Method, device, equipment and storage medium for determining equivalent matrix modulus

Abstract

The present application provides a method, device, equipment and storage medium for determining an equivalent matrix modulus, including: obtaining logging information of an area with unknown mineral content; determining the fluid bulk modulus and the initial shear wave of a target depth point in the area with unknown mineral content based on the logging information; determining the equivalent matrix modulus range of the target depth point based on a preset skeleton model and the initial shear wave; performing an inversion calculation of the equivalent matrix modulus of the target depth point based on the equivalent matrix modulus range, a preset dry rock Poisson's ratio range, the fluid bulk modulus and an objective function to determine the equivalent matrix modulus of the target depth point, so as to determine the equivalent matrix modulus of each depth point in the area with unknown mineral content.

Description

Technical Field

[0001] The present application relates to the field of seismic data interpretation, and in particular to a method, device, equipment and storage medium for determining an equivalent matrix modulus. Background Art

[0002] At present, the widely used shear wave prediction methods require that the matrix modulus at each depth point underground is known, and the equivalent matrix modulus can be calculated according to the corresponding mineral content curve. When the mineral content is unknown, using empirical values ​​to calculate the matrix modulus will have a great impact on the accuracy of shear wave prediction. For example, in the marine carbonate oil and gas reservoirs widely developed in my country, the rock properties are complex and changeable, and the surface exploration conditions are very harsh. The matrix modulus of such complex oil and gas reservoirs is easily affected by various factors such as diagenesis, karstification, ancient faults, pressure and temperature, which makes the matrix modulus calculated by empirical values ​​less accurate. Summary of the invention

[0003] In response to the above problems, the present application provides a method, device, equipment and storage medium for determining an equivalent matrix modulus.

[0004] The present application provides a method for determining an equivalent matrix modulus, comprising:

[0005] Obtain well logging information for areas with unknown mineral content;

[0006] Determine the fluid bulk modulus at the target depth point in the region with unknown mineral content and the initial shear wave at the target depth point based on the well logging information;

[0007] Determining the equivalent matrix modulus range of the target depth point based on a preset skeleton model and the initial shear wave;

[0008] Based on the equivalent matrix modulus range, the preset dry rock Poisson's ratio range, the fluid bulk modulus, and the objective function, an inversion calculation of the equivalent matrix modulus of the target depth point is performed to determine the equivalent matrix modulus of the target depth point, so as to determine the equivalent matrix modulus of each depth point in the area with unknown mineral content, wherein the objective function is to minimize the difference between the first fluid term and the second fluid term, the first fluid term is obtained by a first calculation method based on the equivalent matrix modulus, the dry rock Poisson's ratio, and the fluid bulk modulus, the second fluid term is obtained by a second calculation method based on the equivalent matrix modulus, the dry rock Poisson's ratio, and the fluid bulk modulus, the first calculation method and the second calculation method are different, the equivalent matrix modulus is within the equivalent matrix modulus range, and the dry rock Poisson's ratio is within the dry rock Poisson's ratio range.

[0009] In some embodiments, the determining the equivalent matrix modulus range of the target depth point based on the preset skeleton model and the initial shear wave includes:

[0010] Determine the basic parameters of the preset skeleton model;

[0011] Determining the saturated rock bulk modulus at a target depth point based on the skeleton model and the initial shear wave;

[0012] The equivalent matrix modulus range of the target depth point is determined based on the saturated rock bulk modulus, the skeleton model and the basic parameters.

[0013] In some embodiments, based on the equivalent matrix modulus range, the preset dry rock Poisson's ratio range, the fluid bulk modulus, and the objective function, an inversion calculation of the equivalent matrix modulus of the target depth point is performed to determine the equivalent matrix modulus of the target depth point, so as to determine the equivalent matrix modulus of each depth point in the region with unknown mineral content, including:

[0014] Determining a first initial population based on the equivalent matrix modulus range, and determining a second initial population based on a preset dry rock Poisson's ratio range;

[0015] Determine each population individual based on the first initial population and the second initial population, each population individual includes an individual determined based on the first initial population and an individual determined based on the second initial population;

[0016] Determine the first fluid item corresponding to each population individual by using the first calculation method based on each population individual and the fluid bulk modulus;

[0017] Determine the second fluid item corresponding to each population individual by using the second calculation method based on each population individual and the fluid bulk modulus;

[0018] Determining the fitness value of each population individual based on the first fluid item and the corresponding second fluid item and the objective function;

[0019] Determining the equivalent matrix modulus of the target depth point based on the fitness value of each population individual;

[0020] The equivalent matrix modulus of each depth point in the area with unknown mineral content is determined based on the equivalent matrix modulus of the target depth point.

[0021] In some embodiments, the determining the first fluid item corresponding to each population individual by the first calculation method based on each population individual and the fluid bulk modulus includes:

[0022] Determining the compression coefficient corresponding to each population individual based on each population individual and the fluid bulk modulus;

[0023] A first fluid item corresponding to each population individual is determined by a first calculation method based on the compression coefficient corresponding to each population individual.

[0024] In some embodiments, determining each population individual based on the first initial population and the second initial population includes:

[0025] Performing mutation and crossover operations based on the first initial population to obtain a first temporary population;

[0026] Performing mutation and crossover operations on the second initial population to obtain a second temporary population;

[0027] Individuals of each population are determined based on the first temporary population and the second temporary population.

[0028] In some embodiments, the logging information includes: P-wave velocity and porosity, and the method further includes:

[0029] Determine the distribution range of the pore aspect ratio of the region with unknown material content based on the longitudinal wave velocity and the porosity;

[0030] Determine the target pore aspect ratio of the region with unknown content of the substance by using a particle swarm inversion algorithm based on the distribution range;

[0031] The shear wave velocity at the target depth point is determined based on the target pore aspect ratio and the equivalent matrix modulus.

[0032] In some embodiments, the determining the distribution range of the pore aspect ratio of the region with unknown material content based on the longitudinal wave velocity and the porosity comprises:

[0033] Determine the initial distribution range of pore type and pore aspect ratio of the region with unknown material content based on the longitudinal wave velocity and the porosity;

[0034] The distribution range of the pore aspect ratio is determined based on the pore type and the initial distribution range.

[0035] The present application embodiment provides a device for determining an equivalent matrix modulus, comprising:

[0036] An acquisition module, used to obtain logging information of areas with unknown mineral content;

[0037] A first determination module is used to determine the fluid bulk modulus of the target depth point in the region with unknown mineral content and the initial shear wave of the target depth point based on the well logging information;

[0038] A second determination module is used to determine the equivalent matrix modulus range of the target depth point based on a preset skeleton model and the initial shear wave;

[0039] A calculation module is used to perform inversion calculation of the equivalent matrix modulus of the target depth point based on the equivalent matrix modulus range, the preset dry rock Poisson's ratio range, the fluid bulk modulus, and an objective function to determine the equivalent matrix modulus of the target depth point, so as to determine the equivalent matrix modulus of the area with unknown mineral content, wherein the objective function is to minimize the difference between the first fluid term and the second fluid term, the first fluid term is obtained by a first calculation method based on the equivalent matrix modulus, the dry rock Poisson's ratio, and the fluid bulk modulus, the second fluid term is obtained by a second calculation method based on the equivalent matrix modulus, the dry rock Poisson's ratio, and the fluid bulk modulus, the first calculation method and the second calculation method are different, the equivalent matrix modulus is within the equivalent matrix modulus range, and the dry rock Poisson's ratio is within the dry rock Poisson's ratio range.

[0040] An embodiment of the present application provides a device for determining an equivalent matrix modulus, including a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, any one of the above-mentioned methods for determining the equivalent matrix modulus is executed.

[0041] An embodiment of the present application provides a storage medium, which stores a computer program that can be executed by one or more processors and can be used to implement any of the above-mentioned methods for determining the equivalent matrix modulus.

[0042] The present application provides a method, device, equipment and storage medium for determining an equivalent matrix modulus. The method determines the fluid bulk modulus and initial shear wave through actual logging information of a target depth point in a mineral location area, and then determines the equivalent matrix modulus range of the target depth point based on a preset skeleton model and the initial shear wave. The equivalent matrix modulus of the target depth point is inverted and calculated based on the equivalent matrix modulus range, a preset dry rock Poisson's ratio range, the fluid bulk modulus and an objective function, thereby determining the equivalent matrix modulus of the target depth point. The method can calculate the equivalent matrix modulus of each depth point in an area with unknown mineral content, and the calculation accuracy is higher, so that when shear wave prediction is performed for an area with unknown mineral content, the predicted shear wave is more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The present application will be described in more detail below based on embodiments and with reference to the accompanying drawings.

[0044] Figure 1 A schematic diagram of an implementation flow of a method for determining an equivalent matrix modulus provided in an embodiment of the present application;

[0045] Figure 2 A schematic diagram of the implementation flow of another method for determining equivalent matrix modulus provided in an embodiment of the present application;

[0046] Figure 3 A schematic diagram of a process for performing an inversion calculation provided in an embodiment of the present application;

[0047] Figure 4 A flowchart of an equivalent matrix modulus estimation based on a differential evolution algorithm provided in an embodiment of the present application;

[0048] Figure 5 A schematic diagram of the relationship between velocity and porosity aspect ratio provided in an embodiment of the present application;

[0049] Figure 6 A schematic diagram of an approximate normal distribution law of pore aspect ratio provided in an embodiment of the present application;

[0050] Figure 7 A schematic diagram of determining pore type using a velocity-porosity reference line provided in an embodiment of the present application;

[0051] Figure 8 A schematic diagram of the matrix modulus inversion results of the mineral location area provided in the embodiment of the present application;

[0052] Fig. 9 A schematic diagram of a shear wave prediction result provided in an embodiment of the present application;

[0053] Fig.10 A schematic diagram of the structure of a device for determining an equivalent matrix modulus provided in an embodiment of the present application;

[0054] Fig.11 A schematic diagram of the composition structure of an apparatus for determining the equivalent matrix modulus provided in an embodiment of the present application.

[0055] In the drawings, the same reference numerals are used for the same components, and the drawings are not drawn to scale. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0057] In the following description, reference is made to “some embodiments”, which describe a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0058] If similar descriptions of "first\second\third" appear in the application documents, the following instructions will be added. In the following description, the terms "first\second\third" involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. It can be understood that "first\second\third" can be interchanged in a specific order or sequence where permitted, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0059] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0060] Before introducing a method for determining an equivalent matrix modulus provided in an embodiment of the present application, a brief introduction is given to the problems existing in the related art:

[0061] In recent years, reservoir fluid identification technology has developed rapidly, from the initial "qualitative" identification technology based on seismic amplitude anomalies to the current "quantitative" identification technology based on fluid factors, and velocity information has always played a key role in it. Complete velocity information includes key parameters such as Lame modulus and Poisson's ratio that reflect fluid properties, which can greatly reduce the multi-solution of seismic amplitude interpretation, and is of great significance to AVO analysis of seismic data, fluid identification, logging lithology interpretation, etc. However, in actual production processes, due to the high measurement cost and interpretation difficulty of shear waves, shear wave velocity information is often lacking, so accurate prediction of shear wave velocity is essential, and its importance to reservoir prediction is self-evident. There are many factors that affect the propagation of seismic waves in rocks, including the elastic properties of the rock itself, the lithology of the rock, density, burial depth, tectonic history and geological age. In addition, the porosity, fluid velocity and fluid saturation of the rock can also have a great impact on the seismic wave velocity. Therefore, a suitable and efficient method is very important for shear wave prediction.

[0062] The earliest research on shear wave prediction started with the establishment of relevant empirical formulas. This type of research is mostly conducted in the laboratory on underground rock sampling and analysis, generally describing the relationship between shear wave velocity and other common parameters (such as longitudinal wave velocity, density, etc.). At the beginning, Pickett et al. analyzed a large number of laboratory ultrasonic v p and v S, and gave the corresponding empirical formula; Castagna et al. collected a large amount of experimental data for research, and not only proposed the classic "mudstone baseline" formula, that is, the empirical formula that represents the relationship between the P-wave and S-wave velocities of clastic rocks under water-containing conditions, but also proposed the least squares fitting of the P-wave and S-wave velocities in sandstone and shale based on the collection of a large amount of laboratory data; Han et al. noticed the possible influence of clay content on velocity, and obtained the linear regression equation of velocity in mud sandstone with respect to porosity and clay content by measuring the velocity of rock samples under different pressure conditions. Although this type of method of using empirical formulas to approximate velocity is simple and feasible, it is greatly affected by the actual situation of the underground reservoir, and the accuracy needs to be improved.

[0063] The methods developed later can be collectively referred to as shear wave velocity prediction based on rock physics models. Although such methods are relatively complex, they are more universal and more accurate. In shear wave prediction, if other information such as the matrix modulus of the rock is known, the pore aspect ratio of the rock can be used as a control parameter to adjust the size of the pore aspect ratio to obtain a shear wave velocity close to the actual situation.

[0064] Based on the possible impact of reservoir pore morphology on rock elastic modulus, Xu and White proposed an equivalent medium theory, the Xu-White model, which can be used to solve the modeling problems of conventional sandstone and mudstone reservoirs on the basis of the KT equivalent medium theory. Afterwards, they established a rock physics model of carbonate rocks, the Xu-Payne model, by estimating the pore shape of carbonate rocks, and successfully realized the shear wave prediction in carbonate rocks. Domestically, some scholars have made different improvements on this basis to solve the problem of excessively high parameter requirements in shear wave prediction. However, no matter which rock physics model is based on, some uncertainties in the prediction process will be ignored during use, and assumptions will be made on this basis, so there are often certain differences in the prediction results.

[0065] Before carrying out shear wave prediction, this type of method first needs to calculate the equivalent matrix modulus at each depth point of the logging curve based on the mineral content information on the well. The commonly used method is the Voigt-Reuss-Hill average model, which is a common equivalent medium model and is often used to calculate the rock background equivalent modulus, see formula (1):

[0066]

[0067] In the formula, Where i represents the i-th component, f i and M i Respectively represent the volume fraction of the corresponding mineral and its elastic modulus. The calculated M VRH It is the equivalent elastic modulus.

[0068] After obtaining the equivalent matrix modulus corresponding to each depth point on the well, it is necessary to perform shear wave prediction based on the rock physics model including pore characteristics. The objective function of shear wave prediction is shown in formula (2):

[0069]

[0070] Among them, V p represents the true longitudinal wave velocity, and Represents the P-wave velocity calculated under the current pore aspect ratio. By finding the pore aspect ratio closest to the true P-wave velocity as the actual underground pore shape, the corresponding S-wave velocity is calculated and used as the prediction result.

[0071] The rock physics model selected for shear wave prediction is the equivalent medium self-consistent approximation model (SCA), which is a method that adds inclusions to the rock background to equivalent the equivalent elastic modulus of multiphase elastic media and requires iteration to solve the formula coupling. Berryman gave the calculation formula for the self-consistent elastic modulus of N-phase minerals and pore space, see formula (3):

[0072]

[0073] In the formula, each i represents a mineral phase or pore space, and the volume fraction of each phase is x i , bulk modulus K i and shear modulus μ i ;P i and Q i is the mineral shape factor of the i-th component, which can be calculated according to the flattening and shape of the corresponding pores; and They represent the equivalent bulk modulus and shear modulus finally calculated based on the SCA model, which are closer to the actual rock morphology than the VRH average model.

[0074] In the shear wave prediction process, we first need to determine the search range and step size of the pore aspect ratio according to the actual problem, so as to perform a traversal search within the appropriate pore aspect ratio range, calculate the P- and S-wave velocities corresponding to each pore aspect ratio value, and compare it with the measured P-wave velocity at the current point to find the pore aspect ratio when the objective function is minimized, which is the actual equivalent pore aspect ratio. It is used to calculate the S-wave velocity, which is the final prediction result.

[0075] At present, the widely used shear wave prediction methods require that the matrix modulus at each depth point underground is known, and the equivalent matrix modulus can be calculated according to the corresponding mineral content curve. At the same time, practice has shown that when the mineral content is unknown, the use of empirical values ​​to calculate the matrix modulus will have a great impact on the accuracy of shear wave prediction. In the marine carbonate oil and gas reservoirs widely developed in my country, the rock properties are complex and changeable, and the surface exploration conditions are very harsh. The matrix modulus of such complex oil and gas reservoirs is easily affected by various factors such as diagenesis, karstification, ancient faults, pressure and temperature. The matrix modulus of rocks in the same area often varies greatly. Therefore, how to accurately obtain the matrix modulus of rocks has become the key. Considering that the laboratory measurement method is accurate but costly, it is very necessary to develop an equivalent matrix modulus estimation method suitable for areas with unknown mineral content and accurately obtain the matrix modulus from a small amount of known logging information.

[0076] Based on the problems existing in the related art, an embodiment of the present application provides a method for determining an equivalent matrix modulus, and the method is applied to a device for determining an equivalent matrix modulus, and the device for determining an equivalent matrix modulus may be an electronic device, such as a computer, a mobile terminal, etc. The function implemented by the method for determining an equivalent matrix modulus provided in the embodiment of the present application can be implemented by calling a program code by a processor of the electronic device, wherein the program code may be stored in a computer storage medium.

[0077] Embodiment 1

[0078] The present application embodiment provides a method for determining an equivalent matrix modulus. Figure 1 A schematic diagram of the implementation process of a method for determining an equivalent matrix modulus provided in an embodiment of the present application is shown in FIG. Figure 1 As shown, including:

[0079] Step S101, obtaining well logging information of an area with unknown mineral content.

[0080] In the embodiment of the present application, the logging information may include: longitudinal wave velocity, density, porosity, water saturation, gamma logging information, etc. In the embodiment of the present application, the logging information may be stored in a server, and the device for determining the equivalent matrix modulus obtains the logging information through a communication connection with the server. In some embodiments, the device for determining the equivalent matrix modulus may be directly connected to each test device to directly obtain the logging information from each test device. Since the logging information of the target depth point in the area with unknown mineral content is obtained, the logging information does not include mineral content information.

[0081] Step S102, determining the fluid bulk modulus and the initial shear wave of the target depth point in the region with unknown mineral content based on the well logging information.

[0082] In the embodiment of the present application, the target depth point may be any point in an area with unknown mineral content. The fluid bulk modulus and the initial shear wave of the target depth point may be determined based on well logging information and the Gassmann equation.

[0083] Step S103, determining the equivalent matrix modulus range of the target depth point based on the preset skeleton model and the initial shear wave.

[0084] In the embodiment of the present application, the preset skeleton model may be a Pride model or a Krief model, etc. Determining the equivalent matrix modulus range of the target depth point based on the preset skeleton model and the initial shear wave can be achieved by the following steps: determining the basic parameters of the preset skeleton model; determining the saturated rock bulk modulus of the target depth point based on the skeleton model and the initial shear wave; determining the equivalent matrix modulus range of the target depth point based on the saturated rock bulk modulus, the skeleton model and the basic parameters.

[0085] Step S104, based on the equivalent matrix modulus range, the preset dry rock Poisson's ratio range, the fluid bulk modulus, and the objective function, an inversion calculation of the equivalent matrix modulus of the target depth point is performed to determine the equivalent matrix modulus of the target depth point, and finally the equivalent matrix modulus of each depth point in the area with unknown mineral content is determined.

[0086] In the embodiment of the present application, the objective function is to minimize the difference between the first fluid term and the second fluid term, the first fluid term is obtained by a first calculation method based on the equivalent matrix modulus, the dry rock Poisson's ratio, and the fluid bulk modulus, and the second fluid term is obtained by a second calculation method based on the equivalent matrix modulus, the dry rock Poisson's ratio, and the fluid bulk modulus, the first calculation method and the second calculation method are different, the equivalent matrix modulus is within the equivalent matrix modulus range, and the dry rock Poisson's ratio is within the dry rock Poisson's ratio range. The objective function is shown in formula (4):

[0087] min(f1-f2)(4);

[0088] In formula (4), f1 represents the first fluid term, f2 represents the second fluid term, and in an embodiment of the present application, the first calculation method may be a calculation method based on the Gassmann formula, and the second fluid term may be a calculation method based on the Russell formula. In some embodiments, the second calculation method may be a calculation method based on the Gassmann formula, and the first calculation method may be a calculation method based on the Russell formula.

[0089] In an embodiment of the present application, when performing inversion calculation, the inversion calculation of the equivalent matrix modulus can be performed based on an intelligent optimization algorithm, and the intelligent optimization algorithm can include any one of the following: evolutionary algorithm, genetic algorithm, ant colony algorithm and particle swarm algorithm, etc.

[0090] In an embodiment of the present application, an inversion calculation of the equivalent matrix modulus of the target depth point is performed based on the equivalent matrix modulus range, the preset dry rock Poisson's ratio range, the fluid bulk modulus, and the objective function to determine the equivalent matrix modulus of the target depth point to determine the equivalent matrix modulus of the area with unknown mineral content. This can be achieved by the following steps: determining a first initial population based on the equivalent matrix modulus range, and determining a second initial population based on the preset dry rock Poisson's ratio range; determining each population individual based on the first initial population and the second initial population, wherein the population individuals include individuals determined based on the first initial population. and individuals determined based on the second initial population; determining the first fluid item corresponding to each population individual by the first calculation method based on each population individual and the fluid bulk modulus; determining the second fluid item corresponding to each population individual by the second calculation method based on each population individual and the fluid bulk modulus; determining the fitness value of each population individual based on the first fluid item and the corresponding second fluid item and the objective function; determining the equivalent matrix modulus of the target depth point based on the fitness value of each population individual; determining the equivalent matrix modulus of the area with unknown mineral content based on the equivalent matrix modulus of the target depth point.

[0091] In some embodiments, when the first calculation method is used to determine the first fluid item corresponding to each population individual based on each population individual and the fluid bulk modulus, it can be achieved through the following steps: determine the compression coefficient corresponding to each population individual based on each population individual and the fluid bulk modulus; determine the first fluid item corresponding to each population individual based on the compression coefficient corresponding to each population individual using the first calculation method. And determining the second fluid item corresponding to each population individual based on each population individual and the fluid bulk modulus using the second calculation method can be achieved through the following steps: determine the compression coefficient corresponding to each population individual based on each population individual and the fluid bulk modulus; determine the second fluid item corresponding to each population individual based on the compression coefficient corresponding to each population individual using the second calculation method.

[0092] In some embodiments, the determining of each population individual based on the first initial population and the second initial population can be achieved by the following steps: performing a mutation operation and a crossover operation based on the first initial population to obtain a first temporary population; performing a mutation operation and a crossover operation based on the second initial population to obtain a second temporary population; and determining each population individual based on the first temporary population and the second temporary population.

[0093] The present application provides a method for determining an equivalent matrix modulus. The method determines the fluid bulk modulus and the initial shear wave through actual logging information of a target depth point in a mineral location area, and then determines the equivalent matrix modulus range of the target depth point based on a preset skeleton model and the initial shear wave. The equivalent matrix modulus of the target depth point is inverted and calculated based on the equivalent matrix modulus range, a preset dry rock Poisson's ratio range, the fluid bulk modulus, and an objective function to determine the equivalent matrix modulus of the target depth point. Then, the equivalent matrix modulus of the region with unknown mineral content is determined based on the equivalent matrix modulus of the target depth point. The equivalent matrix modulus of the region with unknown mineral content can be calculated with higher calculation accuracy, so that when the shear wave is predicted for the region with unknown mineral content based on the equivalent matrix modulus, the predicted shear wave is more accurate.

[0094] Embodiment 2

[0095] Based on the above embodiments, the present application further provides a method for determining the equivalent matrix modulus. Figure 2 A schematic diagram of the implementation flow of another method for determining the equivalent matrix modulus provided in an embodiment of the present application is shown in FIG. Figure 2 As shown, including:

[0096] Step S201, obtaining logging information of an area with unknown mineral content.

[0097] In the embodiment of the present application, the logging information may include one or more of the following: P-wave velocity, density, porosity, water saturation, gamma logging information, etc.

[0098] Step S202, determining the fluid bulk modulus and the initial shear wave of the target depth point in the region with unknown mineral content based on the well logging information.

[0099] The initial shear wave can be expressed as V S0 express.

[0100] Step S203, determining basic parameters of the preset skeleton model.

[0101] In the embodiment of the present application, the basic parameter may be the consolidation coefficient of the rock, which may be represented by α.

[0102] Step S204, determining the saturated rock bulk modulus at the target depth point based on the skeleton model and the initial shear wave.

[0103] In the embodiment of the present application, after the initial shear wave and the skeleton model are determined, the saturated rock bulk modulus can be determined. The saturated rock bulk modulus can be expressed as K sat express.

[0104] Step S205: Determine the equivalent matrix modulus range of the target depth point based on the saturated rock bulk modulus and the skeleton model.

[0105] In the embodiments of the present application, the equivalent matrix modulus is denoted by K0. Based on the Krief's rock equivalent compressibility formula and the relationship between K dry <K sat <K0, the range of K0 can be derived, as shown in Formula (5):

[0106]

[0107] where α is the consolidation coefficient of the rock, and the value range of α is usually 2 - 20, and φ is the porosity.

[0108] In the embodiments of the present application, since the saturated rock bulk modulus, the skeleton model, the rock consolidation coefficient, and the porosity are all known quantities, the equivalent matrix modulus range can be determined.

[0109] Step S206: Based on the equivalent matrix modulus range, the preset dry rock Poisson's ratio range, the fluid bulk modulus, and the objective function, perform an inversion calculation of the equivalent matrix modulus of the target depth point to determine the equivalent matrix modulus of the target depth point, so as to determine the equivalent matrix modulus of each depth point in the area with unknown mineral content. Among them, the objective function is that the difference between the first fluid term and the second fluid term is minimized. The first fluid term is obtained by a first calculation method based on the equivalent matrix modulus, the dry rock Poisson's ratio, and the fluid bulk modulus. The second fluid term is obtained by a second calculation method based on the equivalent matrix modulus, the dry rock Poisson's ratio, and the fluid bulk modulus. The first calculation method and the second calculation method are different. The equivalent matrix modulus is within the equivalent matrix modulus range, and the dry rock Poisson's ratio is within the dry rock Poisson's ratio range.

[0110] In the embodiments of the present application, the preset dry rock Poisson's ratio range can be the Poisson's ratio value range of common sedimentary rocks, usually between 0 and 0.45.

[0111] A method for determining the equivalent matrix modulus provided by the present application determines the equivalent matrix modulus range of the target depth point based on a preset skeleton model and an initial shear wave, and then can perform an inversion calculation of the equivalent matrix modulus of the target depth point based on the equivalent matrix modulus range, the preset dry rock Poisson's ratio range, the fluid bulk modulus, and the objective function, so as to determine the equivalent matrix modulus of the target depth point.

[0112] Embodiment 3

[0113] Based on the aforementioned embodiments, the equivalent matrix modulus of the target depth point is inverted and calculated based on the equivalent matrix modulus range, the preset dry rock Poisson's ratio range, the fluid bulk modulus, and the objective function to determine the equivalent matrix modulus of the target depth point, so as to determine the equivalent matrix modulus of the area with unknown mineral content. Figure 3 A schematic diagram of a process for performing an inversion calculation provided in an embodiment of the present application is shown in FIG. Figure 3 As shown, including:

[0114] Step S1, determining a first initial population based on the equivalent matrix modulus range, and determining a second initial population based on a preset dry rock Poisson's ratio range.

[0115] In the embodiment of the present application, the first initial population corresponding to the equivalent matrix modulus range can be determined based on the definition parameters, and the definition parameters can include the maximum evolutionary generation, the population size, the mutation operator and the crossover operator, etc. to determine the first initial population. And the second initial population can be determined based on the definition parameters and the dry rock Poisson's ratio range.

[0116] Step S2: determining individuals of each population based on the first initial population and the second initial population.

[0117] In the embodiment of the present application, the population individuals include individuals determined based on the first initial population and individuals determined based on the second initial population.

[0118] In some embodiments, determining each population individual based on the first initial population and the second initial population can be achieved through the following steps: performing a mutation operation and a crossover operation based on the first initial population to obtain a first temporary population; performing a mutation operation and a crossover operation based on the second initial population to obtain a second temporary population; and determining each population individual based on the first temporary population and the second temporary population.

[0119] In the embodiment of the present application, the differential evolution algorithm is used as an example for explanation. In the differential evolution algorithm, the crossover operation and the mutation operation are more important. The mutation operation is to generate new mutant individuals. The mutation method is shown in formula (6):

[0120]

[0121] Among them, F is the differential weight parameter, also known as the scaling factor, which is generally between 0 and 1, with 0.5 being the most appropriate. p ,x q ,x r There are three unrelated individuals in the current population, and the third individual is mutated by the difference between two of them. The crossover parameter C rControl, control the rate or probability of crossover. The actual crossover can be done in two ways: binomial and exponential. The binomial scheme crosses over each of the d components. By generating random numbers r from a uniform distribution i , so the jth component of a population is expressed as formula (7):

[0122]

[0123] In this way, it is possible to randomly decide whether to exchange a certain component with a mutant individual.

[0124] Step S3: determining the first fluid item corresponding to each population individual by using the first calculation method based on each population individual and the fluid bulk modulus.

[0125] In the embodiment of the present application, the compression coefficient corresponding to each population individual can be determined based on each population individual and the fluid bulk modulus. The first fluid item corresponding to each population individual is determined using a first calculation method based on the compression coefficient corresponding to each population individual.

[0126] In the embodiment of the present application, a compression coefficient formula can be derived in advance, and the compression coefficient formula includes: the relationship between the compression coefficient, the fluid bulk modulus, the Poisson's ratio of dry rock, and the equivalent matrix modulus.

[0127] In the embodiment of the present application, the Gassmann equation, which is widely used in the low-frequency range of earthquakes, is first used to derive the expressions of the longitudinal and transverse wave velocities of rocks when they are filled with fluid saturation. See formula (8):

[0128]

[0129] Where V p 、V s are the longitudinal and transverse wave velocities of the fluid-saturated rock, respectively; ρ sat is the saturated rock density; K sat , μ dry represent the bulk modulus of saturated rock and the shear modulus of dry rock, respectively.

[0130] From the known definition of the longitudinal wave modulus According to formula (8) and Gassmann equation, formula (9) can be derived:

[0131]

[0132] The above formula is the Gassmann-Biot-Geertsma equation. In order to calculate the bulk modulus K of the dry rock skeleton dry , we use the compression coefficient expression in Biot theory, the compression coefficient expression is shown in formula (10):

[0133]

[0134] Substituting formula (10) into formula (8), we get formula (11):

[0135]

[0136] Further introducing the dry rock Poisson's ratio σ dry , see formula (12)

[0137]

[0138] Substituting formula (12) into formula (11), we get formula (13):

[0139]

[0140] Dividing both sides of formula (13) by K0, we can get the quadratic expression of the Gassmann-Biot-Geertsma equation for β, see formula (14):

[0141]

[0142] Based on formula (14), the compression coefficient corresponding to the target depth point can be calculated.

[0143] In the embodiment of the present application, after the compression coefficient is determined, the first fluid term corresponding to each population individual can be calculated based on the Gassmann equation.

[0144] Step S4: determining the second fluid item corresponding to each population individual by using the second calculation method based on each population individual and the fluid bulk modulus.

[0145] In an embodiment of the present application, the compression coefficient corresponding to each population individual is determined based on the each population individual and the fluid bulk modulus; and the second fluid item corresponding to each population individual is determined using a second calculation method based on the compression coefficient corresponding to each population individual.

[0146] Once the compressibility factor is determined, the second fluid term can be calculated based on the Russell formula.

[0147] Step S5, determining the fitness value of each population individual based on the first fluid item and the corresponding second fluid item and the objective function.

[0148] In the embodiment of the present application, the objective function is shown in formula (4). After the first fluid term and the corresponding second fluid term are determined, the value of the objective function can be calculated. The value of the objective function is the fitness value of each population individual.

[0149] Step S6, determining the equivalent matrix modulus of the target depth point based on the fitness value of each population individual.

[0150] In the embodiment of the present application, the equivalent matrix modulus corresponding to the minimum fitness value can be selected as the equivalent matrix modulus of the target depth point.

[0151] Step S7, determining the equivalent matrix modulus of each depth point in the region with unknown mineral content based on the equivalent matrix modulus of the target depth point.

[0152] After calculating one target depth point, the equivalent matrix modulus of another target depth point is calculated, thereby determining the equivalent matrix modulus of each depth point in the area with unknown mineral content.

[0153] The method for determining the equivalent matrix modulus provided in the embodiment of the present application uses an optimization algorithm to perform optimization to obtain the equivalent matrix modulus of the target depth point, so that the equivalent matrix modulus of each depth point obtained is more accurate.

[0154] Embodiment 4

[0155] Based on the above embodiments, the present application further provides a method for determining an equivalent matrix modulus, the method comprising:

[0156] Step S401, obtaining logging information of an area with unknown mineral content.

[0157] In the embodiment of the present application, the logging information at least includes: P-wave velocity and porosity.

[0158] Step S402, determining the fluid bulk modulus and the initial shear wave of the target depth point in the unknown mineral region based on the logging information.

[0159] Step S403: determining the equivalent matrix modulus range of the target depth point based on the preset skeleton model and the initial shear wave.

[0160] Step S404, based on the equivalent matrix modulus range, the preset dry rock Poisson's ratio range, the fluid bulk modulus, and the objective function, an inversion calculation of the equivalent matrix modulus of the target depth point is performed to determine the equivalent matrix modulus of the target depth point, so as to determine the equivalent matrix modulus of each depth point in the area with unknown mineral content.

[0161] In an embodiment of the present application, the objective function is to minimize the difference between the first fluid term and the second fluid term. The first fluid term is obtained by a first calculation method based on the equivalent matrix modulus, the Poisson's ratio of dry rock, and the fluid bulk modulus. The second fluid term is obtained by a second calculation method based on the equivalent matrix modulus, the Poisson's ratio of dry rock, and the fluid bulk modulus. The first calculation method and the second calculation method are different. The equivalent matrix modulus is within the range of the equivalent matrix modulus, and the dry rock Poisson's ratio is within the range of the dry rock Poisson's ratio.

[0162] Step S405, determining the distribution range of the pore aspect ratio of the region with unknown material content based on the longitudinal wave velocity and the porosity.

[0163] In an embodiment of the present application, the initial distribution range of the pore type and the pore aspect ratio of the region with unknown material content can be determined based on the density and the porosity; and the distribution range of the pore aspect ratio can be determined based on the pore type and the initial distribution range.

[0164] In the embodiment of the present application, a porosity-velocity trend reference line can be established based on the longitudinal wave velocity and porosity of each target depth point, and the initial distribution range of pore type and porosity aspect ratio can be determined based on the porosity-velocity trend reference line. The pore type can include rigid pores, intergranular pores and cracks.

[0165] Step S406, determining the target pore aspect ratio of the region with unknown material content by using a particle swarm inversion algorithm based on the distribution range.

[0166] Step S407: determining the shear wave velocity at the target depth point based on the target pore aspect ratio and the equivalent matrix modulus.

[0167] The method for determining the equivalent matrix modulus provided in the embodiment of the present application determines the target porosity aspect ratio of the unknown area by using the determined equivalent matrix modulus and a particle swarm inversion algorithm, so that the predicted shear wave velocity is more accurate during shear wave prediction.

[0168] Embodiment 5

[0169] Based on the foregoing embodiments, the embodiments of the present application provide a two-step shear wave prediction method based on an intelligent optimization algorithm. The two-step method refers to a method that includes two main steps: equivalent matrix modulus inversion and shear wave prediction. First, under the framework of the intelligent optimization algorithm, the equivalent matrix modulus is obtained through an adaptive inversion method, which well solves the problem of unknown mineral content in such areas; and the predicted equivalent matrix modulus is used as the main input parameter in the shear wave prediction part and then participates in the more important link of predicting the shear wave. The calculated equivalent matrix modulus and shear wave information enrich the known information in the reservoir prediction link, can provide more reliable information for rock physics modeling, and provide technical support for high-precision reservoir prediction and fluid identification.

[0170] First, it is necessary to determine the range of the rock matrix modulus (the same as the range of the equivalent matrix modulus in the above embodiments) and the range of the dry rock Poisson's ratio. It is known that the Poisson's ratio of common sedimentary rocks ranges from 0 to 0.45, and the setting of the rock matrix modulus can be combined with the Krief's rock equivalent compressibility formula and K dry <K sat <the relationship of K0 to obtain formula (15):

[0171]

[0172] Thus, reasonable ranges of K0 and σ dry are obtained. However, the dry rock saturated fluid bulk modulus requires the known shear wave velocity in the calculation process, which contradicts the purpose of predicting the shear wave. It is analyzed that the role of K sat in formula (15) is only to provide a more reasonable upper and lower limit for K0 and does not directly participate in the calculation. Therefore, the initial shear wave velocity can be determined by setting a reasonable longitudinal and transverse wave velocity ratio or by using a suitable shear wave fitting formula.

[0173] To efficiently solve the problem of unknown matrix modulus in areas with unknown mineral content, we propose a new adaptive matrix modulus inversion method under the framework of the swarm intelligence optimization algorithm (the same as the inversion calculation in the above embodiments). Such algorithms include evolutionary algorithms, genetic algorithms, ant colony algorithms, and particle swarm algorithms, etc. The objective function of the inversion is defined as min(f1 - f2) (where f1 and f2 respectively refer to the Gassmann fluid term (the same as the first fluid term in the above embodiments) and the Russell fluid factor (the same as the second fluid term in the above embodiments)), and the dry rock Poisson's ratio σ dry and the matrix modulus K0 that meet this objective function are searched for. In this way, the matrix modulus inversion can be regarded as a two-dimensional optimization problem.

[0174] Taking the differential evolution algorithm as an example, after determining the objective function and fitness function (i.e., the minimum value of the above objective function), the initial population is randomly generated according to the maximum evolutionary generation, population size, mutation operator, crossover operator (the same as the definition parameters in the above embodiment), etc. The initial population includes: a first initial population, a second initial population, and the initial population is evaluated, that is, the fitness value of each individual in the population is calculated. The two independent populations (i.e., the first initial population and the second initial population) are continuously subjected to mutation and crossover operations, and the obtained temporary population is evaluated. Through the selection operation, the individual is continuously moved in the direction of a larger fitness value, and the obtained optimal solution includes the equivalent matrix modulus K0 corresponding to the current depth point, which is used in the subsequent shear wave prediction.

[0175] In the embodiment of the present application, the crossover and mutation operations are more important in the differential evolution algorithm. The mutation operation is to generate new mutant individuals. The mutation scheme is as follows:

[0176]

[0177] Among them, F is the differential weight parameter, also known as the scaling factor, which is generally between 0 and 1, with 0.5 being the most appropriate. p ,x q ,x r There are three unrelated individuals in the current population, and the third individual is mutated by the difference between two of them. The crossover parameter C r Control, control the rate or probability of crossover. The actual crossover can be done in two ways: binomial and exponential. The binomial scheme crosses over each of the d components. By generating random numbers r from a uniform distribution i , so the jth component of a population is expressed as

[0178]

[0179] In this way, it is possible to randomly decide whether to exchange a certain component with a mutant individual.

[0180] For the following shear wave prediction problem, the intelligent optimization algorithm is also used to improve it. Taking the particle swarm algorithm as an example, we first need to use the approximate normal distribution law of the pore aspect ratio (considering that the actual pore aspect ratio α varies too much, it is generally believed that -log 10α obeys normal distribution), set an initial state of the aspect ratio at the vertical depth of the logging scale, and then in the subsequent cycles, continuously move the particles by changing the speed of the particles, and use the SCA model to calculate the longitudinal wave velocity corresponding to the moved particles. Similarly, by defining the fitness function to judge the quality of the current particles, the particles are constantly moved to the optimal solution until the end of the cycle, and a reliable equivalent pore aspect ratio value (the same as the target pore aspect ratio in the above embodiment) is obtained, and the calculated corresponding shear wave velocity is used as the best prediction result. Overall, the idea of ​​the particle swarm algorithm is very similar to that of differential evolution. Both use a certain method to obtain new individuals with better fitness in randomly generated populations (differential evolution is achieved through mutation operations, and the particle swarm algorithm changes the particle movement speed to move it to the optimal solution of individuals and groups). Compared with traditional traversal search, swarm intelligence optimization algorithms represented by differential evolution and particle swarm can quickly achieve convergence, which is very helpful for solving nonlinear problems such as equivalent matrix modulus prediction.

[0181] Taking the estimation of equivalent matrix modulus based on differential evolution algorithm as an example, Figure 4 A flowchart of an equivalent matrix modulus estimation based on a differential evolution algorithm is provided in an embodiment of the present application, such as Figure 4 As shown, including:

[0182] Step S11, based on the known logging information (including the rock longitudinal wave velocity v p , density ρ, porosity φ and water saturation s w wait)

[0183] Step S12, estimating the fluid bulk modulus K based on the well logging information fl and the initial shear wave V S0 , and select a suitable skeleton model (such as Pride model or Krief model, etc.) to determine the range of matrix modulus K0;

[0184] Step S13, initialize the parameters such as the maximum number of evolution times and population size of individuals in the x and y directions, calculate the fitness values ​​of the individuals in the population by solving the compression coefficient β; and through the crossover operation, the individuals are continuously moved in the direction of larger fitness values. After multiple evolutions, the entire population finally reaches the termination condition (maximum number of iterations or global optimal solution), and outputs the optimal solution in the y direction obtained by the search.

[0185] Step S14, determining the optimal K0.

[0186] When predicting shear waves, many factors are known to affect the propagation velocity of seismic waves, but in fracture-cavity reservoirs, pore aspect ratio is a common influencing factor. As the pore aspect ratio increases, the stiffness of the pores will also increase, which in turn leads to an increase in the seismic wave velocity. Figure 5The schematic diagram of the relationship between velocity and porosity aspect ratio provided in the embodiment of the present application is as follows: Figure 5 As shown, the velocities in the figure include: shear wave velocity and longitudinal wave velocity. As the pore aspect ratio increases, the velocity also increases.

[0187] Considering that the actual pore aspect ratio α varies greatly, it is generally believed that -log 10 α follows a normal distribution, Figure 6 A schematic diagram of an approximate normal distribution law of pore aspect ratio provided in an embodiment of the present application is shown in FIG. Figure 6 As shown in Figure 1, the approximate normal distribution law of the pore aspect ratio is obtained, based on which the initial distribution state of the pore aspect ratio at the vertical depth of the logging scale can be set.

[0188] In order to facilitate the intelligent optimization algorithm to quickly find the optimal solution, we should reasonably determine the search range of the pore aspect ratio, that is, the solution space. The Kumar-Han method, that is, the velocity-porosity reference line, is used to determine the pore type. Figure 7 A schematic diagram of determining pore type by using a velocity-porosity reference line is provided in an embodiment of the present application, such as Figure 7 As shown in the figure, the points above the reference line have intergranular pores with a relatively large pore aspect ratio; the points below the reference line have mainly fracture-type pores. Figure 7 , the region is mainly dominated by fracture-type pores, and the initial individual distribution of the swarm algorithm should be used as a reference as much as possible.

[0189] Figure 8 A schematic diagram of the matrix modulus inversion results of the mineral location area provided in the embodiment of the present application, such as Figure 8 As shown in the figure, the equivalent matrix modulus obtained by the differential evolution algorithm is still within the Voigt-Reuss limit, indicating that the estimation result of this method is still within a reasonable range and is reasonable as an input parameter for rock physics modeling or subsequent shear wave prediction.

[0190] Comparison of shear wave prediction results with corresponding longitudinal wave velocities. The optimal pore aspect ratio obtained by the intelligent optimization algorithm is basically consistent with the calculated shear wave velocity and the measured shear wave velocity, and the predicted shear wave velocity is also consistent with the basic trend of the longitudinal wave velocity, which proves that the method is very accurate and can achieve the purpose of shear wave prediction.

[0191] Comparison of running time between the traditional method and the particle swarm optimization method. A significant advantage of the particle swarm algorithm is that it can find the target solution quickly compared to the traditional traversal search. The running time of the two methods is statistically analyzed. The calculation time of the particle swarm algorithm is significantly lower than the traversal search time. The calculation time of the optimized method is only related to the size and dimension of the particle swarm, while the traditional method is affected by many factors.

[0192] In order to verify the effect of swarm intelligence optimization algorithm on actual equivalent matrix modulus estimation and shear wave prediction, the well logging data of carbonate reservoir in a certain area was selected for verification. The target layer depth of the actual data is in the range of 2794-2841m, and the logging sampling interval is 0.1524m. It belongs to the dense carbonate formation. The fluid filling the rock pores is mainly water, and a small amount of gas is contained in the layer. The known data of the well logging curve include the P-wave velocity (V P ), density (ρ), porosity (φ), saturation (s o ) and gamma ray (GR), which do not contain information on mineral content.

[0193] Before applying the intelligent optimization algorithm, it is necessary to determine the initial distribution state of the pore aspect ratio based on the velocity-porosity reference line. For example, the area is mainly composed of fracture-type pores, so the initial pore aspect ratio state is set to obey a standard normal distribution with a mean of 1.1 and a variance of 0.15.

[0194] Before making shear wave predictions, it is necessary to estimate the equivalent matrix modulus of the rock. After determining the approximate range of the Poisson's ratio, the Pride model is used to determine the approximate range of the rock matrix modulus. Considering the characteristics of the dense carbonate rock in the work area, the consolidation coefficient is set to 18. After determining the range of the initial shear wave and rock matrix modulus, preparations for differential evolution are started. The number of individuals N in the population is set to 30, the number of population evolutions is 50 times, the individual in the x direction is the value of the dry rock Poisson's ratio, the y direction represents the matrix modulus, and the boundaries of the velocity increments in both directions are set to [-0.01, 0.01]. The final equivalent matrix modulus inversion results, such as the comparison with the Voigt-Reuss limit, prove its rationality as an input parameter for shear wave prediction.

[0195] In order to improve the computational efficiency of shear wave prediction, the present invention adopts the particle swarm method for prediction. After many experiments, it is found that the inertia weight value of the algorithm has the best comprehensive performance with linear decrease, and the number of the entire particle swarm is preferably 200. Fig. 9 A schematic diagram of a shear wave prediction result provided in an embodiment of the present application is shown in FIG. Fig. 9 As shown in the figure, the inverted shear wave velocity and the measured shear wave velocity curve are almost completely overlapped. The error analysis results show that the maximum relative error is 7.28% and the average is only 0.11%, which is much smaller than the tolerance error. This shows that the accuracy of the prediction results is fully guaranteed and the whole process is also relatively reliable in solving the shear wave prediction problem in areas without mineral content information.

[0196] The embodiment of the present application provides a method for solving the problem of equivalent matrix modulus and shear wave prediction in areas with unknown mineral content. The method first effectively solves the problem of unknown matrix modulus by introducing a mineral matrix modulus inversion method based on fluid factor analysis, making it possible to predict the shear wave velocity of carbonate rocks using rock physics models such as Xu-Payne. Afterwards, improvements are made to the problem of low computational efficiency of traditional shear wave prediction methods, and a particle swarm algorithm is used to invert the pore aspect ratio of rocks to accurately predict the shear wave velocity. In the matrix modulus and shear wave prediction work performed using the method, the problem of insufficient existing well data in areas with unknown mineral content can be well solved, and matrix modulus and shear wave information representing the distribution of underground minerals can be effectively obtained. The predicted shear wave information obtained by this method can provide more reliable information for subsequent identification and detection of geological "sweet spots", provide technical support for high-precision reservoir prediction and description, and reduce the risk of failure in oil and gas exploration drilling.

[0197] Embodiment 6

[0198] Based on the foregoing embodiments, the embodiments of the present application provide a device for determining an equivalent matrix modulus. The modules included in the device and the units included in the modules can be implemented by a processor in a computer device; of course, they can also be implemented by a specific logic circuit; in the implementation process, the processor can be a central processing unit (CPU, Central Processing Unit), a microprocessor (MPU, Microprocessor Unit), a digital signal processor (DSP, Digital Signal Processing) or a field programmable gate array (FPGA, Field Programmable Gate Array), etc.

[0199] The present application embodiment provides a device for determining an equivalent matrix modulus. Fig.10 A schematic diagram of a device for determining an equivalent matrix modulus provided in an embodiment of the present application is shown in FIG. Fig.10 As shown, the device 1000 for determining the equivalent matrix modulus includes:

[0200] Acquisition module 1001, used to acquire well logging information of an area with unknown mineral content;

[0201] A first determination module 1002 is used to determine the fluid bulk modulus and the initial shear wave of the target depth point in the region with unknown mineral content based on the well logging information;

[0202] A second determination module 1003 is used to determine the equivalent matrix modulus range of the target depth point based on a preset skeleton model and the initial shear wave;

[0203] The calculation module 1004 is used to perform inversion calculation of the equivalent matrix modulus of the target depth point based on the equivalent matrix modulus range, the preset dry rock Poisson's ratio range, the fluid bulk modulus, and the objective function, and determine the equivalent matrix modulus of the target depth point to determine the equivalent matrix modulus of each depth point in the area with unknown mineral content, wherein the objective function is to minimize the difference between the first fluid item and the second fluid item, the first fluid item is obtained by a first calculation method based on the equivalent matrix modulus, the dry rock Poisson's ratio, and the fluid bulk modulus, the second fluid item is obtained by a second calculation method based on the equivalent matrix modulus, the dry rock Poisson's ratio, and the fluid bulk modulus, the first calculation method and the second calculation method are different, the equivalent matrix modulus is within the equivalent matrix modulus range, and the dry rock Poisson's ratio is within the dry rock Poisson's ratio range.

[0204] In some embodiments, the second determining module 1003 includes:

[0205] A first determining unit determines basic parameters of a preset skeleton model;

[0206] A second determination unit is used to determine the saturated rock bulk modulus at a target depth point based on the skeleton model and the initial shear wave;

[0207] The third determination unit is used to determine the equivalent matrix modulus range of the target depth point based on the skeleton model, the saturated rock bulk modulus and the basic parameters.

[0208] In some embodiments, the computing module 1004 includes:

[0209] a fourth determination unit, configured to determine a first initial population based on the equivalent matrix modulus range, and determine a second initial population based on a preset dry rock Poisson's ratio range;

[0210] a fifth determining unit, configured to determine each population individual based on the first initial population and the second initial population, wherein the population individual includes a value determined based on the first initial population and a value determined based on the second initial population;

[0211] A sixth determining unit, configured to determine, based on each population individual and the fluid bulk modulus, a first fluid item corresponding to each population individual by adopting the first calculation method;

[0212] A seventh determining unit, configured to determine, based on each population individual and the fluid bulk modulus, a second fluid item corresponding to each population individual by adopting the second calculation method;

[0213] an eighth determining unit, configured to determine the fitness value of each population individual based on the first fluid item and the corresponding second fluid item and the objective function;

[0214] A ninth determination unit, configured to determine the equivalent matrix modulus of the target depth point based on the fitness value of each population individual;

[0215] A tenth determining unit is used to determine the equivalent matrix modulus of each depth point in the region with unknown mineral content based on the equivalent matrix modulus of the target depth point.

[0216] In some embodiments, the sixth determining unit includes:

[0217] A first determining subunit, configured to determine a compression coefficient corresponding to each population individual based on each population individual and the fluid bulk modulus;

[0218] The second determination subunit is used to determine the first fluid item corresponding to each population individual by adopting a first calculation method based on the compression coefficient corresponding to each population individual.

[0219] In some embodiments, the fifth determining unit includes:

[0220] A first operation subunit, configured to perform a mutation operation and a crossover operation based on the first initial population to obtain a first temporary population;

[0221] A second operation subunit, configured to perform a mutation operation and a crossover operation based on the second initial population to obtain a second temporary population;

[0222] The third determining subunit is configured to determine each population individual based on the first temporary population and the second temporary population.

[0223] In some embodiments, the logging information includes: P-wave velocity, porosity, and the equivalent matrix modulus determination device 1000 includes:

[0224] A third determination module is used to determine the distribution range of the pore aspect ratio of the region with unknown material content based on the longitudinal wave velocity and the porosity;

[0225] A fourth determination module is used to determine the target pore aspect ratio of the region with unknown material content by using a particle swarm inversion algorithm based on the distribution range;

[0226] A fifth determination module is used to determine the shear wave velocity at the target depth point based on the target pore aspect ratio and the equivalent matrix modulus.

[0227] In some embodiments, the third determination module includes:

[0228] an eleventh determining unit, for determining an initial distribution range of pore types and pore aspect ratios of the region with unknown material content based on the longitudinal wave velocity and the porosity;

[0229] A twelfth determining unit is used to determine the distribution range of the pore aspect ratio based on the pore type and the initial distribution range.

[0230] It should be noted that in the embodiment of the present application, if the above-mentioned method for determining the equivalent matrix modulus is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the embodiment of the present application is essentially 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, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a U disk, a mobile hard disk, a read-only memory (ROM, Read Only Memory), a disk or an optical disk. In this way, the embodiment of the present application is not limited to any specific combination of hardware and software.

[0231] Accordingly, an embodiment of the present application provides a storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps in the method for determining the equivalent matrix modulus provided in the above embodiment are implemented.

[0232] Embodiment 7

[0233] The embodiment of the present application provides a device for determining an equivalent matrix modulus; Fig.11 A schematic diagram of the composition structure of the device for determining the equivalent matrix modulus provided in the embodiment of the present application, such as Fig.11 As shown, the electronic device 1100 includes: a processor 1101, at least one communication bus 1102, a user interface 1103, at least one external communication interface 1104, and a memory 1105. The communication bus 1102 is configured to realize the connection and communication between these components. The user interface 1103 may include a display screen, and the external communication interface 1104 may include a standard wired interface and a wireless interface. The processor 1101 is configured to execute the program of the method for determining the equivalent matrix modulus stored in the memory to implement the steps in the method for determining the equivalent matrix modulus provided in the above embodiment.

[0234] The description of the above display device and storage medium embodiments is similar to the description of the above method embodiments, and has similar beneficial effects as the method embodiments. For technical details not disclosed in the computer device and storage medium embodiments of this application, please refer to the description of the method embodiments of this application for understanding.

[0235] It should be noted here that the description of the above storage medium and device embodiments is similar to the description of the above method embodiments, and has similar beneficial effects as the method embodiments. For technical details not disclosed in the storage medium and device embodiments of this application, please refer to the description of the method embodiments of this application for understanding.

[0236] It should be understood that "one embodiment" or "an embodiment" mentioned throughout the specification means that specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in various embodiments of the present application, the size of the sequence number of the above-mentioned processes does not mean the order of execution, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application. The above-mentioned sequence numbers of the embodiments of the present application are only for description and do not represent the advantages and disadvantages of the embodiments.

[0237] 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.

[0238] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0239] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0240] In addition, all functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may be a separate unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0241] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above-mentioned method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above-mentioned method embodiment; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read Only Memory), disks or optical disks, etc. Various media that can store program codes.

[0242] Alternatively, if the above-mentioned integrated unit of the present application is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application can essentially or in other words, 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 and includes a number of instructions for a controller to execute all or part of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROMs, magnetic disks, or optical disks.

[0243] The above is only an implementation method of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for determining an equivalent matrix modulus, characterized in that: include: Obtain well logging information for areas with unknown mineral content; Determine the fluid bulk modulus at the target depth point in the region with unknown mineral content and the initial shear wave at the target depth point based on the well logging information; Determining the equivalent matrix modulus range of the target depth point based on a preset skeleton model and the initial shear wave; Determining a first initial population based on the equivalent matrix modulus range, and determining a second initial population based on a preset dry rock Poisson's ratio range; Determine each population individual based on the first initial population and the second initial population, each population individual includes an individual determined based on the first initial population and an individual determined based on the second initial population; Determine a first fluid item corresponding to each population individual by a first calculation method based on each population individual and the fluid bulk modulus; Determine the second fluid item corresponding to each population individual by a second calculation method based on each population individual and the fluid bulk modulus; Determining the fitness value of each population individual based on the first fluid term and the corresponding second fluid term and the objective function; Determining the equivalent matrix modulus of the target depth point based on the fitness value of each population individual; Determine the equivalent matrix modulus of each depth point in the region with unknown mineral content based on the equivalent matrix modulus of the target depth point; Among them, the objective function is to minimize the difference between the first fluid item and the second fluid item, the first fluid item is obtained by a first calculation method based on the equivalent matrix modulus, the dry rock Poisson's ratio, and the fluid bulk modulus, and the second fluid item is obtained by a second calculation method based on the equivalent matrix modulus, the dry rock Poisson's ratio, and the fluid bulk modulus. The first calculation method and the second calculation method are different, the equivalent matrix modulus is within the equivalent matrix modulus range, and the dry rock Poisson's ratio is within the dry rock Poisson's ratio range.

2. The method according to claim 1, characterized in that: The determining of the equivalent matrix modulus range of the target depth point based on the preset skeleton model and the initial shear wave comprises: Determine the basic parameters of the preset skeleton model; Determine the saturated rock bulk modulus at the target depth point based on the preset skeleton model and the initial shear wave; The equivalent matrix modulus range of the target depth point is determined based on the saturated rock bulk modulus, the skeleton model and the basic parameters.

3. The method according to claim 1, characterized in that: The determining the first fluid item corresponding to each population individual by using the first calculation method based on each population individual and the fluid bulk modulus includes: Determining the compression coefficient corresponding to each population individual based on each population individual and the fluid bulk modulus; A first fluid item corresponding to each population individual is determined by a first calculation method based on the compression coefficient corresponding to each population individual.

4. The method according to claim 1, characterized in that: The determining of each population individual based on the first initial population and the second initial population comprises: Performing mutation and crossover operations based on the first initial population to obtain a first temporary population; Performing mutation and crossover operations on the second initial population to obtain a second temporary population; Individuals of each population are determined based on the first temporary population and the second temporary population.

5. The method according to claim 1, characterized in that The logging information includes: P-wave velocity and porosity. The method further includes: Determine the distribution range of the pore aspect ratio of the region with unknown material content based on the longitudinal wave velocity and the porosity; Determine the target pore aspect ratio of the region with unknown content of the substance by using a particle swarm inversion algorithm based on the distribution range; The shear wave velocity at the target depth point is determined based on the target pore aspect ratio and the equivalent matrix modulus.

6. The method according to claim 5, characterized in that The determining the distribution range of the pore aspect ratio of the region with unknown material content based on the longitudinal wave velocity and the porosity comprises: Determine the initial distribution range of pore type and pore aspect ratio of the region with unknown material content based on the longitudinal wave velocity and the porosity; The distribution range of the pore aspect ratio is determined based on the pore type and the initial distribution range.

7. A device for determining an equivalent matrix modulus, characterized in that: include: An acquisition module, used to obtain logging information of areas with unknown mineral content; A first determination module is used to determine the fluid bulk modulus of the target depth point in the region with unknown mineral content and the initial shear wave of the target depth point based on the well logging information; A second determination module is used to determine the equivalent matrix modulus range of the target depth point based on a preset skeleton model and the initial shear wave; Computing module, including: a fourth determination unit, configured to determine a first initial population based on the equivalent matrix modulus range, and determine a second initial population based on a preset dry rock Poisson's ratio range; a fifth determining unit, configured to determine each population individual based on the first initial population and the second initial population, wherein each population individual includes an individual determined based on the first initial population and an individual determined based on the second initial population; A sixth determining unit, configured to determine a first fluid item corresponding to each population individual by adopting a first calculation method based on each population individual and the fluid bulk modulus; A seventh determination unit, configured to determine a second fluid item corresponding to each population individual by adopting a second calculation method based on each population individual and the fluid bulk modulus; an eighth determination unit, configured to determine the fitness value of each population individual based on the first fluid item and the corresponding second fluid item and the objective function; A ninth determination unit, configured to determine the equivalent matrix modulus of the target depth point based on the fitness values ​​of the individuals of each population; A tenth determining unit, configured to determine the equivalent matrix modulus of each depth point in the region with unknown mineral content based on the equivalent matrix modulus of the target depth point; Among them, the objective function is to minimize the difference between the first fluid item and the second fluid item, the first fluid item is obtained by a first calculation method based on the equivalent matrix modulus, the dry rock Poisson's ratio, and the fluid bulk modulus, and the second fluid item is obtained by a second calculation method based on the equivalent matrix modulus, the dry rock Poisson's ratio, and the fluid bulk modulus. The first calculation method and the second calculation method are different, the equivalent matrix modulus is within the equivalent matrix modulus range, and the dry rock Poisson's ratio is within the dry rock Poisson's ratio range.

8. A device for determining equivalent matrix modulus, characterized in that: The method 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 method for determining the equivalent matrix modulus according to any one of claims 1 to 6 is executed.

9. A storage medium, characterized in that: The computer program stored in the storage medium can be executed by one or more processors and can be used to implement the method for determining the equivalent matrix modulus as claimed in any one of claims 1 to 6.

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