CN117307129A - Method and system for acquiring porosity of nuclear magnetic resonance logging T2 spectrum and storage medium - Google Patents
Method and system for acquiring porosity of nuclear magnetic resonance logging T2 spectrum and storage medium Download PDFInfo
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- E21—EARTH OR ROCK DRILLING; MINING
- E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B47/00—Survey of boreholes or wells
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Abstract
The invention provides a method and a system for acquiring porosity of a nuclear magnetic resonance logging T2 spectrum, and a storage medium, wherein the method comprises the steps of carrying out standardized treatment on the nuclear magnetic resonance logging T2 spectrum; determining the spectral peak positions of different fluid components according to clay types and core nuclear magnetic resonance experimental data; performing principal component analysis on the normalized nuclear magnetic resonance logging T2 spectrum data to obtain principal component analysis results; according to the analysis result of the main component, a non-negative matrix factorization method is adopted to process a standard nuclear magnetic resonance logging T2 spectrum, and a nuclear magnetic resonance logging T2 sub-spectrum is obtained; analyzing the corresponding relation between the spectrum peak position of the nuclear magnetic resonance logging T2 sub-spectrum and the spectrum peak positions of different fluid components to obtain a clay binding water spectrum, a capillary binding fluid spectrum, a movable oil spectrum and a noise spectrum; and integrating each spectrum to obtain the clay bound water porosity, capillary bound fluid porosity and movable oil hole porosity, and obtaining the porosity value of the nuclear magnetic resonance logging T2 spectrum.
Description
Technical Field
The invention belongs to the technical field of logging, and relates to a method and a system for acquiring porosity of a nuclear magnetic resonance logging T2 spectrum and a storage medium.
Background
Nuclear magnetic resonance logging plays an increasingly important role in oil and gas reservoir exploration and development as a logging method capable of directly providing reservoir pore size and distribution information thereof. By processing and interpreting the nuclear magnetic resonance T2 spectrum, parameters reflecting the total porosity, effective porosity, irreducible water saturation, mobile oil saturation, and permeability of the reservoir can be obtained, wherein the magnitude of the T2 cut-off (T2 cutoff) is critical to influencing these parameters. It was found that the T2 spectral distribution is closely related not only to the size and distribution of rock pores, but also to lithology, measured parameters, formation water mineralization, etc., and thus it was deduced that the T2 cut-off value is also related to the above factors. Due to the large differences in lithology characteristics and formation fluid properties from region to region, from formation to formation, the 33ms (clastic formation) and 92ms (carbonate formation) presently recommended by schlenz are not universally applicable as cut-offs.
According to different measuring objects, the method for determining the nuclear magnetic resonance logging T2 cut-off value is divided into a rock core experiment scale method and an original stratum continuous scale method. The core experiment scale method mainly comprises a porosity accumulation method, a mercury pressing capillary pressure curve method, zhou Cancan, sun Jianmeng and other methods for determining a T2 cut-off value by utilizing NMR experiment data in the state of centrifugal irreducible water saturation and full saturated water. In the aspect of an original stratum continuous scale method, wang Zhonghao and the like establish the correlation between a T2 cut-off value and a rock pore structure comprehensive physical index through the analysis and research of 14 low-hole and low-permeability core samples in a middle region of a tower; however, in this method, the parameter permeability K is difficult to determine. Gao Chuqiao and the like establish an exponential relationship between the T2 cut-off value and the oil column height on the oil-water interface through experimental analysis of 37 rock core samples of the tectonic sea basin. Zhang Wei and the like establish a nuclear magnetic resonance logging variable T2 cut-off value prediction model based on multivariate statistics by optimizing sensitive parameters of the T2 cut-off value. Baldwin et al calculate the irreducible water saturation based on NMR longitudinal relaxation time T1, and the calculation shows that the longitudinal relaxation time and the irreducible water saturation have good correlation and can be used for estimating the irreducible water saturation; however, since the actual measurement time of the T1 spectrum is long, most of the current nuclear magnetic logs mainly measure the T2 spectrum, so that the method cannot be practically applied.
The method is an empirical statistical method based on a core experiment, the method requires core samples to have good representativeness, the number of experimental samples is often required to be increased to improve the representativeness of the samples, the applicability is limited, and the method cannot be popularized and applied on a large scale. On the other hand, the environment of the core experiment is greatly different from the acquisition environment and instrument parameters of the downhole instrument, so that the consistency of the experimental result and downhole actual measurement data is poor, and the experimental result cannot be directly applied to nuclear magnetic resonance logging porosity calculation. In addition, the original nuclear magnetic resonance logging T2 spectrum often has the problems of superposition of strong signal sub-spectrum to adjacent sub-spectrum, tail spectrum noise and the like, and the conventional T2 cut-off method cannot eliminate the influence of the factors, so that the porosity and saturation evaluation error is larger easily, and the reservoir evaluation effect is influenced.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a method and a system for acquiring the porosity of a nuclear magnetic resonance logging T2 spectrum and a storage medium, so that the calculation accuracy of reservoir parameters is effectively improved.
The invention is realized by the following technical scheme:
a method for acquiring porosity of a nuclear magnetic resonance log T2 spectrum, comprising the steps of:
s101: performing standardization treatment on a nuclear magnetic resonance logging T2 spectrum;
s102: determining the spectral peak positions of different fluid components according to clay types and core nuclear magnetic resonance experimental data;
s103: obtaining a principal component analysis result according to principal component analysis of the nuclear magnetic resonance logging T2 spectrum after the standardization treatment in the step S101;
s104: according to the principal component analysis result, a non-negative matrix factorization method is adopted to obtain a nuclear magnetic resonance logging T2 sub-spectrum after the standardization processing in the step S101;
s105: analyzing the corresponding relation between the spectrum peak position of the nuclear magnetic resonance logging T2 sub-spectrum in the step S104 and the spectrum peak positions of the different fluid components in the step S102 to obtain a clay binding water spectrum, a capillary binding fluid spectrum, a movable oil spectrum and a noise spectrum;
s106: and integrating the clay bound water spectrum, the capillary bound fluid spectrum, the movable oil spectrum and the noise spectrum to obtain clay bound water porosity, capillary bound fluid porosity and movable oil hole porosity, and obtaining a porosity value of the nuclear magnetic resonance logging T2 spectrum.
Preferably, in the step S101, the specific process of performing the normalization processing on the nmr logging T2 spectrum is:
wherein y (i) is the normalized nuclear magnetic resonance T2 spectrum, y 0 (i) Is the original nuclear magnetic resonance T2 spectrum;is the total porosity of the formation; m is the number of sample points after the discretization of nuclear magnetic resonance T2 spectrum.
Preferably, the specific process of determining the peak positions of the spectra of the different fluid components in the step S102 is as follows:
wherein T is 2bp The corresponding T2 value of the nuclear magnetic resonance volume relaxation peak, eta is the viscosity of crude oil, and T is the formation temperature.
Preferably, the specific process of obtaining the principal component analysis in step S103 is:
P k×n ·X n×m ＝Y k×m (3)
wherein X is n×m Is a nuclear magnetic data matrix, P k×n Is X n×m A matrix arranged in rows after feature vector unitization of covariance matrix of (a); y is Y k×m Is X n×m And (5) reducing the dimension of the k-dimensional nuclear magnetic resonance data volume matrix.
Preferably, the process of obtaining the nuclear magnetic resonance log T2 sub-spectrum after the normalization processing in step S101 in step S104 specifically includes: and (3) processing the normalized nuclear magnetic resonance logging T2 spectrum by adopting a non-negative matrix factorization method, and separating a source signal matrix and a coefficient matrix to obtain the nuclear magnetic resonance logging T2 sub-spectrum after the normalization in the step (S101).
Preferably, the non-negative matrix factorization method is adopted to process the normalized nuclear magnetic resonance logging T2 spectrum, and the specific process of separating the source signal matrix and the coefficient matrix is as follows:
V n×m ＝W n×k ·H k×n (4)
wherein V is n×m For observing the signal; h k×n Is a coefficient matrix; w (W) n×k Is the source signal matrix.
Preferably, in step S105, the process of obtaining the clay bound water spectrum, the capillary bound fluid spectrum, the movable oil spectrum and the noise spectrum specifically includes: and determining a combination mode of the nuclear magnetic resonance logging T2 sub-spectrum according to the corresponding relation between the spectrum peak position of the nuclear magnetic resonance logging T2 sub-spectrum and the spectrum peak positions of the different fluid components in the step S102, and combining the nuclear magnetic resonance logging T2 sub-spectrum according to the combination mode, so as to obtain the clay bound water spectrum, the capillary bound fluid spectrum, the movable oil spectrum and the noise spectrum.
An acquisition system for porosity of a nuclear magnetic resonance log T2 spectrum, comprising:
the first processing module is used for carrying out standardized processing on the nuclear magnetic resonance logging T2 spectrum;
the second processing module is used for determining the spectral peak positions of different fluid components according to the clay type and core nuclear magnetic resonance experimental data;
the third processing module is used for obtaining a principal component analysis result according to principal component analysis of the normalized nuclear magnetic resonance logging T2 spectrum;
the fourth processing module is used for obtaining a normalized nuclear magnetic resonance logging T2 sub-spectrum according to the main component analysis result;
the fifth processing module is used for analyzing the corresponding relation between the spectrum peak position of the nuclear magnetic resonance logging T2 sub-spectrum and the spectrum peak positions of different fluid components to obtain a clay binding water spectrum, a capillary binding fluid spectrum, a movable oil spectrum and a noise spectrum;
and the sixth processing module is used for integrating the clay bound water spectrum, the capillary bound fluid spectrum, the movable oil spectrum and the noise spectrum to obtain clay bound water porosity, capillary bound fluid porosity and movable oil hole porosity, and acquiring a porosity value of the nuclear magnetic resonance logging T2 spectrum.
Terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the above method when executing the computer program.
A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the above method.
Compared with the prior art, the invention has the following beneficial technical effects:
according to the method, each fluid component is separated from the mixed T2 spectrum, so that the superposition influence of the tail spectrum of the sub-spectrum with high signal intensity on the adjacent sub-spectrum can be effectively avoided, the noise of the nuclear magnetic tail spectrum is eliminated, and the problem that the traditional method relies on a large amount of representative core analysis test data is solved, so that the calculation accuracy of reservoir parameters is greatly improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for acquiring the porosity of a nuclear magnetic resonance log T2 spectrum according to the present invention;
FIG. 2 is a schematic diagram showing structural connection of a system for acquiring porosity of a nuclear magnetic resonance log T2 spectrum according to the present invention;
FIG. 3 is a graph showing the distribution of transverse bulk relaxation peaks of crude oil at different temperatures for crude oil in a target work area in example 2 of the present invention;
FIG. 4 is a chart of the principal component analysis of the nuclear magnetic resonance log T2 spectrum of the target work area to be logged in example 2 of the present invention;
FIG. 5 is a schematic diagram of an automatic separation algorithm of nuclear magnetic resonance logging sub-spectrum of a target work area to be logged in embodiment 2 of the present invention;
FIG. 6 is a chart of the results of the spectrum of each component calculated by the automatic separation algorithm of nuclear magnetic resonance logging sub-spectrum of the target work area to be logged in embodiment 2 of the present invention;
FIG. 7 is a chart showing the comparison of the accuracy of the porosity calculated by the automatic separation algorithm of nuclear magnetic resonance log sub-spectrum of the target work area to be logged and the T2 cut-off value method in the embodiment 2 of the present invention;
FIG. 8 is a chart of the comparison result of the clearance of the movable oil hole calculated by the automatic separation algorithm of nuclear magnetic resonance log sub-spectrum of the target work area to be logged and the T2 cut-off value method in the embodiment 2 of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
In the description of the embodiments of the present invention, it should be noted that, if the terms "upper," "lower," "horizontal," "inner," and the like indicate an azimuth or a positional relationship based on the azimuth or the positional relationship shown in the drawings, or the azimuth or the positional relationship in which the inventive product is conventionally put in use, it is merely for convenience of describing the present invention and simplifying the description, and does not indicate or imply that the apparatus or element to be referred to must have a specific azimuth, be configured and operated in a specific azimuth, and thus should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and the like, are used merely to distinguish between descriptions and should not be construed as indicating or implying relative importance.
Furthermore, the term "horizontal" if present does not mean that the component is required to be absolutely horizontal, but may be slightly inclined. As "horizontal" merely means that its direction is more horizontal than "vertical", and does not mean that the structure must be perfectly horizontal, but may be slightly inclined.
In the description of the embodiments of the present invention, it should also be noted that, unless explicitly specified and limited otherwise, the terms "disposed," "mounted," "connected," and "connected" should be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to the specific circumstances.
The invention is described in further detail below with reference to the attached drawing figures:
example 1
In order to obtain a representative nuclear magnetic resonance logging porosity calculation method which can be directly applied to industrial calculation, the invention provides a nuclear magnetic resonance logging sub-spectrum separation algorithm based on system core effective porosity analysis data and machine learning to determine the porosity of nuclear magnetic resonance logging.
The results of experiments on the response characteristics of clay to nuclear magnetic resonance were published by SPE annual meeting in 1998, M.G.Prammer, E.D.Drack et al. The experiment used was a pristine clay saturated with brine at a seal pressure of 2500psi. The echo interval adopted by the instrument is 0.5ms, and the experimental frequency is 1MHz. Experimental results show that the T2 relaxation peak of montmorillonite is less than 1ms, illite is about 2ms, chlorite is about 5ms, and kaolinite is greater than 10ms. It follows that the peak positions of the T2 spectra corresponding to different clay types are different, and there will be a large error if the clay bound water volume is calculated by taking a fixed T2 cut-off value. Similarly, the calculation of the cutoff value for the movable oil pore volume is also continuously variable in the longitudinal direction, subject to factors such as formation composition, pore structure, wetting phase, and fluid viscosity.
In the artificial intelligence concept, a blind source separation algorithm based on Nonnegative Matrix Factorization (NMF) can be adopted to process nuclear magnetic resonance logging T2 spectrum, a source signal matrix and a coefficient matrix are separated, and a T2 sub-spectrum with independent components after NMF processing is correspondingly obtained; then analyzing the corresponding relation between the spectrum peak position of the sub spectrum after NMF treatment and the spectrum peak positions of different fluid components of nuclear magnetic resonance, and determining the combination mode of the sub spectrum; then combining the sub-spectrums according to the determined combination mode to obtain a clay binding water spectrum, a capillary binding fluid spectrum, a movable oil spectrum and a noise spectrum; finally, integrating each combined spectrum to obtain clay bound water porosity, capillary bound fluid porosity and movable oil porosity, finally realizing the calculation of the porosity of nuclear magnetic resonance logging T2 spectrum,
the blind source separation algorithm of non-Negative Matrix Factorization (NMF) is a special machine learning tool, based on statistical independence, each fluid component can be separated from a mixed T2 spectrum, the superposition influence of a tail spectrum of a sub spectrum with high signal intensity on an adjacent sub spectrum can be effectively avoided, noise of a nuclear magnetic tail spectrum is eliminated, and the problem that a traditional method depends on a large amount of representative core analysis test data is solved, so that the calculation accuracy of reservoir parameters is greatly improved. The theoretical basis is as follows:
the original data of nuclear magnetic resonance logging is a T2 attenuation curve composed of millions of spin echoes, and the T2 distribution representing the pore characteristics and the size of each pore volume can be directly obtained through multi-exponential fitting inversion of echo strings. The rock pores of a reservoir are typically composed of pores of different sizes and multiphase fluids, so that the spin echo train obtained when measuring transverse relaxation with a CMPG sequence is not single exponentially decaying, but rather is a sum of a plurality of exponential decays, which can be expressed as:
wherein: m (t) is the magnetization at time t; m is M i (0) Magnetization as the i-th relaxation component; t (T) 2i T2 relaxation time constant for the i-th relaxation component.
Performing Principal Component Analysis (PCA) on the T2 spectrum to obtain a principal component analysis result with statistically independent components, wherein the principal component analysis result has the mathematical form:
P k×n ·X n×m ＝Y k×m
wherein: x is X n×m Is a nuclear magnetic data matrix; p (P) k×n Is X n×m A matrix arranged in rows after feature vector unitization of covariance matrix of (a); y is Y k×m Is X n×m And (5) reducing the dimension of the k-dimensional nuclear magnetic resonance data volume matrix.
The mathematical significance is that the principal component analysis method transforms the original data into a group of data with each dimension being independent in a linear transformation way, and extracts the principal characteristic components of the data.
Then, a non-Negative Matrix Factorization (NMF) method is adopted to process the nuclear magnetic resonance logging T2 spectrum, a source signal matrix and a coefficient matrix are separated, and a T2 sub-spectrum with independent components after NMF processing is obtained, wherein the mathematical form is as follows:
V n×m ＝W n×k ·H k×n
wherein: v (V) n×m Is an observation signal, a nuclear magnetic data matrix; h k×n Is a semaphore, a matrix of relaxed component content; is the source signal, W n×k A relaxation component nuclear magnetic response matrix.
The mathematical meaning is that for any given non-negative matrix V, by finding one non-negative matrix W and one non-negative matrix H, a new set of substrates is found in the original space and the original data is projected onto it. By matrix decomposition, on the one hand, the dimension of the matrix describing the problem is cut down, and on the other hand, a large amount of data can be compressed and summarized.
Through the data processing, the T2 spectrum distribution of different fluid components which continuously change along with the depth can be obtained, and finally, the effective separation of clay adsorbed water, capillary bound fluid and movable oil nuclear magnetism T2 spectrum is realized.
The blind source separation algorithm of non-Negative Matrix Factorization (NMF) is a special machine learning tool, and based on statistical independence, each fluid component can be separated from a mixed T2 spectrum, so that the problems of the traditional algorithm can be effectively avoided, and the calculation accuracy of reservoir parameters is greatly improved.
The method for acquiring the porosity of the nuclear magnetic resonance logging T2 spectrum specifically comprises the following steps:
s101: performing standardization processing on a nuclear magnetic resonance logging T2 spectrum, wherein the calculation formula of the standardization processing is as follows:
wherein y (i) is the normalized nuclear magnetic resonance T2 spectrum, y 0 (i) Is the original nuclear magnetic resonance T2 spectrum;is the total porosity of the formation; m is the number of sample points after the discretization of nuclear magnetic resonance T2 spectrum.
S102: determining the spectral peak positions of different fluid components according to clay types and core nuclear magnetic resonance experimental data; the specific process of determining the locations of the spectral peaks of the different fluid components in the pores is:
wherein T is 2bp The unit of the T2 value corresponding to the nuclear magnetic resonance volume relaxation peak is ms; η is the viscosity of the crude oil in mpa.s; t is the formation temperature in degrees Celsius.
The nuclear magnetic resonance experimental data are nuclear magnetic resonance T2 spectrums obtained by laboratory measurement of a core sample, and nuclear magnetic resonance logging T2 spectrums are nuclear magnetic resonance T2 spectrums of an actual stratum measured downhole in real time by logging instruments. The clay type determines the position of the clay spectrum peak, the nuclear magnetism experiment determines the positions of other fluids, namely the actual spectrum peak position of a well is obtained, and the position is compared with the sub-spectrum separated subsequently to obtain the combined mode of the sub-spectrum
S103: according to the principal component analysis of the nuclear magnetic resonance logging T2 spectrum after the standardization processing in the step S101, a principal component analysis result is obtained, and the specific process is as follows:
P k×n ·X n×m ＝Y k×m (3)
wherein X is n×m Is a nuclear magnetic data matrix, P k×n Is X n×m A matrix arranged in rows after feature vector unitization of covariance matrix of (a); y is Y k×m Is X n×m And (5) reducing the dimension of the k-dimensional nuclear magnetic resonance data volume matrix.
S104: and according to the principal component analysis result, processing the normalized nuclear magnetic resonance logging T2 spectrum by adopting a non-negative matrix factorization method, and separating a source signal matrix and a coefficient matrix to obtain a nuclear magnetic resonance logging T2 sub-spectrum after the normalization processing in the step S101. And processing the normalized nuclear magnetic resonance logging T2 spectrum by adopting a non-negative matrix factorization method, wherein the specific process for separating the source signal matrix and the coefficient matrix is as follows:
V n×m ＝W n×k ·H k×n (4)
wherein V is n×m For observing the signal; h k×n Is a coefficient matrix; w (W) n×k Is the source signal matrix.
S105: and (3) analyzing the corresponding relation between the spectrum peak position of the nuclear magnetic resonance logging T2 sub-spectrum in the step S104 and the spectrum peak positions of the different fluid components in the step S102, determining a combination mode of the nuclear magnetic resonance logging T2 sub-spectrum, combining the nuclear magnetic resonance logging T2 sub-spectrum according to the combination mode, and then obtaining the clay bound water spectrum, the capillary bound fluid spectrum, the movable oil spectrum and the noise spectrum.
S106: and integrating the clay bound water spectrum, the capillary bound fluid spectrum, the movable oil spectrum and the noise spectrum to obtain clay bound water porosity, capillary bound fluid porosity and movable oil hole porosity, and obtaining a porosity value of the nuclear magnetic resonance logging T2 spectrum.
Referring to fig. 2, an embodiment of the invention discloses a system for acquiring porosity of a nuclear magnetic resonance logging T2 spectrum, which comprises:
a first processing module 1001, configured to perform a normalization process on a nmr logging T2 spectrum;
the second processing module 1002 is configured to determine a spectral peak position of different fluid components according to a clay type of a region to be logged and core nmr experimental data;
a third processing module 1003, configured to obtain a principal component analysis result according to principal component analysis of the normalized nmr log T2 spectrum;
a fourth processing module 1004, configured to obtain a normalized nmr log T2 sub-spectrum according to the principal component analysis result;
a fifth processing module 1005, configured to analyze a correspondence between a spectral peak position of the nuclear magnetic resonance log T2 sub-spectrum and spectral peak positions of different fluid components to obtain a clay bound water spectrum, a capillary bound fluid spectrum, a movable oil spectrum, and a noise spectrum;
and a sixth processing module 1006, configured to integrate the clay bound water spectrum, the capillary bound fluid spectrum, the movable oil spectrum, and the noise spectrum to obtain clay bound water porosity, capillary bound fluid porosity, and movable oil porosity, and obtain a porosity value of the nuclear magnetic resonance logging T2 spectrum.
The embodiment of the invention provides a schematic diagram of terminal equipment. The terminal device of this embodiment includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. The steps of the various method embodiments described above are implemented when the processor executes the computer program. Alternatively, the processor may implement the functions of the modules/units in the above-described device embodiments when executing the computer program.
The computer program may be divided into one or more modules/units, which are stored in the memory and executed by the processor to accomplish the present invention.
The terminal equipment can be computing equipment such as a desktop computer, a notebook computer, a palm computer, a cloud server and the like. The terminal device may include, but is not limited to, a processor, a memory.
The processor may be a central processing unit (CentralProcessingUnit, CPU), but may also be other general purpose processors, digital signal processors (DigitalSignalProcessor, DSP), application specific integrated circuits (ApplicationSpecificIntegratedCircuit, ASIC), off-the-shelf programmable gate arrays (Field-ProgrammableGateArray, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like.
The memory may be used to store the computer program and/or module, and the processor may implement various functions of the terminal device by running or executing the computer program and/or module stored in the memory and invoking data stored in the memory.
The modules/units integrated in the terminal device may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as separate products. Based on such understanding, the present invention may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer memory, a Read-only memory (ROM), a random access memory (RAM, randomAccessMemory), an electrical carrier signal, a telecommunication signal, a software distribution medium, and so forth. It should be noted that the computer readable medium contains content that can be appropriately scaled according to the requirements of jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is subject to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.
Example 2
The invention is illustrated by way of example in a shale oil reservoir in a western oilfield and is described in further detail below with reference to the accompanying drawings and detailed description.
Step S101: and (5) performing standardization treatment on the nuclear magnetic resonance logging T2 spectrum.
The calculation formula for the normalization processing of the nuclear magnetic resonance logging T2 spectrum is as follows:
wherein y (i) is the normalized nuclear magnetic resonance T2 spectrum, y 0 (i) Is the original nuclear magnetic resonance T2 spectrum;is the total porosity of the formation; m is the number of sample points after the discretization of nuclear magnetic resonance T2 spectrum.
Step S102: determining the spectral peak positions of different fluid components in the pores according to clay types and nuclear magnetic resonance experimental data;
pramer et al made T2 relaxation time measurements for various clays and found that the relaxation time of these clays covered a wide range. Experiments show that the T2 distribution peak value of kaolinite is in the range of 8-16ms, the T2 peak value of illite is in the range of 1-2ms, and the T2 peak value of montmorillonite is in the range of 0.3-1 ms. So the types of clay are different, and the positions of the bound water spectrum peaks of the nuclear magnetic resonance logging clay are also different.
The X-ray diffraction analysis data of the target work area show that the main types of clay minerals are mainly montmorillonite and illite mixed layers, the adsorption capacity of clay is strong, and the right envelope curve of the clay adsorbed water relaxation peak is less than 2ms.
The position of the movable oil spectrum peak is determined according to the following formula:
wherein: t (T) 2bp The unit of the T2 value corresponding to the nuclear magnetic resonance volume relaxation peak is ms;
viscosity of eta crude oil in mPas;
t formation temperature in degrees Celsius.
Many factors affect the viscosity of crude oil in a formation, including mainly the chemical composition of the crude oil, the amount of dissolved gas, temperature and pressure, etc. FIG. 3 is a graph showing the transverse bulk relaxation peak distribution of crude oil measured in a laboratory under different temperature conditions in a target working area in this example. The middle temperature of shale oil in a target work area is about 80 ℃, the viscosity of corresponding crude oil is 12.5MP.s, and the calculated volume relaxation time is about 200 ms. Comparing the nuclear magnetic resonance T2 spectrum of the actual well logging of FIG. 6, the apparent bulk relaxation peak of crude oil exists, and the relaxation time is completely consistent with the measurement result of a laboratory, so that the transverse bulk relaxation peak distribution of the movable oil is determined to be about 200 ms.
Step S103: selecting nuclear magnetic resonance logging T2 spectrum data to be logged, and obtaining a principal component analysis result with statistically independent components from PCA analysis of the T2 spectrum based on a principal component analysis method (PCA);
before nonnegative matrix factorization is carried out, a T2 spectrum data body of a well in a target work area is selected, and a principal component analysis method is applied to obtain a principal component analysis result with statistically independent components from PCA analysis of the T2 spectrum. Fig. 4 shows a principal component analysis plot of this well, the principal component analysis defining a principal component difference projection vertical axis, which is useful in reducing interpretation space to locate individual components, where ideally one of the individual components should appear to be the response of oil, which is the primary goal for PCA. By analyzing the number of salient feature values in Principal Component Analysis (PCA), an NMF model of 8 components was finally determined.
The mathematical form of Principal Component Analysis (PCA) is:
P k×n ·X n×m ＝Y k×m (3)
wherein X is n×m Is a nuclear magnetic data matrix, P k×n Is X n×m A matrix arranged in rows after feature vector unitization of covariance matrix of (a); y is Y k×m Is X n×m And (5) reducing the dimension of the k-dimensional nuclear magnetic resonance data volume matrix.
Step S104: according to analysis results obtained by PCA, a non-Negative Matrix Factorization (NMF) method is adopted to process nuclear magnetic resonance logging T2 spectrum, a source signal matrix and a coefficient matrix are separated, and a T2 sub-spectrum with independent components after NMF processing is correspondingly obtained;
processing nuclear magnetic resonance logging T2 spectrum by adopting a non-Negative Matrix Factorization (NMF) method, separating a source signal matrix and a coefficient matrix to obtain a standard spectrum after NMF processing, wherein the mathematical form is as follows:
V n×m ＝W n×k ·H k×n (4)
wherein V is n×m For observing the signal; h k×n Is a coefficient matrix; w (W) n×k Is the source signal matrix.
Fig. 5 shows a plot of the separation effect of the nuclear magnetic resonance log sub-spectra of this well, with small amplitude variations of some signals also detected in sub-spectra 1 to 8, however their contribution to the main peak of the signal is not significant.
Step S105: analyzing the corresponding relation between the spectrum peak position of the sub-spectrum after NMF treatment and the spectrum peak positions of different fluid components of nuclear magnetic resonance, and determining the combination mode of the sub-spectrum; combining the sub-spectrums according to the determined combination mode to obtain a clay bound water spectrum, a capillary bound fluid spectrum, a movable oil spectrum and a noise spectrum;
step S102 shows that the right envelope curve of the clay adsorbed water transverse volume relaxation peak is less than 2ms, and the sub-spectrum separation effect diagram of the figure 5 is compared, wherein the combined accumulation of the sub-spectrum 1 and the sub-spectrum 2 can be interpreted as the clay adsorbed water transverse volume relaxation peak; FIG. 3 shows that the transverse bulk relaxation peak of the movable oil is distributed for about 200ms, and the sub-spectrum 7 can be interpreted as the transverse bulk relaxation peak of the movable oil by comparing with the sub-spectrum separation effect diagram of FIG. 5; the cumulative combination of sub-spectrum 3, sub-spectrum 4, sub-spectrum 5 and sub-spectrum 6 can be interpreted as bound fluid volume relaxation peaks; sub-spectrum 8 is interpreted as noise.
Step S106: and integrating each combined spectrum to obtain the clay binding water porosity, capillary binding fluid porosity and movable oil hole porosity.
And integrating the combined spectrums to obtain the clay bound water porosity, capillary bound fluid porosity and movable oil hole porosity, wherein the sixth to eighth channels of FIG. 8 are used for finally realizing the calculation of the porosity of the nuclear magnetic resonance logging T2 spectrum.
Fig. 6 is a diagram of the results of the nuclear magnetic resonance logging sub-spectrum automatic separation algorithm for the target area to be logged in this embodiment, where the first path is the depth, the second path is the nuclear magnetic resonance logging T2 spectrum, the third path is the clay adsorption water spectrum, the fourth path is the confining fluid spectrum, and the fifth path is the movable oil spectrum, and the blind source separation based on machine learning realizes the effective separation of the nuclear magnetic T2 spectrum, and avoids the calculation error caused by the selection of the nuclear magnetic T2 cut-off value.
FIG. 7 is a graph showing the comparison of the porosity accuracy calculated by the automatic separation method of the sub-spectrum and the T2 cut-off value method of the well to be measured in the target work area in this embodiment. In the figure, the horizontal axis represents depth, the vertical axis represents porosity (square) calculated by a sub-spectrum automatic separation method, porosity (round dot) calculated by a T2 cut-off value method and porosity data (triangle) analyzed by an assay, the relative error calculated by the error analysis table sub-spectrum automatic separation method is 4.54%, the relative error calculated by the T2 cut-off value method is 10.1, and compared with the method of the T2 cut-off value, the calculation precision of the sub-spectrum automatic separation method is obviously improved.
Fig. 8 is a graph of comparison results of a sub-spectrum automatic separation method and a movable oil hole gap calculated by a T2 cut-off value method of a target work area to be logged, wherein the first path is a borehole diameter (CALI) and a natural gamma curve (GR), the second path is a resistivity curve, the third path is a porosity curve, the fourth path is a depth, the fifth path is a nuclear magnetic resonance logging T2 spectrum, the sixth path is a clay adsorbed water spectrum, the seventh path is a capillary constraint fluid spectrum, the eighth path is a movable oil spectrum, the ninth path is a tail noise spectrum, the tenth path is a movable oil hole gap calculated by the T2 cut-off value method, the tenth path is a shale dessert thickness identified by the T2 cut-off value method, the twelfth path is a movable oil hole gap calculated by the shale technology, the tenth path is a shale dessert thickness identified by the technology of the invention, the illustrated well section is identified by using a traditional technology T2 cut-off value method to reach 289m, the shale dessert thickness is only 110m, the movable oil hole is relatively low, the calculated gap is calculated, the cost is guided, and the fracturing result is saved.
In summary, the invention provides an automatic separation algorithm of nuclear magnetic resonance logging sub-spectrums based on machine learning. The method comprises the following steps: performing standardization treatment on a target zone nuclear magnetic resonance logging T2 spectrum, and determining the spectral peak positions of different fluid components in the pores according to clay types and nuclear magnetic resonance experimental data; selecting nuclear magnetic resonance logging T2 spectrum data of a well, and obtaining a principal component analysis result with statistically independent components from PCA analysis of the T2 spectrum based on a principal component analysis method (PCA); according to analysis results obtained by PCA, a non-Negative Matrix Factorization (NMF) method is adopted to process nuclear magnetic resonance logging T2 spectrum, a source signal matrix and a coefficient matrix are separated, and a T2 sub-spectrum with independent components after NMF processing is correspondingly obtained; analyzing the corresponding relation between the spectrum peak position of the sub-spectrum after NMF treatment and the spectrum peak positions of different fluid components of nuclear magnetic resonance, and determining the combination mode of the sub-spectrum; combining the sub-spectrums according to the determined combination mode to obtain a clay bound water spectrum, a capillary bound fluid spectrum, a movable oil spectrum and a noise spectrum; and integrating each combined spectrum to obtain the clay binding water porosity, capillary binding fluid porosity and movable oil hole porosity. The invention solves the problems that the superposition influence of the tail spectrum of the strong signal sub-spectrum on the adjacent sub-spectrum and the noise of the nuclear magnetic resonance logging tail spectrum are not considered in the prior art, and the traditional method relies on a large amount of representative rock core analysis and test data, thereby improving the calculation precision of the porosities of each component of the nuclear magnetic resonance logging.
The invention provides a nuclear magnetic resonance logging sub-spectrum automatic separation algorithm based on machine learning to determine the porosity of nuclear magnetic resonance logging, a non-Negative Matrix Factorization (NMF) blind source separation algorithm is adopted, the components of each fluid can be separated from a mixed T2 spectrum without depending on experiments, the effective separation of clay adsorbed water, capillary bound fluid and movable oil spectrum is realized, the calculation precision of parameters such as bound water saturation, effective porosity and the like is improved, the exploration production of oil and gas fields is effectively guided, the application and popularization are facilitated, and the applicability is good.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The method for acquiring the porosity of the nuclear magnetic resonance logging T2 spectrum is characterized by comprising the following steps of:
s101: performing standardization treatment on a nuclear magnetic resonance logging T2 spectrum;
s102: determining the spectral peak positions of different fluid components according to clay types and core nuclear magnetic resonance experimental data;
s103: obtaining a principal component analysis result according to principal component analysis of the nuclear magnetic resonance logging T2 spectrum after the standardization treatment in the step S101;
s104: according to the principal component analysis result, a non-negative matrix factorization method is adopted to obtain a nuclear magnetic resonance logging T2 sub-spectrum after the standardization processing in the step S101;
s105: analyzing the corresponding relation between the spectrum peak position of the nuclear magnetic resonance logging T2 sub-spectrum in the step S104 and the spectrum peak positions of the different fluid components in the step S102 to obtain a clay binding water spectrum, a capillary binding fluid spectrum, a movable oil spectrum and a noise spectrum;
s106: and integrating the clay bound water spectrum, the capillary bound fluid spectrum, the movable oil spectrum and the noise spectrum to obtain clay bound water porosity, capillary bound fluid porosity and movable oil hole porosity, and obtaining a porosity value of the nuclear magnetic resonance logging T2 spectrum.
2. The method for obtaining the porosity of the nuclear magnetic resonance log T2 spectrum according to claim 1, wherein the specific process of performing the normalization processing on the nuclear magnetic resonance log T2 spectrum in step S101 is as follows:
wherein y (i) is the normalized nuclear magnetic resonance T2 spectrum, y 0 (i) Is the original nuclear magnetic resonance T2 spectrum;is the total porosity of the formation; m is the number of sample points after the discretization of nuclear magnetic resonance T2 spectrum.
3. The method for obtaining the porosity of the nuclear magnetic resonance logging T2 spectrum according to claim 1, wherein the specific process of determining the peak positions of the spectra of the different fluid components in step S102 is as follows:
wherein T is 2bp The corresponding T2 value of the nuclear magnetic resonance volume relaxation peak, eta is the viscosity of crude oil, and T is the formation temperature.
4. The method for obtaining the porosity of the nuclear magnetic resonance logging T2 spectrum according to claim 1, wherein the specific process of obtaining the principal component analysis in step S103 is:
P k×n ·X n×m ＝Y k×m (3)
wherein X is n×m Is a nuclear magnetic data matrix, P k×n Is X n×m A matrix arranged in rows after feature vector unitization of covariance matrix of (a); y is Y k×m Is X n×m And (5) reducing the dimension of the k-dimensional nuclear magnetic resonance data volume matrix.
5. The method for acquiring the porosity of the nuclear magnetic resonance log T2 spectrum according to claim 1, wherein the step S104 is characterized in that the step S101 of acquiring the normalized nuclear magnetic resonance log T2 sub-spectrum comprises the following steps: and (3) processing the normalized nuclear magnetic resonance logging T2 spectrum by adopting a non-negative matrix factorization method, and separating a source signal matrix and a coefficient matrix to obtain the nuclear magnetic resonance logging T2 sub-spectrum after the normalization in the step (S101).
6. The method for obtaining the porosity of the nuclear magnetic resonance logging T2 spectrum according to claim 5, wherein the method for processing the normalized nuclear magnetic resonance logging T2 spectrum by adopting a non-negative matrix factorization method comprises the following specific processes of separating a source signal matrix and a coefficient matrix:
V n×m ＝W n×k ·H k×n (4)
wherein V is n×m For observing the signal; h k×n Is a coefficient matrix; w (W) n×k Is the source signal matrix.
7. The method for acquiring the porosity of the nuclear magnetic resonance logging T2 spectrum according to claim 1, wherein in step S105, the acquiring process of the clay bound water spectrum, the capillary bound fluid spectrum, the movable oil spectrum and the noise spectrum specifically comprises: and determining a combination mode of the nuclear magnetic resonance logging T2 sub-spectrum according to the corresponding relation between the spectrum peak position of the nuclear magnetic resonance logging T2 sub-spectrum and the spectrum peak positions of the different fluid components in the step S102, and combining the nuclear magnetic resonance logging T2 sub-spectrum according to the combination mode, so as to obtain the clay bound water spectrum, the capillary bound fluid spectrum, the movable oil spectrum and the noise spectrum.
8. A system for acquiring porosity of a nuclear magnetic resonance log T2 spectrum, comprising:
the first processing module is used for carrying out standardized processing on the nuclear magnetic resonance logging T2 spectrum;
the second processing module is used for determining the spectral peak positions of different fluid components according to the clay type and core nuclear magnetic resonance experimental data;
the third processing module is used for obtaining a principal component analysis result according to principal component analysis of the normalized nuclear magnetic resonance logging T2 spectrum;
the fourth processing module is used for obtaining a normalized nuclear magnetic resonance logging T2 sub-spectrum according to the main component analysis result;
the fifth processing module is used for analyzing the corresponding relation between the spectrum peak position of the nuclear magnetic resonance logging T2 sub-spectrum and the spectrum peak positions of different fluid components to obtain a clay binding water spectrum, a capillary binding fluid spectrum, a movable oil spectrum and a noise spectrum;
and the sixth processing module is used for integrating the clay bound water spectrum, the capillary bound fluid spectrum, the movable oil spectrum and the noise spectrum to obtain clay bound water porosity, capillary bound fluid porosity and movable oil hole porosity, and acquiring a porosity value of the nuclear magnetic resonance logging T2 spectrum.
9. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1-7 when the computer program is executed.
10. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the method according to any one of claims 1-7.
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