Method and device for determining mineralization degree of formation water, medium and electronic equipment

Abstract

The invention discloses a method and device for determining the mineralization degree of formation water, a medium and electronic equipment. The method comprises the following steps: calculating the resistivity of shaft mud by acquiring the resistivity of ground mud, the temperature of the ground mud and the temperature of a shaft; a formation water mineralization degree model is constructed, parameters of the formation water mineralization degree model are determined, and the formation water mineralization degree model is used for calculating the formation water mineralization degree; and calculating the formation water mineralization degree according to the shaft mud resistivity, the formation water mineralization degree model and the formation water mineralization degree model parameters. According to the technical scheme provided by the invention, the accuracy of calculating the formation water mineralization degree can be improved.

Description

Technical Field

[0001] The present application belongs to the technical field of formation water mineralization, and in particular, relates to a method, device, medium and electronic equipment for determining formation water mineralization. Background Art

[0002] At present, the methods for determining the mineralization of formation water in reservoirs mainly include indoor analysis and testing of formation water sampling to determine the mineralization of formation water, and using natural potential logging data to determine the mineralization of formation water. The method of indoor analysis and testing of formation water sampling to determine the mineralization of formation water is complicated and slow. The method of using natural potential logging data to determine the mineralization of formation water ignores the influence of reservoir physical properties on natural potential logging, which will lead to low accuracy of calculated formation water mineralization. Therefore, a method is needed to improve the accuracy of calculating formation water mineralization. Summary of the invention

[0003] The embodiments of the present application provide a method, device, medium and electronic equipment for determining formation water salinity, wherein the method can improve the accuracy of calculating formation water salinity.

[0004] Other features and advantages of the present application will become apparent from the following detailed description, or may be learned in part by the practice of the present application.

[0005] According to a first aspect of an embodiment of the present application, a method for determining formation water mineralization is provided, characterized in that the method includes: calculating the resistivity of wellbore mud by acquiring the surface mud resistivity, the surface mud temperature, and the wellbore temperature; constructing a formation water mineralization model and determining formation water mineralization model parameters, wherein the formation water mineralization model is used to calculate the formation water mineralization; calculating the formation water mineralization based on the wellbore mud resistivity, the formation water mineralization model, and the formation water mineralization model parameters.

[0006] In some embodiments of the present application, based on the above solution, the method according to claim 1 is characterized in that before constructing the formation water salinity model, the method further comprises:

[0007] By analyzing the relationship between static natural potential and formation water salinity, mud filtrate salinity, and reservoir effective porosity, a static natural potential logging response model is constructed. The static natural potential logging response model is used to construct a formation water salinity model, a first mud filtrate salinity model, and a second mud filtrate salinity model.

[0008] In some embodiments of the present application, based on the above scheme, determining the formation water salinity model parameters includes:

[0009] Constructing a first mud filtrate salinity model according to the static natural potential logging response model, and constructing a second mud filtrate salinity model according to the mud filtrate salinity and the wellbore mud resistivity;

[0010] Calculating the first mud filtrate salinity for determining the formation water salinity model parameters according to the static natural potential, the measured formation water salinity, the reservoir effective porosity, the wellbore temperature, and the first mud filtrate salinity model;

[0011] constructing a regression curve corresponding to the second mud filtrate salinity model according to the wellbore mud resistivity corresponding to the measured formation water salinity and the first mud filtrate salinity;

[0012] Based on the regression curve, the formation water salinity model parameters are determined.

[0013] In some embodiments of the present application, based on the above scheme, after determining the formation water salinity model parameters, the method further includes:

[0014] Calculating the second mud filtrate salinity for verifying the accuracy of the formation water salinity model parameters according to the wellbore mud resistivity corresponding to the measured formation water salinity, the formation water salinity model parameters and the second mud filtrate salinity model;

[0015] Calculating the absolute value of the relative error according to the first mud filtrate salinity and the second mud filtrate salinity, thereby calculating the average value of the absolute value of the relative error;

[0016] By determining the average value, the accuracy of the formation water salinity model parameters is tested.

[0017] In some embodiments of the present application, based on the above scheme, the wellbore mud resistivity is calculated by the following formula:

[0018]

[0019] Among them, R mc is the wellbore mud resistivity, in Ω·m; R md is the ground mud resistivity, in Ω·m; T d is the ground mud temperature, in °C; T c is the wellbore temperature, in °C.

[0020] In some embodiments of the present application, based on the above scheme, the static natural potential logging response model is constructed by the following formula:

[0021]

[0022] Wherein, SSP is the static natural potential, in mV; T c is the wellbore temperature, in °C; Φ is the effective porosity of the reservoir, in %; C w is the mineralization of the formation water, in mg / L; a and b are the model parameters of the mineralization of the formation water, dimensionless; C mf is the mineralization of the mud filtrate, in mg / L.

[0023] In some embodiments of the present application, based on the above scheme, the first mud filtrate salinity model, the second mud filtrate salinity model, and the formation water salinity model are constructed respectively by the following formulas:

[0024] The formula for constructing the first mud filtrate salinity model is as follows:

[0025]

[0026] Among them, C mf is the mineralization degree of the mud filtrate, in mg / L; C w is the formation water mineralization, in mg / L; SSP is the static natural potential, in mV; T c is the wellbore temperature, in °C; Φ is the effective porosity of the reservoir, in %.

[0027] The formula for constructing the second mud filtrate salinity model is as follows:

[0028] C mf =a×e -b×R mc

[0029] Among them, C mf is the salinity of the mud filtrate, in mg / L; a and b are the model parameters of formation water salinity, dimensionless; R mc is the resistivity of wellbore mud, in Ω·m.

[0030] The formula for constructing the formation water salinity model is as follows:

[0031]

[0032] Among them, C w is the formation water mineralization, in mg / L; SSP is the static natural potential, in mV; T c is the wellbore temperature, in °C; Φ is the effective porosity of the reservoir, in %; a and b are the model parameters of formation water salinity, dimensionless; R mc is the resistivity of wellbore mud, in Ω·m.

[0033] Compared with the prior art, the present invention has at least the following beneficial effects:

[0034] The method for determining formation water salinity provided by the present invention first obtains the surface mud resistivity, the surface mud temperature, and the wellbore temperature, and calculates the wellbore mud resistivity.

[0035] Then, by analyzing the relationship between static natural potential and formation water salinity, mud filtrate salinity, and effective reservoir porosity, a static natural potential logging response model is constructed. Since the static natural potential is related to the mud filtrate salinity in the static natural potential logging response model, the first mud filtrate salinity model can be constructed through the static natural potential logging response model. The second mud filtrate salinity model is constructed by analyzing the relationship between the mud filtrate salinity and the wellbore mud resistivity. The formation water salinity model is constructed through the static natural potential logging response model and the second mud filtrate salinity model.

[0036] According to the measured formation water mineralization, static natural potential, wellbore temperature, effective porosity of the reservoir, and the first mud filtrate mineralization model, the first mud filtrate mineralization used to determine the formation water mineralization model parameters is calculated, and then the formation water mineralization model parameters are determined according to the first mud filtrate mineralization, the wellbore mud resistivity corresponding to the measured formation water mineralization, and the second mud filtrate mineralization model.

[0037] The formation water salinity can be accurately calculated by combining static natural potential, wellbore temperature, reservoir effective porosity, formation water salinity model parameters, wellbore mud resistivity, and formation water salinity model.

[0038] In addition, in the present application, the second mud filtrate mineralization for verifying the accuracy of the formation water mineralization model parameters can be calculated according to the wellbore mud resistivity corresponding to the measured formation water mineralization, the formation water mineralization model parameters and the second mud filtrate mineralization model, thereby calculating the absolute value of the relative error between the second mud filtrate mineralization and the first mud filtrate mineralization and the average value of the absolute value of the relative error. The accuracy of the formation water mineralization model parameters can be verified by judging the size of the average value of the absolute value of the relative error.

[0039] Therefore, the method for determining formation water salinity provided by the present invention can improve the accuracy and convenience of calculating formation water salinity.

[0040] According to a second aspect of an embodiment of the present application, a device for determining formation water mineralization is provided, characterized in that the device comprises: a first calculation unit, used to calculate the resistivity of wellbore mud by acquiring the ground mud resistivity, the ground mud temperature, and the wellbore temperature; a construction unit, used to construct a formation water mineralization model and determine the formation water mineralization model parameters, the formation water mineralization model is used to calculate the formation water mineralization; a second calculation unit, used to calculate the formation water mineralization based on the wellbore mud resistivity, the formation water mineralization model, and the formation water mineralization model parameters.

[0041] According to a third aspect of an embodiment of the present application, a computer-readable storage medium is provided, characterized in that at least one program code is stored in the computer-readable storage medium, and the at least one program code is loaded and executed by a processor to implement the operations performed by the described method.

[0042] According to a fourth aspect of an embodiment of the present application, an electronic device is provided, characterized in that the electronic device includes one or more processors and one or more memories, at least one program code is stored in the one or more memories, and the at least one program code is loaded and executed by the one or more processors to implement the operations performed by the method.

[0043] The beneficial effects of each embodiment of the second to fourth aspects mentioned above can refer to the beneficial effects of the first aspect and each embodiment of the first aspect mentioned above, and will not be repeated here.

[0044] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The drawings herein are incorporated into the specification and constitute a part of the specification, showing embodiments consistent with the present application, and together with the specification, are used to explain the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0046] Figure 1 A flow chart showing a method for determining formation water mineralization in an embodiment of the present application is shown;

[0047] Figure 2 The regression curve in the embodiment of the present application is shown;

[0048] Figure 3 A schematic diagram of the structure of a device for determining formation water mineralization in an embodiment of the present application is shown;

[0049] Figure 4 A schematic diagram of the structure of an electronic device in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0050] 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 only 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.

[0051] In addition, described feature, structure or characteristic can be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present application. However, those skilled in the art will appreciate that the technical scheme of the present application can be put into practice without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, known methods, devices, realizations or operations are not shown or described in detail to avoid blurring the various aspects of the application.

[0052] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0053] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.

[0054] The following is a detailed description of this application:

[0055] Figure 1 The flowchart of the method for determining the mineralization degree of formation water in the embodiment of the present application is shown. The method for determining the mineralization degree of formation water can be performed by a device having a computing and processing function, for example, can be performed by a device for determining the mineralization degree of formation water. Figure 1 As shown, the method for determining formation water salinity includes at least steps 110 to 130, which are described in detail as follows:

[0056] In step 110, the wellbore mud resistivity is calculated by acquiring the surface mud resistivity, the surface mud temperature, and the wellbore temperature.

[0057] In the present application, the ground mud resistivity and ground mud temperature can be obtained by real-time measurement. The wellbore temperature can be obtained by real-time measurement or analysis of the relationship between the wellbore temperature and the wellbore depth. After obtaining the ground mud resistivity, the ground mud temperature, and the wellbore temperature, the wellbore mud resistivity can be calculated according to the wellbore mud resistivity calculation formula, which is as follows:

[0058]

[0059] Among them, R mc is the wellbore mud resistivity, in Ω·m; R md is the ground mud resistivity, in Ω·m; T d is the ground mud temperature, in °C; T c is the wellbore temperature in °C.

[0060] Referring to Table 1, Table 1 shows the wellbore mud resistivity corresponding to a portion of the wellbore depths in the implementation of this application. In Table 1, the surface mud resistivity and surface mud temperature will be affected by factors such as temperature and humidity in the natural environment, so the obtained surface mud resistivity and surface mud temperature will fluctuate.

[0061]

[0062]

[0063] Continue to refer to Figure 1 In step 120, a formation water mineralization model is constructed and formation water mineralization model parameters are determined. The formation water mineralization model is used to calculate formation water mineralization.

[0064] In the present application, a static natural potential logging response model can be constructed by analyzing the relationship between the static natural potential and the formation water salinity, the mud filtrate salinity, and the effective porosity of the reservoir.

[0065] Specifically, in the process of constructing the static natural potential logging response model, since the static natural potential is related to the formation water salinity, the mud filtrate salinity, and the effective porosity of the reservoir, and the greater the difference between the formation water salinity and the mud filtrate salinity, the greater the effective porosity of the reservoir, the greater the absolute value of the static natural potential. When the formation water salinity is greater than the mud filtrate salinity, the static natural potential is negative. When the formation water salinity is less than the mud filtrate salinity, the static natural potential is positive. When the formation water salinity is equal to the mud filtrate salinity, the static natural potential is zero.

[0066] Based on this, the static natural potential logging response model can be constructed by the following formula:

[0067]

[0068] Where SSP is the static natural potential, in mV; T c is the wellbore temperature, in °C; Φ is the effective porosity of the reservoir, in %; C w is the mineralization of formation water, in mg / L; a and b are the model parameters of formation water mineralization, dimensionless; C mf It is the mineralization of mud filtrate, and the unit is mg / L.

[0069] In this application, after constructing the static natural potential logging response model, the structure of the static natural potential logging response model is modified with the mud filtrate salinity as the target value, thereby obtaining the first mud filtrate salinity model. The formula for constructing the first mud filtrate salinity model is as follows:

[0070]

[0071] Among them, C mf is the mineralization of mud filtrate, in mg / L; C w is the mineralization of formation water, in mg / L; SSP is the static natural potential, in mV; T c is the wellbore temperature, in °C; Φ is the effective porosity of the reservoir, in %.

[0072] In the present application, a second mud filtrate salinity model may be constructed by analyzing the relationship between the mud filtrate salinity and the wellbore mud resistivity.

[0073] Specifically, in the process of constructing the second mud filtrate mineralization model, since the mud filtrate mineralization is related to the wellbore mud resistivity, and when the wellbore mud resistivity is larger, the mud filtrate mineralization is smaller.

[0074] Based on this, the second mud filtrate mineralization model can be constructed by the following formula:

[0075] C mf =a×e -b×Rmc

[0076] Among them, C mf is the mineralization of mud filtrate, in mg / L; a and b are the model parameters of formation water mineralization, dimensionless; R mc is the resistivity of wellbore mud, in Ω·m.

[0077] In this application, after constructing the static natural potential logging response model and the second mud filtrate salinity model, the structures of the natural potential logging response model and the second mud filtrate salinity model are modified with the formation water salinity as the target value, thereby obtaining the formation water salinity model. The formula for constructing the formation water salinity model is as follows:

[0078]

[0079] Among them, C w is the mineralization of formation water, in mg / L; SSP is the static natural potential, in mV; T c is the wellbore temperature, in °C; Φ is the effective porosity of the reservoir, in %; a and b are the model parameters of formation water salinity, dimensionless; R mc is the resistivity of wellbore mud, in Ω·m.

[0080] In one embodiment of the present application, the determination of formation water salinity model parameters specifically includes steps 121 to 124:

[0081] Step 121, constructing a first mud filtrate salinity model according to the static natural potential logging response model, and constructing a second mud filtrate salinity model according to the mud filtrate salinity and the wellbore mud resistivity.

[0082] Step 122, calculating the first mud filtrate salinity for determining the formation water salinity model parameters based on the static natural potential, the measured formation water salinity, the effective porosity of the reservoir, the wellbore temperature, and the first mud filtrate salinity model.

[0083] Step 123: construct a regression curve corresponding to the second mud filtrate salinity model according to the wellbore mud resistivity corresponding to the measured formation water salinity and the first mud filtrate salinity.

[0084] Step 124: Determine the formation water salinity model parameters based on the regression curve.

[0085] In the present application, after constructing the first mud filtrate mineralization model and the second mud filtrate mineralization model, the measured formation water mineralization, static natural potential, wellbore temperature, and effective reservoir porosity are obtained, and based on the measured formation water mineralization, static natural potential, wellbore temperature, effective reservoir porosity, and the first mud filtrate mineralization model, the first mud filtrate mineralization used to determine the formation water mineralization model parameters is calculated.

[0086] Referring to Table 2, Table 2 shows the measured formation water salinity, static natural potential, wellbore temperature, reservoir effective porosity, and the first mud filtrate salinity calculated according to the first mud filtrate salinity model for determining the formation water salinity model parameters corresponding to the resistivity of some wellbore mud in the implementation of the present application.

[0087] Table 2 is as follows:

[0088]

[0089]

[0090] At the same time, refer to Figure 2 , Figure 2 The regression curve in the embodiment of the present application is shown. Figure 2 It can be seen that based on the first mud filtrate salinity provided in Table 2 for determining the formation water salinity model parameters and the wellbore mud resistivity corresponding to the measured formation water salinity provided in Table 2, and combined with the second mud filtrate salinity model, the regression curve corresponding to the second mud filtrate salinity model is y=81431e -2.402x Therefore, it can be determined that the formation water salinity model parameters a and b are 81431 and 2.402, respectively.

[0091] Continue to refer to Figure 1 In step 130, the formation water salinity is calculated based on the wellbore mud resistivity, the formation water salinity model, and the formation water salinity model parameters.

[0092] In the present application, the formation water mineralization at each wellbore depth can be accurately calculated based on the static natural potential, wellbore temperature, reservoir effective porosity, formation water mineralization model parameters, wellbore mud resistivity, and formation water mineralization model.

[0093] Referring to Table 3, Table 3 shows the wellbore mud resistivity, static natural potential, wellbore temperature, reservoir effective porosity, and formation water salinity calculated according to the formation water salinity model corresponding to a portion of the measured formation water salinity in the implementation of this application, and the Table 3 is as follows:

[0094]

[0095] In the present application, after determining the formation water salinity model parameters, the following steps are specifically included: Step 131 to Step 133:

[0096] Step 131, calculating the second mud filtrate salinity for verifying the accuracy of the formation water salinity model parameters according to the wellbore mud resistivity corresponding to the measured formation water salinity, the formation water salinity model parameters and the second mud filtrate salinity model,

[0097] Step 132: calculating the absolute value of the relative error according to the first mud filtrate salinity and the second mud filtrate salinity, thereby calculating the average value of the absolute value of the relative error.

[0098] Step 133, checking the accuracy of the formation water salinity model parameters by determining the average value.

[0099] In the present application, the formation water mineralization model parameters a and b can be determined based on the first mud filtrate mineralization used to determine the formation water mineralization model parameters and the wellbore mud resistivity corresponding to the measured formation water mineralization, and combined with the second mud filtrate mineralization model.

[0100] For example, according to the first mud filtrate salinity for determining the formation water salinity model parameters provided in Table 2 and the wellbore mud resistivity corresponding to the measured formation water salinity provided in Table 2, combined with the second mud filtrate salinity model, it can be determined that the formation water salinity model parameters a and b are 81431 and 2.402 respectively. Therefore, the second mud filtrate salinity model can be determined as C mf =81431e -2.402×R mc .

[0101] Then, according to the wellbore mud resistivity corresponding to the measured formation water salinity, the formation water salinity model parameters and the second mud filtrate salinity model, the second mud filtrate salinity for verifying the accuracy of the formation water salinity model parameters can be calculated. Based on the first mud filtrate salinity and the second mud filtrate salinity, the relative error absolute value and the average value of the relative error absolute value can be calculated, so as to verify the accuracy of the formation water salinity model parameters by judging the size of the average value of the relative error absolute value.

[0102] Referring to Table 4, Table 4 shows the wellbore mud resistivity corresponding to a portion of the measured formation water salinity in the implementation of this application, the first mud filtrate salinity used to determine the formation water salinity model parameters, and the second mud filtrate salinity calculated according to the second mud filtrate salinity model for verifying the accuracy of the formation water salinity model parameters. The Table 4 is as follows:

[0103]

[0104]

[0105] Based on the same inventive concept, the present application also provides a device for determining the mineralization of formation water, referring to Figure 3 , showing a schematic diagram of the structure of the device for determining formation water mineralization in an embodiment of the present application. The device 300 for determining formation water mineralization includes: a first calculation unit 301, used to calculate the wellbore mud resistivity by acquiring the surface mud resistivity, the surface mud temperature, and the wellbore temperature; a construction unit 302, used to construct a formation water mineralization model and determine the formation water mineralization model parameters, the formation water mineralization model is used to calculate the formation water mineralization; a second calculation unit 303, used to calculate the formation water mineralization according to the wellbore mud resistivity, the formation water mineralization model, and the formation water mineralization model parameters.

[0106] For details not disclosed in the embodiments of the device of the present application, please refer to the embodiments of the above method of the present application.

[0107] Based on the same inventive concept, the present application also provides a computer-readable storage medium, characterized in that the computer-readable storage medium stores at least one program code, and the at least one program code is loaded and executed by a processor to implement the operations performed by the method.

[0108] Based on the same inventive concept, the present application also provides an electronic device, referring to Figure 4 , Figure 4 A schematic diagram of the structure of an electronic device in an embodiment of the present application is shown.

[0109] The electronic device includes one or more memories 404, one or more processors 402, and at least one computer program (program code) stored in the memory 404 and executable on the processor 402. When the processor 402 executes the computer program, the method described above is implemented.

[0110] Among them, Figure 4 In the embodiment of the present invention, a bus architecture (represented by bus 400) is shown, which may include any number of interconnected buses and bridges, and bus 400 links various circuits including one or more processors represented by processor 402 and memory represented by memory 404. Bus 400 may also link various other circuits such as peripherals, voltage regulators, and power management circuits, which are well known in the art and are therefore not further described herein. Bus interface 405 provides an interface between bus 400 and receiver 401 and transmitter 403. Receiver 401 and transmitter 403 may be the same element, namely a transceiver, providing a unit for communicating with various other devices over a transmission medium. Processor 402 is responsible for managing bus 400 and general processing, while memory 404 may be used to store data used by processor 402 when performing operations.

[0111] The functions described herein may be implemented in hardware, software executed by a processor, firmware, or any combination thereof. If implemented in software executed by a processor, the functions may be stored as one or more instructions or codes on a computer-readable medium or transmitted via a computer-readable medium. Other examples and implementations are within the scope and spirit of the present application and the appended claims. For example, due to the nature of software, the functions described above may be implemented using software executed by a processor, hardware, firmware, hard wiring, or a combination of any of these. In addition, each functional unit may be integrated into a processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit.

[0112] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0113] The units described as separate components may or may not be physically separated, and the components of the control device may or may not be physical units, that is, they may be located in one place or distributed in multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0114] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0115] The above description is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present application shall be included in the scope of the claims of the present application.

Claims

1. A method for determining formation water mineralization, characterized in that: The method comprises: By obtaining the surface mud resistivity, the surface mud temperature, and the wellbore temperature, the wellbore mud resistivity is calculated; Constructing a formation water mineralization model and determining parameters of the formation water mineralization model, wherein the formation water mineralization model is used to calculate the formation water mineralization; The formation water salinity is calculated according to the wellbore mud resistivity, the formation water salinity model, and the formation water salinity model parameters.

2. The method according to claim 1, characterized in that Before constructing the formation water salinity model, the method further includes: By analyzing the relationship between static natural potential and formation water salinity, mud filtrate salinity, and reservoir effective porosity, a static natural potential logging response model is constructed. The static natural potential logging response model is used to construct a formation water salinity model, a first mud filtrate salinity model, and a second mud filtrate salinity model.

3. The method according to claim 1, characterized in that Determining the formation water salinity model parameters includes: Constructing a first mud filtrate salinity model according to the static natural potential logging response model, and constructing a second mud filtrate salinity model according to the mud filtrate salinity and the wellbore mud resistivity; Calculating the first mud filtrate salinity for determining the formation water salinity model parameters according to the static natural potential, the measured formation water salinity, the reservoir effective porosity, the wellbore temperature, and the first mud filtrate salinity model; constructing a regression curve corresponding to the second mud filtrate salinity model according to the wellbore mud resistivity corresponding to the measured formation water salinity and the first mud filtrate salinity; Based on the regression curve, the formation water salinity model parameters are determined.

4. The method according to claim 3, characterized in that After determining the formation water salinity model parameters, the method further includes: Calculating the second mud filtrate salinity for verifying the accuracy of the formation water salinity model parameters according to the wellbore mud resistivity corresponding to the measured formation water salinity, the formation water salinity model parameters and the second mud filtrate salinity model; Calculating the absolute value of the relative error according to the first mud filtrate salinity and the second mud filtrate salinity, thereby calculating the average value of the absolute value of the relative error; By determining the average value, the accuracy of the formation water salinity model parameters is tested.

5. The method according to claim 1, characterized in that The wellbore mud resistivity is calculated by the following formula: Among them, R mc is the wellbore mud resistivity, in Ω·m; R md is the ground mud resistivity, in Ω·m; T d is the ground mud temperature, in °C; T c is the wellbore temperature, in °C.

6. The method according to claim 2, characterized in that The static natural potential logging response model is constructed by the following formula: Wherein, SSP is the static natural potential, in mV; T c is the wellbore temperature, in °C; Φ is the effective porosity of the reservoir, in %; C w is the mineralization of the formation water, in mg / L; a and b are the model parameters of the mineralization of the formation water, dimensionless; C mf is the mineralization of the mud filtrate, in mg / L.

7. The method according to claim 2, characterized in that: The first mud filtrate salinity model, the second mud filtrate salinity model, and the formation water salinity model are constructed respectively by the following formulas: The formula for constructing the first mud filtrate salinity model is as follows: Among them, C mf is the mineralization degree of the mud filtrate, in mg / L; C w is the formation water mineralization, in mg / L; SSP is the static natural potential, in mV; T c is the wellbore temperature, in °C; Φ is the effective porosity of the reservoir, in %. The formula for constructing the second mud filtrate salinity model is as follows: C mf =a×e -b×R mc Among them, C mf is the salinity of the mud filtrate, in mg / L; a and b are the model parameters of formation water salinity, dimensionless; R mc is the resistivity of wellbore mud, in Ω·m. The formula for constructing the formation water salinity model is as follows: Among them, C w is the formation water mineralization, in mg / L; SSP is the static natural potential, in mV; T c is the wellbore temperature, in °C; Φ is the effective porosity of the reservoir, in %; a and b are the model parameters of formation water salinity, dimensionless; R mc is the resistivity of wellbore mud, in Ω·m.

8. A device for determining the mineralization of formation water, characterized in that: The device comprises: A first calculation unit is used to calculate the wellbore mud resistivity by acquiring the ground mud resistivity, the ground mud temperature, and the wellbore temperature; A construction unit, used to construct a formation water mineralization model and determine the parameters of the formation water mineralization model, wherein the formation water mineralization model is used to calculate the formation water mineralization; The second calculation unit is used to calculate the formation water salinity according to the wellbore mud resistivity, the formation water salinity model, and the formation water salinity model parameters.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one program code, and the at least one program code is loaded and executed by a processor to implement the operations performed by the method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: The electronic device includes one or more processors and one or more memories, wherein the one or more memories store at least one program code, and the at least one program code is loaded and executed by the one or more processors to implement the operations performed by the method according to any one of claims 1 to 7.

Images