Oil and gas layer identification methods, equipment, media and products

Abstract

The present application provides an oil and gas layer identification method, equipment, medium and product. The method includes: obtaining the first well logging data of the target formation of the target well logging at different depths; obtaining the first apparent granularity median of the target formation of the target well logging at different depths according to the first well logging data and the apparent granularity median model; obtaining the first apparent water layer resistivity of the target formation of the target well logging at different depths according to the first apparent granularity median and the apparent water layer resistivity model; fitting the apparent water layer resistivity curve of the target formation under the target well logging according to the first apparent water layer resistivity; obtaining the deep resistivity curve of the target formation under the target well logging; and identifying the oil and gas layers according to the apparent water layer resistivity curve and the deep resistivity curve. The oil and gas layer identification method provided in the present application can also achieve a good identification effect for unconventional oil and gas layers when identifying the oil and gas layers.

Description

Technical Field

[0001] The present application relates to the technical field of oil reservoir development, and in particular to an oil and gas layer identification method, equipment, medium and product. Background Art

[0002] Oil and gas layer identification is an important part of the oil reservoir development process. The results of oil and gas layer identification can guide the actual production of the oil field. Doing a good job of oil and gas layer identification can reduce the cost of oil and gas extraction. Therefore, relevant technicians have been studying new methods for oil and gas layer identification.

[0003] In the prior art, when developing an oil reservoir, an exploratory well is generally drilled first, and rock samples of a specific stratum are obtained by coring in the exploratory well. A porosity model and a saturation model are established based on the results of rock physics experimental data analysis of the rock samples, combined with logging data such as natural gamma curves, logging density curves, and logging resistivity curves. Finally, the saturation model is used to calculate the oil saturation of the specific stratum of the well to be logged. If the oil saturation is greater than the lower limit standard value, it can be determined that the stratum is an oil and gas layer.

[0004] However, the identification results obtained by the methods used in the existing technology for unconventional oil and gas layers, such as low porosity and low permeability formations with high mud content, often produce large deviations. Summary of the invention

[0005] The present application provides an oil and gas layer identification method, equipment, medium and product to solve the technical problem in the prior art that the identification results obtained for unconventional oil and gas layers, such as low porosity and low permeability formations with high mud content, often have large deviations.

[0006] In a first aspect, the present application provides an oil and gas layer identification method, comprising: obtaining first well logging data of a target stratum of a target well logging at different depths, the well logging data including well logging natural gamma value and well logging density; obtaining a first apparent median granularity of the target stratum of the target well logging at different depths according to the first well logging data and an apparent median granularity model; the apparent median granularity model is pre-established by fitting based on second well logging data and granularity median of the target stratum of a known well logging at different depths; the known well logging and the target well logging belong to the same oil field; obtaining a first apparent water layer of the target stratum of the target well logging at different depths according to the first apparent median granularity and an apparent water layer resistivity model. Resistivity; the apparent water layer resistivity model is pre-established by fitting based on the second apparent granularity median of the pure water layer at multiple depths of the known logging and the measured second apparent water layer resistivity; the second apparent granularity median is obtained according to the third logging data of the pure water layer at multiple depths and the apparent granularity median model; according to the first apparent water layer resistivity, the apparent water layer resistivity curve of the target formation under the target logging is fitted; the deep resistivity curve of the target formation under the target logging is obtained, and the deep resistivity curve is obtained by fitting the resistivity measured at different depths of the target formation of the target logging; according to the apparent water layer resistivity curve and the deep resistivity curve, oil and gas layers are identified.

[0007] Optionally, in the method as described above, before obtaining the first apparent water layer resistivity of the target formation of the target well logging at different depths according to the first apparent grain size median and the apparent water layer resistivity model, the method further comprises: obtaining second well logging data and grain size median of the target formation at different depths of the known well logging by measuring; performing binary fitting on the second well logging data and the grain size median to establish an initial apparent grain size median model, wherein the initial apparent grain size median model is:

[0008] LD = A × GR + B × DEN + C;

[0009] Among them, LD is the median apparent grain size, GR is the logging natural gamma value, DEN is the logging density, and A, B, and C are all constants;

[0010] The apparent median granularity model is established by substituting the known well logging data and the median granularity in the target formation into the initial apparent median granularity model and determining the constants in the apparent median granularity model.

[0011] Optionally, the method as described above, after substituting the well logging data and the median granularity of the known well logging in the target formation into the initial median apparent granularity model to determine the constants in the median apparent granularity model to establish the median apparent granularity model, further comprises: obtaining third well logging data and a second apparent water layer resistivity of the pure water layer of the known well logging at multiple depths by measurement; obtaining the second apparent granularity median of the pure water layer at the multiple depths according to the third well logging data and the median apparent granularity model; establishing the apparent water layer resistivity model by fitting the second median apparent granularity and the second apparent water layer resistivity, wherein the apparent water layer resistivity model is:

[0012] R csw =M×e N×LD

[0013] Among them, R csw is the apparent resistivity of the water layer, LD is the apparent median particle size, and M and N are constants.

[0014] Optionally, in the method as described above, the oil and gas layer identification is performed based on the apparent water layer resistivity curve and the deep resistivity curve, including: if the apparent water layer resistivity curve and the deep resistivity curve do not overlap, determining that the target formation of the target well logging is an oil and gas layer; if the apparent water layer resistivity curve and the deep resistivity curve overlap, determining that the target formation of the target well logging is not an oil and gas layer.

[0015] In a second aspect, an embodiment of the present application provides an oil and gas layer identification device, including: an acquisition module, used to acquire first logging data of a target stratum of a target well logging at different depths, the logging data including a natural gamma value and a logging density; a calculation module, used to obtain a first apparent median granularity of the target stratum of the target well logging at different depths according to the first logging data and an apparent median granularity model; the apparent median granularity model is pre-established by fitting the second logging data and the median granularity of the target stratum at different depths based on a known well logging; the known well logging and the target well logging belong to the same oil field; the calculation module is also used to obtain a first apparent median granularity of the target stratum of the target well logging at different depths according to the first apparent median granularity and an apparent water layer resistivity model. Water layer resistivity; the apparent water layer resistivity model is pre-established by fitting based on the second apparent granularity median of the pure water layer at multiple depths of the known logging and the measured second apparent water layer resistivity; the second apparent granularity median is obtained based on the third logging data of the pure water layer at multiple depths and the apparent granularity median model; the calculation module is also used to fit the apparent water layer resistivity curve of the target formation under the target logging according to the first apparent water layer resistivity; the acquisition module is also used to obtain the deep resistivity curve of the target formation under the target logging, and the deep resistivity curve is obtained by fitting the resistivity of the target formation of the target logging at different depths; the identification module identifies the oil and gas layer according to the apparent water layer resistivity curve and the deep resistivity curve.

[0016] Optionally, the device as described above further includes: a modeling module, specifically used to obtain second well logging data and median granularity of the target formation at different depths of known well logging by measurement; perform binary fitting on the second well logging data and the median granularity to establish an initial median apparent granularity model, wherein the initial median apparent granularity model is:

[0017] LD = A × GR + B × DEN + C;

[0018] Among them, LD is the median apparent grain size, GR is the logging natural gamma value, DEN is the logging density, and A, B, and C are all constants;

[0019] The apparent median granularity model is established by substituting the known well logging data and the median granularity in the target formation into the initial apparent median granularity model and determining the constants in the apparent median granularity model.

[0020] Optionally, in the device as described above, the modeling module is further specifically used to: obtain the third well logging data and the second apparent water layer resistivity of the pure water layer of the known well logging at multiple depths by measuring; obtain the second apparent granularity median of the pure water layer at the multiple depths according to the third well logging data and the apparent granularity median model; establish the apparent water layer resistivity model by fitting the second apparent granularity median and the second apparent water layer resistivity, and the apparent water layer resistivity model is:

[0021] R csw =M×e N×LD

[0022] Among them, R csw is the apparent resistivity of the water layer, LD is the apparent median particle size, and M and N are constants.

[0023] Optionally, in the device as described above, the identification module is specifically used to: if the apparent water layer resistivity curve and the deep resistivity curve do not overlap, determine that the target formation of the target well logging is an oil and gas layer; if the apparent water layer resistivity curve and the deep resistivity curve overlap, determine that the target formation of the target well logging is not an oil and gas layer.

[0024] In a third aspect, an embodiment of the present application provides an oil and gas layer identification device, including:

[0025] Memory, processor;

[0026] Memory; Memory for storing instructions executable by the processor;

[0027] Wherein, the processor is configured to: execute processor-executable instructions stored in the memory to implement the oil and gas layer identification method as described in the first aspect.

[0028] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer execution instructions, and when the computer execution instructions are executed by a processor, they are used to implement the oil and gas layer identification method as described in the first aspect.

[0029] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the oil and gas layer identification method described in the first aspect.

[0030] The embodiment of the present application provides an oil and gas layer identification method, device, medium and product, and pre-establishes an apparent granularity median model and an apparent water layer resistivity model. When it is necessary to identify the oil and gas layers of the target well logging, the apparent water layer resistivity curve can be obtained according to the first well logging data, the apparent granularity median model and the apparent water layer resistivity model, and then the oil and gas layers can be identified according to the apparent water layer resistivity curve and the deep resistivity curve obtained by measurement. The solution provided in the embodiment of the present application can also achieve good identification effect for unconventional oil and gas layers, such as low porosity and low permeability formations with high mud content, when identifying oil and gas layers.

[0031] It should be understood that the contents described in the above application content section are not intended to limit the key or important features of the embodiments of the present application, nor are they intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0033] Figure 1 It is a schematic diagram of the stratigraphic structure;

[0034] Figure 2 A flow chart of the oil and gas layer identification method provided in Example 1 of the present application;

[0035] Figure 3 It is the coordinate superposition diagram of the apparent water layer resistivity curve and the actual measured deep resistivity curve;

[0036] Figure 4 A flow chart of a method for establishing a median model of visual granularity provided in Embodiment 2 of the present application;

[0037] Figure 5 The actual fitting data of a certain formation in a certain oil field;

[0038] Figure 6 It is a comparison chart between the median apparent grain size and the median grain size measured by core sampling;

[0039] Figure 7 A flow chart of a method for establishing an apparent water layer resistivity model provided in Example 3 of the present application;

[0040] Figure 8 This is a comparison chart of the apparent water layer resistivity curve and the actual measured deep resistivity curve of a pure water formation in an oil field;

[0041] Fig. 9 A schematic diagram of the structure of an oil and gas layer identification device provided in Example 4 of the present application;

[0042] Fig.10 This is a structural schematic diagram of an oil and gas layer identification device provided in Example 5 of the present application.

[0043] 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

[0044] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0045] In order to clearly understand the technical solution of the present application, the solution of the prior art is first introduced in detail.

[0046] In the prior art, it is usually necessary to use experimental methods to identify oil and gas layers. Figure 1 As shown, it is a schematic diagram of the stratigraphic structure. The stratigraphic structure of this example includes an oil field 10. Multiple wells 11 are drilled in the oil field 10, and the area under the oil field is stratigraphically distributed. For example, the oil field 10 includes strata 12 and strata 13. Specifically, firstly, the logging data of the exploration well is obtained by coring the exploration well. According to the logging data, the porosity model is obtained by analyzing the relationship between porosity and logging density; then, the coring sample is subjected to a rock electrical experiment to determine the Archie formula.

[0047] S W =(a×b×R W ) / (RT×Φ^m))^(1 / n);

[0048] Among them, a is the lithology coefficient related to lithology, b is the constant related to lithology, m is the cementation index, n is the saturation index, S W is water saturation, R W is the formation water resistivity, RT is the formation resistivity, and Φ is the porosity.

[0049] The values ​​of the parameters a, b, m, and n in are used to establish a saturation model. When oil and gas layer identification is required, the oil saturation of a specific formation is calculated based on the logging data of the well to be logged and the saturation model. If the oil saturation is greater than the lower limit standard value, the formation can be determined to be an oil and gas layer.

[0050] However, the identification results obtained by the methods used in the existing technology for unconventional oil and gas layers, such as low porosity and low permeability formations with high mud content, often produce large deviations.

[0051] In view of the above technical problems, in the embodiments of the present application, an apparent grain size median model and an apparent water layer resistivity model are pre-established. When it is necessary to identify the oil and gas layers for the target well logging, an apparent water layer resistivity curve can be obtained based on the first well logging data, the apparent grain size median model and the apparent water layer resistivity model, and then the oil and gas layers can be identified based on the apparent water layer resistivity curve and the deep resistivity curve obtained by measurement. The solution provided in the embodiments of the present application can also achieve good identification effects for unconventional oil and gas layers, such as low porosity and low permeability formations with high mud content, when identifying oil and gas layers.

[0052] The following is a detailed description of the embodiments of the present application with reference to the accompanying drawings:

[0053] Embodiment 1

[0054] Figure 2 This is a flow chart of the oil and gas layer identification method provided in Example 1 of the present application, as shown in Figure 2 As shown, the execution subject of this embodiment is an oil and gas layer identification device. The oil and gas layer identification method provided by this embodiment includes:

[0055] Step 101, obtaining first well logging data of a target formation of a target well logging at different depths, wherein the well logging data includes a well logging natural gamma value and a well logging density.

[0056] Among them, the target well logging refers to the well to be logged, and the target formation is the formation to be identified. In practical applications, the target formation may be a sandstone stratum, and the solution of the present application is used to identify the target formation and determine whether it is an oil and gas layer. Among them, the well logging data can be obtained by well logging. Specifically, in one embodiment, the first well logging data can be obtained by measuring once every fixed distance in the depth direction of the target well logging, or it can also be measured at irregular intervals, which is not limited in this embodiment.

[0057] For example, when oil and gas layer identification is required, an oil and gas layer identification instruction can be sent to the oil and gas layer identification device. After receiving the oil and gas layer identification instruction, the oil and gas layer identification device can obtain the first well logging data of the target formation of the target well logging at different depths.

[0058] Step 102, obtaining the first apparent median granularity of the target formation of the target logging at different depths according to the first logging data and the apparent median granularity model; the apparent median granularity model is pre-established by fitting based on the second logging data and the median granularity of the target formation of the known logging at different depths; the known logging and the target logging belong to the same oil field.

[0059] Among them, the "median particle size" here refers to the median particle size measured by experiment, expressed as D50. After a sample is broken into pieces, the particle size of each particle is arranged in order from large to small. At 50% of the discharge queue, the corresponding particle size is selected as the median particle size of the sample, that is, the median particle size of the target formation. Its physical meaning is that particles with a particle size larger than it account for 50%, and particles with a particle size smaller than it also account for 50%. D50 is also called the median particle size or median particle size, and is often used to represent the average particle size of powder. Through the apparent median particle size model provided in this application, when it is necessary to obtain the median particle size of a certain formation in a certain well logging, there is no need to perform coring, that is, the apparent median particle size model established can be used to calculate the median particle size that can accurately characterize its median particle size, thereby simplifying the process and reducing costs.

[0060] For example, in combination with practical applications, the acquired first logging data is substituted into a pre-established median apparent granularity model, and the first median apparent granularity of the target formation of the target logging at different depths is calculated in sequence and saved.

[0061] Step 103, based on the first apparent grain size median and the apparent water layer resistivity model, obtain the first apparent water layer resistivity of the target formation of the target logging at different depths; the apparent water layer resistivity model is pre-established by fitting based on the second apparent grain size median of the pure water layer at multiple depths of the known logging and the measured second apparent water layer resistivity; the second apparent grain size median is obtained based on the third logging data of the pure water layer at multiple depths and the apparent grain size median model.

[0062] The apparent water layer resistivity model is used to calculate the apparent water layer resistivity of the target stratum at different depths when the target stratum is assumed to be a pure water layer, that is, the pure water layer resistivity. That is, this step calculates the apparent water layer resistivity at different depths when the target stratum is assumed to be a pure water layer.

[0063] For example, in combination with practical applications, the first apparent granularity median calculated in step 102 is substituted into a pre-established apparent water layer resistivity model to calculate the first apparent water layer resistivity of the target formation of the target well logging at different depths.

[0064] Step 104: fitting an apparent water layer resistivity curve of a target formation under target logging according to the first apparent water layer resistivity.

[0065] For example, in combination with actual application, fitting is performed according to the calculated first apparent water layer resistivity. There are many fitting methods, which are not limited in this embodiment. In this embodiment, the oil and gas layer identification device uses fitting software to fit the first apparent water layer resistivity to obtain an apparent water layer resistivity curve.

[0066] Step 105 , obtaining a deep resistivity curve of the target formation in the target well logging, wherein the deep resistivity curve is obtained by fitting the resistivity of the target formation in the target well logging measured at different depths.

[0067] Taking practical applications as an example, the oil and gas layer identification equipment can obtain the resistivity measured at different depths through the measuring equipment, and obtain the deep resistivity curve after fitting these resistivity. The obtained deep resistivity curve can be stored in the oil and gas layer identification equipment for subsequent oil and gas layer identification.

[0068] Step 106, identifying the oil and gas layers according to the apparent water layer resistivity curve and the deep resistivity curve.

[0069] For example, in combination with actual applications, the method for identifying oil and gas layers can be to compare the apparent water layer resistivity curve and the deep resistivity curve, or to compare them without overlapping. This is not limited in the present embodiment. Exemplarily, based on the overlapping comparison method, if the apparent water layer resistivity curve and the deep resistivity curve do not overlap, it is determined that the target formation of the target well logging is an oil and gas layer; if the apparent water layer resistivity curve and the deep resistivity curve overlap, it is determined that the target formation of the target well logging is not an oil and gas layer. In combination with images, Figure 3 is the coordinate superposition diagram of the apparent water layer resistivity curve and the actual measured deep resistivity curve, where R CSW is the apparent water layer resistivity, R t is the actual measured deep resistivity. Figure 3 As shown, the two curves intersect at 4866.5 meters, and then as the formation depth increases, the two curves gradually separate until the formation depth increases to 4878.5 meters, when the two curves intersect again, forming an obvious island-like area between the formation depths of 4866.5 meters and 4878.5 meters. According to the implementation method provided in this embodiment, it can be judged that there are oil and gas layer resources between the formation depths of 4866.5 meters and 4878.5 meters, and the oil-water interfaces are at 4866.5 meters and 4878.5 meters, respectively. Moreover, it has been confirmed through experiments that the judgment result is consistent with the actual measurement situation, indicating that the implementation method of the present application can effectively guide oil and gas production, and is particularly suitable for the identification of low porosity and low permeability oil and gas layers. In other words, Figure 3 As shown, when the coordinates of the water layer resistivity curve and the deep resistivity curve are superimposed, in a specific stratum, the two curves show a change pattern of intersection-separation-reintersection, forming an island area, and the stratum where the island area is located contains oil and gas layers.

[0070] This scheme calculates the apparent water layer resistivity curve using the apparent water layer resistivity model under the assumption that the target formation of the target logging is a pure water layer, and then compares it with the deep resistivity curve actually measured. If the two are completely coincident, it can be explained that the target formation is not an oil and gas layer, but a water layer; if the two are not completely coincident, it means that the change in the deep resistivity curve is caused by the presence of oil and gas, that is, the target formation is an oil and gas layer.

[0071] In this embodiment, the apparent grain size median model and the apparent water layer resistivity model are pre-established. When it is necessary to identify the oil and gas layers for the target well logging, the apparent water layer resistivity curve can be obtained according to the first well logging data, the apparent grain size median model and the apparent water layer resistivity model, and then the oil and gas layers can be identified according to the apparent water layer resistivity curve and the measured deep resistivity curve. The solution provided in this embodiment can also achieve a good identification effect for unconventional oil and gas layers, such as low porosity and low permeability formations with high mud content.

[0072] Embodiment 2

[0073] Figure 4 This is a flow chart of a method for establishing a median model of visual granularity provided in the second embodiment of the present application. The method provided in this embodiment is used to establish a median model of visual granularity. Accordingly, based on the first embodiment, before step 101, the following steps are included:

[0074] Step 201 : obtaining second well logging data and median granularity of a target formation of known well logging at different depths through measurement.

[0075] Step 202, perform binary fitting on the second well logging data and the median granularity to establish an initial median apparent granularity model. The initial median apparent granularity model is:

[0076] LD = A × GR + B × DEN + C;

[0077] Among them, LD is the median apparent particle size, GR is the logging natural gamma value, DEN is the logging density, and A, B, and C are all constants.

[0078] Step 203, by substituting the known well logging data and the median granularity in the target formation into the initial median apparent granularity model, constants in the median apparent granularity model are determined to establish the median apparent granularity model.

[0079] In this embodiment, the oil and gas layer identification equipment performs binary fitting on the natural gamma value and the logging density value of the well logging. Among them, this embodiment does not limit the software and method used for fitting. For example, in combination with practical applications, the second logging data of the target formation of the known well logging at different depths can be obtained from the logging data of the known well logging; then, through experimental methods, the core is first taken from the target formation of the known well logging to obtain multiple rock samples, and the physical parameters of the rock samples, that is, the rock particle size, are analyzed through the particle size experimental method, and multiple particle size medians are measured. According to the law of binary fitting, the initial apparent particle size median model is established, which can be:

[0080] LD = A × GR + B × DEN + C;

[0081] Among them, LD is the median apparent particle size, GR is the logging natural gamma value, DEN is the logging density, which is the bulk density, and A, B, and C are all constants.

[0082] In order to illustrate the effect of this solution, Figure 5 As shown, Figure 5 This is the actual fitting data of a certain formation in a certain oil field. Figure 5 The three-dimensional coordinate axes in are the median particle size, the well logging natural gamma value, and the well logging density. As an example, through the scheme of this embodiment, the median particle size, the well logging natural gamma value, and the well logging density of the formation are subjected to binary fitting, and the apparent median particle size model is obtained as follows:

[0083] LD=4.729-0.016×GR-1.36×DEN.

[0084] By comparing this functional relationship with the initial apparent grain size median model, the values ​​of constants A, B, and C can be obtained, which can be: A = -0.016, B = -1.36, C = 4.729. Among them, the values ​​of constants A, B, and C are different in different oil fields and different formations. In this embodiment, the same formation of the same oil field is studied, that is, the single apparent grain size median model established based on this scheme can be commonly applied to the same formation of the same oil field. It should be noted that the apparent grain size median model established by the scheme provided in this embodiment is suitable for low porosity and low permeability formations with high mud content. It can not only calculate the apparent grain size median, but also establish the relationship between lithology and physical properties. In the apparent grain size median model, the natural gamma value of the well logging can reflect the mud content. The larger the natural gamma value of the well logging, the higher the mud content; the well logging density can reflect the porosity. The larger the well logging density, the smaller the porosity; the smaller the well logging density, the greater the porosity. It should also be noted that the median apparent particle size in this embodiment is calculated by fitting the natural gamma value of well logging and the well logging density, which is a theoretical value. Figure 6 See, if Figure 6 As shown, Figure 6 The figure is a comparison chart of the apparent median particle size and the median particle size measured by core sampling, where the curve in the figure is the median particle size measured by core sampling, and the scattered points are the median apparent particle size obtained by fitting. Figure 6 It can be seen that the apparent median particle size obtained by the apparent median particle size model provided by this solution is closer to the value of the median particle size obtained by actual measurement. Therefore, the apparent median particle size model obtained by this embodiment has a good effect and can accurately characterize the actual median particle size.

[0085] The method for establishing the apparent median grain size model provided in this embodiment adopts a binary fitting method to fit the functional relationship between the apparent median grain size, the logging natural gamma value, and the logging density value, that is, the apparent median grain size model. Since the apparent median grain size model fitted by this embodiment is compared with the saturation model in the prior art, the apparent median grain size model not only considers the influence of porosity on logging resistivity, but also considers the influence of mud content on logging resistivity. Therefore, for unconventional low-porosity and low-permeability formations with a high mud content, the apparent median grain size calculated by this functional relationship can accurately reflect the measured median grain size.

[0086] Embodiment 3

[0087] Figure 7 The flowchart of the method for establishing the apparent water layer resistivity model provided in the third embodiment of the present application is as follows. The method for establishing the apparent water layer resistivity model provided in the present embodiment, based on any implementation mode, after establishing the apparent granularity median model, comprises the following steps:

[0088] Step 301 : obtaining third well logging data and a second apparent water layer resistivity of a pure water layer of a known well logging at multiple depths by measurement.

[0089] Step 302: Obtain the second median apparent granularity of the pure water layer at multiple depths according to the third well logging data and the median apparent granularity model.

[0090] Step 303, by fitting the second apparent particle size median and the second apparent water layer resistivity, an apparent water layer resistivity model is established. The apparent water layer resistivity model is:

[0091] R csw =M×e N×LD ;

[0092] Among them, R csw is the apparent resistivity of the water layer, LD is the apparent median particle size, and M and N are constants.

[0093] The third logging data is the logging data obtained from the pure water layer, including: the logging natural gamma value and the logging density value; the second apparent water layer resistivity is the actual resistivity of the pure water layer at multiple depths of the known logging obtained through measurement. In practical applications, whether a formation is a pure water layer can be determined through oil testing.

[0094] The following is an example of the process of establishing the apparent water layer resistivity model in conjunction with a pure water layer in a well logging of an oil field: first, the well logging is performed to obtain the third well logging data, and then the third well logging data obtained by measurement is substituted into the apparent granularity median model to obtain the second apparent granularity median at multiple depths; then the second apparent granularity median and the second apparent water layer resistivity are fitted, and the established apparent water layer resistivity model is:

[0095] R csw =M×e N×LD , where R csw is the apparent water layer resistivity, LD is the apparent grain size median, and M and N are both constants. Further fitting, specifically, by importing the second apparent grain size median and the second apparent water layer resistivity into the fitting software, the fitting curve of the apparent water layer resistivity can be obtained, and then the values ​​of M and N can be determined. The apparent water layer resistivity model is: R csw =1.113×e -1.628*LD , where R csw is the apparent water layer resistivity, and LD is the apparent median particle size.

[0096] In the above example, the correlation analysis between the apparent water layer resistivity and the apparent median particle size can be performed by software to obtain the determination coefficient R between the apparent water layer resistivity and the apparent median particle size. 2 =0.8717, which meets the determination standard of the determination coefficient greater than 0.8 commonly used in the art, so it can be shown that the apparent water layer resistivity model provided by this embodiment has a high accuracy. The determination coefficient here is also called the determination coefficient, also known as the goodness of fit. 2 The closer it is to 1, the higher the reference value of the fitting equation and the better the fitting effect; on the contrary, R 2 The closer it is to 0, the lower the reference value of the fitting equation and the worse the fitting effect. Therefore, the apparent water layer resistivity model provided in this embodiment can not only calculate the apparent water layer resistivity, but also establish the relationship between lithology, physical properties and electrical properties. Since the apparent water layer resistivity model obtained by fitting in this embodiment also contains information about the mud content compared to the existing saturation model, for unconventional, low porosity and low permeability formations with high mud content, accurate apparent water layer resistivity can be obtained by calculation.

[0097] In addition, in order to further illustrate the effect of this solution, we will give an example based on actual application. Figure 8 As shown, Figure 8This is a comparison diagram of the apparent water layer resistivity curve and the actual measured deep resistivity curve of a pure water formation in an oil field, where R csw is the apparent water layer resistivity, which is calculated by the apparent water layer resistivity model established in this scheme; R t is the actual measured deep resistivity. The two resistivity curves are superimposed in the depth direction. Figure 8 It can be seen that the two are basically coincident, which indicates that the apparent water layer resistivity model established by this embodiment can fit the apparent water layer resistivity used to accurately characterize the deep resistivity of the pure water layer.

[0098] Embodiment 4

[0099] Fig. 9 A schematic diagram of the structure of an oil and gas layer identification device provided in Example 4 of the present application is shown in FIG. Fig. 9 As shown, the oil and gas layer identification device provided in this embodiment includes: an acquisition module 41, a calculation module 42 and an identification module 43.

[0100] Among them, the acquisition module 41 is used to obtain the first logging data of the target stratum of the target logging at different depths, and the logging data includes the natural gamma value and the logging density of the well logging; the calculation module 42 is used to obtain the first apparent median granularity of the target stratum of the target logging at different depths according to the first logging data and the apparent median granularity model; the apparent median granularity model is pre-established by fitting based on the second logging data and the median granularity of the target stratum of the known well logging at different depths; the known well logging and the target well logging belong to the same oil field; the calculation module 42 is also used to obtain the first apparent water layer resistivity of the target stratum of the target logging at different depths according to the first apparent median granularity and the apparent water layer resistivity model; The layer resistivity model is pre-established by fitting based on the second apparent grain size median of the pure water layer at multiple depths of the known logging and the measured second apparent water layer resistivity; the second apparent grain size median is obtained based on the third logging data of the pure water layer at multiple depths and the apparent grain size median model; the calculation module 42 is also used to fit the apparent water layer resistivity curve of the target formation under the target logging based on the first apparent water layer resistivity; the acquisition module 41 is also used to obtain the deep resistivity curve of the target formation under the target logging, and the deep resistivity curve is obtained by fitting the resistivity measured at different depths of the target formation of the target logging; the identification module 43 identifies the oil and gas layer based on the apparent water layer resistivity curve and the deep resistivity curve.

[0101] The oil and gas layer identification device provided in this embodiment can execute the technical solution of the aforementioned method embodiment 1, and its implementation principle and technical effects are similar, which will not be repeated here.

[0102] Optionally, the above device further includes: a modeling module 44, which is specifically used to obtain second logging data and median granularity of a target formation of known logging at different depths by measurement; perform binary fitting on the second logging data and the median granularity to establish an initial median apparent granularity model, and the initial median apparent granularity model is:

[0103] LD = A × GR + B × DEN + C;

[0104] Among them, LD is the median apparent grain size, GR is the logging natural gamma value, DEN is the logging density, and A, B, and C are all constants;

[0105] The apparent median granularity model is established by substituting the known well logging data and the median granularity in the target formation into the initial apparent median granularity model and determining the constants in the apparent median granularity model.

[0106] Optionally, in the above device, the modeling module 44 is further specifically used to: obtain the third well logging data and the second apparent water layer resistivity of the pure water layer at multiple depths of the known well logging by measuring; obtain the second apparent grain size median of the pure water layer at multiple depths according to the third well logging data and the apparent grain size median model; establish the apparent water layer resistivity model by fitting the second apparent grain size median and the second apparent water layer resistivity, and the apparent water layer resistivity model is:

[0107] R csw =M×e N×LD

[0108] Among them, R csw is the apparent resistivity of the water layer, LD is the apparent median particle size, and M and N are constants.

[0109] Optionally, in the above device, the identification module 43 is specifically used to: if the apparent water layer resistivity curve and the deep resistivity curve do not overlap, determine that the target formation of the target well logging is an oil and gas layer; if the apparent water layer resistivity curve and the deep resistivity curve overlap, determine that the target formation of the target well logging is not an oil and gas layer.

[0110] The oil and gas layer identification device provided in this embodiment can execute the technical solution of the aforementioned method embodiment, and its implementation principle and technical effect are similar, which will not be repeated here.

[0111] Embodiment 5

[0112] Fig.10 This is a schematic diagram of the structure of an oil and gas layer identification device provided in Example 5 of the present application. Fig.10 As shown, the oil and gas layer identification device includes: a memory 1 and a processor 2.

[0113] The memory 1 is configured to store processor executable instructions. The memory 1 can be implemented by any type of volatile or non-volatile memory device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0114] The memory 1 and the processor 2 are interconnected by circuits. Specifically, the various components are interconnected by buses and can be installed on a common motherboard or in other ways as required. The processor can process instructions executed in the oil and gas layer identification device.

[0115] Embodiment 6

[0116] Embodiment 6 of the present application provides a computer-readable storage medium.

[0117] When the instructions in the storage medium are executed by the processor, the oil and gas layer identification device can execute the above-mentioned oil and gas layer identification method.

[0118] Embodiment 7

[0119] Embodiment 7 of the present application provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the above-mentioned oil and gas layer identification method is implemented.

[0120] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the application disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes 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, and the true scope and spirit of the present application are indicated by the following claims.

[0121] 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 identifying oil and gas layers, characterized in that: include: Acquire first well logging data of a target formation of a target well logging at different depths, wherein the well logging data includes a well logging natural gamma value and a well logging density; According to the first well logging data and the median apparent granularity model, a first median apparent granularity of a target formation of the target well logging at different depths is obtained; the median apparent granularity model is pre-established by fitting based on second well logging data and median granularity of the target formation at different depths of a known well logging; the known well logging and the target well logging belong to the same oil field; The second well logging data and the median granularity of the target formation at different depths of the known well logging are obtained by measurement, and the second well logging data and the median granularity are subjected to binary fitting to establish an initial median apparent granularity model. The initial median apparent granularity model is: LD = A × GR + B × DEN + C; Among them, LD is the median apparent grain size, GR is the logging natural gamma value, DEN is the logging density, and A, B, and C are all constants; Substituting the known well logging data and the median granularity in the target formation into the initial median apparent granularity model, determining constants in the median apparent granularity model, so as to establish the median apparent granularity model; The third well logging data and the second apparent water layer resistivity of the pure water layer at multiple depths of the known well logging are obtained by measurement, and the second apparent grain size median value of the pure water layer at the multiple depths is obtained according to the third well logging data and the apparent grain size median model. The apparent water layer resistivity model is established by fitting the second apparent grain size median value and the second apparent water layer resistivity. The apparent water layer resistivity model is: R csw =M×e N×LD Among them, R csw is the apparent water layer resistivity, LD is the apparent median particle size, and M and N are constants; According to the first apparent grain size median value and the apparent water layer resistivity model, the first apparent water layer resistivity of the target formation of the target well logging at different depths is obtained; the apparent water layer resistivity model is pre-established by fitting based on the second apparent grain size median value of the pure water layer at multiple depths of the known well logging and the measured second apparent water layer resistivity; the second apparent grain size median value is obtained based on the third well logging data of the pure water layer at multiple depths and the apparent grain size median value model; fitting an apparent water layer resistivity curve of a target formation under the target well logging according to the first apparent water layer resistivity; Acquire a deep resistivity curve of the target formation in the target well logging, wherein the deep resistivity curve is obtained by fitting the resistivity of the target formation in the target well logging measured at different depths; Oil and gas layers are identified based on the apparent water layer resistivity curve and the deep resistivity curve. If the apparent water layer resistivity curve and the deep resistivity curve do not overlap, it is determined that the target formation of the target well logging is an oil and gas layer. If the apparent water layer resistivity curve and the deep resistivity curve overlap, it is determined that the target formation of the target well logging is not an oil and gas layer.

2. An oil and gas layer identification device, comprising: An acquisition module, used for acquiring first well logging data of a target formation of a target well logging at different depths, wherein the well logging data includes a well logging natural gamma value and a well logging density; A calculation module, for obtaining a first apparent granularity median of a target formation of the target well logging at different depths according to the first well logging data and an apparent granularity median model; the apparent granularity median model is pre-established by fitting based on second well logging data and granularity median of the target formation at different depths of a known well logging; the known well logging and the target well logging belong to the same oil field; The modeling module is further used to obtain the second well logging data and the median granularity of the target formation at different depths of the known well logging by measurement; perform binary fitting on the second well logging data and the median granularity to establish an initial median apparent granularity model, wherein the initial median apparent granularity model is: LD = A × GR + B × DEN + C; Wherein, LD is the median apparent granularity, GR is the natural gamma value of the well logging, DEN is the well logging density, and A, B, and C are all constants; the constants in the median apparent granularity model are determined by substituting the well logging data and the median granularity of the known well logging in the target formation into the initial median apparent granularity model, so as to establish the median apparent granularity model; The modeling module is further used to obtain the third well logging data and the second apparent water layer resistivity of the pure water layer of the known well logging at multiple depths by measurement, obtain the second apparent grain size median of the pure water layer at the multiple depths according to the third well logging data and the apparent grain size median model, and establish the apparent water layer resistivity model by fitting the second apparent grain size median and the second apparent water layer resistivity, wherein the apparent water layer resistivity model is: R csw =M×e N×LD Among them, R csw is the apparent water layer resistivity, LD is the apparent median particle size, and M and N are constants; The calculation module is further used to obtain the first apparent water layer resistivity of the target formation of the target well logging at different depths according to the first apparent grain size median value and the apparent water layer resistivity model; the apparent water layer resistivity model is pre-established by fitting based on the second apparent grain size median value of the pure water layer at multiple depths of the known well logging and the measured second apparent water layer resistivity; the second apparent grain size median value is obtained based on the third well logging data of the pure water layer at multiple depths and the apparent grain size median value model; The calculation module is further used to fit the apparent water layer resistivity curve of the target formation under the target well logging according to the first apparent water layer resistivity; The acquisition module is further used to acquire a deep resistivity curve of the target formation in the target well logging, wherein the deep resistivity curve is obtained by fitting the resistivity of the target formation in the target well logging measured at different depths; The identification module identifies the oil and gas layer according to the apparent water layer resistivity curve and the deep resistivity curve. If the apparent water layer resistivity curve and the deep resistivity curve do not overlap, it is determined that the target formation of the target well logging is the oil and gas layer. If the apparent water layer resistivity curve and the deep resistivity curve overlap, it is determined that the target formation of the target well logging is not the oil and gas layer.

3. An oil and gas layer identification device, comprising: Memory, processor; Memory; a memory for storing instructions executable by the processor; Wherein, the processor is configured to: execute processor-executable instructions stored in the memory to implement the oil and gas layer identification method as described in claim 1.

4. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the oil and gas layer identification method as claimed in claim 1 when executed by a processor.

5. A computer program product, comprising a computer program, which implements the oil and gas layer identification method described in claim 1 when executed by a processor.

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