Complex reservoir saturation calculation method, device and equipment and storage medium

Abstract

The invention relates to the technical field of geophysical exploration, in particular to a saturation calculation method, device and equipment of a complex reservoir and a storage medium, and the saturation calculation method of the complex reservoir comprises the steps that the movable fluid porosity of the complex reservoir is determined based on nuclear magnetic well logging and conventional well logging; acquiring water resistivity in an oil layer of the complex reservoir, and acquiring formation resistivity and a first lithologic coefficient of the complex reservoir; determining a cementation index, a saturation index and a second lithologic coefficient of the complex reservoir based on the regression model and the movable fluid porosity; and determining the saturation of the complex reservoir based on the movable fluid porosity, the water resistivity, the formation resistivity, the first lithologic coefficient, the cementation index, the saturation index and the second lithologic coefficient. According to the saturation calculation method of the complex reservoir, the error between the porosity obtained through conventional well logging calculation and the effective movable fluid porosity and the error of the formation water resistivity are avoided, and the accuracy of saturation calculation of the complex reservoir is comprehensively improved.

Description

Technical Field

[0001] The present application relates to the field of geophysical exploration technology, and in particular to a method, device, equipment and storage medium for calculating the saturation of a complex reservoir. Background Art

[0002] Under the premise that the lithology of the reservoir is the same, the classic Archie formula is usually used to calculate the saturation of sandstone reservoirs.

[0003] However, in complex reservoirs, the lithology of the rock formations is variable. The same reservoir is usually associated with more than two lithologies. The lithology components between different layers are also complex and variable. The porosity used in conventional logging calculations will also have a certain gap with the effective movable fluid pore space due to the complexity of the lithology. At the same time, the formation water resistivity calculated using the conventional method used in typical pure water layers is also different. The above calculation parameters directly determine the accuracy of the saturation calculation. In the above case, there is a great deal of uncertainty in the saturation calculated by the Archie formula, resulting in a large deviation between the logging interpretation conclusion based on saturation and the oil test conclusion.

[0004] Therefore, how to improve the accuracy of saturation calculation of complex reservoirs has become a technical problem that needs to be solved urgently in this field. Summary of the invention

[0005] The main purpose of the present application is to provide a method, device, equipment and storage medium for calculating the saturation of a complex reservoir, aiming to solve the technical problem of how to improve the accuracy of saturation calculation of a complex reservoir.

[0006] To achieve the above object, the present application provides a method for calculating the saturation of a complex reservoir, the method comprising:

[0007] Determine the movable fluid porosity of complex reservoirs based on the operation of nuclear magnetic logging and conventional logging;

[0008] Acquire the water resistivity in the oil layer of the complex reservoir, and acquire the formation resistivity and the first lithology coefficient of the complex reservoir;

[0009] Determining a cementation index, a saturation index, and a second lithology coefficient of the complex reservoir based on a regression model and the movable fluid porosity;

[0010] The saturation of the complex reservoir is determined based on the movable fluid porosity, the water resistivity, the formation resistivity, the first lithology coefficient, the cementation index, the saturation index, and the second lithology coefficient.

[0011] Optionally, in a feasible embodiment, the step of determining the movable fluid porosity of a complex reservoir based on nuclear magnetic logging and conventional logging operations includes:

[0012] Conducting nuclear magnetic logging and conventional logging on reservoir samples to determine a movable fluid porosity model for calculating movable fluid porosity;

[0013] Conventional logging is performed on the complex reservoir to obtain logging information, and the logging information is substituted into the movable fluid porosity model to calculate the movable fluid porosity of the complex reservoir.

[0014] Optionally, in a feasible embodiment, the step of performing nuclear magnetic logging and conventional logging on the reservoir sample to determine a movable fluid porosity model for calculating the movable fluid porosity includes:

[0015] Performing nuclear magnetic resonance logging on the reservoir sample to obtain the sample movable fluid porosity of the reservoir sample;

[0016] Performing conventional logging on the reservoir sample to obtain a first acoustic time difference logging value, a first compensated neutron logging value, and a first density logging value of the reservoir sample;

[0017] Based on the movable fluid porosity of the sample, a multivariate regression calculation is performed on the first acoustic time difference logging value, the first compensated neutron logging value and the first density logging value to obtain a movable fluid porosity model with the acoustic time difference logging value, the compensated neutron logging value and the density logging value as parameters.

[0018] Optionally, in a feasible embodiment, the step of performing conventional well logging on the complex reservoir to obtain well logging information, and substituting the well logging information into the movable fluid porosity model to calculate the movable fluid porosity of the complex reservoir includes:

[0019] Performing conventional logging on the complex reservoir to obtain a second acoustic time difference logging value, a second compensated neutron logging value, and a second density logging value of the complex reservoir;

[0020] The second acoustic time difference logging value, the second compensated neutron logging value and the second density logging value are substituted into the movable fluid porosity model to obtain the movable fluid porosity of the complex reservoir.

[0021] Optionally, in a feasible embodiment, the regression model includes: a cementation index regression model;

[0022] Before the step of determining the cementation index, saturation index and second lithology coefficient of the complex reservoir based on the regression model and the movable fluid porosity, the method further includes:

[0023] Obtaining sample cementation index and sample mobile liquid porosity of reservoir samples;

[0024] A regression analysis is performed on the sample cementation index and the flowable liquid porosity of the sample to obtain a cementation index regression model with the flowable liquid porosity as a parameter.

[0025] Optionally, in a feasible embodiment, the regression model includes: a saturation index regression model;

[0026] Before the step of determining the cementation index, saturation index and second lithology coefficient of the complex reservoir based on the regression model and the movable fluid porosity, the method further includes:

[0027] Obtaining sample saturation index and sample mobile liquid porosity of reservoir samples;

[0028] A regression analysis is performed on the sample saturation index and the sample flowable liquid porosity to obtain a saturation index regression model with the flowable liquid porosity as a parameter.

[0029] Optionally, in a feasible embodiment, the regression model includes: a second lithology coefficient regression model;

[0030] Before the step of determining the cementation index, saturation index and second lithology coefficient of the complex reservoir based on the regression model and the movable fluid porosity, the method further includes:

[0031] Obtaining a sample second lithology coefficient and a sample mobile liquid porosity of a reservoir sample;

[0032] A regression analysis is performed on the second lithology coefficient of the sample and the flowable liquid porosity of the sample to obtain a second lithology coefficient regression model with the flowable liquid porosity as a parameter.

[0033] In addition, to achieve the above-mentioned purpose, the present application also provides a saturation calculation device for a complex reservoir, wherein the saturation calculation device for a complex reservoir is a virtual device, and the saturation calculation device for a complex reservoir comprises:

[0034] A porosity determination module for determining the movable fluid porosity of complex reservoirs based on the operation of nuclear magnetic logging and conventional logging;

[0035] A parameter acquisition module, used to acquire the water resistivity in the oil layer of the complex reservoir, and to acquire the formation resistivity and the first lithology coefficient of the complex reservoir;

[0036] A parameter determination module, for determining a cementation index, a saturation index and a second lithology coefficient of the complex reservoir based on a regression model and the movable fluid porosity;

[0037] A saturation calculation module is used to determine the saturation of the complex reservoir based on the movable fluid porosity, the water resistivity, the formation resistivity, the first lithology coefficient, the cementation index, the saturation index and the second lithology coefficient.

[0038] In addition, to achieve the above-mentioned purpose, the present application also provides a saturation calculation device for a complex reservoir, and the saturation calculation device for a complex reservoir includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the computer program is executed by the processor, the steps of the saturation calculation method for a complex reservoir as described above are implemented.

[0039] The present application also provides a storage medium, which is a computer-readable storage medium. A computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the complex reservoir saturation calculation method as described above are implemented.

[0040] The present application provides a method, device, equipment and storage medium for calculating the saturation of a complex reservoir. The method for calculating the saturation of a complex reservoir includes: determining the movable fluid porosity of the complex reservoir based on nuclear magnetic resonance logging and conventional logging operations; obtaining the water resistivity in the oil layer of the complex reservoir, and obtaining the formation resistivity and a first lithology coefficient of the complex reservoir; determining the cementation index, saturation index and second lithology coefficient of the complex reservoir based on a regression model and the movable fluid porosity; determining the saturation of the complex reservoir based on the movable fluid porosity, the water resistivity, the formation resistivity, the first lithology coefficient, the cementation index, the saturation index and the second lithology coefficient.

[0041] Compared with the conventional technical means of calculating reservoir saturation using the Archie formula, the saturation calculation method for complex reservoirs in the present application determines the movable fluid porosity of complex rock formations through nuclear magnetic resonance logging and conventional logging operations to replace the porosity in the Archie formula, and then obtains the water resistivity in the oil layer of the complex reservoir to replace the formation water resistivity in the Archie formula. The formation resistivity and the first lithology coefficient are the same as those in the conventional Archie formula. Then, the movable fluid porosity is used as an input parameter, and the movable fluid porosity is input into the regression model to obtain the cementation index, saturation index and second lithology coefficient of the complex reservoir to replace the cementation index, saturation index and second lithology coefficient in the Archie formula, and then the saturation of the complex rock formation is calculated using the formula after parameter replacement.

[0042] Thus, the present application is based on the above-mentioned method of calculating saturation based on the movable fluid porosity, the water resistivity in the oil layer and various parameters obtained by regression calculation. Compared with the conventional method of calculating reservoir saturation using the Archie formula, the saturation calculation method for complex reservoirs of the present application avoids the error between the porosity obtained by conventional logging calculations and the effective movable fluid porosity due to the complexity of the lithology, avoids the error of formation water resistivity calculated by conventional methods in complex reservoirs, and can also adaptively calculate various parameters under the condition of variable lithology according to the regression model, thereby comprehensively improving the accuracy of saturation calculation for complex reservoirs. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The accompanying drawings herein are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present application, and together with the description are used to explain the principles of the present application.

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without any creative work.

[0045] Figure 1 A schematic diagram of the structure of a saturation calculation device for a complex reservoir in the device hardware operating environment involved in the embodiment of the present application;

[0046] Figure 2 This is a schematic diagram of the implementation process of the first embodiment of the complex reservoir saturation calculation method of the present application;

[0047] Figure 3 A relationship diagram between the cementation index m and the movable fluid porosity involved in the first embodiment of the complex reservoir saturation calculation method of the present application;

[0048] Figure 4 A relationship diagram between the saturation index n and the movable fluid porosity involved in the first embodiment of the saturation calculation method for complex reservoirs of the present application;

[0049] Figure 5 A relationship diagram between the second lithology coefficient a and the movable fluid porosity involved in the first embodiment of the complex reservoir saturation calculation method of the present application;

[0050] Figure 6 A calculation scene diagram related to an embodiment of a method for calculating saturation of a complex reservoir of the present application;

[0051] Figure 7 A calculation interaction diagram related to an embodiment of a method for calculating saturation of a complex reservoir of the present application;

[0052] Figure 8 This is a schematic diagram of the functional modules of a saturation calculation device for a complex reservoir according to an embodiment of the present application.

[0053] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0054] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0055] It should be noted that, under the premise that the lithology of the reservoir is the same, the classic Archie formula is usually used to calculate the saturation of sandstone reservoirs.

[0056] However, in complex reservoirs, the lithology of the rock formations is variable. The same reservoir is usually associated with more than two lithologies. The lithology components between different layers are also complex and variable. The porosity used in conventional logging calculations will also have a certain gap with the effective movable fluid pore space due to the complexity of the lithology. At the same time, the formation water resistivity calculated using the conventional method used in typical pure water layers is also different. The above calculation parameters directly determine the accuracy of the saturation calculation. In the above case, there is a great deal of uncertainty in the saturation calculated by the Archie formula, resulting in a large deviation between the logging interpretation conclusion based on saturation and the oil test conclusion.

[0057] Therefore, how to improve the accuracy of saturation calculation of complex reservoirs has become a technical problem that needs to be solved urgently in this field.

[0058] In response to the above problems, the present application provides a method, device, equipment and storage medium for calculating the saturation of a complex reservoir. The method for calculating the saturation of a complex reservoir includes: determining the movable fluid porosity of the complex reservoir based on nuclear magnetic resonance logging and conventional logging operations; obtaining the water resistivity in the oil layer of the complex reservoir, and obtaining the formation resistivity and a first lithology coefficient of the complex reservoir; determining the cementation index, saturation index and second lithology coefficient of the complex reservoir based on a regression model and the movable fluid porosity; determining the saturation of the complex reservoir based on the movable fluid porosity, the water resistivity, the formation resistivity, the first lithology coefficient, the cementation index, the saturation index and the second lithology coefficient.

[0059] Compared with the conventional technical means of calculating reservoir saturation using the Archie formula, the saturation calculation method for complex reservoirs in the present application determines the movable fluid porosity of complex rock formations through nuclear magnetic resonance logging and conventional logging operations to replace the porosity in the Archie formula, and then obtains the water resistivity in the oil layer of the complex reservoir to replace the formation water resistivity in the Archie formula. The formation resistivity and the first lithology coefficient are the same as those in the conventional Archie formula. Then, the movable fluid porosity is used as an input parameter, and the movable fluid porosity is input into the regression model to obtain the cementation index, saturation index and second lithology coefficient of the complex reservoir to replace the cementation index, saturation index and second lithology coefficient in the Archie formula, and then the saturation of the complex rock formation is calculated using the formula after parameter replacement.

[0060] Thus, the present application is based on the above-mentioned method of calculating saturation based on the movable fluid porosity, the water resistivity in the oil layer and various parameters obtained by regression calculation. Compared with the conventional method of calculating reservoir saturation using the Archie formula, the saturation calculation method for complex reservoirs of the present application avoids the error between the porosity obtained by conventional logging calculations and the effective movable fluid porosity due to the complexity of the lithology, avoids the error of formation water resistivity calculated by conventional methods in complex reservoirs, and can also adaptively calculate various parameters under the condition of variable lithology according to the regression model, thereby comprehensively improving the accuracy of saturation calculation for complex reservoirs.

[0061] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are 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.

[0062] Please refer to Figure 1 , Figure 1 This is a schematic diagram of the structure of a saturation calculation device for a complex reservoir in the device hardware operating environment involved in an embodiment of the present application.

[0063] It should be noted that the terminal device involved in the embodiment of the present invention can be a data storage control terminal, a PC or a portable computer in the complex reservoir saturation calculation device that executes the complex reservoir saturation calculation method of the present application.

[0064] like Figure 1As shown, the complex reservoir saturation calculation device may include: a processor 1001, such as a CPU, a memory 1005, and a communication bus 1002. The communication bus 1002 is used to realize the connection and communication between the processor 1001 and the memory 1005. The memory 1005 may be a high-speed RAM memory or a stable memory (non-volatile memory), such as a disk memory. The memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0065] Optionally, the complex reservoir saturation calculation device may further include a user interface 1003, a network interface 1004, a camera, an RF (Radio Frequency) circuit, a sensor, an audio circuit, a WiFi module, etc. The user interface may include a display screen (Display), an input submodule such as a keyboard (Keyboard), and optionally, the user interface may also include a standard wired interface and a wireless interface. The network interface may optionally include a standard wired interface and a wireless interface (such as a WIFI interface).

[0066] Those skilled in the art will understand that Figure 1 The structure of the complex reservoir saturation calculation device shown in the figure does not constitute a limitation to the complex reservoir saturation calculation device, and may include more or less components than those shown in the figure, or combine certain components, or arrange the components differently.

[0067] like Figure 1 As shown, the memory 1005 as a computer-readable storage medium may include an operating system, a network communication module, a user interface module, and a computer program. The operating system is a program that manages and controls the hardware and software resources of the complex reservoir saturation calculation device, and supports the operation of the computer program and other software and / or programs. The network communication module is used to realize the communication between the components inside the memory 1005, and the communication with other hardware and software in the complex reservoir saturation calculation device.

[0068] exist Figure 1 In the complex reservoir saturation calculation device shown, the processor 1001 is used to execute the computer program stored in the memory 1005 and perform the following operations:

[0069] Determine the movable fluid porosity of complex reservoirs based on the operation of nuclear magnetic logging and conventional logging;

[0070] Acquire the water resistivity in the oil layer of the complex reservoir, and acquire the formation resistivity and the first lithology coefficient of the complex reservoir;

[0071] Determining a cementation index, a saturation index, and a second lithology coefficient of the complex reservoir based on a regression model and the movable fluid porosity;

[0072] The saturation of the complex reservoir is determined based on the movable fluid porosity, the water resistivity, the formation resistivity, the first lithology coefficient, the cementation index, the saturation index, and the second lithology coefficient.

[0073] Further, the processor 1001 may call the computer program stored in the memory 1005 and perform the following operations:

[0074] Conducting nuclear magnetic logging and conventional logging on reservoir samples to determine a movable fluid porosity model for calculating movable fluid porosity;

[0075] Conventional logging is performed on the complex reservoir to obtain logging information, and the logging information is substituted into the movable fluid porosity model to calculate the movable fluid porosity of the complex reservoir.

[0076] Further, the processor 1001 may call the computer program stored in the memory 1005 and perform the following operations:

[0077] Performing nuclear magnetic resonance logging on the reservoir sample to obtain the sample movable fluid porosity of the reservoir sample;

[0078] Performing conventional logging on the reservoir sample to obtain a first acoustic time difference logging value, a first compensated neutron logging value, and a first density logging value of the reservoir sample;

[0079] Based on the movable fluid porosity of the sample, a multivariate regression calculation is performed on the first acoustic time difference logging value, the first compensated neutron logging value and the first density logging value to obtain a movable fluid porosity model with the acoustic time difference logging value, the compensated neutron logging value and the density logging value as parameters.

[0080] Further, the processor 1001 may call the computer program stored in the memory 1005 and perform the following operations:

[0081] Performing conventional logging on the complex reservoir to obtain a second acoustic time difference logging value, a second compensated neutron logging value, and a second density logging value of the complex reservoir;

[0082] The second acoustic time difference logging value, the second compensated neutron logging value and the second density logging value are substituted into the movable fluid porosity model to obtain the movable fluid porosity of the complex reservoir.

[0083] Further, the processor 1001 may call the computer program stored in the memory 1005 and perform the following operations:

[0084] Before the step of determining the cementation index, saturation index and second lithology coefficient of the complex reservoir based on the regression model and the movable fluid porosity, the method further includes:

[0085] Obtaining sample cementation index and sample mobile liquid porosity of reservoir samples;

[0086] A regression analysis is performed on the sample cementation index and the flowable liquid porosity of the sample to obtain a cementation index regression model with the flowable liquid porosity as a parameter.

[0087] Further, the processor 1001 may call the computer program stored in the memory 1005 and perform the following operations:

[0088] Before the step of determining the cementation index, saturation index and second lithology coefficient of the complex reservoir based on the regression model and the movable fluid porosity, the method further includes:

[0089] Obtaining sample saturation index and sample mobile liquid porosity of reservoir samples;

[0090] A regression analysis is performed on the sample saturation index and the sample flowable liquid porosity to obtain a saturation index regression model with the flowable liquid porosity as a parameter.

[0091] Further, the processor 1001 may call the computer program stored in the memory 1005 and perform the following operations:

[0092] Before the step of determining the cementation index, saturation index and second lithology coefficient of the complex reservoir based on the regression model and the movable fluid porosity, the method further includes:

[0093] Obtaining a sample second lithology coefficient and a sample mobile liquid porosity of a reservoir sample;

[0094] A regression analysis is performed on the second lithology coefficient of the sample and the flowable liquid porosity of the sample to obtain a second lithology coefficient regression model with the flowable liquid porosity as a parameter.

[0095] Based on the above structure, various embodiments of a method for calculating saturation of a complex reservoir are proposed.

[0096] In the first embodiment of the method for calculating the saturation of a complex reservoir of the present application, please refer to Figure 2 , Figure 2 This is a schematic flow chart of the first embodiment of the method for calculating saturation of a complex reservoir of the present application.

[0097] The embodiment of the present invention provides an embodiment of a method for calculating the saturation of a complex reservoir. It should be noted that although a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than that shown here. In this embodiment, the saturation calculation method for a complex reservoir is performed by a saturation calculation device for a complex reservoir. For ease of description, the following description of each embodiment is omitted. In this embodiment, the saturation calculation method for a complex reservoir includes:

[0098] Step S10, determining the movable fluid porosity of the complex reservoir based on the operation of nuclear magnetic logging and conventional logging;

[0099] It should be noted that, in the present embodiment, nuclear magnetic logging (i.e., nuclear magnetic resonance logging) is a logging method for measuring the free precession of hydrogen nuclei in the formation in the geomagnetic field. There are many methods for measuring nuclear magnetic resonance, and the prepolarization method and the spin echo method are mainly used in nuclear magnetic resonance logging. The former is simple and easy to measure underground, and the latter can eliminate the error caused by diffusion to the measurement result, making the result more accurate and improving the signal-to-noise ratio. Nuclear magnetic logging is based on the interaction between hydrogen nuclei and an external magnetic field. It is the only logging method that can directly measure the seepage volume characteristics of free fluids (oil, gas, water) in any lithology reservoir, and has obvious advantages. It can directly measure the characteristics of pore fluids, is not affected by rock skeleton minerals, and can provide rich formation information, such as total porosity, effective porosity, bound water porosity, pore size distribution and permeability parameters of the formation.

[0100] Conventional logging methods mainly refer to the logging methods that must be measured in the exploration and development of oil and gas, such as exploration well logging, evaluation well logging, and development well logging projects, namely the so-called "nine" curve series: three lithology curves of natural gamma, natural potential, and well diameter, three resistivity curves of shallow, medium, and deep, and three porosity curves of sonic wave, neutron, and density.

[0101] In this embodiment, the movable fluid porosity is the porosity of the effective pore space. The porosity calculated by conventional logging may be inaccurate due to the complex lithology of complex reservoirs, so the nuclear magnetic logging method can be introduced to measure the movable fluid porosity of the effective pore space in the complex reservoir to improve the calculation accuracy of saturation.

[0102] Step S20, obtaining the water resistivity in the oil layer of the complex reservoir, and obtaining the formation resistivity and the first lithology coefficient of the complex reservoir;

[0103] It should be noted that, in this embodiment, R in the classic Archie formula w =R 0 *φ m / a,R 0It is the resistivity of the formation when the formation contains 100% water, that is, the resistivity of the formation water. The resistivity of the formations with different lithology combinations in complex reservoirs when they contain 100% water is also different, which also affects the porosity φ and the rock coefficient a, making it difficult to determine which standard water layer resistivity to use to assign R when applying Archie's formula. w .

[0104] Therefore, in this embodiment, in order to avoid the influence of different reservoir lithology on the resistivity of the formation when it contains 100% water, the resistivity obtained by chemical analysis of the oil layer in the reservoir is used as the water resistivity R w , so that the water resistivity can be determined without being affected by the lithology, thus improving the calculation accuracy of the saturation.

[0105] Step S30, determining the cementation index, saturation index and second lithology coefficient of the complex reservoir based on the regression model and the movable fluid porosity;

[0106] In this embodiment, in the conventional Archie formula, when calculating the oil saturation of the reservoir, it is assumed that the cementation index m, saturation index n, and second lithology coefficient a in the same set of stratum and the same set of stratum system formulas are fixed, but the saturation calculated with a set of fixed parameters in a complex reservoir will have a large deviation, leading to a misjudgment of the conclusion. Therefore, in this embodiment, the movable fluid porosity is used as a parameter, and a regression model is established to calculate m, n, and a of the complex reservoir. There are many ways to establish the regression equation, which are not limited here.

[0107] Step S40, determining the saturation of the complex reservoir based on the movable fluid porosity, the water resistivity, the formation resistivity, the first lithology coefficient, the cementation index, the saturation index and the second lithology coefficient.

[0108] It should be noted that, in this embodiment, Archie's formula is as follows:

[0109]

[0110] in, is the saturation, a is the second lithology coefficient, b is the first lithology coefficient, R w is the formation water resistivity, R t is the formation resistivity, φ is the porosity, and m is the cementation index. In the conventional Archie formula, m, n, a and formation water resistivity R w They are all constant values. When calculating the saturation for reservoirs with complex lithology, there is a deviation between the calculated saturation and the actual saturation.

[0111] In this embodiment, the above-mentioned movable fluid porosity φ and water resistivity R are calculated. w, m, n, a, and obtain the conventional formation resistivity R t After obtaining the first lithology coefficient b, the parameters calculated above can be used to replace the parameters in the Archie formula, and then the saturation of the complex reservoir can be calculated based on the updated Archie formula.

[0112] Thus, the present application is based on the above-mentioned method of calculating saturation based on the movable fluid porosity, the water resistivity in the oil layer and various parameters obtained by regression calculation. Compared with the conventional method of calculating reservoir saturation using the Archie formula, the saturation calculation method for complex reservoirs of the present application avoids the error between the porosity obtained by conventional logging calculations and the effective movable fluid porosity due to the complexity of the lithology, avoids the error of formation water resistivity calculated by conventional methods in complex reservoirs, and can also adaptively calculate various parameters under the condition of variable lithology according to the regression model, thereby comprehensively improving the accuracy of saturation calculation for complex reservoirs.

[0113] Further, in a feasible embodiment, the above step S10, the step of determining the movable fluid porosity of the complex reservoir based on the operation of nuclear magnetic logging and conventional logging, includes:

[0114] Step S101, performing nuclear magnetic logging and conventional logging on a reservoir sample to determine a movable fluid porosity model for calculating movable fluid porosity;

[0115] It should be noted that, in this embodiment, the reservoir sample can be a variety of different complex reservoirs, which are not limited here, but it needs to be a well that can perform nuclear magnetic logging in order to obtain the logging information of nuclear magnetic logging.

[0116] In this embodiment, nuclear magnetic resonance logging and conventional logging are first performed on the reservoir samples to obtain information from nuclear magnetic resonance logging and conventional logging, respectively. By comparing the information from nuclear magnetic resonance logging and conventional logging, a movable fluid porosity model for calculating the porosity of movable fluid can be established. There are many ways to establish the model, which are not limited here.

[0117] Step S102, performing conventional well logging on the complex reservoir to obtain well logging information, and substituting the well logging information into the movable fluid porosity model to calculate the movable fluid porosity of the complex reservoir.

[0118] In this embodiment, after obtaining the movable fluid porosity model, conventional logging is performed on the complex reservoir that needs to be calculated for saturation to obtain logging information, and then the logging information is substituted into the movable fluid porosity model to calculate the movable fluid porosity of the complex reservoir.

[0119] In this embodiment, a method is proposed for calculating the movable fluid porosity of complex reservoirs based on the movable fluid porosity model after establishing the movable fluid porosity model based on reservoir samples. On the basis of improving the accuracy of saturation calculation, the saturation of reservoirs of different lithologies can be adaptively calculated, thereby improving the versatility of the method.

[0120] Further, in a feasible embodiment, the above step S101, the step of performing nuclear magnetic logging and conventional logging on the reservoir sample to determine a movable fluid porosity model for calculating the movable fluid porosity, includes:

[0121] Step S1011, performing nuclear magnetic resonance logging on the reservoir sample to obtain the movable fluid porosity of the reservoir sample;

[0122] In this embodiment, there may be multiple reservoir samples. NMR logging is first performed on each reservoir sample to obtain the total porosity, clay bound water porosity and capillary bound water porosity of each reservoir sample. The movable fluid porosity of each reservoir sample can be calculated.

[0123] Step S1012, performing conventional logging on the reservoir sample to obtain a first acoustic time difference logging value, a first compensated neutron logging value and a first density logging value of the reservoir sample;

[0124] It should be noted that, in this embodiment, only the three porosity curves of sonic wave, neutron and density obtained by conventional logging are used, that is, only neutron logging, density logging and sonic wave logging are needed.

[0125] In this embodiment, conventional logging is performed on each reservoir sample to obtain a first acoustic time difference logging value, a first compensated neutron logging value, and a first density logging value related to porosity in the conventional logging.

[0126] Step S1013, based on the movable fluid porosity of the sample, a multivariate regression calculation is performed on the first acoustic time difference logging value, the first compensated neutron logging value and the first density logging value to obtain a movable fluid porosity model with the acoustic time difference logging value, the compensated neutron logging value and the density logging value as parameters.

[0127] In this embodiment, for the same reservoir sample, there is a certain internal connection between the movable fluid porosity obtained by nuclear magnetic logging and the first acoustic time difference logging value, the first compensated neutron logging value and the first density logging value obtained by conventional logging. Therefore, the movable fluid porosity obtained by nuclear magnetic logging can be used as a standard to calculate the relationship between the acoustic time difference logging value, the compensated neutron logging value and the density logging value and the movable fluid porosity by means of multiple regression, thereby establishing a movable fluid porosity model with the acoustic time difference logging value, the compensated neutron logging value and the density logging value as parameters. The movable fluid porosity model established in the above manner can, on the basis of improving the accuracy of saturation calculation, adaptively establish a calculation model of movable fluid porosity based on the lithological characteristics of multiple complex reservoirs, further improving the accuracy of saturation calculation.

[0128] For example, when modeling the movable fluid porosity, nuclear magnetic resonance logging is first performed on the reservoir sample to measure the total porosity, clay bound water porosity and capillary bound water porosity of the reservoir sample. The movable fluid porosity of the reservoir sample can be obtained by calculation. The calculation formula of the movable fluid porosity is as follows:

[0129] PORM = PORT-PORCL-PORCC;

[0130] Wherein, PORM is the movable fluid porosity, unit: %, PORT is the total porosity, unit: %, PORCL is the clay bound water porosity, unit: %, and PORCC is the capillary bound water porosity, unit: %.

[0131] Considering that not every well can measure NMR logging, based on this, the NMR movable fluid porosity is linked to the conventional three porosities (acoustic time difference logging value, compensated neutron logging value and density logging value), and a universally applicable movable fluid porosity model is constructed using the multivariate regression method:

[0132] PORM=C 1 *AC-C 2 *CN-C 3 *DEN-C 4 ;

[0133] Where: PORM is the porosity of movable fluid, unit: %, AC is the acoustic time difference logging value, unit: us / m, CN is the compensated neutron logging value, unit: %, DEN is the density logging value, unit: g / cm3, C 1 , C 2 , C 3 , C 4 is the regression coefficient, dimensionless. Each regression coefficient can be adaptively adjusted according to the actual logging data and is not limited here.

[0134] Further, in a feasible embodiment, the above step S102, performing conventional logging on the complex reservoir to obtain logging information, and substituting the logging information into the movable fluid porosity model to calculate the movable fluid porosity of the complex reservoir, includes:

[0135] Step S1021, performing conventional logging on the complex reservoir to obtain a second acoustic time difference logging value, a second compensated neutron logging value, and a second density logging value of the complex reservoir;

[0136] Step S1022, substituting the second acoustic time difference logging value, the second compensated neutron logging value and the second density logging value into the movable fluid porosity model to obtain the movable fluid porosity of the complex reservoir.

[0137] In this embodiment, conventional logging is first performed on the complex reservoir to obtain the second acoustic time difference logging value, the second compensated neutron logging value and the second density logging value in the complex reservoir logging results, and then the second acoustic time difference logging value, the second compensated neutron logging value and the second density logging value are substituted into the established movable fluid porosity model for calculation, and the movable fluid porosity can be obtained by calculation. The above method can be applied to wells where nuclear magnetic logging cannot be performed on the basis of improving the saturation calculation accuracy. When nuclear magnetic logging cannot be performed, accurate movable fluid porosity can be obtained only through the parameters obtained by conventional logging, thereby calculating and obtaining accurate saturation.

[0138] Furthermore, based on the first embodiment of the method for calculating the saturation of a complex reservoir of the present application, a second embodiment of the method for calculating the saturation of a complex reservoir of the present application is proposed.

[0139] In the second embodiment of the method for calculating saturation of a complex reservoir of the present application, the regression model includes: a cementation index regression model;

[0140] Before the above step S30, the step of determining the cementation index, saturation index and second lithology coefficient of the complex reservoir based on the regression model and the movable fluid porosity, the method further includes:

[0141] Step A10, obtaining a sample cementation index and a sample flowable liquid porosity of the reservoir sample;

[0142] Step A20, performing regression analysis on the sample cementation index and the sample flowable liquid porosity to obtain a cementation index regression model with the flowable liquid porosity as a parameter.

[0143] In this embodiment, before calculating the cementation index, saturation index and second lithology coefficient based on the regression model, it is necessary to establish a cementation index regression model for the cementation index. When establishing the cementation index regression model, multiple reservoir samples are used as samples to collect data, and the sample cementation index and sample movable fluid porosity of each of the multiple reservoir samples are collected. Then, the relationship between the multiple sample cementation index and the multiple sample movable fluid porosity is compared, and the cementation index regression model can be obtained by using regression analysis with movable fluid porosity as a parameter. The cementation index calculated in the above manner can better adapt to the lithology characteristics of the local complex reservoir, and improve the accuracy of the complex reservoir cementation index on the basis of improving the accuracy of the saturation calculation, thereby further improving the calculation accuracy of the saturation.

[0144] For example, using the data of 582 rock electrical experimental samples from 28 wells in the Liushagang Formation of the Fushan Sag, the relationship between the cementation index m value and the movable fluid porosity was analyzed, and the corresponding relationship chart was established. Please refer to Figure 3 , Figure 3 This is a relationship diagram between the cementation index m and the porosity of the movable fluid involved in the first embodiment of the method for calculating the saturation of a complex reservoir of the present application, such as Figure 3 As shown in the figure, the horizontal axis is the movable fluid porosity (abbreviated as porosity in the figure), and the vertical axis is the cementation index m. The points in the figure correspond to the sample cementation index and the sample movable fluid porosity of different reservoir samples. The straight line in the figure is the fitted cementation index regression model. The relationship chart shows that the m value and porosity of the complex reservoir in the Fushan Sag are significantly positively correlated, that is, the m value increases with the increase of porosity; the m value decreases with the decrease of porosity. After fitting calculation, the calculation formula of m can be obtained as m=0.0187φ+1.4194. When calculating the complex reservoir that needs to calculate the cementation index, the movable fluid porosity of the complex reservoir can be directly substituted into the formula to obtain the cementation index of the complex reservoir.

[0145] Further, in a feasible embodiment, the regression model includes: a saturation index regression model;

[0146] Before the above step S30, the step of determining the cementation index, saturation index and second lithology coefficient of the complex reservoir based on the regression model and the movable fluid porosity, the method further includes:

[0147] Step B10, obtaining a sample saturation index and a sample flowable liquid porosity of the reservoir sample;

[0148] Step B20, performing regression analysis on the sample saturation index and the sample flowable liquid porosity to obtain a saturation index regression model with the flowable liquid porosity as a parameter.

[0149] In this embodiment, before calculating the cementation index, saturation index and second lithology coefficient based on the regression model, it is also necessary to establish a saturation index regression model for the saturation index. When establishing the saturation index regression model, multiple reservoir samples are used as samples to collect data, and the sample saturation index and sample movable fluid porosity of each of the multiple reservoir samples are collected. Then, the relationship between the multiple sample saturation index and the multiple sample movable fluid porosity is compared, and the saturation index regression model can be obtained by using the movable fluid porosity as a parameter through regression analysis. The saturation index calculated in the above manner can better adapt to the lithology characteristics of the local complex reservoir, and improve the accuracy of the complex reservoir saturation index on the basis of improving the accuracy of the saturation calculation, thereby further improving the calculation accuracy of the saturation.

[0150] For example, using the data of 582 rock electrical experimental samples from 28 wells in the Liushagang Formation of the Fushan Sag, the relationship between the saturation index n value and the movable fluid porosity was analyzed, and the corresponding relationship chart was established. Please refer to Figure 4 , Figure 4 This is a relationship diagram between the saturation index n and the movable fluid porosity involved in the first embodiment of the saturation calculation method for complex reservoirs of the present application, such as Figure 4 As shown in the figure, the horizontal axis is the movable fluid porosity (abbreviated as porosity in the figure), and the vertical axis is the saturation index n. The points in the figure correspond to the sample saturation index and sample movable fluid porosity of different reservoir samples. The curve in the figure is the saturation index regression model obtained by fitting. The relationship chart shows that when the movable fluid porosity is less than 10%, the n value is negatively correlated with the porosity, that is, as the movable fluid porosity decreases, the n value increases. When the movable fluid porosity is between 10% and 22%, the n value shows a slight positive correlation with the movable fluid porosity, that is, as the movable fluid porosity increases, the n value increases slightly. When the movable fluid porosity is greater than 22%, the n value basically remains unchanged at 1.8. Therefore, when the movable fluid porosity is less than 10%, the n value is calculated using the following formula:

[0151] n=-0.1529φ+3.2294;

[0152] When the porosity of the movable fluid is between 10% and 22%, the n value is calculated using the following formula:

[0153] n=0.0333φ+1.3667;

[0154] When calculating a complex reservoir that needs to calculate the saturation index, the saturation index of the complex reservoir can be obtained by directly substituting the movable fluid porosity of the complex reservoir into the formula of the corresponding range.

[0155] Further, in a feasible embodiment, the regression model includes: a second lithology coefficient regression model;

[0156] Before the above step S30, the step of determining the cementation index, saturation index and second lithology coefficient of the complex reservoir based on the regression model and the movable fluid porosity, the method further includes:

[0157] Step C10, obtaining a sample second lithology coefficient and a sample mobile liquid porosity of the reservoir sample;

[0158] Step C20, performing regression analysis on the second lithology coefficient of the sample and the flowable liquid porosity of the sample to obtain a second lithology coefficient regression model with the flowable liquid porosity as a parameter.

[0159] In this embodiment, before calculating the cementation index, saturation index and second lithology coefficient based on the regression model, it is necessary to establish a second lithology coefficient regression model for the second lithology coefficient. When establishing the second lithology coefficient regression model, multiple reservoir samples are used as samples to collect data, and the sample second lithology coefficients and sample movable fluid porosity of each of the multiple reservoir samples are collected. Then, the relationship between the second lithology coefficients of the multiple samples and the movable fluid porosity of the multiple samples is compared, and the second lithology coefficient regression model can be obtained by using the movable fluid porosity as a parameter through regression analysis. The second lithology coefficient calculated in the above manner can better adapt to the lithology characteristics of the local complex reservoir, and improve the accuracy of the second lithology coefficient of the complex reservoir on the basis of improving the accuracy of the saturation calculation, thereby further improving the calculation accuracy of the saturation.

[0160] For example, in the Liushagang Formation of the Fushan Sag, the relationship between the porosity index a value and the oil and gas saturation parameter was plotted using the rock electrical experimental data of 23 wells. Please refer to Figure 5 , Figure 5 This is a relationship diagram between the second lithology coefficient a and the movable fluid porosity involved in the first embodiment of the complex reservoir saturation calculation method of the present application, such as Figure 5 As shown in the figure, the horizontal axis is the movable fluid porosity (abbreviated as porosity in the figure), and the vertical axis is the second lithology coefficient a. The points in the figure correspond to the sample second lithology coefficient and sample movable fluid porosity of different reservoir samples. The curve in the figure is the fitted second lithology coefficient regression model. The study found that when the movable fluid porosity is greater than 17%, the a value tends to be close to 1.0. When the movable fluid porosity is less than 17, the a value increases continuously as the movable fluid porosity decreases. When the movable fluid porosity is less than 10, the a value is greater than 2.0. The a value formula is as follows:

[0161] a=-0.002φ 3 +0.1098φ 2 -2.0378φ+13.62;

[0162] When calculating a complex reservoir that needs to calculate the second lithology coefficient, the second lithology coefficient of the complex reservoir can be obtained by directly substituting the movable fluid porosity of the complex reservoir into the formula.

[0163] For example, please refer to Figure 6 , Figure 6 This is a calculation scenario diagram involved in an embodiment of a method for calculating saturation of a complex reservoir of the present application, such as Figure 6 As shown in the figure, when calculating the saturation, it is necessary to first establish a movable fluid porosity model, determine the formation water resistivity, establish a cementation index (m) model, establish a saturation index (n) model, and establish a lithology coefficient (a) model related to lithology. Then, the parameter calculation formula in the model is brought into the conventional Archie formula for saturation calculation, thereby improving the accuracy of saturation calculation for complex reservoirs.

[0164] For example, Figure 7 This is a calculation interaction diagram related to an embodiment of a method for calculating saturation of a complex reservoir of the present application, such as Figure 7 As shown, the user first inputs the acoustic time difference logging values, compensated neutron logging values ​​and density logging values ​​obtained by conventional logging into the movable fluid porosity model to obtain the movable fluid porosity φ of the complex reservoir, and then determines the formation water resistivity based on the water test analysis of the oil layer, and then inputs φ into the cementation index model, saturation index model and lithology coefficient model to obtain the output m, n, a, and then inputs the parameters into the Archie formula to obtain the saturation of the complex reservoir.

[0165] In addition, please refer to Figure 8 , Figure 8 This is a functional module diagram of a saturation calculation device for a complex reservoir of the present application. The present application also provides a saturation calculation device for a complex reservoir, and the saturation calculation device for a complex reservoir includes:

[0166] A porosity determination module 10, for determining the movable fluid porosity of a complex reservoir based on the operation of nuclear magnetic logging and conventional logging;

[0167] A parameter acquisition module 20, used to acquire the water resistivity in the oil layer of the complex reservoir, and to acquire the formation resistivity and the first lithology coefficient of the complex reservoir;

[0168] A parameter determination module 30, for determining a cementation index, a saturation index and a second lithology coefficient of the complex reservoir based on a regression model and the movable fluid porosity;

[0169] The saturation calculation module 40 is used to determine the saturation of the complex reservoir based on the movable fluid porosity, the water resistivity, the formation resistivity, the first lithology coefficient, the cementation index, the saturation index and the second lithology coefficient.

[0170] Optionally, the porosity determination module is further configured to:

[0171] Conducting nuclear magnetic logging and conventional logging on reservoir samples to determine a movable fluid porosity model for calculating movable fluid porosity;

[0172] Conventional logging is performed on the complex reservoir to obtain logging information, and the logging information is substituted into the movable fluid porosity model to calculate the movable fluid porosity of the complex reservoir.

[0173] Optionally, the porosity determination module is further configured to:

[0174] Performing nuclear magnetic resonance logging on the reservoir sample to obtain the sample movable fluid porosity of the reservoir sample;

[0175] Performing conventional logging on the reservoir sample to obtain a first acoustic time difference logging value, a first compensated neutron logging value, and a first density logging value of the reservoir sample;

[0176] Based on the movable fluid porosity of the sample, a multivariate regression calculation is performed on the first acoustic time difference logging value, the first compensated neutron logging value and the first density logging value to obtain a movable fluid porosity model with the acoustic time difference logging value, the compensated neutron logging value and the density logging value as parameters.

[0177] Optionally, the porosity determination module is further configured to:

[0178] Performing conventional logging on the complex reservoir to obtain a second acoustic time difference logging value, a second compensated neutron logging value, and a second density logging value of the complex reservoir;

[0179] The second acoustic time difference logging value, the second compensated neutron logging value and the second density logging value are substituted into the movable fluid porosity model to obtain the movable fluid porosity of the complex reservoir.

[0180] Optionally, the device for calculating saturation of a complex reservoir further comprises:

[0181] A modeling module for obtaining a sample cementation index and a sample mobile liquid porosity of a reservoir sample;

[0182] A regression analysis is performed on the sample cementation index and the flowable liquid porosity of the sample to obtain a cementation index regression model with the flowable liquid porosity as a parameter.

[0183] Optionally, the modeling module is also used to:

[0184] Obtaining sample saturation index and sample mobile liquid porosity of reservoir samples;

[0185] A regression analysis is performed on the sample saturation index and the sample flowable liquid porosity to obtain a saturation index regression model with the flowable liquid porosity as a parameter.

[0186] Optionally, the modeling module is also used to:

[0187] Obtaining a sample second lithology coefficient and a sample mobile liquid porosity of a reservoir sample;

[0188] A regression analysis is performed on the second lithology coefficient of the sample and the flowable liquid porosity of the sample to obtain a second lithology coefficient regression model with the flowable liquid porosity as a parameter.

[0189] The specific implementation of the complex reservoir saturation calculation device of the present application is basically the same as the various embodiments of the complex reservoir saturation calculation method described above, and will not be repeated here.

[0190] In addition, the present application also proposes a storage medium, which is a computer-readable storage medium. A computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the complex reservoir saturation calculation method of the present application as described above are implemented.

[0191] The specific embodiments of the storage medium of the present application are basically the same as the embodiments of the above-mentioned complex reservoir saturation calculation method, and will not be described in detail here.

[0192] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0193] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0194] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0195] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for calculating the saturation of complex reservoirs. It is characterized in that The saturation calculation method of the complex reservoir comprises: Determine the movable fluid porosity of complex reservoirs based on the operation of nuclear magnetic logging and conventional logging; Acquire the water resistivity in the oil layer of the complex reservoir, and acquire the formation resistivity and the first lithology coefficient of the complex reservoir; Determining a cementation index, a saturation index, and a second lithology coefficient of the complex reservoir based on a regression model and the movable fluid porosity; The saturation of the complex reservoir is determined based on the movable fluid porosity, the water resistivity, the formation resistivity, the first lithology coefficient, the cementation index, the saturation index, and the second lithology coefficient.

2. The method for calculating the saturation of a complex reservoir according to claim 1, It is characterized in that The step of determining the movable fluid porosity of a complex reservoir based on the operation of nuclear magnetic logging and conventional logging comprises: Conducting nuclear magnetic logging and conventional logging on reservoir samples to determine a movable fluid porosity model for calculating movable fluid porosity; Conventional logging is performed on the complex reservoir to obtain logging information, and the logging information is substituted into the movable fluid porosity model to calculate the movable fluid porosity of the complex reservoir.

3. The method for calculating the saturation of a complex reservoir according to claim 2, It is characterized in that The step of performing nuclear magnetic logging and conventional logging on the reservoir sample to determine a movable fluid porosity model for calculating the movable fluid porosity comprises: Performing nuclear magnetic resonance logging on the reservoir sample to obtain the sample movable fluid porosity of the reservoir sample; Performing conventional logging on the reservoir sample to obtain a first acoustic time difference logging value, a first compensated neutron logging value, and a first density logging value of the reservoir sample; Based on the movable fluid porosity of the sample, a multivariate regression calculation is performed on the first acoustic time difference logging value, the first compensated neutron logging value and the first density logging value to obtain a movable fluid porosity model with the acoustic time difference logging value, the compensated neutron logging value and the density logging value as parameters.

4. The method for calculating the saturation of a complex reservoir according to claim 3, It is characterized in that The step of performing conventional logging on the complex reservoir to obtain logging information, and substituting the logging information into the movable fluid porosity model to calculate the movable fluid porosity of the complex reservoir comprises: Performing conventional logging on the complex reservoir to obtain a second acoustic time difference logging value, a second compensated neutron logging value, and a second density logging value of the complex reservoir; The second acoustic time difference logging value, the second compensated neutron logging value and the second density logging value are substituted into the movable fluid porosity model to obtain the movable fluid porosity of the complex reservoir.

5. The method for calculating saturation of a complex reservoir according to claim 1, It is characterized in that The regression model includes: a cementation index regression model; Before the step of determining the cementation index, saturation index and second lithology coefficient of the complex reservoir based on the regression model and the movable fluid porosity, the method further includes: Obtaining sample cementation index and sample mobile liquid porosity of reservoir samples; A regression analysis is performed on the sample cementation index and the flowable liquid porosity of the sample to obtain a cementation index regression model with the flowable liquid porosity as a parameter.

6. The method for calculating the saturation of a complex reservoir according to claim 3, It is characterized in that The regression model includes: a saturation index regression model; Before the step of determining the cementation index, saturation index and second lithology coefficient of the complex reservoir based on the regression model and the movable fluid porosity, the method further includes: Obtaining sample saturation index and sample mobile liquid porosity of reservoir samples; A regression analysis is performed on the sample saturation index and the sample flowable liquid porosity to obtain a saturation index regression model with the flowable liquid porosity as a parameter.

7. The method for calculating saturation of a complex reservoir according to claim 4, It is characterized in that The regression model includes: a second lithology coefficient regression model; Before the step of determining the cementation index, saturation index and second lithology coefficient of the complex reservoir based on the regression model and the movable fluid porosity, the method further includes: Obtaining a sample second lithology coefficient and a sample mobile liquid porosity of a reservoir sample; A regression analysis is performed on the second lithology coefficient of the sample and the flowable liquid porosity of the sample to obtain a second lithology coefficient regression model with the flowable liquid porosity as a parameter.

8. A saturation calculation device for complex reservoirs, It is characterized in that The complex reservoir saturation calculation device comprises: A porosity determination module for determining the movable fluid porosity of complex reservoirs based on the operation of nuclear magnetic logging and conventional logging; A parameter acquisition module, used to acquire the water resistivity in the oil layer of the complex reservoir, and to acquire the formation resistivity and the first lithology coefficient of the complex reservoir; A parameter determination module, for determining a cementation index, a saturation index and a second lithology coefficient of the complex reservoir based on a regression model and the movable fluid porosity; A saturation calculation module is used to determine the saturation of the complex reservoir based on the movable fluid porosity, the water resistivity, the formation resistivity, the first lithology coefficient, the cementation index, the saturation index and the second lithology coefficient.

9. A saturation calculation device for complex reservoirs. It is characterized in that The complex reservoir saturation calculation device comprises: a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the steps of the complex reservoir saturation calculation method according to any one of claims 1 to 7 are implemented.

10. A storage medium, It is characterized in that The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the complex reservoir saturation calculation method according to any one of claims 1 to 7 are implemented.

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