Reservoir fluid identification method, system and device and readable storage medium

Abstract

The invention discloses a reservoir fluid identification method, system and device and a readable storage medium. The method comprises the following steps: acquiring seismic data, logging data and electromagnetic data; respectively calculating seismic apparent resistivity data, logging apparent resistivity data and electromagnetic apparent resistivity data according to the seismic data, the logging data and the electromagnetic data; performing normalization processing on the seismic apparent resistivity data, the logging apparent resistivity data and the electromagnetic apparent resistivity data to obtain seismic conversion data, logging conversion data and electromagnetic conversion data of a depth domain; reconstructing the seismic conversion data, the logging conversion data and the electromagnetic conversion data by using wavelet transform to obtain diploid resistivity data; and reservoir fluid identification is carried out based on the diploid resistivity data. According to the reservoir fluid identification method, system and device and the readable storage medium provided by the embodiment of the invention, the accuracy of reservoir fluid identification can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field related to oil and gas exploration and development, and in particular to a reservoir fluid identification method, system, device and readable storage medium. Background Art

[0002] Commonly used reservoir fluid identification methods include seismic, well logging and electromagnetic methods. Seismic data is usually used to provide high-resolution structures and sequences. Methods used for fluid identification include post-stack attribute intersection analysis and pre-stack amplitude variation with offset (AVO) inversion method, but the ability to distinguish between oil-bearing and water-bearing reservoirs is not strong enough; well logging has high vertical resolution, but only one hole is seen and the coverage is small; electromagnetic exploration is very sensitive to water changes, but the resolution is relatively low due to various factors. Therefore, a single method is used with strong multi-solution and insufficient reservoir fluid identification accuracy.

[0003] At present, there is a method in the prior art to fuse seismic resistivity, electromagnetic resistivity and logging resistivity to identify reservoir fluids, but the method of fusing seismic resistivity, electromagnetic resistivity and logging resistivity adopts the method of principal component analysis. However, the principal component analysis method will simplify the data in three dimensions, resulting in information loss and reducing the credibility of the data. At the same time, principal component analysis is only applicable to linear data, while there is a nonlinear relationship between the three resistivities. The effect after fusion is not good and the characteristics of the data cannot be fully captured, resulting in insufficient accuracy in the reservoir fluid identification. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, the present invention provides a reservoir fluid identification method, which can improve the accuracy of reservoir fluid identification.

[0005] The present invention also provides a reservoir fluid identification system, a control device for executing the reservoir fluid identification method, and a computer-readable storage medium.

[0006] According to a reservoir fluid identification method of a first aspect of the present invention, the method comprises: Acquisition of seismic, well-log, and electromagnetic data; According to the seismic data, the well logging data and the electromagnetic data, respectively, seismic apparent resistivity data, well logging apparent resistivity data and electromagnetic apparent resistivity data are calculated; Normalizing the seismic apparent resistivity data, the well logging apparent resistivity data, and the electromagnetic apparent resistivity data to obtain seismic conversion data, well logging conversion data, and electromagnetic conversion data in the depth domain; Reconstructing the seismic conversion data, the well logging conversion data and the electromagnetic conversion data by wavelet transform to obtain complex resistivity data; Reservoir fluid identification is performed based on the complex resistivity data.

[0007] The reservoir fluid identification method according to the embodiment of the present invention has at least the following beneficial effects: The embodiment of the present invention normalizes the seismic apparent resistivity data, the logging apparent resistivity data and the electromagnetic apparent resistivity data into seismic conversion data, logging conversion data and electromagnetic conversion data in the depth domain, and reconstructs the seismic conversion data, logging conversion data and electromagnetic conversion data using wavelet transform to obtain complex apparent resistivity data. Wavelet transform is a global transform, which has good positioning ability in the wavelet domain and can focus on any details of the processed data. Therefore, the complex apparent resistivity data after wavelet transform fusion in the embodiment of the present invention has a high resolution and can improve the accuracy of reservoir fluid identification.

[0008] According to some embodiments of the present invention, the reconstructing the seismic conversion data, the well logging conversion data and the electromagnetic conversion data by wavelet transform to obtain complex resistivity data includes: The seismic conversion data, the well logging conversion data and the electromagnetic conversion data are transformed into the same scale range and the same frequency range by using wavelet transform to obtain seismic wavelet data, well logging wavelet data and electromagnetic wavelet data; Determine a plurality of reconstruction coefficients corresponding to different scales and frequencies according to the seismic wavelet data, the well logging wavelet data and the electromagnetic wavelet data; The seismic wavelet data, the well logging wavelet data and the electromagnetic wavelet data are subjected to inverse wavelet transformation according to the plurality of reconstruction coefficients to reconstruct the complex apparent resistivity data.

[0009] According to some embodiments of the present invention, determining a plurality of reconstruction coefficients corresponding to different scales and frequencies according to the seismic wavelet data, the well logging wavelet data and the electromagnetic wavelet data includes: Calculating variance data of the seismic wavelet data, the well logging wavelet data and the electromagnetic wavelet data at the same scale and frequency; Calculate the variance weights of the frequencies corresponding to the scale according to all the variance data corresponding to all the frequencies at the same scale, and obtain multiple variance weights corresponding to different scales and different frequencies; The plurality of variance weights are used as the plurality of reconstruction coefficients.

[0010] According to some embodiments of the present invention, the variance weight is obtained by the following steps: Calculate the sum of the pairwise products of all variance data corresponding to all frequencies at the same scale to obtain the variance product; The product of all variance data of the remaining frequencies except the corresponding frequency under the scale is determined as the variance weight, and the proportion of the product in the variance product is determined as the variance weight.

[0011] According to some embodiments of the present invention, the seismic apparent resistivity data is obtained by the following steps: Based on the rock physics relationship between seismic wave impedance and resistivity, the seismic apparent resistivity data is calculated according to the well logging data and the seismic data, wherein the rock physics relationship is derived according to the Faust formula and the Gardner formula.

[0012] According to some embodiments of the present invention, the well logging data includes depth data, resistivity curve, neutron curve, acoustic wave curve and density curve, and the seismic apparent resistivity data is calculated based on the well logging data and the seismic data based on the rock physical relationship between seismic wave impedance and well logging resistivity, including: Obtaining seismic wave impedance through post-stack seismic inversion according to the seismic data; The seismic apparent resistivity data is obtained by calculation according to the depth data, the resistivity curve, the neutron curve, the acoustic wave curve, the density curve and the seismic wave impedance.

[0013] According to some embodiments of the present invention, the normalizing the seismic apparent resistivity data, the well logging apparent resistivity data and the electromagnetic apparent resistivity data to obtain seismic conversion data, well logging conversion data and electromagnetic conversion data in the depth domain includes: Aligning the seismic apparent resistivity data, the logging apparent resistivity data and the electromagnetic apparent resistivity data to the calibration of plane coordinates and the vertical unification to obtain first seismic data, first logging data and first electromagnetic data in the depth domain; Unifying the processing bin sizes of the first seismic data, the first well logging data and the first electromagnetic data on the plane coordinates and the sampling interval size in the vertical direction to obtain second seismic data, second well logging data and second electromagnetic data; The second seismic data, the second well logging data and the second electromagnetic data are normalized into a unified data range to obtain the seismic conversion data, the well logging conversion data and the electromagnetic conversion data.

[0014] According to a reservoir fluid identification system of a second aspect of the present invention, the system comprises: A data acquisition module, used to acquire seismic data, well logging data and electromagnetic data; an apparent resistivity determination module, used to calculate seismic apparent resistivity data, well logging apparent resistivity data and electromagnetic apparent resistivity data according to the seismic data, the well logging data and the electromagnetic data; A normalization processing module, used for normalizing the seismic apparent resistivity data, the well logging apparent resistivity data and the electromagnetic apparent resistivity data to obtain seismic conversion data, well logging conversion data and electromagnetic conversion data in the depth domain; A wavelet transform module, used for reconstructing the seismic conversion data, the well logging conversion data and the electromagnetic conversion data by using wavelet transform to obtain complex resistivity data; A reservoir fluid identification module is used to identify reservoir fluid based on the complex resistivity data.

[0015] The reservoir fluid identification system according to the embodiment of the present invention has at least the following beneficial effects: The embodiment of the present invention normalizes the seismic apparent resistivity data, the logging apparent resistivity data and the electromagnetic apparent resistivity data into seismic conversion data, logging conversion data and electromagnetic conversion data in the depth domain, and reconstructs the seismic conversion data, logging conversion data and electromagnetic conversion data using wavelet transform to obtain complex apparent resistivity data. Wavelet transform is a global transform, which has good positioning ability in the wavelet domain and can focus on any details of the processed data. Therefore, the complex apparent resistivity data after wavelet transform fusion in the embodiment of the present invention has a high resolution and can improve the accuracy of reservoir fluid identification.

[0016] According to the third aspect of the present invention, the control device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor implements the reservoir fluid identification method as described in the first aspect of the present invention when executing the computer program. Since the control device adopts all the technical solutions of the reservoir fluid identification method of the above embodiment, it has at least all the beneficial effects brought by the technical solutions of the above embodiment.

[0017] The computer-readable storage medium according to the fourth aspect of the present invention stores computer-executable instructions, and the computer-executable instructions are used to execute the reservoir fluid identification method as described in the first aspect of the present invention. Since the computer-readable storage medium adopts all the technical solutions of the reservoir fluid identification method of the above embodiment, it at least has all the beneficial effects brought by the technical solutions of the above embodiment.

[0018] Other features and advantages of the present invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which: Figure 1 4 is a flow chart of a reservoir fluid identification method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0020] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0021] In the description of the present invention, if there is a description of first, second, etc., it is only for the purpose of distinguishing technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0022] In the description of the present invention, it should be understood that descriptions involving orientation, such as orientation or positional relationship indicated as up, down, etc., are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present invention.

[0023] In the description of the present invention, it should be noted that, unless otherwise clearly defined, terms such as setting, installing, connecting, etc. should be understood in a broad sense, and technicians in the relevant technical field can reasonably determine the specific meanings of the above terms in the present invention in combination with the specific content of the technical solution.

[0024] The following will be combined Figure 1 A clear and complete description is given of the reservoir fluid identification method according to the embodiment of the present invention. Obviously, the embodiment described below is only a part of the embodiments of the present invention, but not all of the embodiments.

[0025] According to a reservoir fluid identification method of a first aspect of the present invention, the method comprises: Acquisition of seismic, well-log, and electromagnetic data; Seismic apparent resistivity data, well logging apparent resistivity data and electromagnetic apparent resistivity data are calculated respectively according to seismic data, well logging data and electromagnetic data; Normalizing the seismic apparent resistivity data, well logging apparent resistivity data and electromagnetic apparent resistivity data to obtain seismic conversion data, well logging conversion data and electromagnetic conversion data in the depth domain; Using wavelet transform, seismic conversion data, logging conversion data and electromagnetic conversion data are reconstructed to obtain complex resistivity data; Reservoir fluid identification based on complex resistivity data.

[0026] It is understandable that seismic data, well logging data and electromagnetic data are pre-processed. Among them, well logging data is obtained by processing well logging data through depth correction, curve smoothing, environmental correction, numerical standardization, etc., so that it can meet the requirements of inversion; electromagnetic data is obtained by removing abnormalities and preliminary filtering of electromagnetic data; seismic data is post-stack pure wave seismic data after pre-stack amplitude preservation processing of seismic data.

[0027] It should be noted that the specific pretreatment principle and process are prior arts known to those skilled in the art and will not be described in detail here.

[0028] The electromagnetic apparent resistivity data is obtained by performing wide-area electromagnetic apparent resistivity inversion on the basis of the seismic geological framework. The well logging apparent resistivity data is obtained by performing three-dimensional data volume calculation of well logging apparent resistivity on the basis of the geological framework, and by simulating and interpolating the well logging resistivity of multiple wells. The calculation principles and processes of electromagnetic apparent resistivity data and well logging apparent resistivity data are all existing technologies known to those skilled in the art, and will not be described in detail here.

[0029] In some embodiments of the present invention, seismic apparent resistivity data is obtained by the following steps: Based on the rock physical relationship between seismic wave impedance and resistivity, seismic apparent resistivity data are calculated according to logging data and seismic data, wherein the rock physical relationship is derived according to Faust formula and Gardner formula.

[0030] Well logging data includes depth data, resistivity curve, neutron curve, acoustic wave curve and density curve. Based on the rock physical relationship between seismic wave impedance and well logging resistivity, seismic apparent resistivity data is calculated from well logging data and seismic data, including: Seismic wave impedance is obtained through post-stack seismic inversion based on seismic data; The seismic apparent resistivity data is calculated based on the depth data, resistivity curve, neutron curve, acoustic wave curve, density curve and seismic wave impedance.

[0031] The Faust formula is: ;Formula (1) in, is the longitudinal wave velocity, h is the depth data, F The stratigraphic factor.

[0032] Gardner's formula is: ;Formula (2) in, is the density, is the longitudinal wave velocity, c and m is a constant.

[0033] The rock physics relationship can be derived from the Faust formula and the Gardner formula: ;Formula (3) ;Formula (4) in, is the formation water resistivity, R is the seismic apparent resistivity data, AI is the seismic wave impedance.

[0034] First, the parameters are obtained by using the resistivity curve and the acoustic wave curve Calculate the formation water resistivity The method is based on Archie's formula: ;Formula (5) in, is the formation resistivity, a, b is a coefficient related to lithology and can be obtained by laboratory measurement. m’ is the cementation index, n is the saturation index, is the formation water saturation. Taking the logarithm of both sides of formula (5), we get formula (6): =-m'lg ;Formula (6) The formation water resistivity is the value corresponding to the water saturation of 100% and the porosity of 100%. First, select the typical water-producing layer in the work area, that is, the water saturation of this layer is 100%, and the formula The porosity value in the section is calculated by the neutron curve, acoustic wave curve and density curve of the water-producing layer, and then the formation resistivity (i.e. resistivity curve) and porosity of the typical water-producing layer are statistically analyzed. The formation resistivity and porosity of the typical water-producing well are analyzed by the PICKETT intersection diagram method to obtain the mathematical relationship between the two, i.e. the relationship between formation resistivity and porosity. For a fixed work area, is a fixed constant, that is is a fixed constant, and formation resistivity and porosity are variables. In this relationship, when porosity is equal to 100%, -m'lg is 0, the intercept term can be used to obtain the formation resistivity , the formation water resistivity can be obtained by calculation , so that we can get the parameters .

[0035] It should be noted that the parameter It can also be obtained by laboratory formation water sample determination method, natural potential method, imaging formation water resistivity method, which should not be regarded as a limitation of the present invention.

[0036] Then the parameters can be obtained by linear regression using the acoustic wave curve and density curve. c and m The acoustic wave curve can be used to directly measure the acoustic wave time difference DT of the underground strata, and then the longitudinal wave velocity can be obtained by calculation. The density curve can directly measure the density of the underground strata , collect the P-wave velocity and density of the target layer, perform intersection analysis, and then fit the mathematical relationship of the exponential formula (i.e., the Gardner formula), so that the parameters can be obtained c and m .

[0037] In the prior art, seismic apparent resistivity data is obtained from seismic wave impedance by means of multiple regression and interactive verification based on wave impedance inversion. The multiple regression and interactive verification methods are based on a mathematical algorithm, obtained by calculating the correlation between each other, and are not based on a certain rock physics basis of the connection existing in rock physics itself, which is equivalent to simplifying the relationship between the two. The embodiment of the present invention adopts a certain rock physics relationship between the two, and the mathematical relationship between the two is more complex, and the seismic apparent resistivity data obtained has higher accuracy.

[0038] The embodiment of the present invention uses wavelet transform to convert the signals of the depth domain of the three types of data into the wavelet domain for unified integration and analysis, performs wavelet frequency division on data of different scales, performs energy calibration on each scale, determines the optimal reconstruction coefficient, and finally reconstructs the seismic conversion data, the logging conversion data and the electromagnetic conversion data into complex resistivity data close to the white spectrum. In this way, the complex resistivity data contains logging information, seismic information and electromagnetic information, which is more beneficial for reservoir fluid identification and has higher accuracy.

[0039] In some embodiments of the present invention, normalization processing is performed on seismic apparent resistivity data, well logging apparent resistivity data, and electromagnetic apparent resistivity data to obtain seismic conversion data, well logging conversion data, and electromagnetic conversion data in the depth domain, including: Aligning the seismic apparent resistivity data, the logging apparent resistivity data and the electromagnetic apparent resistivity data to the calibration of the plane coordinates and the vertical unification to obtain the first seismic data, the first logging data and the first electromagnetic data in the depth domain; Unifying the processing bin size of the first seismic data, the first well logging data and the first electromagnetic data in the plane coordinates and the sampling interval size in the vertical direction to obtain the second seismic data, the second well logging data and the second electromagnetic data; The second seismic data, the second well logging data and the second electromagnetic data are normalized into a unified data range to obtain seismic conversion data, well logging conversion data and electromagnetic conversion data.

[0040] It can be understood that the plane coordinates are unified in the geodetic coordinate system. Vertically, the logging apparent resistivity data itself is measured in the depth domain; the electromagnetic apparent resistivity data needs to be converted to wide-area electromagnetic depth. The skin depth of electromagnetic waves in the underground mainly depends on the resistivity of the underground medium and the frequency of the electromagnetic waves: ;Formula (7) in, is the skin depth, ' is the resistivity of the earth, is the magnetic permeability of the medium, is the angular frequency of the electromagnetic wave. Skin depth refers to the depth below the ground in a uniform half space. The intensity of electromagnetic waves at the location is 1 / e of the intensity of electromagnetic waves on the ground, so it is often roughly interpreted as the approximate depth of electromagnetic waves penetrating into the medium. It can be used to roughly estimate the detection depth of electromagnetic methods. Using a series of electromagnetic waves with different frequencies can detect geoelectric information at different depths. The time-domain depth conversion of seismic apparent resistivity data can use the acoustic wave curve measured on the well for well seismic calibration in the work area to establish a three-dimensional seismic velocity data volume, and then convert the seismic time domain data to the depth domain through time-to-depth conversion.

[0041] In the process of unifying the processing facet size of the first seismic data, the first logging data and the first electromagnetic data in the plane coordinates and the sampling interval size in the vertical direction, the size of each processing facet in the plane coordinates is first determined. It must be comprehensively determined based on the distances when different data are collected. The unified facet size of the three is generally 20m*20m, and the sampling interval in the vertical direction must also be unified to the standard interval. The three data are unified by linear sampling thinning or encryption.

[0042] It should be noted that the specific numerical values ​​mentioned in the above embodiments cannot be regarded as limitations of the present invention.

[0043] It is understandable that in order to avoid the influence of dimensions and magnitudes of different types of apparent resistivity data, the second seismic data, the second well logging data and the second electromagnetic data need to be normalized to a unified data range. The specific unified formula is: ;Formula (8) in, represents the normalized value, Represents a sample value in the sample group. Represents the minimum value of the sample group, Represents the maximum value of the sample point group.

[0044] In some embodiments of the present invention, wavelet transform is used to reconstruct seismic conversion data, well logging conversion data and electromagnetic conversion data to obtain complex resistivity data, including: The seismic conversion data, the well logging conversion data and the electromagnetic conversion data are transformed into the same scale range and the same frequency range by using wavelet transform to obtain seismic wavelet data, well logging wavelet data and electromagnetic wavelet data; Determine multiple reconstruction coefficients corresponding to different scales and frequencies according to seismic wavelet data, well logging wavelet data and electromagnetic wavelet data; According to multiple reconstruction coefficients, the seismic wavelet data, the logging wavelet data and the electromagnetic wavelet data are subjected to inverse wavelet transformation to reconstruct the complex apparent resistivity data.

[0045] In some embodiments of the present invention, multiple reconstruction coefficients corresponding to different scales and frequencies are determined based on seismic wavelet data, well logging wavelet data, and electromagnetic wavelet data, including: Calculate the variance data of seismic wavelet data, well logging wavelet data and electromagnetic wavelet data at the same scale and frequency; According to all variance data corresponding to all frequencies at the same scale, the variance weights of the corresponding frequencies at the scale are calculated to obtain multiple variance weights corresponding to different scales and frequencies; Multiple variance weights are used as multiple reconstruction coefficients.

[0046] In some embodiments of the present invention, the variance weight is obtained by the following steps: Calculate the sum of the pairwise products of all variance data corresponding to all frequencies at the same scale to obtain the variance product; The product of all variance data of the remaining frequencies except the corresponding frequency under this scale is determined as the variance weight, and its proportion in the variance product.

[0047] In some embodiments, seismic conversion data is defined as X ( h ), the electromagnetic conversion data is Y ( h ), the logging conversion data is Z ( h ), h is the depth value of a certain depth underground. The three resistivities are transformed into the wavelet domain through wavelet transform. In order to facilitate fusion analysis, the three data are uniformly transformed to the same scale range and the same frequency range. That is, the transformed three data are respectively (i, j), (i, j), (i, j), (i, j) represents the i At the scale j Frequency of seismic wavelet data, usually 1 , 1 .

[0048] The calculation formula for variance data is: ]; Formula (9) = ;Formula (10) by i =5, j =3 is used as an example to illustrate the calculation of the reconstruction coefficient.

[0049] Let the variance weight be S ,when j =3, i The variance weights of the three frequencies corresponding to the scale are: ;Formula (11) ;Formula (12) ;Formula (13) It can be seen from formula (11), formula (12) and formula (13) that the reconstruction coefficients of the three data at the same scale and the same frequency are equal, but the reconstruction coefficients between different frequencies at the same scale are different, and the reconstruction coefficients at different scales are different.

[0050] The embodiment of the present invention calculates the variance data of the three data at different scales at the same frequency to indicate the degree of fluctuation of the data at different scales at the frequency. The larger the variance data, the larger the fluctuation of the three data at different scales at the frequency, and the more unstable it is. The variance weight at this frequency is the ratio of the product of the variance data of the remaining frequencies to the variance product. The larger the product of the variance data of the remaining frequencies, the larger the fluctuation of the data of the remaining frequencies, and the more unstable it is. This proportion (variance weight) is used as the reconstruction coefficient of the frequency, which fully improves the proportion of stability data and effectively controls the numerical error in the fusion process. The conventional mean fusion method of each feature component, especially the method of using a single fusion weight, directly assigns weight coefficients based on the original three data for fusion, which will weaken the most effective feature information, and the proportion of introducing invalid or interfering feature information will also increase accordingly.

[0051] At the same time, the three data are combined to calculate the variance weight of a certain frequency data at the corresponding scale, which makes full use of the characteristics of data at different scales, including large-scale fluctuation trend information and small-scale detail oscillation information. The independent variance weight fusion method of the three data at different scales and frequencies is conducive to extracting and fusing effective information that can best characterize the structural characteristics at the current scale. That is, the fusion result is the dynamic weight fusion of the spatial feature information of the multi-dimensional fusion data source from coarse grid to fine grid.

[0052] According to the reservoir fluid identification method of the embodiment of the present invention, the embodiment of the present invention normalizes the seismic apparent resistivity data, the logging apparent resistivity data and the electromagnetic apparent resistivity data into seismic conversion data, logging conversion data and electromagnetic conversion data in the depth domain, and reconstructs the seismic conversion data, logging conversion data and electromagnetic conversion data using wavelet transform to obtain complex apparent resistivity data. Wavelet transform is a global transform, which has good positioning ability in the wavelet domain and can focus on any details of the processed data. Therefore, the complex apparent resistivity data after wavelet transform fusion in the embodiment of the present invention has a high resolution and can improve the accuracy of reservoir fluid identification.

[0053] According to the reservoir fluid identification system of the second aspect of the present invention, the system includes a data acquisition module, an apparent resistivity determination module, a normalization processing module, a wavelet transformation module and a reservoir fluid identification module.

[0054] A data acquisition module, used to acquire seismic data, well logging data and electromagnetic data; An apparent resistivity determination module is used to calculate seismic apparent resistivity data, well logging apparent resistivity data and electromagnetic apparent resistivity data according to seismic data, well logging data and electromagnetic data respectively; A normalization processing module is used to perform normalization processing on seismic apparent resistivity data, well logging apparent resistivity data and electromagnetic apparent resistivity data to obtain seismic conversion data, well logging conversion data and electromagnetic conversion data in the depth domain; A wavelet transform module is used to reconstruct the seismic conversion data, the logging conversion data and the electromagnetic conversion data by using wavelet transform to obtain the complex resistivity data; The reservoir fluid identification module is used to identify reservoir fluids based on complex resistivity data.

[0055] In some embodiments of the present invention, seismic apparent resistivity data is obtained by the following steps: Based on the rock physical relationship between seismic wave impedance and resistivity, seismic apparent resistivity data are calculated according to logging data and seismic data, wherein the rock physical relationship is derived according to Faust formula and Gardner formula.

[0056] Well logging data includes depth data, resistivity curve, neutron curve, acoustic wave curve and density curve. Based on the rock physical relationship between seismic wave impedance and well logging resistivity, seismic apparent resistivity data is calculated from well logging data and seismic data, including: Seismic wave impedance is obtained through post-stack seismic inversion based on seismic data; The seismic apparent resistivity data is calculated based on the depth data, resistivity curve, neutron curve, acoustic wave curve, density curve and seismic wave impedance.

[0057] In the prior art, seismic apparent resistivity data is obtained from seismic wave impedance by means of multiple regression and interactive verification based on wave impedance inversion. The multiple regression and interactive verification methods are based on a mathematical algorithm, obtained by calculating the correlation between each other, and are not based on a certain rock physics basis of the connection existing in rock physics itself, which is equivalent to simplifying the relationship between the two. The embodiment of the present invention adopts a certain rock physics relationship between the two, and the mathematical relationship between the two is more complex, and the seismic apparent resistivity data obtained has higher accuracy.

[0058] The embodiment of the present invention uses wavelet transform to convert the signals of the depth domain of the three types of data into the wavelet domain for unified integration and analysis, performs wavelet frequency division on data of different scales, performs energy calibration on each scale, determines the optimal reconstruction coefficient, and finally reconstructs the seismic conversion data, the logging conversion data and the electromagnetic conversion data into complex resistivity data close to the white spectrum. In this way, the complex resistivity data contains logging information, seismic information and electromagnetic information, which is more beneficial for reservoir fluid identification and has higher accuracy.

[0059] In some embodiments of the present invention, normalization processing is performed on seismic apparent resistivity data, well logging apparent resistivity data, and electromagnetic apparent resistivity data to obtain seismic conversion data, well logging conversion data, and electromagnetic conversion data in the depth domain, including: Aligning the seismic apparent resistivity data, the logging apparent resistivity data and the electromagnetic apparent resistivity data to the calibration of the plane coordinates and the vertical unification to obtain the first seismic data, the first logging data and the first electromagnetic data in the depth domain; Unifying the processing bin size of the first seismic data, the first well logging data and the first electromagnetic data in the plane coordinates and the sampling interval size in the vertical direction to obtain the second seismic data, the second well logging data and the second electromagnetic data; The second seismic data, the second well logging data and the second electromagnetic data are normalized into a unified data range to obtain seismic conversion data, well logging conversion data and electromagnetic conversion data.

[0060] In some embodiments of the present invention, wavelet transform is used to reconstruct seismic conversion data, well logging conversion data and electromagnetic conversion data to obtain complex resistivity data, including: The seismic conversion data, the well logging conversion data and the electromagnetic conversion data are transformed into the same scale range and the same frequency range by using wavelet transform to obtain seismic wavelet data, well logging wavelet data and electromagnetic wavelet data; Determine multiple reconstruction coefficients corresponding to different scales and frequencies according to seismic wavelet data, well logging wavelet data and electromagnetic wavelet data; According to multiple reconstruction coefficients, the seismic wavelet data, the logging wavelet data and the electromagnetic wavelet data are subjected to inverse wavelet transformation to reconstruct the complex apparent resistivity data.

[0061] In some embodiments of the present invention, multiple reconstruction coefficients corresponding to different scales and frequencies are determined based on seismic wavelet data, well logging wavelet data, and electromagnetic wavelet data, including: Calculate the variance data of seismic wavelet data, well logging wavelet data and electromagnetic wavelet data at the same scale and frequency; According to all variance data corresponding to all frequencies at the same scale, the variance weights of the corresponding frequencies at the scale are calculated to obtain multiple variance weights corresponding to different scales and frequencies; Multiple variance weights are used as multiple reconstruction coefficients.

[0062] In some embodiments of the present invention, the variance weight is obtained by the following steps: Calculate the sum of the pairwise products of all variance data corresponding to all frequencies at the same scale to obtain the variance product; The product of all variance data of the remaining frequencies except the corresponding frequency under this scale is determined as the variance weight, and its proportion in the variance product.

[0063] The embodiment of the present invention calculates the variance data of the three data at different scales at the same frequency to indicate the degree of fluctuation of the data at different scales at the frequency. The larger the variance data, the larger the fluctuation of the three data at different scales at the frequency, and the more unstable it is. The variance weight at this frequency is the ratio of the product of the variance data of the remaining frequencies to the variance product. The larger the product of the variance data of the remaining frequencies, the larger the fluctuation of the data of the remaining frequencies, and the more unstable it is. This proportion (variance weight) is used as the reconstruction coefficient of the frequency, which fully improves the proportion of stability data and effectively controls the numerical error in the fusion process. The conventional mean fusion method of each feature component, especially the method of using a single fusion weight, directly assigns weight coefficients based on the original three data for fusion, which will weaken the most effective feature information, and the proportion of introducing invalid or interfering feature information will also increase accordingly.

[0064] At the same time, the three data are combined to calculate the variance weight of a certain frequency data at the corresponding scale, which makes full use of the characteristics of data at different scales, including large-scale fluctuation trend information and small-scale detail oscillation information. The independent variance weight fusion method of the three data at different scales and frequencies is conducive to extracting and fusing effective information that can best characterize the structural characteristics at the current scale. That is, the fusion result is the dynamic weight fusion of the spatial feature information of the multi-dimensional fusion data source from coarse grid to fine grid.

[0065] According to the reservoir fluid identification system of the embodiment of the present invention, the embodiment of the present invention normalizes the seismic apparent resistivity data, the logging apparent resistivity data and the electromagnetic apparent resistivity data into seismic conversion data, logging conversion data and electromagnetic conversion data in the depth domain, and reconstructs the seismic conversion data, logging conversion data and electromagnetic conversion data by wavelet transform to obtain complex apparent resistivity data. Wavelet transform is a global transform, which has good positioning ability in the wavelet domain and can focus on any details of the processed data. Therefore, the complex apparent resistivity data after wavelet transform fusion in the embodiment of the present invention has a high resolution and can improve the accuracy of reservoir fluid identification.

[0066] In addition, an embodiment of the present invention further provides a control device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor and the memory may be connected via a bus or other means.

[0067] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0068] The non-transient software program and instructions required to implement the reservoir fluid identification method of the above embodiment are stored in the memory, and when executed by the processor, the reservoir fluid identification method of the above embodiment is executed.

[0069] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0070] In addition, an embodiment of the present invention also provides a computer-readable storage medium, which stores computer-executable instructions, and the computer-executable instructions are executed by a processor or controller, for example, by the processor of the above embodiment, so that the above processor can execute the reservoir fluid identification method in the above embodiment.

[0071] It will be appreciated by those skilled in the art that all or some of the steps and systems in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or non-transitory medium) and a communication medium (or transient medium). As known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media typically embodies computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0072] The embodiments of the present invention are described in detail above in conjunction with the accompanying drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge scope of ordinary technicians in the relevant technical field without departing from the purpose of the present invention.

Claims

1. A reservoir fluid identification method, characterized in that: The method comprises: Acquisition of seismic, well-log, and electromagnetic data; According to the seismic data, the well logging data and the electromagnetic data, respectively, seismic apparent resistivity data, well logging apparent resistivity data and electromagnetic apparent resistivity data are calculated; Normalizing the seismic apparent resistivity data, the well logging apparent resistivity data, and the electromagnetic apparent resistivity data to obtain seismic conversion data, well logging conversion data, and electromagnetic conversion data in the depth domain; Reconstructing the seismic conversion data, the well logging conversion data and the electromagnetic conversion data by wavelet transform to obtain complex resistivity data; Reservoir fluid identification is performed based on the complex resistivity data.

2. The reservoir fluid identification method according to claim 1, characterized in that: The method of reconstructing the seismic conversion data, the well logging conversion data and the electromagnetic conversion data by wavelet transform to obtain complex resistivity data includes: The seismic conversion data, the well logging conversion data and the electromagnetic conversion data are transformed into the same scale range and the same frequency range by using wavelet transform to obtain seismic wavelet data, well logging wavelet data and electromagnetic wavelet data; Determine a plurality of reconstruction coefficients corresponding to different scales and frequencies according to the seismic wavelet data, the well logging wavelet data and the electromagnetic wavelet data; The seismic wavelet data, the well logging wavelet data and the electromagnetic wavelet data are subjected to inverse wavelet transformation according to the plurality of reconstruction coefficients to reconstruct the complex apparent resistivity data.

3. The reservoir fluid identification method according to claim 2, characterized in that: The determining of a plurality of reconstruction coefficients corresponding to different scales and frequencies according to the seismic wavelet data, the well logging wavelet data and the electromagnetic wavelet data comprises: Calculating variance data of the seismic wavelet data, the well logging wavelet data and the electromagnetic wavelet data at the same scale and frequency; Calculate the variance weights of the frequencies corresponding to the scale according to all the variance data corresponding to all the frequencies at the same scale, and obtain multiple variance weights corresponding to different scales and different frequencies; The plurality of variance weights are used as the plurality of reconstruction coefficients.

4. The reservoir fluid identification method according to claim 3, characterized in that: The variance weight is obtained by the following steps: Calculate the sum of the pairwise products of all variance data corresponding to all frequencies at the same scale to obtain the variance product; The product of all variance data of the remaining frequencies except the corresponding frequency under the scale is determined as the variance weight, and the proportion of the product in the variance product is determined as the variance weight.

5. The reservoir fluid identification method according to claim 1, characterized in that: The seismic apparent resistivity data is obtained by the following steps: Based on the rock physics relationship between seismic wave impedance and resistivity, the seismic apparent resistivity data is calculated according to the well logging data and the seismic data, wherein the rock physics relationship is derived according to the Faust formula and the Gardner formula.

6. The reservoir fluid identification method according to claim 5, characterized in that: The logging data includes depth data, resistivity curve, neutron curve, acoustic wave curve and density curve. The seismic apparent resistivity data is calculated based on the logging data and the seismic data based on the rock physical relationship between seismic wave impedance and logging resistivity, and includes: Obtaining seismic wave impedance through post-stack seismic inversion according to the seismic data; The seismic apparent resistivity data is obtained by calculation according to the depth data, the resistivity curve, the neutron curve, the acoustic wave curve, the density curve and the seismic wave impedance.

7. The reservoir fluid identification method according to claim 1, characterized in that: The normalization process of the seismic apparent resistivity data, the well logging apparent resistivity data and the electromagnetic apparent resistivity data to obtain seismic conversion data, well logging conversion data and electromagnetic conversion data in the depth domain includes: Aligning the seismic apparent resistivity data, the logging apparent resistivity data and the electromagnetic apparent resistivity data to the calibration of plane coordinates and the vertical unification to obtain first seismic data, first logging data and first electromagnetic data in the depth domain; Unifying the processing bin sizes of the first seismic data, the first well logging data and the first electromagnetic data on the plane coordinates and the sampling interval size in the vertical direction to obtain second seismic data, second well logging data and second electromagnetic data; The second seismic data, the second well logging data and the second electromagnetic data are normalized into a unified data range to obtain the seismic conversion data, the well logging conversion data and the electromagnetic conversion data.

8. A reservoir fluid identification system, characterized in that: The system comprises: A data acquisition module, used to acquire seismic data, well logging data and electromagnetic data; an apparent resistivity determination module, used to calculate seismic apparent resistivity data, well logging apparent resistivity data and electromagnetic apparent resistivity data according to the seismic data, the well logging data and the electromagnetic data; A normalization processing module, used for normalizing the seismic apparent resistivity data, the well logging apparent resistivity data and the electromagnetic apparent resistivity data to obtain seismic conversion data, well logging conversion data and electromagnetic conversion data in the depth domain; A wavelet transform module, used for reconstructing the seismic conversion data, the well logging conversion data and the electromagnetic conversion data by using wavelet transform to obtain complex resistivity data; A reservoir fluid identification module is used to identify reservoir fluid based on the complex resistivity data.

9. A control device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the reservoir fluid identification method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing computer-executable instructions, characterized in that: The computer executable instructions are used to execute the reservoir fluid identification method according to any one of claims 1 to 7.

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