationship between each logging parameter data and the core indoor test data provided by the embodiments of the present application;

[0052] Figure 4 The flowchart of the method for establishing the geological parameter interpretation model provided by the embodiments of the present application;

[0053] Figure 5 The flowchart of the method for obtaining the sensitive logging parameter attribute volume provided by the embodiments of the present application;

[0054] Figure 6 The P-S wave velocity ratio attribute volume slice diagram provided by the embodiments of the present application;

[0055] Figure 7An organic carbon content attribute body slice map provided for an embodiment of the present application;

[0056] Figure 8 A shale porosity attribute body slice map provided for an embodiment of the present application;

[0057] Figure 9 A free hydrocarbon content attribute body slice map provided for an embodiment of the present application;

[0058] Figure 10 An override effect attribute body slice map provided for an embodiment of the present application;

[0059] Figure 11 A structural schematic diagram of a device for predicting a continental shale formation geological sweet spot parameter provided for an embodiment of the present application.

[0060] The specific embodiments of the present application have been shown and described in the above-described drawings, and will be described in more detail hereinafter. These drawings and written descriptions are not intended to limit the scope of the concept of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0061] The technical solutions in the embodiments of the present application will be described clearly and completely below in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without any creative work fall within the scope of protection of the present application.

[0062] Unless otherwise defined, all the technical terms used in the embodiments of the present application have the same meanings as commonly understood by those skilled in the art.

[0063] In order to make the technical solutions and advantages of the present application more clear, the embodiments of the present application will be described in further detail below in combination with the drawings.

[0064] At present, the continental shale formation is one of important unconventional oil and gas reservoirs in China, which is widely distributed and has abundant reserves of oil and gas resources. In order to maximize the economic benefits in the process of oil and gas development, it is necessary to accurately explore the oil and gas resources in the reservoir of the continental shale formation, and the geological sweet spot parameter can accurately reflect the distribution and storage of the oil and gas resources in the continental shale formation. Therefore, in order to accurately explore the oil and gas resources of the continental shale formation, it is necessary to obtain accurate geological sweet spot parameters.

[0065] In the related art, a method for predicting a geological sweet spot parameter of a continental shale formation generally determines a corresponding relationship between a conventional logging curve and a sweet spot parameter after fitting a logging curve (for example, a natural potential curve, a resistivity curve, or a sonic travel time curve) obtained based on logging data and sweet spot parameter (for example, rock type, porosity) data measured based on a core, and then determines the sweet spot parameter of the target continental shale formation based on the corresponding relationship and a logging characteristic curve of the target continental shale formation.

[0066] However, on the one hand, the logging characteristic curve obtained based on the logging data and the sweet spot parameter data measured based on the core are scattered in distribution when fitting, so that the value of the sweet spot parameter predicted based on the corresponding relationship between the logging characteristic and the sweet spot parameter is inaccurate, and cannot provide technical support for the exploration of oil and gas resources in the reservoir of the continental shale formation. On the other hand, the sweet spot parameter predicted by fitting the logging characteristic curve obtained based on the logging data and the sweet spot parameter data measured based on the core can only reflect the region near the wellbore, and cannot obtain the sweet spot parameter condition of the entire target continental shale formation region, and cannot provide sufficient technical support for the exploration of oil and gas resources.

[0067] To solve the technical problems in the related art, the embodiments of the present application provide a method for predicting a geological sweet spot parameter of a continental shale formation, which can provide technical support for the exploration of oil and gas resources in the reservoir of the continental shale formation.

[0068] The method for predicting a geological sweet spot parameter of a continental shale formation provided in the embodiments of the present application can be executed by a device for predicting a geological sweet spot parameter of a continental shale formation. The device can be a terminal device such as a fixed terminal or a mobile terminal. The terminal device can be any electronic product that can interact with a user through one or more of a keyboard, a touchpad, a touch screen, a remote control, voice interaction, or a handwriting device, such as a PC (Personal Computer), a mobile phone, a smart phone, a PDA (Personal Digital Assistant), a PPC (Pocket PC), a tablet computer, and the like.

[0069] Figure 1 A flowchart of a method for predicting a geological sweet spot parameter of a continental shale formation is provided in the embodiments of the present application. Referring to Figure 1 , the method includes the following steps:

[0070] In step 101, core laboratory test data and logging curve data of a target continental shale formation are obtained, wherein the core laboratory test data include geological parameters of multiple depth points, and the logging curve data include logging curve data corresponding to multiple logging parameters of the multiple depth points;

[0071] In step 102, a corresponding relationship between each logging parameter data and the core laboratory test data is established based on the geological parameters of the multiple depth points and the logging curve data corresponding to the multiple logging parameters of the multiple depth points in the logging curve data;

[0072] In step 103, a sensitive logging parameter is determined based on the corresponding relationship between each logging parameter data and the core laboratory test data, and a corresponding relationship between the sensitive logging parameter data and the core laboratory test data is determined as a target corresponding relationship;

[0073] In step 104, a geological parameter interpretation model is established based on logging curve data corresponding to the sensitive logging parameter.

[0074] In step 105, a first sensitive logging parameter attribute body is obtained by using an inversion technique based on the target corresponding relationship.

[0075] In step 106, a geological parameter attribute body of the target continental shale formation is obtained based on the first sensitive logging parameter attribute body and the geological parameter interpretation model.

[0076] The method for predicting a geological sweet spot parameter of a continental shale formation provided by the embodiments of the present application uses core laboratory test data and actual logging curve data of a target continental shale formation to establish a corresponding relationship between the core laboratory test data and the logging curve data, selects a sensitive logging parameter sensitive to the geological sweet spot parameter from the corresponding relationship, identifies a rock type of the target continental shale formation based on logging curve data corresponding to the sensitive logging parameter, establishes a geological parameter interpretation model, obtains a geological parameter attribute body of the target continental shale formation based on the geological parameter interpretation model and a sensitive logging parameter attribute body obtained by using an inversion technique, and obtains a parameter of a geological sweet spot of the target continental shale formation. The method predicts the geological sweet spot parameter based on the sensitive logging parameter, accurately predicts the geological sweet spot parameter, and provides technical support for exploration of oil and gas resources in a reservoir of the continental shale formation.

[0077] In some embodiments, establishing a corresponding relationship between each logging parameter data and the core laboratory test data based on the geological parameters of the multiple depth points and the logging curve data corresponding to the multiple logging parameters of the multiple depth points in the logging curve data includes:

[0078] For each logging parameter corresponding to the logging curve data, a two-dimensional coordinate system is established with the logging parameter as the horizontal axis and the geological parameter as the vertical axis;

[0079] Based on the two-dimensional coordinate system, the geological parameters and corresponding logging parameters of multiple depth points are fitted to obtain the corresponding relationship between the geological parameters and the logging parameters.

[0080] In some embodiments, the geological parameters include porosity, free hydrocarbon content and organic carbon content, the target corresponding relationship includes a target corresponding relationship corresponding to the porosity, a target corresponding relationship corresponding to the free hydrocarbon content, and a target corresponding relationship corresponding to the organic carbon content, and the first sensitive logging parameter attribute volume is obtained based on the target corresponding relationship using an inversion technique, including:

[0081] Based on the target corresponding relationship corresponding to the porosity, a pre-stack geostatistical inversion technique is used to obtain a first sensitive logging parameter attribute volume corresponding to the porosity;

[0082] Based on the target corresponding relationship corresponding to the free hydrocarbon content, an elastic parameter inversion technique is used to obtain a first sensitive logging parameter attribute volume corresponding to the free hydrocarbon content;

[0083] Based on the target corresponding relationship corresponding to the organic carbon content, an elastic parameter inversion technique is used to obtain a first sensitive logging parameter attribute volume corresponding to the organic carbon content.

[0084] In some embodiments, the geological parameters include porosity, free hydrocarbon content and organic carbon content, the sensitive logging parameters include sensitive logging parameters corresponding to the porosity, sensitive logging parameters corresponding to the free hydrocarbon content, and sensitive logging parameters corresponding to the organic carbon content, and the geological parameter interpretation model is established based on the logging curve data corresponding to the sensitive logging parameters, including:

[0085] Based on the logging curve data corresponding to the sensitive logging parameters corresponding to the porosity, a porosity interpretation model is established;

[0086] Based on the logging curve data corresponding to the sensitive logging parameters corresponding to the free hydrocarbon content, a free hydrocarbon content interpretation model is established through multiple linear regression analysis;

[0087] Based on the logging curve data corresponding to the sensitive logging parameters corresponding to the organic carbon content, an organic carbon content interpretation model is established through multiple linear regression analysis.

[0088] In some embodiments, the sensitive logging parameters corresponding to the porosity include sensitive logging parameters corresponding to shale layer porosity and sensitive logging parameters corresponding to sandstone porosity, and the porosity interpretation model is established based on the logging curve data corresponding to the sensitive logging parameters corresponding to the porosity, including:

[0089] The shale formation porosity interpretation model is established based on the sensitive logging parameters corresponding to the shale formation porosity.

[0090] The sandstone porosity interpretation model is established based on the sensitive logging parameters corresponding to the sandstone porosity.

[0091] The porosity interpretation model is obtained based on the shale formation porosity interpretation model and the sandstone porosity interpretation model.

[0092] In some embodiments, the geological sweet spot parameter further includes the overshoot effect, and the method further includes:

[0093] The overshoot effect attribute volume of the target continental shale formation is obtained based on the geological parameter attribute volume of the target continental shale formation.

[0094] In some embodiments, the geological parameter attribute volume includes a free hydrocarbon content attribute volume, an organic carbon content attribute volume, and a shale porosity attribute volume, and obtaining the overshoot effect attribute volume of the target continental shale formation based on the geological parameter attribute volume of the target continental shale formation includes:

[0095] The overshoot effect attribute volume of the target continental shale formation is obtained by attribute fusion based on the free hydrocarbon content attribute volume and the organic carbon content attribute volume in the geological parameter attribute volume.

[0096] In some embodiments, the geological sweet spot parameter further includes a rock type, and the method further includes:

[0097] The attribute volume of the second sensitive logging parameter is obtained based on the target corresponding relationship;

[0098] The attribute volume of the second sensitive logging parameter is subjected to slice processing to obtain an attribute volume slice.

[0099] The rock type of the target continental shale formation is determined based on the attribute volume slice.

[0100] In some embodiments, the geological parameter attribute volume includes a free hydrocarbon content attribute volume, an organic carbon content attribute volume, and a shale porosity attribute volume, and the method further includes:

[0101] The free hydrocarbon content attribute volume, the organic carbon content attribute volume, and the shale porosity attribute volume are subjected to slice processing respectively to obtain a free hydrocarbon content slice, an organic carbon content slice, and a shale porosity slice.

[0102] Figure 2 Another flowchart of a method for predicting a geological sweet spot parameter of a continental shale formation is provided in the embodiments of the present application. In the method, the geological sweet spot parameter includes a geological parameter, an overshoot effect, and a rock type.

[0103] Referring to Figure 2 The method includes the following steps:

[0104] In step 201, core indoor test data and logging curve data of a target continental shale series are obtained, wherein the core indoor test data include geological parameters of multiple depth points, and the logging curve data include logging curve data corresponding to multiple logging parameters.

[0105] Since the core is obtained from the formation, it can reflect the actual storage and distribution of underground oil and gas resources to a certain extent. Therefore, by performing core indoor testing to obtain core indoor test data, in combination with the actually measured logging curve data, preparation can be made for subsequent establishment of an attribute body.

[0106] The geological parameters include porosity, free hydrocarbon content, and organic carbon content.

[0107] After obtaining the logging curve data, the method for predicting the geological sweet spot parameters of the continental shale series provided in the embodiments of the present application further includes: obtaining a caliper curve in the logging curve data, wherein each data point on the caliper curve corresponds to a caliper baseline value; and for each data point, in response to the caliper baseline value corresponding to the data point being greater than a second threshold value, performing a smoothing process on the logging curve data corresponding to the data point.

[0108] When the caliper baseline value corresponding to a data point on the caliper curve is greater than the second threshold value, it indicates that the degree of borehole collapse at this time is obvious and has a greater impact on the measurement value of the logging instrument, and thus it is necessary to perform a smoothing process on the logging curve. The natural gamma curve in the logging curve, which is least affected by the borehole collapse, is used to correct other logging curves in a linear fitting manner.

[0109] In some embodiments, the second threshold value can be 28 cm.

[0110] In step 202, a corresponding relationship between each logging parameter data and the core indoor test data is established based on the geological parameters of the multiple depth points and the logging curve data corresponding to the multiple logging parameters of the multiple depth points in the logging curve data.

[0111] By establishing the corresponding relationship between each logging parameter data and the core indoor test data, it is convenient to determine the sensitive logging parameter based on the corresponding relationship subsequently.

[0112] The corresponding relationship between each logging parameter data and the core indoor test data has a correlation coefficient.

[0113] In some embodiments, referring to Figure 3 Step 202 includes the following sub-steps:

[0114] In step 2021, for the logging curve data corresponding to each logging parameter, a two-dimensional coordinate system with the logging parameter as the horizontal axis and the geological parameter as the vertical axis is established.

[0115] In step 2022, based on the two-dimensional coordinate system, the geological parameters and the corresponding logging parameters of the plurality of depth points are fitted to obtain the corresponding relationship between the geological parameters and the logging parameters.

[0116] The two-dimensional coordinate system is established based on the logging parameters and the geological parameters, and the corresponding relationship between the logging parameters and the geological parameters can be more intuitively reflected.

[0117] In step 203, based on the corresponding relationship between each logging parameter data and the core indoor test data, a sensitive logging parameter is determined, and the corresponding relationship between the sensitive logging parameter data and the core indoor test data is determined as a target corresponding relationship.

[0118] By determining the sensitive logging parameter, the sensitive logging parameter attribute body is obtained, and the subsequent target continental shale layer system geological parameter attribute body is prepared.

[0119] In some embodiments, based on the corresponding relationship between each logging parameter data and the core indoor test data, the sensitive logging parameter is determined, including: in response to the correlation coefficient of the corresponding relationship between the logging parameter data and the core indoor test data being greater than a first threshold, determining the logging parameter corresponding to the logging parameter data as the sensitive logging parameter.

[0120] By setting the first threshold, the corresponding relationship with the correlation coefficient meeting the requirements is screened out, and then the sensitive logging parameter is determined, which is convenient for subsequent establishment of the sensitive logging parameter attribute body and the geological parameter interpretation model.

[0121] In some embodiments, the first threshold can be 0.9.

[0122] In step 204, based on the logging curve data corresponding to the sensitive logging parameter, a geological parameter interpretation model is established.

[0123] The sensitive logging parameter includes a sensitive logging parameter corresponding to porosity, a sensitive logging parameter corresponding to free hydrocarbon content, and a sensitive logging parameter corresponding to organic carbon content.

[0124] Referring to Figure 4 , step 204 specifically includes the following sub-steps:

[0125] In step 2041, based on the logging curve data corresponding to the sensitive logging parameter corresponding to the porosity, a porosity interpretation model is established.

[0126] In some embodiments, the sensitive logging parameter corresponding to the porosity can be the P-wave velocity Vp and the Young's modulus E.

[0127] For example, the porosity interpretation model can be:

[0128] Psh = 54.41312 - 0.0101317*Vp - 0.643423*E;

[0129] wherein Psh is shale porosity, Vp is P-wave velocity, and E is Young's modulus.

[0130] In some embodiments, the sensitive well logging parameters corresponding to porosity include sensitive well logging parameters corresponding to shale formation porosity and sensitive well logging parameters corresponding to sandstone porosity.

[0131] Further, the step 2041 specifically includes: establishing a shale formation porosity interpretation model based on the sensitive well logging parameters corresponding to shale formation porosity; establishing a sandstone porosity interpretation model based on the sensitive well logging parameters corresponding to sandstone porosity; and obtaining the porosity interpretation model based on the shale formation porosity interpretation model and the sandstone porosity interpretation model.

[0132] The porosity interpretation model here refers to a shale porosity interpretation model.

[0133] Since the shale formation includes sandstone, in order to obtain the shale porosity interpretation model, the shale formation total porosity interpretation model and the sandstone porosity interpretation model need to be obtained first, and the difference between the shale formation total porosity interpretation model and the sandstone porosity interpretation model is used to calculate the shale porosity interpretation model. After determining the sensitive well logging parameters corresponding to shale formation porosity, the shale formation total porosity interpretation model is established based on the logging curve data corresponding to the sensitive well logging parameters corresponding to shale formation porosity; after determining the sensitive well logging parameters corresponding to sandstone porosity, the sandstone porosity interpretation model is established based on the logging curve data corresponding to the sensitive well logging parameters corresponding to sandstone porosity.

[0134] Step 2042, establishing a free hydrocarbon content interpretation model through multiple linear regression analysis based on the logging curve data corresponding to the sensitive well logging parameters corresponding to free hydrocarbon content.

[0135] In some embodiments, the sensitive well logging parameters corresponding to free hydrocarbon content can be acoustic time difference AC, density DEN, and resistivity RT.

[0136] For example, the free hydrocarbon content interpretation model can be:

[0137] S1 = -0.132173 + 0.00385419*AC / DEN + 0.0691439*log(RT);

[0138] wherein S1 is free hydrocarbon content, AC is acoustic time difference, DEN is density, and RT is resistivity.

[0139] In step 2043, based on the logging curve data corresponding to the sensitive logging parameter corresponding to the organic carbon content, an organic carbon content interpretation model is established by multiple linear regression analysis.

[0140] In some embodiments, the sensitive logging parameter corresponding to the organic carbon content can be the P-wave velocity Vp, the acoustic time difference AC, and the density DEN.

[0141] For example, the interpretation model of the organic carbon content can be:

[0142] TOC = -0.584 + 1.36175 * 1019 * VP - 5.433 + 0.001 * AC / DEN;

[0143] In the formula, TOC is the organic carbon content, Vp is the P-wave velocity, AC is the acoustic time difference, and DEN is the density.

[0144] In step 205, based on the target correspondence relationship, the sensitive logging parameter attribute volume is obtained by using inversion technology.

[0145] The target correspondence relationship includes a target correspondence relationship corresponding to porosity, a target correspondence relationship corresponding to free hydrocarbon content, and a target correspondence relationship corresponding to organic carbon content.

[0146] It should be noted that the present step is realized based on Jason software.

[0147] Referring to Figure 5 , step 205 includes the following sub-steps:

[0148] In step 2051, based on the target correspondence relationship corresponding to the porosity, a first sensitive logging parameter attribute volume corresponding to the porosity is obtained by using pre-stack geostatistics inversion technology.

[0149] In step 2052, based on the target correspondence relationship corresponding to the free hydrocarbon content, a first sensitive logging parameter attribute volume corresponding to the free hydrocarbon content is obtained by using elastic parameter inversion technology.

[0150] In step 2053, based on the target correspondence relationship corresponding to the organic carbon content, a first sensitive logging parameter attribute volume corresponding to the organic carbon content is obtained by using elastic parameter inversion technology.

[0151] In step 206, based on the sensitive logging parameter attribute volume and the geological parameter interpretation model, a geological parameter attribute volume of the target continental shale layer system is obtained.

[0152] By using the geological parameter interpretation model, the sensitive logging parameter attribute volume is fused by using attribute fusion technology to obtain the geological parameter attribute volume of the target continental shale layer system.

[0153] The geological parameter attribute body includes a free hydrocarbon content attribute body, an organic carbon content attribute body, and a shale porosity attribute body.

[0154] Specifically, the first sensitive well logging parameter attribute body corresponding to the free hydrocarbon content is substituted into the free hydrocarbon content interpretation model, the first sensitive well logging parameter attribute body corresponding to the organic carbon content is substituted into the organic carbon content interpretation model, and the first sensitive well logging parameter attribute body corresponding to the porosity is substituted into the porosity interpretation model, to obtain the free hydrocarbon content attribute body, the organic carbon content attribute body, and the shale porosity attribute body, respectively.

[0155] In addition, since the geological sweet spot parameter further includes an overmatch effect, the method for predicting the geological sweet spot parameter of the terrestrial shale formation series provided in the embodiments of the present application further includes obtaining an overmatch effect attribute body of the target terrestrial shale formation series based on the geological parameter attribute body of the target terrestrial shale formation series. Since the geological sweet spot parameter can further include the overmatch effect, the overmatch effect attribute body of the target terrestrial shale formation series can also be obtained based on the geological parameter attribute body of the target terrestrial shale formation series.

[0156] Specifically, obtaining the overmatch effect attribute body of the target terrestrial shale formation series based on the geological parameter attribute body of the target terrestrial shale formation series includes substituting the free hydrocarbon content attribute body and the organic carbon content attribute body in the geological parameter attribute body into the following formula to obtain the overmatch effect attribute body:

[0157] Overmatch effect = S1 / TOC.

[0158] In the formula, S1 is the free hydrocarbon content, and TOC is the organic carbon content.

[0159] Since the geological sweet spot parameter further includes a rock type, the method for predicting the geological sweet spot parameter of the terrestrial shale formation series provided in the embodiments of the present application further includes obtaining an attribute body of a second sensitive well logging parameter based on a target corresponding relationship, performing slice processing on the attribute body of the second sensitive well logging parameter to obtain an attribute body slice, and determining the rock type of the target terrestrial shale formation series based on the attribute body slice.

[0160] The attribute body of the sensitive well logging parameter corresponding to the rock type is subjected to slice processing, so that a slice capable of identifying the rock type can be obtained. For example, as shown in Figure 6 .

[0161] In some embodiments, the second sensitive well logging parameter can be a ratio of P-wave velocity to S-wave velocity.

[0162] In step 207, the free hydrocarbon content attribute body, the organic carbon content attribute body, and the shale porosity attribute body are subjected to slice processing, respectively, to obtain a free hydrocarbon content slice, an organic carbon content slice, and a shale porosity slice.

[0163] By obtaining the free hydrocarbon content slice, the organic carbon content slice and the shale porosity slice, an operator can identify the distribution characteristics of the shale series geological dessert elements.

[0164] For example, the organic carbon content slice, the shale porosity slice and the free hydrocarbon content slice are as shown in Figure 7 , Figure 8 and Figure 9 . In addition, the exceeding effect attribute body can also be sliced to obtain an exceeding effect slice, for example, as shown in Figure 10 .

[0165] It should be noted that, in the prediction method of the land facies shale series geological dessert parameters provided in the embodiments of the present application, except for the steps realized based on the Jason software, other steps can be realized through general office software, for example, through the Excel software.

[0166] In general, the prediction method of the land facies shale series geological dessert parameters provided in the embodiments of the present application uses the core indoor test data and the logging curve data of the target land facies shale series to establish the corresponding relationship between each logging parameter data and the core indoor test data, and determines the sensitive logging parameters sensitive to the geological dessert parameters based on the corresponding relationship, establishes the geological parameter interpretation model based on the logging curve data corresponding to the sensitive logging parameters, and obtains the geological parameter attribute body of the target land facies shale series based on the geological parameter interpretation model and the sensitive logging parameter attribute body obtained by using the inversion technology. Here, the rock type and the geological parameter attribute body of the target land facies shale series are the parameters of the predicted target land facies shale series geological dessert. The method predicts the geological dessert parameters based on the sensitive logging parameters, realizes the accurate prediction of the geological dessert parameters, and provides technical support for the exploration of the oil and gas resources in the reservoir of the land facies shale series.

[0167] Figure 11 is a structural schematic diagram of a prediction device for land facies shale series geological dessert parameters provided in the embodiments of the present application. Please refer to Figure 11 , the device 1100 comprises:

[0168] The first obtaining module 1101 is configured to obtain core indoor test data and logging curve data of a target land facies shale series, wherein the core indoor test data comprises geological parameters of a plurality of depth points, and the logging curve data comprises logging curve data corresponding to a plurality of logging parameters of a plurality of depth points;

[0169] The first establishing module 1102 is configured to establish a corresponding relationship between each logging parameter data and the core indoor test data based on the geological parameters of the plurality of depth points and the logging curve data corresponding to the plurality of logging parameters of the plurality of depth points in the logging curve data;

[0170] The determining module 1103 is configured to determine a sensitive logging parameter based on the correspondence between each logging parameter data and the core indoor test data, and determine the correspondence between the sensitive logging parameter data and the core indoor test data as a target correspondence.

[0171] The second establishing module 1104 is configured to establish a geological parameter interpretation model based on logging curve data corresponding to the sensitive logging parameter.

[0172] The second obtaining module 1105 is configured to obtain a first sensitive logging parameter attribute volume by using an inversion technique based on the target correspondence.

[0173] The obtaining module 1106 is configured to obtain a geological parameter attribute volume of a target continental shale layer based on the first sensitive logging parameter attribute volume and the geological parameter interpretation model.

[0174] In some embodiments, the first establishing module 1102 includes:

[0175] The first establishing submodule is configured to, for logging curve data corresponding to each logging parameter, establish a two-dimensional coordinate system with the logging parameter as the horizontal axis and the geological parameter as the vertical axis.

[0176] The first obtaining submodule is configured to, based on the two-dimensional coordinate system, fit the geological parameters and the corresponding logging parameters of a plurality of depth points to obtain a correspondence between the geological parameters and the logging parameters.

[0177] In some embodiments, the geological parameters include porosity, free hydrocarbon content, and organic carbon content, the target correspondence includes a target correspondence corresponding to the porosity, a target correspondence corresponding to the free hydrocarbon content, and a target correspondence corresponding to the organic carbon content, and the second obtaining module 1105 includes:

[0178] The first obtaining submodule is configured to obtain a first sensitive logging parameter attribute volume corresponding to the porosity by using a pre-stack geostatistical inversion technique based on the target correspondence corresponding to the porosity.

[0179] The second obtaining submodule is configured to obtain a first sensitive logging parameter attribute volume corresponding to the free hydrocarbon content by using an elastic parameter inversion technique based on the target correspondence corresponding to the free hydrocarbon content.

[0180] The third obtaining submodule is configured to obtain a first sensitive logging parameter attribute volume corresponding to the organic carbon content by using an elastic parameter inversion technique based on the target correspondence corresponding to the organic carbon content.

[0181] In some embodiments, the geological parameters include porosity, free hydrocarbon content, and organic carbon content, the sensitive logging parameters include a sensitive logging parameter corresponding to the porosity, a sensitive logging parameter corresponding to the free hydrocarbon content, and a sensitive logging parameter corresponding to the organic carbon content, and the second establishing module 1104 includes:

[0182] A second establishing submodule is configured to establish a porosity interpretation model based on the logging curve data corresponding to the sensitive logging parameter corresponding to the porosity.

[0183] A third establishing submodule is configured to establish a free hydrocarbon content interpretation model based on the logging curve data corresponding to the sensitive logging parameter corresponding to the free hydrocarbon content through multivariate linear regression analysis.

[0184] A fourth establishing submodule is configured to establish an organic carbon content interpretation model based on the logging curve data corresponding to the sensitive logging parameter corresponding to the organic carbon content through multivariate linear regression analysis.

[0185] In some embodiments, the sensitive logging parameter corresponding to the porosity includes a sensitive logging parameter corresponding to shale formation porosity and a sensitive logging parameter corresponding to sandstone porosity, and the second establishing submodule includes:

[0186] A fifth establishing submodule is configured to establish a shale formation porosity interpretation model based on the sensitive logging parameter corresponding to the shale formation porosity.

[0187] A sixth establishing submodule is configured to establish a sandstone porosity interpretation model based on the sensitive logging parameter corresponding to the sandstone porosity.

[0188] A second obtaining submodule is configured to obtain the porosity interpretation model based on the shale formation porosity interpretation model and the sandstone porosity interpretation model.

[0189] In some embodiments, the apparatus 1100 further includes:

[0190] An attribute body obtaining module is configured to obtain a transitive effect attribute body of the target continental shale formation based on a geological parameter attribute body of the target continental shale formation.

[0191] In some embodiments, the geological parameter attribute body includes a free hydrocarbon content attribute body, an organic carbon content attribute body, and a shale porosity attribute body, and the attribute body obtaining module includes:

[0192] A third obtaining submodule is configured to obtain the transitive effect attribute body of the target continental shale formation through attribute fusion based on the free hydrocarbon content attribute body and the organic carbon content attribute body in the geological parameter attribute body.

[0193] In some embodiments, the geological sweet spot parameter further includes a rock type, and the apparatus 1100 further includes:

[0194] The third acquisition module is configured to acquire an attribute body of the second sensitive well logging parameter based on the target correspondence relationship.

[0195] The first slicing module is configured to perform slicing processing on the attribute body of the second sensitive well logging parameter to obtain an attribute body slice.

[0196] The type determination module is configured to determine a rock type of the target continental shale formation based on the attribute body slice.

[0197] In some embodiments, the geological parameter attribute body includes a free hydrocarbon content attribute body, an organic carbon content attribute body, and a shale porosity attribute body, and the device 1100 further includes:

[0198] The second slicing module is configured to perform slicing processing on the free hydrocarbon content attribute body, the organic carbon content attribute body, and the shale porosity attribute body respectively to obtain a free hydrocarbon content slice, an organic carbon content slice, and a shale porosity slice.

[0199] It should be noted that the device for predicting a geological sweet spot parameter of a continental shale formation provided in the above embodiments is only used as an example for the division of the above functional modules during data processing. In actual applications, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device for predicting a geological sweet spot parameter of a continental shale formation and the method for predicting a geological sweet spot parameter of a continental shale formation provided in the above embodiments belong to the same concept, and the specific implementation process is described in the method embodiments, which will not be repeated here.

[0200] In some embodiments, a non-transitory computer readable storage medium is also provided, and the storage medium stores a computer program. When the computer program is executed by a processor, the steps of the method for predicting a geological sweet spot parameter of a continental shale formation in the above embodiments are implemented. For example, the computer readable storage medium can be a ROM, a RAM, a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.

[0201] It should be noted that the computer readable storage medium mentioned in the embodiments of the present application can be a non-volatile storage medium, in other words, a non-transitory storage medium.

[0202] It should be understood that all or part of the steps of the above embodiments can be implemented by software, hardware, firmware, or any combination thereof. When implemented by software, it can be implemented in the form of a computer program product in whole or in part. The computer program product includes one or more computer instructions. The computer instructions can be stored in the computer readable storage medium mentioned above.

[0203] That is, in some embodiments, there is also provided a computer program product comprising instructions which, when the program is run by a computer, cause the computer to carry out the steps of the above-described method of predicting parameters of geological sweet spots in a terrestrial shale formation.

[0204] In this application, the terms "first" and "second" are used only for descriptive purposes and are not to be construed as indicating or implying relative importance. The term "plurality" means two or more, unless expressly specified otherwise.

[0205] Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. It is intended that the specification and examples be considered as exemplary only, with a true scope of the application being indicated by the following claims.

[0206] It is to be understood that the application is not limited to the precise details of construction and the above-described and shown in the drawings, and that various modifications and changes can be applied to the application without departing from the scope thereof or sacrificing any substantive or essential features thereof. The scope of the application should be determined, not with reference to the above description, but should instead be determined with reference to the appended claims, along with their full scope of equivalents.

Claims

1. A method of predicting geologic sweet spot parameters for a continental shale formation, characterized by, The geological dessert parameter comprises a geological parameter, and the method comprises: obtaining core indoor test data and logging curve data of a target continental shale series, wherein the core indoor test data comprises geological parameters of multiple depth points, and the logging curve data comprises logging curve data corresponding to multiple logging parameters of multiple depth points; based on the geological parameters of the multiple depth points and the logging curve data corresponding to the multiple logging parameters of the multiple depth points, establishing a corresponding relationship between each logging parameter data and the core indoor test data; based on the corresponding relationship between each logging parameter data and the core indoor test data, determining a sensitive logging parameter, and determining the corresponding relationship between the sensitive logging parameter data and the core indoor test data as a target corresponding relationship; based on the logging curve data corresponding to the sensitive logging parameter, establishing a geological parameter interpretation model, wherein the geological parameter interpretation model represents the corresponding relationship between the geological parameter and the sensitive logging parameter; based on the target corresponding relationship, obtaining a first sensitive logging parameter attribute body by using an inversion technique; based on the first sensitive logging parameter attribute body and the geological parameter interpretation model, obtaining a geological parameter attribute body of the target continental shale series; the geological parameter comprises porosity, free hydrocarbon content and organic carbon content, the target corresponding relationship comprises a target corresponding relationship corresponding to the porosity, a target corresponding relationship corresponding to the free hydrocarbon content and a target corresponding relationship corresponding to the organic carbon content, and the obtaining of the first sensitive logging parameter attribute body by using the inversion technique based on the target corresponding relationship comprises: based on the target corresponding relationship corresponding to the porosity, obtaining a first sensitive logging parameter attribute body corresponding to the porosity by using a pre-stack geostatistics inversion technique; based on the target corresponding relationship corresponding to the free hydrocarbon content, obtaining a first sensitive logging parameter attribute body corresponding to the free hydrocarbon content by using an elastic parameter inversion technique; based on the target corresponding relationship corresponding to the organic carbon content, obtaining a first sensitive logging parameter attribute body corresponding to the organic carbon content by using an elastic parameter inversion technique.

2. The method of predicting parameters of a geological sweet spot in a continental shale formation of claim 1, wherein, the establishing of the corresponding relationship between each logging parameter data and the core indoor test data based on the geological parameters of the multiple depth points and the logging curve data corresponding to the multiple logging parameters of the multiple depth points comprises: for the logging curve data corresponding to each logging parameter, a two-dimensional coordinate system is established, wherein the logging parameter is taken as the horizontal axis, and the geological parameter is taken as the vertical axis; based on the two-dimensional coordinate system, the geological parameters of the multiple depth points and the corresponding logging parameters are fitted to obtain the corresponding relationship between the geological parameters and the logging parameters.

3. The method for predicting parameters of a geological sweet spot in a continental shale formation according to claim 1, wherein, the sensitive logging parameter comprises a sensitive logging parameter corresponding to the porosity, a sensitive logging parameter corresponding to the free hydrocarbon content and a sensitive logging parameter corresponding to the organic carbon content, and the establishing of the geological parameter interpretation model based on the logging curve data corresponding to the sensitive logging parameter comprises: based on the logging curve data corresponding to the sensitive logging parameter corresponding to the porosity, a porosity interpretation model is established; The free hydrocarbon content interpretation model is established by multivariate linear regression analysis based on the logging curve data corresponding to the sensitive logging parameter corresponding to the free hydrocarbon content; The organic carbon content interpretation model is established by multivariate linear regression analysis based on the logging curve data corresponding to the sensitive logging parameter corresponding to the organic carbon content.

4. The method of predicting parameters of a geological sweet spot in a continental shale formation of claim 3, wherein, The sensitive logging parameter corresponding to the porosity includes a sensitive logging parameter corresponding to shale series porosity and a sensitive logging parameter corresponding to sandstone porosity, and the porosity interpretation model is established based on the logging curve data corresponding to the sensitive logging parameter corresponding to the porosity, including: The shale series porosity interpretation model is established based on the sensitive logging parameter corresponding to the shale series porosity; The sandstone porosity interpretation model is established based on the sensitive logging parameter corresponding to the sandstone porosity; The porosity interpretation model is obtained based on the shale series porosity interpretation model and the sandstone porosity interpretation model.

5. The method for predicting parameters of a geological sweet spot in a continental shale formation of claim 1, wherein, The method further includes: The exceeding effect attribute body of the target continental shale series is obtained based on the geological parameter attribute body of the target continental shale series.

6. The method of predicting parameters of a geological sweet spot in a continental shale formation of claim 5, wherein, The geological parameter attribute body includes a free hydrocarbon content attribute body, an organic carbon content attribute body, and a shale porosity attribute body, and the exceeding effect attribute body of the target continental shale series is obtained based on the free hydrocarbon content attribute body and the organic carbon content attribute body in the geological parameter attribute body through attribute fusion. The method further includes:

7. The method for predicting parameters of a geological sweet spot of a continental shale formation according to claim 1, wherein, The attribute body of the second sensitive logging parameter is obtained based on the target corresponding relationship; The attribute body of the second sensitive logging parameter is sliced to obtain an attribute body slice; The rock type of the target continental shale series is determined based on the attribute body slice. The method further includes:

8. The method for predicting parameters of a geological sweet spot of a continental shale formation according to claim 1, wherein, The free hydrocarbon content slice, the organic carbon content slice, and the shale porosity slice are obtained by slicing the free hydrocarbon content attribute body, the organic carbon content attribute body, and the shale porosity attribute body, respectively. The geological sweet spot parameter includes a geological parameter, and the device includes:

9. A device for predicting geological sweet spot parameters of terrestrial shale strata, characterized in that, A first obtaining module is configured to obtain core laboratory test data of a target continental shale series and logging curve data, wherein the core laboratory test data includes geological parameters of a plurality of depth points, and the logging curve data includes logging curve data corresponding to a plurality of logging parameters; A first establishing module is configured to establish a corresponding relationship between each logging parameter data and the core laboratory test data based on the geological parameters of the plurality of depth points and the logging curve data corresponding to the plurality of logging parameters corresponding to the plurality of depth points in the logging curve data; A determining module is configured to determine a sensitive logging parameter based on the corresponding relationship between each logging parameter data and the core laboratory test data, and determine the corresponding relationship between the sensitive logging parameter data and the core laboratory test data as a target corresponding relationship; and A second establishing module is configured to establish a geological sweet spot parameter of the target continental shale series based on the target corresponding relationship. The second establishing module is configured to establish a geological parameter interpretation model based on the logging curve data corresponding to the sensitive well logging parameter, and the geological parameter interpretation model represents a corresponding relationship between the geological parameter and the sensitive well logging parameter; The second obtaining module is configured to obtain a first sensitive well logging parameter attribute body by using an inversion technique based on the target corresponding relationship; The obtaining module is configured to obtain a geological parameter attribute body of a target continental shale layer system based on the first sensitive well logging parameter attribute body and the geological parameter interpretation model; The geological parameter includes porosity, free hydrocarbon content and organic carbon content, the target corresponding relationship includes a target corresponding relationship corresponding to the porosity, a target corresponding relationship corresponding to the free hydrocarbon content and a target corresponding relationship corresponding to the organic carbon content, and the second obtaining module includes: The first obtaining submodule is configured to obtain a first sensitive well logging parameter attribute body corresponding to the porosity by using a pre-stack geostatistics inversion technique based on the target corresponding relationship corresponding to the porosity; The second obtaining submodule is configured to obtain a first sensitive well logging parameter attribute body corresponding to the free hydrocarbon content by using an elastic parameter inversion technique based on the target corresponding relationship corresponding to the free hydrocarbon content; The third obtaining submodule is configured to obtain a first sensitive well logging parameter attribute body corresponding to the organic carbon content by using an elastic parameter inversion technique based on the target corresponding relationship corresponding to the organic carbon content.

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