CN119179117A - Reservoir fluid identification method, system, device and readable storage medium - Google Patents
Reservoir fluid identification method, system, device and readable storage medium Download PDFInfo
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- G01V3/00—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation
- G01V3/18—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation specially adapted for well-logging
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- G01V3/26—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation specially adapted for well-logging operating with magnetic or electric fields produced or modified either by the surrounding earth formation or by the detecting device
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Abstract
The invention discloses a reservoir fluid identification method, a system, a device and a readable storage medium, wherein the method comprises the steps of obtaining seismic data, logging data and electromagnetic data; the method comprises the steps of respectively calculating seismic apparent resistivity data, logging apparent resistivity data and electromagnetic apparent resistivity data according to seismic data, logging data and electromagnetic data, carrying out 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 in a depth domain, reconstructing the seismic conversion data, the logging conversion data and the electromagnetic conversion data by utilizing wavelet transformation to obtain multi-apparent resistivity data, and carrying out reservoir fluid identification based on the multi-apparent resistivity data. The reservoir fluid identification method, the system, the device and the readable storage medium can improve the accuracy of reservoir fluid identification.
Description
Technical Field
The invention relates to the technical field related to oil and gas exploration and development, in particular to a reservoir fluid identification method, a system, a device and a readable storage medium.
Background
Common reservoir fluid identification methods include earthquake, well logging, electromagnetic methods and the like, earthquake data are commonly used for providing high-resolution structures and layer sequences, the methods for fluid identification include post-stack attribute intersection analysis and pre-stack amplitude-to-offset change (Amplitude variation with offset, AVO) inversion methods, but the discrimination strength of an oil-containing water reservoir is insufficient, the longitudinal resolution of the well logging is high, but only one hole is visible, the coverage is small, and the electromagnetic exploration method is very sensitive to water body changes, but the resolution is affected by various factors to be lower. Therefore, the single method is adopted, the multiple solution is strong, and the reservoir fluid identification precision is not enough.
At present, a method for fusing the seismic resistivity, the electromagnetic resistivity and the logging resistivity to identify the reservoir fluid exists in the prior art, but the method for fusing the seismic resistivity, the electromagnetic resistivity and the logging resistivity adopts a principal component analysis method, but the principal component analysis method can simplify data in three dimensions, so that information is lost, the reliability of the data is reduced, meanwhile, the principal component analysis is only applicable to linear data, nonlinear relations exist among the three resistivities, the effect after fusion is poor, the characteristics of the data cannot be completely captured, and the identification accuracy of the obtained reservoir fluid is insufficient.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems existing in the prior art. Therefore, the invention provides a reservoir fluid identification method which can improve the accuracy of reservoir fluid identification.
The invention also provides a reservoir fluid identification system, a control device for executing the reservoir fluid identification method and a computer readable storage medium.
A reservoir fluid identification method according to an embodiment of the first aspect of the present invention, the method comprising:
acquiring seismic data, logging data and electromagnetic data;
Respectively calculating to obtain 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;
Normalizing 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 in a depth domain;
Reconstructing the seismic conversion data, the logging conversion data and the electromagnetic conversion data by utilizing wavelet transformation to obtain multi-view resistivity data;
and carrying out reservoir fluid identification based on the multi-view resistivity data.
The reservoir fluid identification method provided by the embodiment of the invention has at least the following beneficial effects:
According to the embodiment of the invention, the seismic apparent resistivity data, the logging apparent resistivity data and the electromagnetic apparent resistivity data are normalized to the seismic conversion data, the logging conversion data and the electromagnetic conversion data in the depth domain, and the seismic conversion data, the logging conversion data and the electromagnetic conversion data are reconstructed by utilizing wavelet transformation to obtain the multi-apparent resistivity data. The wavelet transformation is global transformation, has good positioning capability in a wavelet domain, and can focus on any detail of processed data, so that the embodiment of the invention has high resolution of the multi-view resistivity data after wavelet transformation fusion, and can improve the accuracy of reservoir fluid identification.
According to some embodiments of the invention, reconstructing the seismic conversion data, the logging conversion data, and the electromagnetic conversion data using wavelet transforms to obtain multi-apparent resistivity data comprises:
transforming the seismic conversion data, the logging conversion data and the electromagnetic conversion data into the same scale range and the same frequency range by utilizing wavelet transformation to obtain seismic wavelet data, logging wavelet data and electromagnetic wavelet data;
determining a plurality of reconstruction coefficients corresponding to different scales and different frequencies according to the seismic wavelet data, the logging wavelet data and the electromagnetic wavelet data;
and carrying out wavelet inverse transformation on the seismic wavelet data, the logging wavelet data and the electromagnetic wavelet data according to a plurality of reconstruction coefficients, and reconstructing the complex apparent resistivity data.
According to some embodiments of the invention, the determining a corresponding plurality of reconstruction coefficients at different frequencies at different scales from the seismic wavelet data, the logging wavelet data, and the electromagnetic wavelet data includes:
Calculating variance data of the seismic wavelet data, the logging wavelet data and the electromagnetic wavelet data under the same scale and the same frequency;
calculating each variance weight of the corresponding frequency under the same scale according to all variance data corresponding to all frequencies under the same scale to obtain a plurality of variance weights corresponding to different scales and different frequencies;
And taking a plurality of variance weights as a plurality of reconstruction coefficients.
According to some embodiments of the invention, the variance weight is obtained by:
calculating the sum of products of all variance data corresponding to all frequencies under the same scale to obtain a variance product;
And (3) determining the duty ratio in the variance product as the variance weight by multiplying all variance data of the residual frequencies except the corresponding frequency under the scale.
According to some embodiments of the invention, the seismic apparent resistivity data is obtained by:
And calculating the seismic apparent resistivity data according to the logging data and the seismic data based on a petrophysical relationship between the seismic wave impedance and the resistivity, wherein the petrophysical relationship is derived according to a Faust formula and a Gardner formula.
According to some embodiments of the invention, the logging data includes depth data, resistivity curves, neutron curves, acoustic curves, and density curves, the calculating the seismic apparent resistivity data from the logging data and the seismic data based on a petrophysical relationship between seismic wave impedance and logging resistivity includes:
Obtaining the impedance of the earthquake wave through post-stack earthquake inversion according to the earthquake data;
and calculating according to the depth data, the resistivity curve, the neutron curve, the acoustic curve, the density curve and the seismic wave impedance to obtain the seismic apparent resistivity data.
According to some embodiments of the present invention, the normalizing the seismic apparent resistivity data, the logging apparent resistivity data, and the electromagnetic apparent resistivity data to obtain depth-domain seismic conversion data, logging conversion data, and electromagnetic conversion data includes:
Registering the seismic apparent resistivity data, the logging apparent resistivity data and the electromagnetic apparent resistivity data to the alignment of plane coordinates and unification in the longitudinal direction to obtain first seismic data, first logging data and first electromagnetic data of a depth domain;
Unifying the size of the processing surface element of the first seismic data, the first logging data and the first electromagnetic data on the plane coordinates and the size of the sampling interval in the longitudinal direction to obtain second seismic data, second logging data and second electromagnetic data;
Normalizing the second seismic data, the second logging data and the second electromagnetic data to a unified data range to obtain the seismic conversion data, the logging conversion data and the electromagnetic conversion data.
A reservoir fluid identification system according to an embodiment of the second aspect of the present invention, the system comprising:
The data acquisition module is used for acquiring seismic data, logging data and electromagnetic data;
The apparent resistivity determining module is used for respectively calculating to obtain 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;
The normalization processing module is used for carrying out 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;
the wavelet transformation module is used for reconstructing the seismic transformation data, the logging transformation data and the electromagnetic transformation data by utilizing wavelet transformation to obtain multi-view resistivity data;
and the reservoir fluid identification module is used for carrying out reservoir fluid identification based on the multiple vision resistivity data.
The reservoir fluid identification system provided by the embodiment of the invention has at least the following beneficial effects:
According to the embodiment of the invention, the seismic apparent resistivity data, the logging apparent resistivity data and the electromagnetic apparent resistivity data are normalized to the seismic conversion data, the logging conversion data and the electromagnetic conversion data in the depth domain, and the seismic conversion data, the logging conversion data and the electromagnetic conversion data are reconstructed by utilizing wavelet transformation to obtain the multi-apparent resistivity data. The wavelet transformation is global transformation, has good positioning capability in a wavelet domain, and can focus on any detail of processed data, so that the embodiment of the invention has high resolution of the multi-view resistivity data after wavelet transformation fusion, and can improve the accuracy of reservoir fluid identification.
An embodiment of the third aspect of the present invention provides a control device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing a reservoir fluid identification method as described in the embodiment of the first aspect when executing the computer program. The control device adopts all the technical schemes of the reservoir fluid identification method of the embodiment, so that the control device has at least all the beneficial effects brought by the technical schemes of the embodiment.
A computer readable storage medium according to an embodiment of the fourth aspect of the present invention stores computer executable instructions for performing the reservoir fluid identification method as described in the embodiment of the first aspect. Since the computer-readable storage medium adopts all the technical solutions of the reservoir fluid identification method of the above embodiments, it has at least all the advantageous effects brought by the technical solutions of the above embodiments.
Additional features and advantages of the 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 invention.
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The foregoing and/or additional aspects and advantages of the invention will become apparent and may be better understood from the following description of embodiments taken in conjunction with the accompanying drawings in which:
FIG. 1 is a flow chart of a reservoir fluid identification method according to an embodiment of the present invention.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative only and are not to be construed as limiting the invention.
In the description of the present invention, the description of first, second, etc. is for the purpose of distinguishing between technical features only and should not be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated or implicitly indicating the precedence of the technical features indicated.
In the description of the present invention, it should be understood that the direction or positional relationship indicated with respect to the description of the orientation, such as up, down, etc., is based on the direction or positional relationship shown in the drawings, is merely for convenience of describing the present invention and simplifying the description, and does not indicate or imply that the apparatus or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention.
In the description of the present invention, unless explicitly defined otherwise, terms such as arrangement, installation, connection, etc. should be construed broadly and the specific meaning of the terms in the present invention can be determined reasonably by a person skilled in the art in combination with the specific content of the technical solution.
A method for identifying reservoir fluids according to embodiments of the present invention will be described more clearly and fully below with reference to fig. 1, it being apparent that the embodiments described below are some, but not all, embodiments of the present invention.
A reservoir fluid identification method according to an embodiment of the first aspect of the present invention, the method comprising:
acquiring seismic data, logging data and electromagnetic data;
respectively calculating to obtain 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;
Normalizing 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 in a depth domain;
reconstructing the seismic conversion data, the logging conversion data and the electromagnetic conversion data by utilizing wavelet transformation to obtain complex apparent resistivity data;
Reservoir fluid identification is performed based on the multi-view resistivity data.
It is understood that the seismic data, logging data, and electromagnetic data are preprocessed. The well logging data are obtained by carrying out depth correction, curve smoothing, environment correction, numerical standardization and the like on the well logging data, so that inversion requirements can be met, electromagnetic data are obtained by carrying out anomaly removal and preliminary filtering on electromagnetic data, and seismic data are post-stack pure wave seismic data after carrying out pre-stack amplitude preservation on the seismic data.
It should be noted that the specific pretreatment principle and process are known to those skilled in the art, and are not described herein.
The electromagnetic apparent resistivity data is obtained by carrying out wide-area electromagnetic apparent resistivity inversion on the basis of building a seismic geological framework. The logging apparent resistivity data is obtained by performing three-dimensional data volume calculation of logging apparent resistivity on the basis of geological framework construction and performing simulation interpolation calculation of logging resistivity of a plurality of wells. The calculation principle and process of the electromagnetic apparent resistivity data and the logging apparent resistivity data are known to those skilled in the art, and are not described herein.
In some embodiments of the invention, the seismic apparent resistivity data is obtained by:
based on the petrophysical relation between the earthquake wave impedance and the resistivity, the earthquake apparent resistivity data is obtained through calculation according to the well logging data and the earthquake data, wherein the petrophysical relation is obtained through deduction according to a Faust formula and a Gardner formula.
The logging data comprises depth data, a resistivity curve, a neutron curve, a sound wave curve and a density curve, the seismic apparent resistivity data is calculated according to the logging data and the seismic data based on a petrophysical relationship between seismic wave impedance and logging resistivity, and the method comprises the following steps:
obtaining the impedance of the earthquake wave through post-stack earthquake inversion according to the earthquake data;
and calculating according to the depth data, the resistivity curve, the neutron curve, the acoustic curve, the density curve and the seismic wave impedance to obtain the seismic apparent resistivity data.
The Faust formula is:
formula (1)
Wherein, And h is depth data, and F is stratum factor.
The Gardner formula is:
formula (2)
Wherein, In order to achieve a density of the particles,For longitudinal wave velocity, c and m are constants.
Deriving the petrophysical relationship from the Faust formula and the Gardner formula:
Formula (3)
Formula (4)
Wherein, The water resistivity of the stratum is R, the seismic apparent resistivity data is R, and the AI is seismic wave impedance.
Firstly, obtaining parameters by using a resistivity curve and a sound wave curve. Calculating formation water resistivityThe method of (1) is based on the Alqi formula:
Formula (5)
Wherein, A, b are coefficients related to lithology, which are obtained from laboratory measurements, m' is the cementing index, n is the saturation index,Is the water saturation of the formation. Taking the logarithm of the two sides of the formula (5) to obtain a formula (6):
=-m’lg Formula (6)
The resistivity of the stratum water is 100% of the water saturation and 100% of the porosity, corresponding toIs a value of (2). Firstly, selecting a typical water producing layer in a work area, namely the water saturation of the layer is 100 percent, whereinAnd 0, calculating a porosity value in the section through a neutron curve, a sound wave curve and a density curve of the water producing layer, then counting the formation resistivity (namely, a resistivity curve) and the porosity of the typical water producing layer, and analyzing the formation resistivity and the porosity intersection of the typical water producing well by using a PICKETT intersection graph method to obtain a mathematical relationship between the formation resistivity and the porosity. For a certain fixed work area,Is a fixed constant, i.eIs a fixed constant, the formation resistivity and the porosity are variables, and the relation is that-m' lg is taken when the porosity is equal to 100 percentAt 0, the intercept term can obtain the formation resistivityThe formation water resistivity can be obtained through calculationThereby obtaining parameters。
Parameters of the productAnd can also be obtained by a laboratory stratum water sample measuring method, a natural potential method and an imaging stratum water resistivity method, and the method is not limited by the invention.
The parameters c and m can then be found by linear regression using the acoustic curve and the density curve. The acoustic wave time difference DT of the underground stratum can be directly measured by using the acoustic wave curve, and then the longitudinal wave speed can be obtained by calculation. The density curve can directly measure the density of the subsurface formationThe longitudinal wave speed and density of the target layer are collected, intersection analysis is carried out, and then the mathematical relationship of an exponential formula (namely a Gardner formula) is fitted, so that parameters c and m can be obtained.
In the prior art, the seismic apparent resistivity data is obtained from the seismic wave impedance by means of multiple regression and interactive verification based on a mathematical algorithm, and the multiple regression and interactive verification are obtained by calculating correlations among the seismic wave impedance data and not based on a petrophysical basis of a certain petrophysical relationship, which is equal to simplifying the relationship between the seismic wave impedance data and the petrophysical relationship. The embodiment of the invention adopts a certain petrophysical relationship between the two, the mathematical relationship between the two is more complex, and the obtained seismic apparent resistivity data has higher precision.
According to the embodiment of the invention, signals in depth domains of three data are converted into wavelet domains by utilizing wavelet transformation to be integrated and analyzed uniformly, wavelet frequency division is carried out on data with different scales, energy calibration is carried out on each scale, the optimal reconstruction coefficient is determined, and finally, the seismic transformation data, the logging transformation data and the electromagnetic transformation data are reconstructed into multi-view resistivity data close to white spectrum, so that the multi-view resistivity data comprise logging information, seismic information and electromagnetic information, and the multi-view resistivity data is more beneficial to reservoir fluid identification and has higher precision.
In some embodiments of the present invention, normalizing the seismic apparent resistivity data, the logging apparent resistivity data, and the electromagnetic apparent resistivity data to obtain depth domain seismic conversion data, logging conversion data, and electromagnetic conversion data, includes:
Registering the seismic apparent resistivity data, the logging apparent resistivity data and the electromagnetic apparent resistivity data to the alignment of plane coordinates and unification in the longitudinal direction to obtain first seismic data, first logging data and first electromagnetic data of a depth domain;
Unifying the size of a processing bin of the first seismic data, the first logging data and the first electromagnetic data on a plane coordinate and the size of a sampling interval in the longitudinal direction to obtain second seismic data, second logging data and second electromagnetic data;
normalizing the second seismic data, the second logging data and the second electromagnetic data to a unified data range to obtain seismic conversion data, logging conversion data and electromagnetic conversion data.
It can be understood that the plane coordinates are unified in a geodetic coordinate system, the logging apparent resistivity data is measured in a depth domain in a longitudinal direction, the electromagnetic apparent resistivity data needs to be subjected to wide-area electromagnetic depth conversion, and the skin depth of the electromagnetic wave in the underground mainly depends on the resistivity of an underground medium and the frequency of the electromagnetic wave:
Formula (7)
Wherein, For the skin depth of the skin, the skin is of a depth,'Is the resistivity of the earth's earth,Is the magnetic permeability of the medium, and is the magnetic permeability of the medium,Is the angular frequency of the electromagnetic wave. Skin depth refers to depth below ground in uniform half spaceThe intensity of electromagnetic wave is 1/e of the intensity of the electromagnetic wave on the ground, so that the intensity is often roughly interpreted as the general depth of penetration of the electromagnetic wave in the medium, the intensity can be used for roughly estimating the detection depth of the electromagnetic method, and a series of electromagnetic waves with different frequencies can be used for detecting the ground electric information with different depths;
The time domain depth conversion of the seismic apparent resistivity data can be used for establishing a three-dimensional seismic velocity data volume by adopting an acoustic curve measured on a well for well earthquake calibration in a work area, and then the time domain data of the earthquake is converted into a depth domain by the time domain depth conversion.
In the process of unifying the sizes of the processing surface elements of the first seismic data, the first logging data and the first electromagnetic data on the plane coordinates and the sampling interval in the longitudinal direction, the size of each processing surface element on the plane coordinates is firstly determined, the size of each processing surface element is comprehensively determined according to the distance of different data acquisition, the unified surface element sizes of the three are generally 20m x 20m, the sampling interval in the longitudinal direction is unified to the standard interval, and the unification of the three data is carried out in a linear sampling thinning or encrypting mode.
It should be noted that the specific numerical values mentioned in the above embodiments should not be construed as limiting the present invention.
It can be appreciated that, in order to avoid the influence of the dimensions and orders of magnitude of the apparent resistivity data of different types, the second seismic data, the second logging data and the second electromagnetic data need to be normalized to be within a unified data range, and a specific unified formula is as follows:
Formula (8)
Wherein, Representing the value after the normalization,Represents a certain point value in the sample point group,Representing the minimum value of the set of samples,Representing the maximum value of the set of samples.
In some embodiments of the invention, reconstructing the seismic, logging, and electromagnetic conversion data using wavelet transforms to obtain multi-apparent resistivity data comprises:
Transforming the seismic conversion data, the logging conversion data and the electromagnetic conversion data into the same scale range and the same frequency range by utilizing wavelet transformation to obtain seismic wavelet data, logging wavelet data and electromagnetic wavelet data;
Determining a plurality of reconstruction coefficients corresponding to different scales and different frequencies according to the seismic wavelet data, the logging wavelet data and the electromagnetic wavelet data;
And performing wavelet inverse transformation on the seismic wavelet data, the logging wavelet data and the electromagnetic wavelet data according to the plurality of reconstruction coefficients to reconstruct the composite apparent resistivity data.
In some embodiments of the present invention, determining a corresponding plurality of reconstruction coefficients at different scales and different frequencies from the seismic wavelet data, the logging wavelet data, and the electromagnetic wavelet data comprises:
calculating the variance data of the seismic wavelet data, the logging wavelet data and the electromagnetic wavelet data under the same scale and the same frequency;
Calculating each variance weight of the corresponding frequency under the same scale according to all variance data corresponding to all frequencies under the same scale to obtain a plurality of variance weights corresponding to different scales and different frequencies;
the plurality of variance weights are used as a plurality of reconstruction coefficients.
In some embodiments of the invention, the variance weight is obtained by:
calculating the sum of products of all variance data corresponding to all frequencies under the same scale to obtain a variance product;
The product of all variance data of the remaining frequencies at the scale except the corresponding frequency, and the duty ratio in the variance product is determined as the variance weight.
In some embodiments, the seismic conversion data is defined as X (h), the electromagnetic conversion data is defined as Y (h), the logging conversion data is defined as Z (h), h is a depth value of a certain depth of the underground, the resistivity of the three is transformed into a wavelet domain through wavelet transformation, and for the convenience of fusion analysis, the three data are uniformly transformed into the same scale range and the same frequency range, namely the transformed three data are respectively(i,j)、(i,j)、(i,j),(I, j) seismic wavelet data expressed as j frequencies at the ith scale, typically 1,1。
The calculation formula of the variance data is:
];
Formula (9)
=Formula (10)
The calculation of the reconstruction coefficient is described taking i= 5,j =3 as an example.
Let the variance weight be S, when j=3, the variance weights of the corresponding 3 frequencies at the i-th scale are respectively:
formula (11)
Formula (12)
Formula (13)
As can be seen from the formula (11), the formula (12) and the formula (13), the reconstruction coefficients of the three data under the same scale and the same frequency are equal, but the reconstruction coefficients of different frequencies under the same scale are different, and the reconstruction coefficients under different scales are different.
According to the embodiment of the invention, the fluctuation degree of the data with different scales at the same frequency is represented by calculating the variance data of the data with the same frequency and different scales, and the larger the variance data is, the larger the fluctuation of the data with different scales of the data with the same frequency is, the more unstable the fluctuation of the data with different scales is. The variance weight under the frequency is the ratio of the product of the residual frequency variance data to the variance product, and the larger the product of the residual frequency variance data is, the larger the fluctuation of the residual frequency data is, and the more unstable the residual frequency data is. The duty ratio (variance weight) is used as a reconstruction coefficient of the frequency, so that the proportion of stability data is fully improved, and the numerical error in the fusion process is effectively controlled. The conventional mean value fusion mode of the characteristic components, particularly a single fusion weight mode is adopted, and the weight coefficients are directly given to fusion based on the original three data, so that the most effective characteristic information is weakened, and the proportion of the introduced invalid or interference characteristic information is increased.
Meanwhile, the three data are combined to calculate the variance weight of certain frequency data under the corresponding scale, the characteristics of the data under different scales are fully utilized, the characteristics comprise large-scale fluctuation trend information and small-scale detail oscillation information, the three data are combined in a scale-by-scale frequency-by-layer independent variance weight fusion mode, so that effective information which can represent the structural characteristics under the current scale most is extracted and fused, namely, the fusion result is the dynamic weight fusion of the spatial characteristic information of the multi-element fusion data source from the coarse grid to the fine grid.
According to the reservoir fluid identification method provided by the embodiment of the invention, the seismic apparent resistivity data, the logging apparent resistivity data and the electromagnetic apparent resistivity data are normalized into the seismic conversion data, the logging conversion data and the electromagnetic conversion data in the depth domain, and the seismic conversion data, the logging conversion data and the electromagnetic conversion data are reconstructed by utilizing wavelet transformation to obtain the multi-apparent resistivity data. The wavelet transformation is global transformation, has good positioning capability in a wavelet domain, and can focus on any detail of processed data, so that the embodiment of the invention has high resolution of the multi-view resistivity data after wavelet transformation fusion, and can improve the accuracy of reservoir fluid identification.
A reservoir fluid identification system according to an embodiment of the second aspect of the present invention includes a data acquisition module, a apparent resistivity determination module, a normalization processing module, a wavelet transform module, and a reservoir fluid identification module.
The data acquisition module is used for acquiring seismic data, logging data and electromagnetic data;
the apparent resistivity determining module is used for respectively calculating and obtaining the seismic apparent resistivity data, the well logging apparent resistivity data and the electromagnetic apparent resistivity data according to the seismic data, the well logging data and the electromagnetic data;
The normalization processing module is used for carrying out 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;
the wavelet transformation module is used for reconstructing the seismic transformation data, the logging transformation data and the electromagnetic transformation data by utilizing wavelet transformation to obtain multi-view resistivity data;
And the reservoir fluid identification module is used for carrying out reservoir fluid identification based on the multi-view resistivity data.
In some embodiments of the invention, the seismic apparent resistivity data is obtained by:
based on the petrophysical relation between the earthquake wave impedance and the resistivity, the earthquake apparent resistivity data is obtained through calculation according to the well logging data and the earthquake data, wherein the petrophysical relation is obtained through deduction according to a Faust formula and a Gardner formula.
The logging data comprises depth data, a resistivity curve, a neutron curve, a sound wave curve and a density curve, the seismic apparent resistivity data is calculated according to the logging data and the seismic data based on a petrophysical relationship between seismic wave impedance and logging resistivity, and the method comprises the following steps:
obtaining the impedance of the earthquake wave through post-stack earthquake inversion according to the earthquake data;
and calculating according to the depth data, the resistivity curve, the neutron curve, the acoustic curve, the density curve and the seismic wave impedance to obtain the seismic apparent resistivity data.
In the prior art, the seismic apparent resistivity data is obtained from the seismic wave impedance by means of multiple regression and interactive verification based on a mathematical algorithm, and the multiple regression and interactive verification are obtained by calculating correlations among the seismic wave impedance data and not based on a petrophysical basis of a certain petrophysical relationship, which is equal to simplifying the relationship between the seismic wave impedance data and the petrophysical relationship. The embodiment of the invention adopts a certain petrophysical relationship between the two, the mathematical relationship between the two is more complex, and the obtained seismic apparent resistivity data has higher precision.
According to the embodiment of the invention, signals in depth domains of three data are converted into wavelet domains by utilizing wavelet transformation to be integrated and analyzed uniformly, wavelet frequency division is carried out on data with different scales, energy calibration is carried out on each scale, the optimal reconstruction coefficient is determined, and finally, the seismic transformation data, the logging transformation data and the electromagnetic transformation data are reconstructed into multi-view resistivity data close to white spectrum, so that the multi-view resistivity data comprise logging information, seismic information and electromagnetic information, and the multi-view resistivity data is more beneficial to reservoir fluid identification and has higher precision.
In some embodiments of the present invention, normalizing the seismic apparent resistivity data, the logging apparent resistivity data, and the electromagnetic apparent resistivity data to obtain depth domain seismic conversion data, logging conversion data, and electromagnetic conversion data, includes:
Registering the seismic apparent resistivity data, the logging apparent resistivity data and the electromagnetic apparent resistivity data to the alignment of plane coordinates and unification in the longitudinal direction to obtain first seismic data, first logging data and first electromagnetic data of a depth domain;
Unifying the size of a processing bin of the first seismic data, the first logging data and the first electromagnetic data on a plane coordinate and the size of a sampling interval in the longitudinal direction to obtain second seismic data, second logging data and second electromagnetic data;
normalizing the second seismic data, the second logging data and the second electromagnetic data to a unified data range to obtain seismic conversion data, logging conversion data and electromagnetic conversion data.
In some embodiments of the invention, reconstructing the seismic, logging, and electromagnetic conversion data using wavelet transforms to obtain multi-apparent resistivity data comprises:
Transforming the seismic conversion data, the logging conversion data and the electromagnetic conversion data into the same scale range and the same frequency range by utilizing wavelet transformation to obtain seismic wavelet data, logging wavelet data and electromagnetic wavelet data;
Determining a plurality of reconstruction coefficients corresponding to different scales and different frequencies according to the seismic wavelet data, the logging wavelet data and the electromagnetic wavelet data;
And performing wavelet inverse transformation on the seismic wavelet data, the logging wavelet data and the electromagnetic wavelet data according to the plurality of reconstruction coefficients to reconstruct the composite apparent resistivity data.
In some embodiments of the present invention, determining a corresponding plurality of reconstruction coefficients at different scales and different frequencies from the seismic wavelet data, the logging wavelet data, and the electromagnetic wavelet data comprises:
calculating the variance data of the seismic wavelet data, the logging wavelet data and the electromagnetic wavelet data under the same scale and the same frequency;
Calculating each variance weight of the corresponding frequency under the same scale according to all variance data corresponding to all frequencies under the same scale to obtain a plurality of variance weights corresponding to different scales and different frequencies;
the plurality of variance weights are used as a plurality of reconstruction coefficients.
In some embodiments of the invention, the variance weight is obtained by:
calculating the sum of products of all variance data corresponding to all frequencies under the same scale to obtain a variance product;
The product of all variance data of the remaining frequencies at the scale except the corresponding frequency, and the duty ratio in the variance product is determined as the variance weight.
According to the embodiment of the invention, the fluctuation degree of the data with different scales at the same frequency is represented by calculating the variance data of the data with the same frequency and different scales, and the larger the variance data is, the larger the fluctuation of the data with different scales of the data with the same frequency is, the more unstable the fluctuation of the data with different scales is. The variance weight under the frequency is the ratio of the product of the residual frequency variance data to the variance product, and the larger the product of the residual frequency variance data is, the larger the fluctuation of the residual frequency data is, and the more unstable the residual frequency data is. The duty ratio (variance weight) is used as a reconstruction coefficient of the frequency, so that the proportion of stability data is fully improved, and the numerical error in the fusion process is effectively controlled. The conventional mean value fusion mode of the characteristic components, particularly a single fusion weight mode is adopted, and the weight coefficients are directly given to fusion based on the original three data, so that the most effective characteristic information is weakened, and the proportion of the introduced invalid or interference characteristic information is increased.
Meanwhile, the three data are combined to calculate the variance weight of certain frequency data under the corresponding scale, the characteristics of the data under different scales are fully utilized, the characteristics comprise large-scale fluctuation trend information and small-scale detail oscillation information, the three data are combined in a scale-by-scale frequency-by-layer independent variance weight fusion mode, so that effective information which can represent the structural characteristics under the current scale most is extracted and fused, namely, the fusion result is the dynamic weight fusion of the spatial characteristic information of the multi-element fusion data source from the coarse grid to the fine grid.
According to the reservoir fluid identification system provided by the embodiment of the invention, the seismic apparent resistivity data, the logging apparent resistivity data and the electromagnetic apparent resistivity data are normalized into the seismic conversion data, the logging conversion data and the electromagnetic conversion data in the depth domain, and the seismic conversion data, the logging conversion data and the electromagnetic conversion data are reconstructed by utilizing wavelet transformation to obtain the multi-apparent resistivity data. The wavelet transformation is global transformation, has good positioning capability in a wavelet domain, and can focus on any detail of processed data, so that the embodiment of the invention has high resolution of the multi-view resistivity data after wavelet transformation fusion, and can improve the accuracy of reservoir fluid identification.
In addition, one embodiment of the present invention also provides a control device including a memory, a processor, and a computer program stored on the memory and executable on the processor. The processor and the memory may be connected by a bus or other means.
The memory, as a non-transitory computer readable storage medium, may be used to store non-transitory software programs as well as non-transitory computer executable programs. In addition, the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory optionally includes memory remotely located relative to the processor, the remote memory being connectable to the processor through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The non-transitory software programs and instructions required to implement the reservoir fluid identification method of the above embodiments are stored in memory and when executed by a processor, perform the reservoir fluid identification method of the above embodiments.
The above described apparatus embodiments are merely illustrative, wherein the units illustrated as separate components may or may not be physically separate, i.e. may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
Furthermore, an embodiment of the present invention provides a computer-readable storage medium storing computer-executable instructions that are executed by a processor or controller, for example, by the processor of the above embodiment, so that the above processor performs the reservoir fluid identification method of the above embodiment.
Those of ordinary skill in the art will appreciate that all or some of the steps, systems, and methods disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as known to those skilled in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computer. Furthermore, as is well known to those of ordinary skill in the art, 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 includes any information delivery media.
The embodiments of the present invention have been described in detail with reference to the accompanying drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge of one of ordinary skill in the art without departing from the spirit of the present invention.
Claims (10)
1. A method of reservoir fluid identification, the method comprising:
acquiring seismic data, logging data and electromagnetic data;
Respectively calculating to obtain 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;
Normalizing 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 in a depth domain;
Reconstructing the seismic conversion data, the logging conversion data and the electromagnetic conversion data by utilizing wavelet transformation to obtain multi-view resistivity data;
and carrying out reservoir fluid identification based on the multi-view resistivity data.
2. The reservoir fluid identification method of claim 1, wherein reconstructing the seismic conversion data, the logging conversion data, and the electromagnetic conversion data using wavelet transforms to obtain multi-apparent resistivity data comprises:
transforming the seismic conversion data, the logging conversion data and the electromagnetic conversion data into the same scale range and the same frequency range by utilizing wavelet transformation to obtain seismic wavelet data, logging wavelet data and electromagnetic wavelet data;
determining a plurality of reconstruction coefficients corresponding to different scales and different frequencies according to the seismic wavelet data, the logging wavelet data and the electromagnetic wavelet data;
and carrying out wavelet inverse transformation on the seismic wavelet data, the logging wavelet data and the electromagnetic wavelet data according to a plurality of reconstruction coefficients, and reconstructing the complex apparent resistivity data.
3. The reservoir fluid identification method of claim 2, wherein the determining a corresponding plurality of reconstruction coefficients at different scales and different frequencies from the seismic wavelet data, the logging wavelet data, and the electromagnetic wavelet data comprises:
Calculating variance data of the seismic wavelet data, the logging wavelet data and the electromagnetic wavelet data under the same scale and the same frequency;
calculating each variance weight of the corresponding frequency under the same scale according to all variance data corresponding to all frequencies under the same scale to obtain a plurality of variance weights corresponding to different scales and different frequencies;
And taking a plurality of variance weights as a plurality of reconstruction coefficients.
4. The reservoir fluid identification method of claim 3, wherein the variance weight is obtained by:
calculating the sum of products of all variance data corresponding to all frequencies under the same scale to obtain a variance product;
And (3) determining the duty ratio in the variance product as the variance weight by multiplying all variance data of the residual frequencies except the corresponding frequency under the scale.
5. The reservoir fluid identification method of claim 1, wherein the seismic apparent resistivity data is obtained by:
And calculating the seismic apparent resistivity data according to the logging data and the seismic data based on a petrophysical relationship between the seismic wave impedance and the resistivity, wherein the petrophysical relationship is derived according to a Faust formula and a Gardner formula.
6. The reservoir fluid identification method of claim 5, wherein the well logging data comprises depth data, resistivity curves, neutron curves, acoustic curves, and density curves, the calculating the seismic apparent resistivity data from the well logging data and the seismic data based on a petrophysical relationship between seismic wave impedance and well logging resistivity comprises:
Obtaining the impedance of the earthquake wave through post-stack earthquake inversion according to the earthquake data;
and calculating according to the depth data, the resistivity curve, the neutron curve, the acoustic curve, the density curve and the seismic wave impedance to obtain the seismic apparent resistivity data.
7. The reservoir fluid identification method of claim 1, wherein normalizing the seismic apparent resistivity data, the logging apparent resistivity data, and the electromagnetic apparent resistivity data to obtain depth domain seismic conversion data, logging conversion data, and electromagnetic conversion data comprises:
Registering the seismic apparent resistivity data, the logging apparent resistivity data and the electromagnetic apparent resistivity data to the alignment of plane coordinates and unification in the longitudinal direction to obtain first seismic data, first logging data and first electromagnetic data of a depth domain;
Unifying the size of the processing surface element of the first seismic data, the first logging data and the first electromagnetic data on the plane coordinates and the size of the sampling interval in the longitudinal direction to obtain second seismic data, second logging data and second electromagnetic data;
Normalizing the second seismic data, the second logging data and the second electromagnetic data to a unified data range to obtain the seismic conversion data, the logging conversion data and the electromagnetic conversion data.
8. A reservoir fluid identification system, the system comprising:
The data acquisition module is used for acquiring seismic data, logging data and electromagnetic data;
The apparent resistivity determining module is used for respectively calculating to obtain 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;
The normalization processing module is used for carrying out 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;
the wavelet transformation module is used for reconstructing the seismic transformation data, the logging transformation data and the electromagnetic transformation data by utilizing wavelet transformation to obtain multi-view resistivity data;
and the reservoir fluid identification module is used for carrying out reservoir fluid identification based on the multiple vision resistivity data.
9. A control device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the reservoir fluid identification method of any one of claims 1 to 7 when the computer program is executed.
10. A computer-readable storage medium storing computer-executable instructions for performing the reservoir fluid identification method of any one of claims 1 to 7.
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