Prediction method and device for geological dessert parameters of continental facies shale strata series

Abstract

The invention discloses a method and a device for predicting geological sweet spot parameters of a continental facies shale bed series. The method comprises the following steps: acquiring core indoor test data and logging curve data of a target continental facies shale bed series; based on the geological parameters of the plurality of depth points and logging curve data corresponding to a plurality of logging parameters corresponding to the plurality of depth points in the logging curve data, establishing a corresponding relation between each piece of logging parameter data and test data in the core chamber; based on the corresponding relation between each piece of logging parameter data and the test data in the core chamber, determining the corresponding relation between the sensitive logging parameters and the target; based on the logging curve data corresponding to the sensitive logging parameters, establishing a geological parameter interpretation model; based on the target corresponding relation, 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 facies shale bed series. The method can provide technical support for oil and gas resource exploration of continental facies shale strata series.

Description

Technical Field

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

[0002] The continental shale formations are important oil and gas reservoirs in my country. 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 reservoirs of the continental shale formations, and therefore it is necessary to obtain accurate geological sweet spot parameters.

[0003] In the related art, the method for predicting the geological sweet spot parameters of continental shale formations is usually to fit the logging curves (such as natural potential curves, resistivity curves or acoustic time difference curves) obtained based on logging data and the sweet spot parameters (such as rock type, porosity) obtained based on core measurements, and then determine the correspondence between the conventional logging curves and the sweet spot parameters, and then determine the sweet spot parameters of the target continental shale formation based on the correspondence and the logging characteristic curves of the target continental shale formation.

[0004] However, when fitting the logging characteristic curve obtained based on the logging data and the sweet spot parameter data obtained based on the core measurement in the related technology, the characteristic points are distributed in a scattered manner, which makes the values ​​of the sweet spot parameters predicted based on the correspondence between the logging characteristics and the sweet spot parameters inaccurate, and cannot provide technical support for the exploration of oil and gas resources in the reservoir of the continental shale formation. Summary of the invention

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

[0006] Specifically, the following technical solutions are included:

[0007] In a first aspect, an embodiment of the present application provides a method for predicting geological sweet spot parameters of a continental shale formation, the method comprising:

[0008] Acquire core indoor test data and well logging curve data of the target continental shale formation, wherein the core indoor test data includes geological parameters of multiple depth points, and the well logging curve data includes well logging curve data corresponding to multiple well logging parameters;

[0009] Based on the geological parameters of the multiple depth points and the logging curve data corresponding to the multiple logging parameters corresponding to the multiple depth points in the logging curve data, a corresponding relationship between each logging parameter data and the core chamber test data is established;

[0010] Based on the correspondence between each well logging parameter data and the core indoor test data, a sensitive well logging parameter is determined, and the correspondence between the sensitive well logging parameter data and the core indoor test data is determined as a target correspondence;

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

[0012] Based on the target corresponding relationship, using inversion technology to obtain a first sensitive logging parameter attribute body;

[0013] Based on the first sensitive logging parameter attribute body and the geological parameter interpretation model, a geological parameter attribute body of the target continental shale formation is obtained.

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

[0015] For the well logging curve data corresponding to each well logging parameter, a two-dimensional coordinate system is established with the well 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 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.

[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 porosity, a target correspondence corresponding to free hydrocarbon content and a target correspondence corresponding to organic carbon content, and based on the target correspondence, obtaining the first sensitive logging parameter attribute body by using the inversion technology includes:

[0018] Based on the target corresponding relationship corresponding to the porosity, a first sensitive logging parameter attribute body corresponding to the porosity is obtained by using a prestack geostatistical inversion technique;

[0019] Based on the target corresponding relationship corresponding to the free hydrocarbon content, using elastic parameter inversion technology, a first sensitive logging parameter attribute body corresponding to the free hydrocarbon content is obtained;

[0020] Based on the target corresponding relationship corresponding to the organic carbon content, the elastic parameter response technology is used to obtain a first sensitive logging parameter attribute body corresponding to the organic carbon content.

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

[0022] Based on the well logging curve data corresponding to the sensitive logging parameters corresponding to the porosity, a porosity interpretation model is established;

[0023] Based on the well logging curve data corresponding to the sensitive logging parameters corresponding to the free hydrocarbon content, a free hydrocarbon content interpretation model is established through multivariate linear regression analysis;

[0024] Based on the well logging curve data corresponding to the sensitive logging parameters corresponding to the organic carbon content, the organic carbon content interpretation model was established through multivariate linear regression analysis.

[0025] In some embodiments, the sensitive logging parameters corresponding to the porosity include sensitive logging parameters corresponding to the porosity of shale formations and sensitive logging parameters corresponding to the porosity of sandstones. The step of establishing a porosity interpretation model based on the logging curve data corresponding to the sensitive logging parameters corresponding to the porosity includes:

[0026] Based on the sensitive logging parameters corresponding to the porosity of shale formations, a shale formation porosity interpretation model is established;

[0027] Based on the sensitive logging parameters corresponding to the sandstone porosity, a sandstone porosity interpretation model is established;

[0028] The porosity interpretation model is obtained 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 overrun effect, and the method further includes:

[0030] Based on the geological parameter attribute body of the target continental shale formation, a transcendence effect attribute body of the target continental shale formation is obtained.

[0031] 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. Based on the geological parameter attribute body of the target continental shale formation, obtaining the overriding effect attribute body of the target continental shale formation includes:

[0032] Based on the free hydrocarbon content attribute body and the organic carbon content attribute body in the geological parameter attribute body, the surpassing effect attribute body of the target continental shale formation is obtained by attribute fusion.

[0033] In some embodiments, the geological sweet spot parameter further includes rock type, and the method further includes:

[0034] Based on the target corresponding relationship, obtaining an attribute body of a second sensitive logging parameter;

[0035] Slicing the attribute body of the second sensitive logging parameter to obtain attribute body slices;

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

[0037] 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 method further includes:

[0038] The free hydrocarbon content attribute body, the organic carbon content attribute body and the shale porosity attribute body are sliced ​​respectively to obtain free hydrocarbon content slices, organic carbon content slices and shale porosity slices.

[0039] In a second aspect, the present application also provides a device for predicting parameters of geological sweet spots in continental shale formations, the device comprising:

[0040] The first acquisition module is used to acquire the core indoor test data and well logging curve data of the target continental shale formation, wherein the core indoor test data includes geological parameters of multiple depth points, and the well logging curve data includes well logging curve data corresponding to multiple well logging parameters;

[0041] The first establishment module is used to establish a corresponding relationship between each well logging parameter data and the core chamber test data based on the geological parameters of the multiple depth points and the well logging curve data corresponding to the multiple well logging parameters corresponding to the multiple depth points in the well logging curve data;

[0042] A determination module, configured to determine a sensitive logging parameter based on a correspondence between each logging parameter data and the core chamber test data, and determine the correspondence between the sensitive logging parameter data and the core chamber test data as a target correspondence;

[0043] The second establishment module is used to establish a geological parameter interpretation model based on the well logging curve data corresponding to the sensitive well logging parameters;

[0044] A second acquisition module is used to acquire a first sensitive logging parameter attribute body based on the target corresponding relationship by using an inversion technology;

[0045] An obtaining module is used to obtain the geological parameter attribute body of the target continental shale formation based on the first sensitive logging parameter attribute body and the geological parameter interpretation model.

[0046] The beneficial effects of the technical solution provided by the embodiments of the present application include at least:

[0047] The method for predicting the geological sweet spot parameters of the continental shale formation provided in the embodiment of the present application uses the core indoor test data and the logging curve data of the target continental shale formation to establish the corresponding relationship between each logging parameter data and the core indoor test data, and based on the corresponding relationship, the sensitive logging parameters that are sensitive to the geological sweet spot parameters are determined, and the geological parameter interpretation model is established based on the logging curve data corresponding to the sensitive logging parameters. Based on the geological parameter interpretation model and the sensitive logging parameter attribute body obtained by the inversion technology, the geological parameter attribute body of the target continental shale formation is obtained. Here, the rock type and geological parameter attribute body of the target continental shale formation are the parameters of the predicted target continental shale formation address sweet spot. 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 can provide technical support for the exploration of oil and gas resources in the reservoir of the continental shale formation. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0049] Figure 1 A flow chart of a method for predicting geological sweet spot parameters of a continental shale formation provided in an embodiment of the present application;

[0050] Figure 2 A flowchart of another method for predicting geological sweet spot parameters of continental shale formations provided in an embodiment of the present application;

[0051] Figure 3 A flowchart of a method for establishing a corresponding relationship between each well logging parameter data and the test data in the core chamber provided in an embodiment of the present application;

[0052] Figure 4 A flow chart of a method for establishing a geological parameter interpretation model provided in an embodiment of the present application;

[0053] Figure 5 A flowchart of a method for obtaining a sensitive well logging parameter attribute body provided in an embodiment of the present application;

[0054] Figure 6 A longitudinal and transverse wave velocity ratio attribute volume slice diagram provided in an embodiment of the present application;

[0055] Figure 7A slice diagram of an organic carbon content attribute body provided in an embodiment of the present application;

[0056] Figure 8 A shale porosity attribute volume slice diagram provided in an embodiment of the present application;

[0057] Fig. 9 A free hydrocarbon content attribute volume slice diagram provided in an embodiment of the present application;

[0058] Fig.10 A slice diagram of a transcendence effect attribute body provided in an embodiment of the present application;

[0059] Fig.11 A schematic diagram of the structure of a device for predicting geological sweet spot parameters of a continental shale formation provided in an embodiment of the present application.

[0060] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope 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 following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

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

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

[0064] At present, continental shale formations are one of the important unconventional oil and gas reservoirs in my country. They are widely distributed and have rich 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 reservoirs of continental shale formations. The geological sweet spot parameters can accurately reflect the distribution and storage conditions of oil and gas resources in continental shale formations. Therefore, in order to accurately explore the oil and gas resources in continental shale formations, it is necessary to obtain accurate geological sweet spot parameters.

[0065] In the related art, the method for predicting the geological sweet spot parameters of continental shale formations is usually to fit the logging curves (such as natural potential curves, resistivity curves or acoustic time difference curves) obtained based on logging data and the sweet spot parameters (such as rock type, porosity) obtained based on core measurements, and then determine the correspondence between the conventional logging curves and the sweet spot parameters, and then determine the sweet spot parameters of the target continental shale formation based on the correspondence and the logging characteristic curves of the target continental shale formation.

[0066] However, on the one hand, when fitting the logging characteristic curve obtained based on the logging data and the sweet spot parameter data obtained based on the core measurement in the related technology, the characteristic points are distributed in a scattered manner, which makes the values ​​of the sweet spot parameters predicted based on the correspondence between the logging characteristics and the sweet spot parameters inaccurate, and cannot provide technical support for the exploration of oil and gas resources in the reservoirs of the continental shale formations; on the other hand, the sweet spot parameters predicted by fitting the logging characteristic curve obtained based on the logging data and the sweet spot parameter data obtained based on the core measurement in the related technology can only reflect the area near the wellbore, and cannot obtain the sweet spot parameters of the entire target continental shale formation area, and cannot provide sufficient technical support for the exploration of oil and gas resources.

[0067] In order to solve the technical problems existing in the related technologies, the embodiments of the present application provide a method for predicting the geological sweet spot parameters 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 the parameters of the geological sweet spot of the continental shale formation provided in the embodiment of the present application can be executed by a device for predicting the parameters of the geological sweet spot of the continental shale formation, and the device for predicting the parameters of the geological sweet spot of the continental shale formation can be a terminal device such as a fixed terminal or a mobile terminal. Among them, the terminal device can be any electronic product that can interact with the user through one or more methods such as a keyboard, a touch pad, 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 handheld computer PPC (Pocket PC), a tablet computer, etc.

[0069] Figure 1 A flowchart of a method for predicting geological sweet spot parameters of a continental shale formation provided in an embodiment of the present application. Figure 1 , including the following steps:

[0070] Step 101, obtaining core indoor test data and well logging curve data of the target continental shale formation, wherein the core indoor test data includes geological parameters of multiple depth points, and the well logging curve data includes well logging curve data corresponding to multiple well logging parameters;

[0071] Step 102, based on the geological parameters of the multiple depth points and the logging curve data corresponding to the multiple logging parameters corresponding to the multiple depth points in the logging curve data, establish a corresponding relationship between each logging parameter data and the core chamber test data;

[0072] Step 103, based on the correspondence between each well logging parameter data and the test data in the core chamber, determine the sensitive well logging parameters, and determine the correspondence between the sensitive well logging parameter data and the test data in the core chamber as the target correspondence;

[0073] Step 104, establishing a geological parameter interpretation model based on the well logging curve data corresponding to the sensitive well logging parameters;

[0074] Step 105, based on the target corresponding relationship, using the inversion technology to obtain the first sensitive logging parameter attribute body;

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

[0076] The method for predicting the geological sweet spot parameters of the continental shale formation provided in the embodiment of the present application uses the core indoor test data and the actual logging curve data of the target continental shale formation to establish a corresponding relationship between the core indoor test data and the logging curve data, and selects the sensitive logging parameters that are sensitive to the geological sweet spot parameters from the corresponding relationship. Based on the logging curve data corresponding to the sensitive logging parameters, the rock type of the target continental shale formation is identified, and a geological parameter interpretation model is established. Based on the geological parameter interpretation model and the sensitive logging parameter attribute body obtained by the inversion technology, the geological parameter attribute body of the target continental shale formation is obtained. Here, the rock type and geological parameter attribute body of the target continental shale formation are the parameters of the predicted target continental shale formation address sweet spot. This method predicts the geological sweet spot parameters based on the sensitive logging parameters, realizes the accurate prediction of the geological sweet spot parameters, and can provide technical support for the exploration of oil and gas resources in the reservoir of the continental shale formation.

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

[0078] For the well logging curve data corresponding to each well logging parameter, a two-dimensional coordinate system is established with the well 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 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 based on the target correspondence, obtaining the first sensitive logging parameter attribute body by using the inversion technology includes:

[0081] Based on the target correspondence relationship corresponding to the porosity, the first sensitive logging parameter attribute body corresponding to the porosity is obtained by using the prestack geostatistical inversion technology;

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

[0083] Based on the target corresponding relationship with the organic carbon content, the elastic parameter response technology is used to obtain the first sensitive logging parameter attribute body 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 porosity, sensitive logging parameters corresponding to free hydrocarbon content and sensitive logging parameters corresponding to organic carbon content, and based on the logging curve data corresponding to the sensitive logging parameters, establishing the geological parameter interpretation model includes:

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

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

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

[0088] In some embodiments, the sensitive logging parameters corresponding to the porosity include sensitive logging parameters corresponding to the porosity of shale formations and sensitive logging parameters corresponding to the porosity of sandstones. Based on the logging curve data corresponding to the sensitive logging parameters corresponding to the porosity, establishing the porosity interpretation model includes:

[0089] Based on the sensitive logging parameters corresponding to the porosity of shale formations, a shale formation porosity interpretation model is established;

[0090] Based on the sensitive logging parameters corresponding to the sandstone porosity, a sandstone porosity interpretation model is established;

[0091] Based on the shale formation porosity interpretation model and the sandstone porosity interpretation model, a porosity interpretation model is obtained.

[0092] In some embodiments, the geological sweet spot parameter further includes an override effect, the method further comprising:

[0093] Based on the geological parameter attribute body of the target continental shale formation, the surpassing effect attribute body of the target continental shale formation is obtained.

[0094] 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. Based on the geological parameter attribute body of the target continental shale formation, the overriding effect attribute body of the target continental shale formation is obtained, which includes:

[0095] Based on the free hydrocarbon content attribute body and the organic carbon content attribute body in the geological parameter attribute body, the surpassing effect attribute body of the target continental shale formation is obtained through attribute fusion.

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

[0097] Based on the target corresponding relationship, an attribute body of a second sensitive logging parameter is obtained;

[0098] Slicing the attribute body of the second sensitive logging parameter to obtain attribute body slices;

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

[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 body, the organic carbon content attribute body and the shale porosity attribute body are sliced ​​to obtain free hydrocarbon content slices, organic carbon content slices and shale porosity slices.

[0102] Figure 2 A flowchart of another method for predicting geological sweet spot parameters of continental shale formations provided in an embodiment of the present application. In this method, the geological sweet spot parameters include geological parameters, overrun effect and rock type.

[0103] See also Figure 2 , the method comprises the following steps:

[0104] Step 201, obtaining core indoor test data and logging curve data of the target continental shale formation, wherein the core indoor test data includes geological parameters of multiple depth points, and the logging curve data includes 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 conducting core indoor testing on the core, the core indoor test data is obtained. When combined with the logging curve data obtained by actual measurement, it can prepare for the establishment of subsequent attribute bodies.

[0106] Among them, 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 formation provided in the embodiment of the present application also includes: obtaining the wellbore curve in the logging curve data, wherein each data point on the wellbore curve corresponds to a wellbore baseline value; for each data point, in response to the wellbore baseline value corresponding to the data point being greater than a second threshold, performing slow processing 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, it indicates that the borehole collapse is obvious at this time, which has a great impact on the measurement value of the logging instrument. Therefore, the logging curve needs to be slowed down and the natural gamma curve that is least affected by the borehole collapse in the logging curve is used to correct other logging curves in a linear fitting manner.

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

[0110] Step 202, based on the geological parameters of the multiple depth points and the logging curve data corresponding to the multiple logging parameters corresponding to the multiple depth points in the logging curve data, establish a corresponding relationship between each logging parameter data and the core chamber test data.

[0111] By establishing a corresponding relationship between each logging parameter data and the test data in the core chamber, it is convenient to determine the sensitive logging parameters based on the corresponding relationship later.

[0112] Among them, the corresponding relationship between each logging parameter data and the core chamber test data has a correlation coefficient.

[0113] In some embodiments, see Figure 3 , step 202 includes the following sub-steps:

[0114] Step 2021: 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.

[0115] Step 2022: Based on the two-dimensional coordinate system, the geological parameters of multiple depth points and the corresponding logging parameters are fitted to obtain the corresponding relationship between the geological parameters and the logging parameters.

[0116] Establishing a two-dimensional coordinate system based on well logging parameters and geological parameters can more intuitively reflect the corresponding relationship between various well logging parameters and various geological parameters.

[0117] Step 203: based on the correspondence between each well logging parameter data and the core chamber test data, determine the sensitive well logging parameters, and determine the correspondence between the sensitive well logging parameter data and the core chamber test data as the target correspondence.

[0118] By determining the sensitive logging parameters, it is convenient to obtain the sensitive logging parameter attribute body, so as to prepare for the acquisition of the geological parameter attribute body of the subsequent target continental shale formation.

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

[0120] By setting the first threshold, corresponding relationships whose correlation coefficients meet the requirements are screened out, and then sensitive logging parameters are determined, so as to facilitate the subsequent establishment of sensitive logging parameter attribute bodies and geological parameter interpretation models.

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

[0122] Step 204: Establish a geological parameter interpretation model based on the well logging curve data corresponding to the sensitive well logging parameters.

[0123] The sensitive logging parameters include sensitive logging parameters corresponding to porosity, sensitive logging parameters corresponding to free hydrocarbon content, and sensitive logging parameters corresponding to organic carbon content.

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

[0125] Step 2041: Establish a porosity interpretation model based on the logging curve data corresponding to the sensitive logging parameters corresponding to the porosity.

[0126] In some embodiments, the sensitive logging parameters corresponding to the porosity may be the longitudinal wave velocity Vp and the Young's modulus E.

[0127] For example, a porosity interpretation model could be:

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

[0129] Where: Psh is the shale porosity, Vp is the longitudinal wave velocity, and E is the Young's modulus.

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

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

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

[0134] Step 2042: Based on the well logging curve data corresponding to the sensitive well logging parameters corresponding to the free hydrocarbon content, a free hydrocarbon content interpretation model is established through multivariate linear regression analysis.

[0135] In some embodiments, the sensitive logging parameters corresponding to the free hydrocarbon content may be acoustic transit time 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] Where: S1 is the free hydrocarbon content, AC is the acoustic time difference, DEN is the density, and RT is the resistivity.

[0139] Step 2043: Based on the well logging curve data corresponding to the sensitive well logging parameters corresponding to the organic carbon content, an organic carbon content interpretation model is established through multivariate linear regression analysis.

[0140] In some embodiments, the sensitive logging parameters corresponding to the organic carbon content may be the compressional wave velocity Vp, the acoustic wave time difference AC, and the density DEN.

[0141] For example, an explanatory model for organic carbon content could be:

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

[0143] Where: TOC is the organic carbon content, Vp is the longitudinal wave velocity, AC is the acoustic time difference, and DEN is the density.

[0144] Step 205: Based on the target correspondence, the sensitive logging parameter attribute body is obtained using the inversion technology.

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

[0146] It should be noted that this step is implemented based on Jason software.

[0147] See also Figure 5 , step 205 includes the following sub-steps:

[0148] Step 2051: Based on the target corresponding relationship corresponding to the porosity, a first sensitive logging parameter attribute body corresponding to the porosity is obtained by using the prestack geostatistical inversion technology.

[0149] Step 2052: Based on the target corresponding relationship corresponding to the free hydrocarbon content, the elastic parameter inversion technology is used to obtain a first sensitive logging parameter attribute body corresponding to the free hydrocarbon content.

[0150] Step 2053: Based on the target corresponding relationship corresponding to the organic carbon content, the elastic parameter response technology is used to obtain a first sensitive logging parameter attribute body corresponding to the organic carbon content.

[0151] Step 206: Based on the sensitive logging parameter attribute body and the geological parameter interpretation model, the geological parameter attribute body of the target continental shale formation is obtained.

[0152] By using the geological parameter interpretation model and the attribute fusion technology, the sensitive logging parameter attribute body is fused to obtain the geological parameter attribute body of the target continental shale formation.

[0153] Among them, the geological parameter attribute bodies include free hydrocarbon content attribute bodies, organic carbon content attribute bodies and shale porosity attribute bodies.

[0154] Specifically, the first sensitive logging parameter attribute body corresponding to the free hydrocarbon content is substituted into the free hydrocarbon content interpretation model, the first sensitive logging parameter attribute body corresponding to the organic carbon content is substituted into the organic carbon content interpretation model, and the first sensitive 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 parameters also include the overtaking effect, the prediction method of the geological sweet spot parameters of the continental shale formation provided in the embodiment of the present application also includes: obtaining the overtaking effect attribute body of the target continental shale formation based on the geological parameter attribute body of the target continental shale formation. Since the geological sweet spot parameters can also include the overtaking effect, the overtaking effect attribute body of the target continental shale formation can also be obtained here based on the geological parameter attribute body of the target continental shale formation.

[0156] Specifically, based on the geological parameter attribute body of the target continental shale formation, obtaining the surpassing effect attribute body of the target continental shale formation 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 surpassing effect attribute body:

[0157] Transcendence effect = S1 / TOC;

[0158] Where: S1 is the free hydrocarbon content, TOC is the organic carbon content.

[0159] Since the geological sweet spot parameters also include rock type, the prediction method of the geological sweet spot parameters of the continental shale formation provided in the embodiment of the present application also includes: obtaining the attribute body of the second sensitive logging parameter based on the target correspondence relationship; slicing the attribute body of the second sensitive logging parameter to obtain attribute body slices; and determining the rock type of the target continental shale formation based on the attribute body slices.

[0160] By slicing the attribute body of the sensitive logging parameters corresponding to the rock type, a slice that can identify the rock type can be obtained. For example, Figure 6 shown.

[0161] In some embodiments, the second sensitive logging parameter may be the ratio of longitudinal and transverse wave velocities.

[0162] Step 207 , slicing the free hydrocarbon content attribute body, the organic carbon content attribute body and the shale porosity attribute body respectively to obtain free hydrocarbon content slices, organic carbon content slices and shale porosity slices.

[0163] By obtaining free hydrocarbon content slices, organic carbon content slices and shale porosity slices, operators can easily identify the distribution characteristics of geological sweet spot elements in shale formations.

[0164] For example, organic carbon content slices, shale porosity slices, and free hydrocarbon content slices are shown in Figure 2. Figure 7 , Figure 8 and Fig. 9 In addition, the transcendence effect attribute body can be sliced ​​to obtain transcendence effect slices. For example, Fig.10 shown.

[0165] It should be noted that in the method for predicting the geological sweet spot parameters of the continental shale formation provided in the embodiment of the present application, except for the steps implemented based on Jason software, other steps can be implemented by general office software, for example, by Excel software.

[0166] In general, the method for predicting the geological sweet spot parameters of the continental shale formation provided in the embodiment of the present application uses the core indoor test data and logging curve data of the target continental shale formation to establish a correspondence between each logging parameter data and the core indoor test data, and based on the correspondence, determine the sensitive logging parameters that are sensitive to the geological sweet spot parameters, and establish a geological parameter interpretation model based on the logging curve data corresponding to the sensitive logging parameters. Based on the geological parameter interpretation model and the sensitive logging parameter attribute body obtained by inversion technology, the geological parameter attribute body of the target continental shale formation is obtained. Here, the rock type and geological parameter attribute body of the target continental shale formation are the parameters of the predicted target continental shale formation address sweet spot. This method predicts the geological sweet spot parameters based on the sensitive logging parameters, realizes the accurate prediction of the geological sweet spot parameters, and can provide technical support for the exploration of oil and gas resources in the reservoir of the continental shale formation.

[0167] Fig.11 This is a schematic diagram of the structure of a device for predicting the geological sweet spot parameters of a continental shale formation provided in an embodiment of the present application. Fig.11 , the device 1100 comprises:

[0168] The first acquisition module 1101 is used to acquire core indoor test data and well logging curve data of the target continental shale formation, wherein the core indoor test data includes geological parameters of multiple depth points, and the well logging curve data includes well logging curve data corresponding to multiple well logging parameters;

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

[0170] A determination module 1103 is used to determine the sensitive logging parameters based on the correspondence between each logging parameter data and the test data in the core chamber, and determine the correspondence between the sensitive logging parameter data and the test data in the core chamber as a target correspondence;

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

[0172] The second acquisition module 1105 is used to acquire the first sensitive logging parameter attribute body based on the target corresponding relationship by using the inversion technology;

[0173] The obtaining module 1106 is used to obtain the geological parameter attribute body of the target continental shale formation based on the first sensitive logging parameter attribute body and the geological parameter interpretation model.

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

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

[0176] The first obtaining submodule is used to fit the geological parameters and corresponding logging parameters of multiple depth points based on a two-dimensional coordinate system to obtain the corresponding relationship 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 porosity, a target correspondence corresponding to free hydrocarbon content and a target correspondence corresponding to organic carbon content, and the second acquisition module 1105 includes:

[0178] A first acquisition submodule is used to acquire a first sensitive logging parameter attribute body corresponding to the porosity based on a target corresponding relationship corresponding to the porosity by using a prestack geostatistical inversion technique;

[0179] The second acquisition submodule is used to acquire a first sensitive logging parameter attribute body corresponding to the free hydrocarbon content by using an elastic parameter inversion technique based on a target corresponding relationship corresponding to the free hydrocarbon content;

[0180] The third acquisition submodule is used to acquire a first sensitive logging parameter attribute body corresponding to the organic carbon content by using elastic parameter response technology based on the target corresponding relationship 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 sensitive logging parameters corresponding to porosity, sensitive logging parameters corresponding to free hydrocarbon content and sensitive logging parameters corresponding to organic carbon content, and the second establishing module 1104 includes:

[0182] The second establishment submodule is used to establish a porosity interpretation model based on the well logging curve data corresponding to the sensitive well logging parameters corresponding to the porosity;

[0183] The third submodule is used to establish a free hydrocarbon content interpretation model through multivariate linear regression analysis based on the well logging curve data corresponding to the sensitive well logging parameters corresponding to the free hydrocarbon content;

[0184] The fourth establishment submodule is used to establish an organic carbon content interpretation model through multivariate linear regression analysis based on the logging curve data corresponding to the sensitive logging parameters corresponding to the organic carbon content.

[0185] In some embodiments, the sensitive logging parameters corresponding to the porosity include sensitive logging parameters corresponding to the porosity of shale formations and sensitive logging parameters corresponding to the porosity of sandstones, and the second establishment submodule includes:

[0186] The fifth establishment submodule is used to establish a shale formation porosity interpretation model based on sensitive logging parameters corresponding to the shale formation porosity;

[0187] The sixth establishment submodule is used to establish a sandstone porosity interpretation model based on sensitive logging parameters corresponding to the sandstone porosity;

[0188] Secondly, a submodule is obtained, and a porosity interpretation model is obtained based on the shale formation porosity interpretation model and the sandstone porosity interpretation model.

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

[0190] The attribute body obtaining module is used to obtain the transcendence effect attribute body of the target continental shale formation based on the 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] The third submodule is used to obtain the transcendence 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 rock type, and the apparatus 1100 further includes:

[0194] A third acquisition module is used to acquire an attribute body of a second sensitive logging parameter based on the target corresponding relationship;

[0195] A first slicing module is used to slice the attribute body of the second sensitive logging parameter to obtain attribute body slices;

[0196] The type determination module is used to determine the rock type of the target continental shale formation based on the attribute volume 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 apparatus 1100 further includes:

[0198] The second slicing module is used to slice the free hydrocarbon content attribute body, the organic carbon content attribute body and the shale porosity attribute body respectively to obtain free hydrocarbon content slices, organic carbon content slices and shale porosity slices.

[0199] It should be noted that: the device for predicting the geological sweet spot parameters of continental shale formations provided in the above embodiment only uses the division of the above functional modules as an example when performing data processing. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the device for predicting the geological sweet spot parameters of continental shale formations provided in the above embodiment and the method for predicting the geological sweet spot parameters of continental shale formations are of the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

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

[0201] It is worth noting that the computer-readable storage medium mentioned in the embodiments of the present application may 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 to implement the above embodiments can be implemented by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. The computer instructions can be stored in the above-mentioned computer readable storage medium.

[0203] That is, in some embodiments, a computer program product comprising instructions is also provided, which, when executed on a computer, enables the computer to execute the steps of the above-mentioned method for predicting the geological sweet spot parameters of the continental shale formation.

[0204] In the present application, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance. The term "plurality" refers to two or more than two, unless otherwise clearly defined.

[0205] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the present application disclosed herein. The present application is intended to cover any variations, uses or adaptations of the present application, which follow the general principles of the present application and include common knowledge or customary techniques in the art that are not disclosed in the present application. The specification and examples are intended to be exemplary only.

[0206] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A method for predicting geological sweet spot parameters of continental shale formations, characterized in that: The geological sweet spot parameters include geological parameters, and the method includes: Acquire core indoor test data and well logging curve data of the target continental shale formation, wherein the core indoor test data includes geological parameters of multiple depth points, and the well logging curve data includes well logging curve data corresponding to multiple well logging parameters; Based on the geological parameters of the multiple depth points and the logging curve data corresponding to the multiple logging parameters corresponding to the multiple depth points in the logging curve data, a corresponding relationship between each logging parameter data and the core chamber test data is established; Based on the correspondence between each well logging parameter data and the core indoor test data, a sensitive well logging parameter is determined, and the correspondence between the sensitive well logging parameter data and the core indoor test data is determined as a target correspondence; Establish a geological parameter interpretation model based on the well logging curve data corresponding to sensitive well logging parameters; Based on the target corresponding relationship, using inversion technology to obtain a first sensitive logging parameter attribute body; Based on the first sensitive logging parameter attribute body and the geological parameter interpretation model, a geological parameter attribute body of the target continental shale formation is obtained.

2. The method for predicting the parameters of the continental shale formation geological sweet spot according to claim 1, characterized in that: The establishing of a correspondence between each well logging parameter data and the core chamber test data based on the geological parameters of the multiple depth points and the well logging curve data corresponding to the multiple well logging parameters corresponding to the multiple depth points in the well logging curve data comprises: For the well logging curve data corresponding to each well logging parameter, a two-dimensional coordinate system is established with the well logging parameter as the horizontal axis and the geological parameter 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 geological sweet spot parameters of continental shale formations according to claim 1, characterized in that: The geological parameters include porosity, free hydrocarbon content and organic carbon content, the target correspondence includes a target correspondence corresponding to porosity, a target correspondence corresponding to free hydrocarbon content and a target correspondence corresponding to organic carbon content, and based on the target correspondence, obtaining the first sensitive logging parameter attribute body by using the inversion technology includes: Based on the target corresponding relationship corresponding to the porosity, a first sensitive logging parameter attribute body corresponding to the porosity is obtained by using a prestack geostatistical inversion technique; Based on the target corresponding relationship corresponding to the free hydrocarbon content, using elastic parameter inversion technology, a first sensitive logging parameter attribute body corresponding to the free hydrocarbon content is obtained; Based on the target corresponding relationship corresponding to the organic carbon content, the elastic parameter response technology is used to obtain a first sensitive logging parameter attribute body corresponding to the organic carbon content.

4. The method for predicting geological sweet spot parameters of continental shale formations according to claim 1, characterized in that: The geological parameters include porosity, free hydrocarbon content and organic carbon content, the sensitive logging parameters include sensitive logging parameters corresponding to porosity, sensitive logging parameters corresponding to free hydrocarbon content and sensitive logging parameters corresponding to organic carbon content, and the establishment of a geological parameter interpretation model based on logging curve data corresponding to the sensitive logging parameters includes: Based on the well logging curve data corresponding to the sensitive logging parameters corresponding to the porosity, a porosity interpretation model is established; Based on the well logging curve data corresponding to the sensitive logging parameters corresponding to the free hydrocarbon content, a free hydrocarbon content interpretation model is established through multivariate linear regression analysis; Based on the well logging curve data corresponding to the sensitive logging parameters corresponding to the organic carbon content, the organic carbon content interpretation model was established through multivariate linear regression analysis.

5. The method for predicting the parameters of the geological sweet spot of the continental shale formation according to claim 4, characterized in that: The sensitive logging parameters corresponding to the porosity include sensitive logging parameters corresponding to the porosity of shale formations and sensitive logging parameters corresponding to the porosity of sandstones. The porosity interpretation model is established based on the logging curve data corresponding to the sensitive logging parameters corresponding to the porosity, including: Based on the sensitive logging parameters corresponding to the porosity of shale formations, a shale formation porosity interpretation model is established; Based on the sensitive logging parameters corresponding to the sandstone porosity, a sandstone porosity interpretation model is established; The porosity interpretation model is obtained based on the shale formation porosity interpretation model and the sandstone porosity interpretation model.

6. The method for predicting geological sweet spot parameters of continental shale formations according to claim 1, characterized in that: The geological sweet spot parameter also includes an overrun effect, and the method further includes: Based on the geological parameter attribute body of the target continental shale formation, a transcendence effect attribute body of the target continental shale formation is obtained.

7. The method for predicting geological sweet spot parameters of continental shale formations according to claim 6, characterized in that: The geological parameter attribute body includes a free hydrocarbon content attribute body, an organic carbon content attribute body and a shale porosity attribute body. Based on the geological parameter attribute body of the target continental shale formation, the surpassing effect attribute body of the target continental shale formation is obtained, which includes: Based on the free hydrocarbon content attribute body and the organic carbon content attribute body in the geological parameter attribute body, the surpassing effect attribute body of the target continental shale formation is obtained by attribute fusion.

8. The method for predicting geological sweet spot parameters of continental shale formations according to claim 1, characterized in that: The geological sweet spot parameters also include rock type, and the method further includes: Based on the target corresponding relationship, obtaining an attribute body of a second sensitive logging parameter; Slicing the attribute body of the second sensitive logging parameter to obtain attribute body slices; Based on the attribute volume slice, the rock type of the target continental shale formation is determined.

9. The method for predicting geological sweet spot parameters of continental shale formations according to claim 1, characterized in that: 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 method further includes: The free hydrocarbon content attribute body, the organic carbon content attribute body and the shale porosity attribute body are sliced ​​respectively to obtain free hydrocarbon content slices, organic carbon content slices and shale porosity slices.

10. A device for predicting geological sweet spot parameters of continental shale formations, characterized in that: The device comprises: The first acquisition module is used to acquire the core indoor test data and well logging curve data of the target continental shale formation, wherein the core indoor test data includes geological parameters of multiple depth points, and the well logging curve data includes well logging curve data corresponding to multiple well logging parameters; A first establishing module is used to establish a corresponding relationship between each well logging parameter data and the core chamber test data based on the geological parameters of the multiple depth points and the well logging curve data corresponding to the multiple well logging parameters corresponding to the multiple depth points in the well logging curve data; A determination module, configured to determine a sensitive logging parameter based on a correspondence between each logging parameter data and the core chamber test data, and determine the correspondence between the sensitive logging parameter data and the core chamber test data as a target correspondence; The second establishment module is used to establish a geological parameter interpretation model based on the well logging curve data corresponding to the sensitive well logging parameters; A second acquisition module is used to acquire a first sensitive logging parameter attribute body based on the target corresponding relationship by using an inversion technology; An obtaining module is used to obtain the geological parameter attribute body of the target continental shale formation based on the first sensitive logging parameter attribute body and the geological parameter interpretation model.

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