Reservoir water production rate calculation method and device, electronic equipment and storage medium

Abstract

The application provides a reservoir water production rate calculation method and device, electronic equipment and storage medium, and relates to the technical field of geological exploration. The method comprises the following steps: first, obtaining the rock-soil sample of the target reservoir, obtaining the rock-electricity experiment data and rock cation exchange measurement data, constructing the digital core data body, extracting the pore network model of the digital core data body, and obtaining the corresponding simulation data according to the pore network model; according to the rock-electricity experiment data and the rock cation exchange measurement data, the resistivity increase coefficient is corrected, and then the influence of the argillaceous additional conductivity is eliminated; then, according to the pore structure parameters introduced in the simulation data, the I-Kr model considering the pore structure is established, the conversion of the resistivity index and the relative permeability is completed, and the problem that the residual wet phase saturation and the residual non-wetting phase saturation are difficult to obtain is solved. Finally, according to the corrected resistivity increase coefficient and the pore structure parameters, the water production rate of the target reservoir is calculated, and the calculation result is more accurate.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of geological exploration, and particularly relates to a reservoir water production rate calculation method and device, electronic equipment and a storage medium. BACKGROUND

[0002] In the field of oil and gas exploration technology, according to the relevant provisions of the state, the specific type of the target reservoir can be determined by the value of the water production rate in the target reservoir. If the water production rate in the target reservoir is less than 10%, it can be determined that it belongs to an oil layer or a gas layer. Therefore, the accuracy of the water production rate as an important parameter for determining the type of the target reservoir is very important. The calculation of the water production rate is determined by formula calculation according to the calculation results of the water saturation and the relative permeability.

[0003] In the related art, the calculation method of the water production rate is generally applicable to relatively homogeneous rocks such as pure sandstone containing water or artificial cores, and the calculation accuracy for reservoirs with a certain amount of shale content is low, thus resulting in low accuracy in determining the type of reservoirs with a certain amount of shale content. SUMMARY

[0004] The present application provides a reservoir water production rate calculation method, device, electronic equipment and storage medium, which aims to solve the problems in the above background technology.

[0005] In order to solve the above technical problems, the present application is implemented as follows:

[0006] In a first aspect, the present application provides a reservoir water production rate calculation method, which comprises:

[0007] Obtaining a rock-soil sample of a target reservoir, and obtaining rock-electricity experiment data and rock cation exchange measurement data based on the rock-soil sample;

[0008] Constructing a digital core data body of the rock-soil sample, extracting a pore network model of the digital core data body, and obtaining mercury injection experiment data and nuclear magnetic data according to the pore network model;

[0009] Correcting the resistivity increase coefficient according to the rock-electricity experiment data and the rock cation exchange measurement data;

[0010] Extracting pore structure parameters according to the mercury injection experiment data and the nuclear magnetic data;

[0011] Calculating the water production rate of the target reservoir according to the corrected resistivity increase coefficient and the pore structure parameters.

[0012] Optionally, the step of constructing the digital core data body of the rock-soil sample comprises:

[0013] Multiple sampling of the target reservoir is performed to obtain multiple groups of rock-soil samples with different shale content;

[0014] For any group of rock-soil samples, multiple planar scanning images of the rock-soil samples are obtained;

[0015] The multiple planar scanning images are combined and spliced to obtain a three-dimensional space model of the rock-soil sample; wherein the three-dimensional space model is a digital core data volume.

[0016] Optionally, the step of correcting the resistivity increase coefficient according to the rock-electricity experiment data and the rock cation exchange measurement data comprises:

[0017] The pore water salinity of the target reservoir is determined according to the rock-electricity experiment data, and the clay additional conductivity of the target reservoir is determined according to the rock cation exchange measurement data;

[0018] The conductivity of the water-saturated state and the conductivity of the oil and gas bearing formation are corrected according to the salinity of the target reservoir and the clay additional conductivity of the target reservoir;

[0019] The resistivity increase coefficient is calculated according to the corrected conductivity of the water-saturated state and the conductivity of the oil and gas bearing formation.

[0020] Optionally, the step of extracting the pore structure parameters according to the mercury injection experiment data and the nuclear magnetic data comprises:

[0021] The throat pore diameter of the target reservoir is determined according to the mercury injection curve shape of the mercury injection experiment data, and the pore structure model of the target reservoir is determined according to the nuclear magnetic data;

[0022] The pore structure parameters of the target reservoir are determined according to the throat pore diameter and the pore structure model.

[0023] Optionally, the step of calculating the water production rate of the target reservoir according to the corrected resistivity increase coefficient and the pore structure parameters comprises:

[0024] The water saturation of the target reservoir is calculated according to the corrected resistivity increase coefficient;

[0025] The rock physics conversion model is constructed according to the corrected resistivity increase coefficient and the pore structure parameters;

[0026] The calculated relative permeability of the target reservoir is determined according to the rock physics conversion model;

[0027] The water production rate of the target reservoir is calculated according to the water saturation of the target reservoir and the calculated relative permeability of the target reservoir.

[0028] Optionally, the method further comprises: performing a relative permeability-resistivity joint test according to the rock-soil sample of the target reservoir to obtain the actual relative permeability of the target reservoir;

[0029] According to the difference between the actual relative permeability and the calculated relative permeability, the rock physical conversion model is corrected.

[0030] The second aspect of the embodiment of the application provides a reservoir water production rate calculation device, which comprises:

[0031] An acquisition module is configured to acquire a rock-soil sample of a target reservoir and obtain rock-electricity experiment data and rock cation exchange measurement data based on the rock-soil sample;

[0032] A test module is configured to construct a digital core data volume of the rock-soil sample, extract a pore network model of the digital core data volume, and obtain mercury injection experiment data and nuclear magnetic data according to the pore network model;

[0033] A calibration module is configured to correct a resistivity increase coefficient according to the rock-electricity experiment data and the rock cation exchange measurement data;

[0034] An extraction module is configured to extract pore structure parameters according to the mercury injection experiment data and the nuclear magnetic data;

[0035] A calculation module is configured to calculate a water production rate of the target reservoir according to the corrected resistivity increase coefficient and the pore structure parameters.

[0036] Optionally, the test module comprises:

[0037] A sampling sub-module is configured to sample the target reservoir multiple times to obtain multiple groups of rock-soil samples with different shale contents;

[0038] A scanning sub-module is configured to acquire multiple planar scanning images of the rock-soil sample for any one group of rock-soil samples;

[0039] A splicing sub-module is configured to combine and splice the multiple planar scanning images to obtain a three-dimensional space model of the rock-soil sample; the three-dimensional space model is a digital core data volume.

[0040] The third aspect of the embodiment of the application provides an electronic device, which comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete mutual communication through the communication bus.

[0041] The memory is configured to store a computer program.

[0042] The processor is configured to execute the program stored on the memory to implement the method steps provided in the first aspect of the embodiment of the application.

[0043] The fourth aspect of the embodiment of the application provides a computer readable storage medium, which stores a computer program, and the program is executed by the processor to implement the method provided in the first aspect of the embodiment of the application.

[0044] The embodiments of the present application include the following advantages:

[0045] The embodiments of the present application firstly acquire a rock sample of a target reservoir, and obtain rock-electricity experiment data and rock cation exchange measurement data, construct a digital core data volume, extract a pore network model of the digital core data volume, and obtain corresponding simulation data according to the pore network model; then correct a resistivity increase coefficient according to the rock-electricity experiment data and the rock cation exchange measurement data, and further eliminate the influence of shale additional conductivity; then introduce a pore structure parameter according to mercury injection experiment data and nuclear magnetic data, establish an I-Kr model considering the pore structure, complete the conversion of the resistivity index and the relative permeability, and solve the problem that residual wet phase saturation and residual non-wetting phase saturation are difficult to obtain; finally, the water production rate of the target reservoir can be calculated according to the corrected resistivity increase coefficient and the pore structure parameter, and the calculation of the water production rate considers the influence of shale and pore structure and other factors, so that the calculation result is more accurate than that of the prior art. BRIEF DESCRIPTION OF DRAWINGS

[0046] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0047] Figure 1 is a step flow chart of a reservoir water production rate calculation method in the embodiments of the present application;

[0048] Figure 2 is an equivalent path model of a rock sample in the embodiments of the present application;

[0049] Figure 3 is a Df and tortuosity ratio relationship graph of a No. 2 sample provided by the embodiments of the present application;

[0050] Figure 4 is a Df and tortuosity ratio relationship graph of a No. 4 sample provided by the embodiments of the present application;

[0051] Figure 5 is a resistivity index and wet phase tortuosity ratio relationship graph of six samples provided by the embodiments of the present application;

[0052] Figure 6 is a mercury injection curve classification graph provided by the embodiments of the present application;

[0053] Figure 7 is a tortuosity ratio and resistivity index classification relationship graph provided by the embodiments of the present application;

[0054] Figure 8 is an equivalent wetting phase saturation and resistivity index classification relationship diagram provided by an embodiment of the present application;

[0055] Figure 9 is a rock sample RQI and permeability classification relationship diagram provided by an embodiment of the present application;

[0056] Figure 10 is a No. 3 sample conversion result diagram provided by an embodiment of the present application;

[0057] Figure 11 is a No. 4 sample conversion result diagram provided by an embodiment of the present application;

[0058] Figure 12 is a No. 6 sample conversion result diagram provided by an embodiment of the present application;

[0059] Figure 13 is a well logging comprehensive interpretation effect diagram provided by an embodiment of the present application;

[0060] Figure 14 is a module schematic diagram of a reservoir water production rate calculation device in an embodiment of the present application. DETAILED DESCRIPTION

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

[0062] In the related art, the saturation calculation has always used a simple Archie formula, such as the Simandoux model, the W-S (waxman-smits saturation evaluation model) model, etc. For the calculation of relative permeability, it is generally assumed that the average flow paths of fluid and current in the porous medium are the same, and a method for calculating the relative permeability of the wetting phase and the non-wetting phase in a two-phase system is proposed. For a reservoir with a certain shale content, the influence of shale cannot be ignored, the pore structure is more complex, and the porosity and permeability are relatively small. It is relatively difficult to establish an interpretation model of water saturation, relative permeability and water production rate for such a reservoir. And the traditional resistivity index- relative permeability conversion model is generally applicable to relatively homogeneous rocks such as pure sandstone containing water or artificial cores. The influence of shale and pore structure is not considered.

[0063] Therefore, the present application is proposed based on the inventive concept of the applicant: based on the digital core simulation data and the real core measurement data, the influence of shale content and pore structure is considered, and the I-Kr (rock physics conversion model) model is improved to calculate the water production rate.

[0064] The embodiment of the present application provides a reservoir water production rate calculation method, referring to Figure 1 , Figure 1 A step flow chart of a reservoir water production rate calculation method is shown, and the method comprises the following steps:

[0065] Step S101: Obtain a rock-soil sample of a target reservoir, and obtain rock-electricity experiment data and rock cation exchange measurement data based on the rock-soil sample.

[0066] In the process of oil and gas exploration, when a target reservoir is selected as an exploration target, the target reservoir is sampled, representative rocks are selected, a rock-soil sample is obtained, and the rock-soil sample includes a standard plunger sample and a rock-soil fragment sample. The standard plunger sample is drilled by using a core drilling machine, and the rock-soil near the standard plunger sample is selected as the rock-soil fragment sample. The full-rock experiment and the CEC (Cation Exchange Capacity, cation exchange capacity) experiment are performed based on the rock-soil fragment sample to obtain the rock-electricity experiment data and the rock cation exchange measurement data, and the full-rock experiment is used to measure the content and components of mineral components in the rock-soil sample.

[0067] Step S102: Construct a digital core data volume of the rock-soil sample, extract a pore network model of the digital core data volume, and obtain mercury injection experiment data and nuclear magnetic data according to the pore network model.

[0068] The digital core data volume corresponding to the rock-soil sample obtained according to the sampling or the parallel sample is obtained, the digital core data volume is a simulated three-dimensional model of the rock-soil sample, and the pore network model is determined based on the digital core data volume. Since the digital core data volume contains all components of the rock-soil sample, that is, elements such as mineral components and pore network models, the mineral components are composed of what types of minerals. The elements other than the pore network model are removed from the digital core data volume, that is, the pore network model of the digital core data volume is obtained. The pore network model is simulated based on the pore network model to obtain pore size distribution, relative permeability-resistivity joint measurement, nuclear magnetic T2 distribution, and mercury injection curve simulation results. The nuclear magnetic T2 distribution is the nuclear magnetic data, and the mercury injection curve is the mercury injection experiment data.

[0069] Step S103: Correct the resistivity increase coefficient according to the rock-electricity experiment data and the rock cation exchange measurement data.

[0070] For argillaceous formation, due to the influence of argillaceous, the formation conductivity and the formation water conductivity no longer meet the simple Archie law. When the salinity is low, the additional conductivity caused by argillaceous increases with the increase of the salinity, resulting in the sharp increase of the formation conductivity with the increase of the salinity. When the salinity is high, the additional conductivity caused by argillaceous does not change with the change of the salinity, and the formation conductivity increases linearly with the increase of the salinity. Therefore, for argillaceous formation, the influence of argillaceous on rock conductivity law must be considered. Therefore, based on the rock electrical experiment data and the rock cation exchange measurement data, the resistivity increase coefficient expression is established to correct the resistivity increase coefficient.

[0071] Step S104: According to the mercury injection experiment data and the nuclear magnetic data, the pore structure parameters are extracted.

[0072] Step S105: According to the corrected resistivity increase coefficient and the pore structure parameters, the water production rate of the target reservoir is calculated.

[0073] In the embodiment of steps S101 to S105, since the influence of the additional conductivity of argillaceous on the conductivity is taken as an influencing factor, the influence of the additional conductivity of argillaceous is eliminated, the corrected resistivity index is established, the pore structure parameters are introduced, and the I-Kr model considering the pore structure is established. In this way, the problem that the residual wetting phase saturation and the residual non-wetting phase saturation are difficult to obtain is solved. The calculation result of the water production rate of the target reservoir is more accurate.

[0074] In an available embodiment, the step of constructing the digital core data volume of the rock and soil sample comprises:

[0075] Step S101-1: A plurality of rock and soil samples with different argillaceous contents are obtained by sampling the target reservoir multiple times.

[0076] Step S101-2: For any one group of rock and soil samples, a plurality of plane scanning images of the rock and soil sample are obtained.

[0077] Step S101-3: The plurality of plane scanning images are combined and spliced to obtain a three-dimensional space model of the rock and soil sample; wherein the three-dimensional space model is a digital core data volume.

[0078] In the embodiment of steps S101-1 to S101-3, in the process of obtaining the rock and soil sample of the target reservoir, multiple sampling is performed to obtain rock and soil samples with different argillaceous contents. As an example, during sampling, rock and soil sample 1 with argillaceous content A is obtained, rock and soil sample 2 with argillaceous content B is obtained, and rock and soil sample 3 with argillaceous content C is obtained. A, B, and C are different. For any one group of rock and soil samples, CT scanning is performed to obtain a plurality of plane scanning images of the rock and soil sample, and the plurality of plane scanning images are combined and spliced to obtain a three-dimensional space model of the rock and soil sample.

[0079] In an implementable embodiment, according to the rock electricity experiment data and the rock cation exchange measurement data, the step of correcting the resistivity increase coefficient comprises:

[0080] Step S103-1: determining the pore water salinity of the target reservoir according to the rock electricity experiment data, and determining the clay additional conductivity of the target reservoir according to the rock cation exchange measurement data;

[0081] Step S103-2: correcting the conductivity in the water-saturated state and the conductivity of the oil and gas bearing formation according to the salinity of the target reservoir and the clay additional conductivity of the target reservoir;

[0082] Step S103-3: calculating the resistivity increase coefficient according to the corrected conductivity in the water-saturated state and the conductivity of the oil and gas bearing formation.

[0083] In the embodiment of steps S103-1 to S103-3, first, the salinity of the target reservoir is determined according to the rock electricity experiment data, and the clay additional conductivity of the target reservoir is determined according to the rock cation exchange measurement data, and the influence of the salinity on the clay additional conductivity is that: when the salinity is low, the additional conductivity Cex caused by the shale increases with the increase of Cw, resulting in that the formation conductivity increases sharply with the increase of Cw; when the salinity is high, the additional conductivity Ce caused by the shale does not change with Cw, and the formation conductivity increases linearly with the increase of Cw. Therefore, for the shale formation, the influence of the shale on the rock conductivity rule must be considered. The W-S model divides the conductivity equation into two parts, one part is the conductivity of the clay component, and the other part is the conductivity of the electrolyte, and the specific steps are as follows:

[0084] First, the conductivity in the water-saturated state is corrected, and the correction process is shown in formula 1,

[0085]

[0086] Wherein, Cw is the conductivity of the electrolyte, Ce is the conductivity of the "clay exchange cation", that is, the clay additional conductivity, F* is the formation factor of the shale sandstone, and C e =BQ v . Q v is the salinity, and it is assumed that Obtain:

[0087] C0=C 0sd +C 0sh (2)

[0088] The conductivity of the oil and gas bearing formation can be expressed as:

[0089] C t =C tsd+C tsh (3)

[0090] wherein,

[0091] According to formula (2), (3) can be obtained:

[0092] C 0sd =C0-C 0sh (4)

[0093] C tsd =C t -C tsh (5)

[0094] The resistivity increase coefficient can be expressed in the form of formula (6):

[0095]

[0096] In the formula, C 0sd is the corrected conductivity in the water-saturated state, C tsd is the corrected conductivity of the oil and gas bearing formation, and based on the corrected conductivity in the water-saturated state and the corrected conductivity of the oil and gas bearing formation, the resistivity increase coefficient can be corrected based on formula 6, I * is the corrected resistivity increase coefficient. The clay additional conductivity will make the calculated conductivity of the rock larger, resulting in lower experimental measurement values. Therefore, based on the improved W-S model, the influence of the shale additional conductivity is eliminated.

[0097] In a feasible implementation, according to the mercury injection experiment data and the nuclear magnetic data, the step of extracting the pore structure parameters includes:

[0098] According to the mercury injection curve shape of the mercury injection experiment data, the throat pore diameter of the target reservoir is determined, and according to the nuclear magnetic data, the pore structure model of the target reservoir is determined;

[0099] According to the throat pore diameter and the pore structure model, the pore structure parameters of the target reservoir are determined.

[0100] In the present embodiment, Figure 2 is the equivalent path model of the rock sample, and the model center is the capillary pore channel, wherein La is the equivalent length in the saturated wet phase state, Lw is the equivalent path length in the current wet phase saturation state, and L is the length of the rock sample. τa=La / L, τw=Lw / L, so λrw=La / Lw. This is an ideal model, and application of this model is based on the assumption that all sample pore structures are similar and the pore size distribution is uniform. However, in real applications, the rock samples from the same study area have complex pore structures.

[0101] Figure 3 andFigure 4 Figure 25 is a plot of the fractal dimension Df and tortuosity ratio of the relative permeability curves obtained by different methods for two samples. The higher the wetting phase saturation, the more consistent the Df and tortuosity ratio of the three relative permeability curves, the closer to the irreducible water saturation, the more obvious the difference between different methods, and the smaller the fractal dimension. The closer the fractal dimension is to 2, the lower the tortuosity ratio. Since the pore tortuosity of the wetting phase at the wetting phase saturation is considered to be a constant value for the same sample, the closer the fractal dimension is to 2, the greater the pore tortuosity of the wetting phase at the current saturation, and since the wetting phase is completely bound, the equivalent wetting phase flow length is equivalent to an infinite length. It is considered that the wetting phase tortuosity ratio is a function of the resistivity index Figure 5 Figure 26 is a plot of the resistivity index and the wetting phase tortuosity ratio of six samples, and the data points are obviously distributed in two trends. The whole rock analysis shows that the illite / montmorillonite content of these samples is 19-25%, and the chlorite content is 1-3%, and there is little difference between each sample. After not considering the influence of argillaceous, it causes Figure 4 The reason for this phenomenon may be related to the pore structure. Therefore, the pore structure is considered as an influencing factor for correcting the I-Kr model. In order to study whether it is the pore structure that causes Figure 5 This phenomenon, we select samples to measure capillary pressure curves by mercury injection method, according to the mercury injection curve shape and pore size distribution, they are divided into three categories, Figure 6 The first category of pore structure is the best, the green solid line is the second category, and the red dotted line is the third category. And in the first and second categories, each select 2 samples for verification, A and B samples are the first category, and C and D samples are the second category. Figure 7 Figure 27 is a plot of the tortuosity ratio and the resistivity index of the first sample A and B, and the second sample C and D. The two categories of samples have obvious differences. From this figure, the relationship between the tortuosity ratio and the resistivity index of the two categories of samples in the region can be obtained:

[0102] First category: λ rw = 3.291 * e -1.129*I (7)

[0103] Second category: λ rw = 1.6699 * e -0.56*I (8)

[0104] Similarly Figure 8 Figure 28 is a plot of the equivalent saturation Sw* and the resistivity index of the two categories of samples, and the empirical formula in the region is obtained:

[0105] First category: S w * = -1.332 * ln(I) + 1.8057 (9)

[0106] Second category: S w* = -0.841 * ln(I) + 1.5845 (10)

[0107] Based on the above test results, further determine the calculation formula of the pore structure parameter as shown in formula 11

[0108]

[0109] In a feasible implementation, the step of calculating the water production rate of the target reservoir according to the corrected resistivity increase coefficient and the pore structure parameter comprises:

[0110] According to the corrected resistivity increase coefficient, the water saturation of the target reservoir is calculated;

[0111] According to the corrected resistivity increase coefficient and the pore structure parameter, a rock physical conversion model is constructed;

[0112] According to the rock physical conversion model, the calculated relative permeability of the target reservoir is determined;

[0113] According to the water saturation of the target reservoir and the calculated relative permeability of the target reservoir, the water production rate of the target reservoir is calculated.

[0114] In this embodiment, after the corrected resistivity increase coefficient I * is obtained, the water saturation of the rock sample, i.e. the water saturation of the target reservoir, can be calculated according to the W-S model. Then, a rock physical conversion model as shown in formula 12 is constructed according to the corrected resistivity increase coefficient and the pore structure parameter, and the calculated relative permeability of the target reservoir is calculated according to the rock physical conversion model, so as to realize the conversion of the resistivity index and the relative permeability, and obtain a more actual result.

[0115]

[0116] Based on the improved rock physical conversion model, the calculated relative permeability of the target reservoir is determined; when the relative permeability and the water saturation are calculated, the water production rate of the target reservoir can be calculated according to formula 13.

[0117]

[0118] In a feasible implementation, the method further comprises:

[0119] According to the rock sample of the target reservoir, a relative permeability-resistivity joint test is performed to obtain the actual relative permeability of the target reservoir;

[0120] According to the difference between the actual relative permeability and the calculated relative permeability, the rock physical conversion model is corrected.

[0121] In the embodiment, in order to verify the accuracy of the calculation method, after the rock and soil sample of the target reservoir is obtained, the relative permeability-resistivity joint measurement is performed, wherein the temperature and pressure meet the in-situ conditions of the underground, the actual relative permeability of the target reservoir is obtained under the simulation of the real environment, and the calculated relative permeability is compared, and the rock physical conversion model is corrected based on the difference. Figure 9 is a rock sample quality factor RQI and permeability classification relationship diagram of a target research area, the RQI reflects a macroscopic pore structure of a sample, Through the formula, the sample pore structure classification result can be obtained under the condition that the porosity and permeability are known.

[0122] Table 1 is basic information of verification samples, according to Figure 9 the classification standard, the No. 3 and No. 4 samples belong to the first type, and the No. 6 sample belongs to the second type. According to the classification empirical formula, the wet phase tortuosity ratio and the equivalent wet phase saturation are obtained, and then the formula (11) and the formula (12) are used to obtain Figure 10-12 The conversion results of the samples are good. Figure 13 is an application effect of the method.

[0123] Table 1 is basic information of verification samples

[0124]

[0125]

[0126] The embodiment of the application further provides a reservoir water production rate calculation device, referring to Figure 14 , a functional module diagram of a first aspect of a reservoir water production rate calculation device embodiment in the application is shown, the device can include the following modules:

[0127] The acquisition module 1401 is used for acquiring a rock and soil sample of a target reservoir, and obtaining rock and electricity experimental data and rock cation exchange measurement data based on the rock and soil sample;

[0128] The test module 1402 is used for constructing a digital core data body of the rock and soil sample, extracting a pore network model of the digital core data body, and obtaining mercury injection experiment data and nuclear magnetic data according to the pore network model;

[0129] The calibration module 1403 is used for correcting the resistivity increase coefficient according to the rock and electricity experimental data and the rock cation exchange measurement data;

[0130] The extraction module 1404 is used for extracting pore structure parameters according to the mercury injection experiment data and the nuclear magnetic data;

[0131] The computing module 1405 is configured to calculate the water production rate of the target reservoir according to the corrected resistivity increase coefficient and the pore structure parameter.

[0132] In an implementation, the testing module 1402 includes:

[0133] The sampling sub-module is configured to sample the target reservoir multiple times to obtain multiple groups of rock-soil samples with different shale contents.

[0134] The scanning sub-module is configured to obtain multiple planar scanning images of the rock-soil sample.

[0135] The splicing sub-module is configured to combine and splice the multiple planar scanning images to obtain a three-dimensional space model of the rock-soil sample, where the three-dimensional space model is a digital core data volume.

[0136] In an implementation, the calibration module 1403 includes:

[0137] The determining sub-module is configured to determine the mineralization degree of the target reservoir according to the rock-electricity experiment data, and determine the clay additional conductivity of the target reservoir according to the rock cation exchange measurement data.

[0138] The first correcting sub-module is configured to correct the conductivity in the water-saturated state and the conductivity of the oil and gas bearing formation according to the pore water mineralization degree of the target reservoir and the clay additional conductivity of the target reservoir.

[0139] The second correcting sub-module is configured to calculate the resistivity increase coefficient according to the corrected conductivity in the water-saturated state and the conductivity of the oil and gas bearing formation.

[0140] In an implementation, the extracting module 1404 includes:

[0141] The first determining sub-module is configured to determine the throat aperture of the target reservoir according to the mercury injection curve morphology of the mercury injection experiment data, and determine the pore structure model of the target reservoir according to the nuclear magnetic data.

[0142] The second determining sub-module is configured to determine the pore structure parameter of the target reservoir according to the throat aperture and the pore structure model.

[0143] In an implementation, the computing module 1405 includes:

[0144] The first calculating sub-module is configured to calculate the water saturation of the target reservoir according to the corrected resistivity increase coefficient.

[0145] The constructing sub-module is configured to construct a rock physics conversion model according to the corrected resistivity increase coefficient and the pore structure parameter.

[0146] The second calculation sub-module is configured to determine the calculated relative permeability of the target reservoir according to a rock physics conversion model;

[0147] The third calculation sub-module is configured to calculate the water production rate of the target reservoir according to the water saturation of the target reservoir and the calculated relative permeability of the target reservoir.

[0148] Based on the same inventive concept, another embodiment of the present application provides an electronic device, comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete communication with each other through the communication bus,

[0149] The memory is configured to store a computer program.

[0150] The processor is configured to execute the program stored in the memory, and realize the steps of the first aspect of the embodiment of the present application.

[0151] The communication bus of the terminal mentioned above can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The communication bus can be divided into an address bus, a data bus, a control bus, etc. For the convenience of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or only one type of bus.

[0152] The communication interface is configured to complete communication between the terminal and other devices.

[0153] The memory can include a random access memory (RAM) and can also include a non-volatile memory, for example, at least one disk memory. Optionally, the memory can also be at least one storage device located away from the aforementioned processor.

[0154] The processor described above can be a general processor, including a central processing unit (CPU), a network processor (NP), etc.; can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component.

[0155] In yet another embodiment provided by the present application, a computer readable storage medium is also provided, and the computer readable storage medium stores instructions, which, when executed on a computer, cause the computer to perform the reservoir water production rate calculation method of the first aspect of the above-mentioned embodiment.

[0156] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, device, or computer program product. Therefore, the embodiments of the present application can adopt a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the embodiments of the present application can adopt a computer program product in the form of being implemented on one or more computer readable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) containing computer usable program codes.

[0157] The embodiments of the present application are described with reference to flowcharts and / or block diagrams of the method, terminal device (apparatus), and computer program product according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to produce a machine, so that the instructions executed by the computer or other programmable data processing terminal device produce a device implemented in the flowcharts and / or block diagrams. Figure 1 The device for implementing the functions specified in one flow or multiple flows and / or one block or multiple blocks. Figure 1 The device for implementing the functions specified in one flow or multiple flows and / or one block or multiple blocks.

[0158] These computer program instructions can also be stored in a computer readable memory capable of guiding the computer or other programmable data processing terminal device to work in a specific manner, so that the instructions stored in the computer readable memory produce a product including instruction devices, which implement the flowcharts and / or block diagrams. Figure 1one or more processes and / or blocks Figure 1 the function specified in the one or more blocks.

[0159] These computer program instructions can also be loaded into a computer or other programmable data processing terminal device, so that a series of operational steps are performed on the computer or other programmable terminal device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable terminal device provide a process for implementing the flow Figure 1 one or more processes and / or blocks Figure 1 the function specified in the one or more blocks.

[0160] Finally, it should be noted that, in this paper, relational terms such as first and second are used merely to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. "And / or" means that any one of the two or both can be selected. Moreover, the terms "include", "contain" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or terminal device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or terminal device. Without more limitations, the element defined by the statement "including a" does not exclude the presence of other identical elements in the process, method, article or terminal device including the element.

[0161] The above describes in detail the reservoir water production rate calculation method, device, electronic equipment and storage medium provided by the present application. The principles and implementation modes of the present application are described by applying specific examples in this paper. The above example is only used to help understand the method and core idea of the present application; at the same time, for those skilled in the art, according to the idea of the present application, the specific implementation mode and application range will be changed; in view of the above, the content of the specification should not be understood as a limitation of the present application.

Claims

1. A method for calculating reservoir water production rate, characterized in that, The method includes: Obtain rock and soil samples from the target reservoir, and obtain rock electrical experimental data and rock cation exchange measurement data based on the rock and soil samples; A digital core data volume of the soil and rock sample was constructed, a pore network model of the digital core data volume was extracted, and mercury intrusion porosimetry data and nuclear magnetic resonance data were obtained based on the pore network model. Based on the rock electrical experiment data and the rock cation exchange measurement data, the resistivity increase factor was corrected. The resistivity increase factor is expressed in the form of: in, This is the corrected conductivity under saturated conditions. The corrected electrical conductivity of the oil and gas-bearing formation. This is the corrected resistivity increase factor; , F* represents the stratigraphic factors of argillaceous sandstone. Mineralization; Based on the mercury intrusion porosimetry data and the nuclear magnetic resonance data, pore structure parameters were extracted; The formula for calculating the pore structure parameters is as follows: The water production rate of the target reservoir is calculated based on the corrected resistivity increase coefficient and the pore structure parameters. A rock physics transformation model is constructed based on the corrected resistivity increase factor and the pore structure parameters: Based on the improved rock physics transformation model, the calculated relative permeability of the target reservoir is determined; the water production rate of the target reservoir is calculated according to the water production rate formula, which is: 。 2. The method according to claim 1, characterized in that, The steps for constructing the digital core data volume of the soil and rock samples include: Multiple samples were taken from the target reservoir to obtain multiple groups of soil and rock samples with different clay contents; For any set of soil and rock samples, acquire multiple planar scan images of the soil and rock samples; The multiple planar scan images are combined and stitched together to obtain a three-dimensional spatial model of the soil and rock sample; wherein, the three-dimensional spatial model is the digital core data volume.

3. The method according to claim 1, characterized in that, Based on the rock electrical experiment data and the rock cation exchange measurement data, the steps for correcting the resistivity increase factor include: The pore water salinity of the target reservoir is determined based on the rock electrical experiment data, and the clay-added conductivity of the target reservoir is determined based on the rock cation exchange measurement data. Based on the mineralization of the target reservoir and the clay-added electrical conductivity of the target reservoir, the electrical conductivity under water saturation and the electrical conductivity of the oil and gas-bearing formation are corrected. The resistivity increase factor is calculated based on the corrected conductivity under saturated water conditions and the conductivity of the oil and gas-bearing formation.

4. The method according to claim 1, characterized in that, The steps for extracting pore structure parameters based on the mercury intrusion porosimetry data and the nuclear magnetic resonance data include: The throat pore size of the target reservoir is determined based on the shape of the mercury intrusion curve from the mercury intrusion test data, and the pore structure model of the target reservoir is determined based on the nuclear magnetic resonance data. Based on the throat pore size and the pore structure model, the pore structure parameters of the target reservoir are determined.

5. The method according to claim 1, characterized in that, The steps for calculating the water production rate of the target reservoir based on the corrected resistivity increase coefficient and the pore structure parameters include: Calculate the water saturation of the target reservoir based on the corrected resistivity increase coefficient; Based on the corrected resistivity increase coefficient and the pore structure parameters, a rock physics transformation model is constructed. Based on the aforementioned rock physics transformation model, the calculated relative permeability of the target reservoir is determined; The water production rate of the target reservoir is calculated based on the water saturation of the target reservoir and the measured relative permeability of the target reservoir.

6. The method according to claim 5, characterized in that, The method further includes: Based on the rock and soil samples of the target reservoir, a relative permeability-resistivity joint test was conducted to obtain the actual relative permeability of the target reservoir; The rock physical conversion model is corrected based on the difference between the actual relative permeability and the calculated relative permeability.

7. A reservoir water production rate calculation device, characterized in that, The device includes: The acquisition module is used to acquire rock and soil samples from the target reservoir and obtain rock electrical experimental data and rock cation exchange measurement data based on the rock and soil samples. The experimental module is used to construct a digital core data volume of the soil and rock sample, extract the pore network model of the digital core data volume, and obtain mercury intrusion porosimetry experimental data and nuclear magnetic resonance data based on the pore network model. The calibration module is used to correct the resistivity increase factor based on the rock electrical experiment data and the rock cation exchange measurement data; The resistivity increase factor is expressed in the form of: in, This is the corrected conductivity under saturated conditions. The corrected electrical conductivity of the oil and gas-bearing formation. This is the corrected resistivity increase factor; , F* represents the stratigraphic factors of argillaceous sandstone. Mineralization; The extraction module is used to extract pore structure parameters based on the mercury intrusion porosimetry data and the nuclear magnetic resonance data; The formula for calculating the pore structure parameters is as follows: The calculation module is used to calculate the water production rate of the target reservoir based on the corrected resistivity increase coefficient and the pore structure parameters. A rock physics transformation model is constructed based on the corrected resistivity increase factor and the pore structure parameters: Based on the improved rock physics transformation model, the calculated relative permeability of the target reservoir is determined; the water production rate of the target reservoir is calculated according to the water production rate formula, which is: 。 8. The apparatus according to claim 7, characterized in that, The test module includes: The sampling submodule is used to perform multiple samplings of the target reservoir to obtain multiple sets of soil and rock samples with different clay contents. The scanning submodule is used to acquire multiple planar scanning images of any set of soil and rock samples. The stitching submodule is used to combine and stitch together the multiple planar scan images to obtain a three-dimensional spatial model of the soil and rock sample; wherein, the three-dimensional spatial model is the digital core data volume.

9. An electronic device, characterized in that, It includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor, when executing a program stored in memory, implements the steps of the method described in any one of claims 1-6.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1-6.

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