CN112394394B - Identification method of ultra-deep volcanic rock - Google Patents
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Abstract
The invention discloses an identification method of ultra-deep volcanic rock, which comprises the following steps: filtering the seismic data volume of the volcanic development zone in a transverse forced manner, and highlighting the discontinuity of the longitudinal seismic data volume; enhancing the abnormal characteristics of the volcanic channel by adopting a principal component analysis data fusion method; performing chaotic attribute processing on the reflection chaotic characteristics to obtain a chaotic reflection value; performing clustering analysis on the chaotic reflection value to predict lithofacies; processing the chaotic reflection value by adopting a K-prototype algorithm to obtain a lithofacies classification result; and (4) integrating the volcanic channel abnormal characteristics and the lithofacies classification results to identify volcanic rocks. The geological meaning multi-resolution of the seismic abnormal body is reduced, the identification method of the ultra-deep volcanic rock is provided, and the identification precision of the ultra-deep volcanic rock reservoir is greatly improved.
Description
Technical Field
The invention relates to the field of petroleum and natural gas exploration and development, in particular to an identification method of ultra-deep volcanic rocks.
Background
Volcanic rock oil and gas reservoirs are important targets for oil and gas exploration, and volcanic rock seismic identification is a key for volcanic rock oil and gas reservoir exploration. The buried depths of the Xujia surrounding gas reservoir, the Sonnan gas reservoir and the Clariti gas reservoir are all 2000-4000 m in China, and the buried depths of the volcanic gas reservoirs discovered at present abroad are not more than 4000m, so that the volcanic gas reservoirs in the West of Sichuan have the characteristics of ultra-deep buried, ultra-6000 m buried depths, strong concealment and the like compared with other volcanic gas reservoirs at home and abroad. The method is influenced by the ultra-deep burial, the seismic data quality is low, and the identification difficulty is high; the lower part of the lava in western Sichuan has no obvious volcanic cone type characteristic, the magma overflow channel is narrow, and the seismic reflection characteristic is not obvious; and the volcanic rock drilling is less, and the plane distribution characteristics of the volcanic rock are not clear. At present, a western-chuan volcanic rock recognition method is mostly based on a conventional seismic profile recognition means, a stratum thickness method reflects the development of volcanic rock, the characteristics of a volcanic rock profile have a hillock-shaped raised appearance and an internal disordered characteristic, the characteristics are similar to the characteristics of a biological reef seismic profile, effective distinguishing cannot be achieved, and the recognition method has strong ambiguity.
Therefore, the chinese patent application CN201611142635.0 discloses a volcanic lithofacies prediction method, which comprises the following steps: and (3) forward modeling: performing forward modeling of a section model on the basis of seismic geological horizon calibration; and (3) seismic attribute extraction: extracting the seismic attribute of the boundary layer aiming at the target layer position, and determining the range of an attribute time window; the attributes are preferably: preferably reflecting the seismic attributes of volcanic rock distribution, and carrying out pretreatment; volcanic lithofacies classification: according to the drilling result, carrying out classification on volcanic rock facies; performing multi-attribute neural network pattern recognition; and (3) lithofacies prediction: and fusing the plurality of seismic attributes according to the neural network parameters obtained by training, and predicting the distribution change rule of the volcanic lithofacies in the unknown area. However, the volcanic burst phase and the spray-overflow phase are separated and affected by the buried depth of the stratum and the data quality, the phase boundary is difficult to determine, and the seismic attribute plane regularity is poor, so that the distribution characteristics of the volcanic phase are not clear.
Disclosure of Invention
The invention aims to overcome the problem of unclear lithofacies distribution characteristics of ultra-deep volcanic rocks in the prior art and provides an identification method of the ultra-deep volcanic rocks.
In order to achieve the above purpose, the invention provides the following technical scheme:
an identification method of ultra-deep volcanic rocks comprises the following steps:
filtering the seismic data volume of the volcanic development zone in a transverse forced manner, and highlighting the discontinuity of the longitudinal seismic data volume; enhancing the abnormal characteristics of the volcanic channel by adopting a principal component analysis data fusion method to obtain a volcanic cone distribution map;
performing chaotic attribute processing on the reflection chaotic characteristics of the volcanic development zone to obtain a chaotic reflection value; classifying lithofacies through clustering analysis on the chaotic reflection values to obtain a volcanic lithofacies prediction plan;
and predicting volcanic mechanism distribution and volcanic facies plane distribution according to the volcanic facies prediction plane graph and the volcanic cone distribution diagram.
Preferably, the clustering analysis is to process the chaotic reflection value by adopting a K-prototype algorithm to obtain a lithofacies classification result.
Preferably, the transverse mandatory filtering adopts a self-adaptive construction orientation denoising method, automatically adjusts the orientation according to the main construction direction, specifies the signal dominant direction in the filtering process, and performs filtering iteration for multiple times to remove tuning and random noise.
Preferably, the principal component analysis data fusion method includes:
solving a covariance matrix or a correlation coefficient matrix of the independent variables;
solving the eigenvalue of the covariance matrix or the relation matrix and the corresponding eigenvector;
arranging the eigenvectors into a matrix from top to bottom according to the size of the corresponding eigenvalue, and taking the first k rows to form a matrix a, wherein the matrix a is k × p dimension;
reducing the dimension of the matrix a to k dimensions, and obtaining data Y ═ aTX, wherein Y is k X1 dimension.
Preferably, the mathematical model of the chaotic reflection value is
Wherein C (k) is the chaotic reflection value; alpha is alphakThe inclination angle value at any sample point is obtained; alpha is alphasIs the average of the tilt angles.
According to another aspect of the invention, there is provided an electronic device comprising at least one processor, and a memory communicatively coupled to the at least one processor; the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of the above.
Compared with the prior art, the invention has the beneficial effects that:
the method analyzes substrate fracture based on regional background control, determines volcanic development zones, improves the abnormal characteristics of a volcanic channel in an ultra-deep layer through transverse mandatory filtering, enhances the abnormal conditions of the volcanic channel by adopting a principal component analysis data fusion method, matches and searches a channel development zone and an upper earthquake abnormal zone, identifies a volcanic miscellaneous reflection zone through a chaotic reflection value, and predicts volcanic mechanisms and plane lithofacies distribution characteristics by adopting a clustering analysis means.
Description of the drawings:
FIG. 1 is a seismic section based on a laterally forced filtering process.
Figure 2 is a volcanic pathway identification map using principal component analysis data fusion.
FIG. 3 is a volcanic clustering lithofacies section.
FIG. 4 is a plan view of volcanic lithofacies prediction.
Fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the invention.
Detailed Description
The present invention will be described in further detail with reference to test examples and specific embodiments. It should be understood that the scope of the above-described subject matter is not limited to the following examples, and any techniques implemented based on the disclosure of the present invention are within the scope of the present invention.
The volcanic rock in the southwest Sichuan region is subjected to large-scale fracture type eruption along fracture, the energy release is fast, the overflow phase basalt is taken as the main component, the explosive phase pyroclastic rock is few, the distribution trend of the volcanic rock in the middle region of the southwest Sichuan region has better correlation with the fracture of the No. 13 basement, and the small-scale central eruption along fracture is presumed, the energy release is slow, and the explosive phase pyroclastic rock is much. In combination with aeromagnetic anomaly measurement data, 11 basal fracture systems can develop in the Sichuan basin and the periphery, and the Sichuan basin have control effects on development of Chunxi volcanic rocks, namely, Hakko break (No. 13 basal fracture) and Longquan mountain-Zheba fracture. The deep and large fracture of the basin foundation not only has deep influence on the structure of a cover layer and the deposition pattern, but also has strong control effect on volcanic activity because the deep and large fracture of the basin foundation has large cutting depth and can communicate with a deep rock slurry room, and a fracture zone can also be used as a rock slurry upwelling channel, and the junction of the deep and large fractures is the more advantageous position of rock slurry upwelling overflow.
The volcanic channel seismic response characteristics are nearly vertical, disordered inside, bright spots, blank reflection and weak boundary reflection characteristics, and the upper part of the two-fold system abnormal body is disordered hill reflection. Influenced by the size of the channel, the volcanic channel wall is not obviously reflected, and the identification of the channel boundary has certain difficulty.
The volcano channel can be well identified through the seismic pixel processing technology, and the main content of the technology comprises the steps of carrying out transverse forced filtering on the seismic data, highlighting the discontinuity of the longitudinal seismic data and identifying the small-scale volcano channel. The forced filtering adopts a self-adaptive construction orientation denoising method for removing tuning and random noise and simultaneously has a good storage effect on tiny details in data, such as boundary corners and the like. The method can automatically adjust the azimuth according to the main construction direction without pre-calculating an inclination angle and an azimuth data volume, has good denoising effect in a chaotic reflection area, can specify the dominant direction of a signal in the filtering process, and performs filtering iteration for multiple times. The reflection characteristics of the vertical volcanic channels can be more clearly seen on the seismic section after filtering (figure 1).
Extracting an earthquake dip angle change rate attribute body, an azimuth angle change rate attribute body and a coherent attribute body based on multiple times of filtering data, and realizing attribute body fusion by adopting a principal component analysis dimensionality reduction technology, wherein the PCA main algorithm comprises the following steps:
solving a covariance matrix (or a correlation coefficient matrix) of the independent variables;
solving the eigenvalue of the covariance matrix (or relation matrix) and the corresponding eigenvector;
arranging the eigenvectors into a matrix from top to bottom according to the size of the corresponding eigenvalue, and taking the first k rows to form a matrix a (k × p dimension);
Y＝aTx (Y is k X1 dimension) is data from dimension reduction to k dimension, and the principal component score of each sample is calculated;
the principal component score of each sample can be plotted in a scatter plot and clustered, or the principal component score can be regarded as a new dependent variable and subjected to linear regression, and the like. The fused data volume after the principal component analysis has clear volcanic channel abnormal characteristics, and the volcanic channel development area longitudinal seismic reflection structural abnormality and the upper binary system abnormal volume have obvious corresponding relation (figure 2), thus forming a volcanic cone distribution diagram.
The volcanic explosive phase mainly takes chaotic reflection as a main part, the overflow phase mainly takes parallel reflection characteristics as a main part, and according to the characteristics, a tensor algorithm is adopted, the change condition of the main azimuth is quantified by using characteristic values, the irregularity of seismic reflection characteristics can be reflected, if the value is low, the internal reflection rule of the structure is indicated, the stratum change is not large, and if the value is large, the irregular change area in the structure is indicated, and the reflection chaotic characteristics are obvious. The mathematical model is as follows:
in the formula: c (k) is the chaotic reflection value; alpha is alphakIs the dip angle value at any sample point; alpha is alphasIs the average of the tilt angles.
Based on chaotic attribute processing, facies is predicted through clustering analysis, and K-prototype is a typical algorithm for processing mixed attribute clustering and inherits the ideas of a Kmean algorithm and a Kmode algorithm. And a dissimilarity degree calculation formula between the prototype and the mixed attribute data describing the data cluster is added. The K-prototype algorithm sets an objective function, similar to the Sum of Squared Errors (SSE) of kmean, and iterates until the objective function value is unchanged.
Meanwhile, the K-prototype algorithm provides a prototype of the mixed attribute cluster, wherein the prototype is the centroid of the numerical attribute cluster. The mixed attribute comprises a numerical attribute and a classification attribute, the definition of the prototype is the mean value of all attribute values in the attribute for the numerical attribute prototype, and the classification attribute prototype is the attribute with the highest frequency of the selected attribute value in the classification attribute.
The dissimilarity degree of the numerical attributes is generally selected from Euclidean distances, and the dissimilarity degree of the mixed attributes in the K-prototype algorithm is divided into the numerical attributes and the classification attributes which are separately solved and added.
For the classification attribute: the Hamming distance is used, namely the attribute values are the same and are 0; the attribute value is 1, differently.
For the classification attribute:
for the numerical attribute:
calculating Euclidean distance corresponding to numerical attribute
The distance (degree of dissimilarity) between the data and the cluster is:
wherein the first p are numerical attributes, the last m are classification attributes, which are j attributes of the prototype of the cluster Q, and μ is a weight factor of the classification attributes
The target function of K-prototype is:
the lithofacies classification result processed based on the chaotic attribute is consistent with the reflection characteristics in the seismic profile, can reflect the irregularity degree in the seismic information, and can provide a reliable basis for volcanic lithofacies identification, as shown in fig. 3. The volcanic lithofacies prediction plan (figure 4) and the volcanic cone distribution diagram are generally matched, the volcanic cone distribution area is mainly based on the explosive facies, and the volcanic mechanism three-dimensional distribution characteristics can be drawn by integrating the characteristics of the volcanic facies prediction plan and the volcanic cone distribution diagram, so that an important basis is provided for the next favorable reservoir prediction and target optimization.
FIG. 5 illustrates an electronic device (e.g., a computer server with program execution functionality) including at least one processor, a power source, and a memory and input-output interface communicatively coupled to the at least one processor, according to an exemplary embodiment of the invention; the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method disclosed in any one of the preceding embodiments; the input and output interface can comprise a display, a keyboard, a mouse and a USB interface and is used for inputting and outputting data; the power supply is used for supplying electric energy to the electronic equipment.
Those skilled in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a computer readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: various media that can store program codes, such as a removable Memory device, a Read Only Memory (ROM), a magnetic disk, or an optical disk.
When the integrated unit of the present invention is implemented in the form of a software functional unit and sold or used as a separate product, it may also be stored in a computer-readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present invention may be essentially implemented or a part contributing to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, a ROM, a magnetic or optical disk, or other various media that can store program code.
The foregoing is merely a detailed description of specific embodiments of the invention and is not intended to limit the invention. Various alterations, modifications and improvements will occur to those skilled in the art without departing from the spirit and scope of the invention.
Claims (6)
1. The identification method of the ultra-deep volcanic rock is characterized by comprising the following steps of:
filtering the seismic data volume of the volcanic development zone in a transverse forced manner, and highlighting the discontinuity of the longitudinal seismic data volume; enhancing the abnormal characteristics of the volcanic channel by adopting a principal component analysis data fusion method to obtain a volcanic cone distribution map;
performing chaotic attribute processing on the reflection chaotic characteristics of the volcanic development zone to obtain a chaotic reflection value; classifying lithofacies through clustering analysis on the chaotic reflection values to obtain a volcanic lithofacies prediction plan;
and predicting volcanic mechanism distribution and volcanic facies plane distribution according to the volcanic facies prediction plane graph and the volcanic cone distribution diagram.
2. The identification method of the ultra-deep volcanic rock as claimed in claim 1, wherein the clustering analysis is performed by processing the chaotic reflection value by a K-prototype algorithm to obtain a lithofacies classification result.
3. The identification method of the ultra-deep volcanic rock as claimed in claim 1, wherein the transverse mandatory filtering adopts a self-adaptive construction orientation de-noising method, automatically adjusts the orientation according to the main construction direction, specifies the dominant direction of the signal in the filtering process, and performs filtering iteration for multiple times to remove tuning and random noise.
4. The identification method of the ultra-deep volcanic rock as claimed in claim 1, wherein the principal component analysis data fusion method comprises:
solving a covariance matrix or a correlation coefficient matrix of the independent variables;
solving the eigenvalue of the covariance matrix or the relation matrix and the corresponding eigenvector;
arranging the eigenvectors into a matrix from top to bottom according to the size of the corresponding eigenvalue, and taking the first k rows to form a matrix a, wherein the matrix a is k × p dimension;
reducing the dimension of the matrix a to k dimensions, and obtaining data Y ═ aTX, wherein Y is k X1 dimension.
5. The method for identifying the ultra-deep volcanic rock as claimed in claim 1, wherein the mathematical model of the chaotic reflecting value is
Wherein n is the number of feature vectors; c (k) is the chaotic reflection value; alpha is alphakIs the dip angle value at any sample point; alpha (alpha) ("alpha")sIs the average of the tilt angles.
6. An electronic device comprising at least one processor, and a memory communicatively coupled to the at least one processor; the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 4.
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