A method for predicting underground magmatic rock masses based on post-stack seismic data

Abstract

The present invention discloses a method for predicting underground magmatic rock masses based on post-stack seismic data, comprising the following steps: S1. obtaining a dip data volume, an azimuth data volume, and a structure-guided filter data volume with a maximum similarity window as the analysis point; S2. obtaining a sensitive attribute volume; S3. processing each attribute volume separately; and S4. completing the prediction of the underground magmatic rock mass. The underground magmatic rock mass prediction method provided by the present invention integrates multiple magmatic rock mass prediction methods, helping resource exploration units improve the accuracy and efficiency of underground magmatic rock mass interpretation, and providing strong support for magmatic rock-related oil and gas resource evaluation and metal mineral exploration.

Description

Technical Field

[0001] The present invention belongs to the field of geophysical exploration technology, relates to a fracture prediction method in geophysics and the petroleum industry, and specifically is a method for predicting underground magma rock bodies based on post-stack seismic data. Background Art

[0002] Magmatic rocks are formed by condensation and crystallization of high-temperature molten magma that intrudes into strata or erupts to the surface. Based on their mineral composition, magmatic rocks are primarily classified into four categories: ultramafic, basic, neutral, and acidic. Detailed characterization of the location, extent, and morphology of underground magmatic bodies is crucial for numerous fields, including scientific research, energy exploration, and engineering construction. In particular, significant breakthroughs have been made in identifying magmatic oil and gas reservoirs in major oil and gas basins in my country, demonstrating promising development prospects. However, magmatic rock eruptions and intrusions are irregular, resulting in irregular external morphological contours and complex internal structures with strong heterogeneity. Geophysical characteristics suggest that magmatic rocks strongly shield and absorb seismic waves, often resulting in strong reflections and multiple waves. This results in poor seismic data quality for the underlying strata, severely limiting the accuracy of predicting the comprehensive characteristics of magmatic rocks.

[0003] Previous methods for predicting magmatic rock masses using post-stack seismic data have been subject to numerous limitations in both methodology and application. In particular, the inability to clearly characterize rock mass boundaries through seismic wave characteristics makes it difficult to meet the requirements for high-precision rock mass prediction, significantly limiting the efficiency and accuracy of magmatic rock geological evaluation. Furthermore, the applicability of each method and technique varies, and may not be suitable for predicting data in a specific region. Furthermore, relying on a single method provides very limited information, necessitating the integration of multiple effective methods to comprehensively reflect relevant information about magmatic rock masses, and ultimately verifying this information with other geological findings. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for predicting underground magmatic rock bodies based on post-stack seismic data, to identify magmatic rock information with complete morphology, clear boundaries, strong contrast and high fidelity in conventional post-stack seismic data, to accurately depict the location and spatial combination characteristics of magmatic rock bodies of different sizes, to assist in the interpretation of magmatic rock bodies more efficiently and accurately, and to provide the necessary basis for subsequent geological evaluation.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is:

[0006] A method for predicting underground magma rock mass based on post-stack seismic data, the method comprising the following steps performed in sequence:

[0007] S1. Obtain the maximum similarity window as the inclination data volume, azimuth data volume and construction guidance filter data volume of the analysis point

[0008] S11. Collect post-stack seismic data, perform multi-window dip scanning, and use vertical calculation windows to estimate the dip and azimuth of the seismic reflection interface. Select the window with the highest seismic waveform similarity as the dip and azimuth data volume of the analysis point.

[0009] S12. Using information from the dip data volume and the azimuth data volume to locate reflection interfaces in the post-stack seismic data, smoothing continuous reflection interfaces in the post-stack seismic data, and amplifying waveform differences for discontinuous reflection interfaces to generate a structural guidance filter data volume;

[0010] S2. Get sensitive attributes

[0011] Using the dip data volume, azimuth data volume, and structure-guided filtering data volume, a sensitive attribute volume capable of identifying igneous rocks is obtained. In this sensitive attribute volume, the amplitude energy difference between the igneous rock mass and the surrounding rock formation is amplified, and the rock mass boundary is displayed more clearly and completely.

[0012] S3. Process the attribute body separately

[0013] The sensitive attribute bodies are jointly displayed in plane, section and 3D visualization, and qualified attribute bodies are selected for processing to obtain result attribute bodies;

[0014] S4. Complete the underground magma rock prediction

[0015] In the three-dimensional visualization space, the differential amplitude of the magmatic body is extracted from the result attribute body to complete the three-dimensional characterization of the magmatic body, and the morphological range and relative position of the underground magmatic body in the study area are summarized to complete the underground magmatic body prediction.

[0016] As a limitation, in step S2, the sensitive attribute body includes amplitude curvature attribute, texture attribute, amplitude difference attribute, image edge detection attribute, and coherent energy amplitude gradient attribute.

[0017] As another limitation, in step S3, the selection criteria are that the magmatic body has a complete morphology, clear boundaries, a contrast ratio increased by 1 times, and is consistent with drilling data, gravity, magnetic and electrical data, and seismic forward modeling results.

[0018] As a third limitation, in step S3, the processing includes the following methods:

[0019] S31. Use sensitive attribute bodies to identify magmatic rock patterns

[0020] Using the sample set established by the confirmed information within the attribute body as a classifier, the data in the sensitive attribute body is classified to distinguish the data related to the magmatic rock body, and the pattern recognition attribute body is obtained. The pattern recognition attribute body further highlights the amplitude energy difference between the magmatic rock body and the surrounding rock formation;

[0021] S32. Perform waveform cluster analysis on sensitive attribute bodies

[0022] Based on the correlation between the overall change of the attribute body amplitude and the lithology, the sensitive attribute bodies are divided into several categories of waveform characteristics, and the attribute bodies representing the waveform characteristics of the magmatic rock body are selected to identify the scope and morphology of the magmatic rock body;

[0023] S33. Data fusion of sensitive attributes

[0024] The sensitive attribute bodies are fused to complement each other with relevant information of the magmatic rock body to form a fused attribute body, making the identification results of the magmatic rock body richer and more accurate;

[0025] S34. Select result attribute body

[0026] The attribute body that best matches the characteristics of the magmatic body among the attribute bodies obtained from S31, S32, and S33 is selected as the result attribute body. The result attribute body is jointly displayed in plane, section, and three-dimensional visualization, highlighting the morphological range and combination characteristics of magmatic bodies at different locations and scales.

[0027] Due to the adoption of the above technical solution, the present invention has the following technical advancements compared with the prior art:

[0028] The underground magmatic rock body prediction method based on post-stack seismic data provided by the present invention has been successfully applied to many large domestic oil and gas fields, such as Shengli Oilfield in the Bohai Bay Basin, Tarim Oilfield in the Tarim Basin, and Anhui Provincial Exploration Institute. It helps resource exploration units improve the accuracy and work efficiency of underground magmatic rock body interpretation, and provides strong support for magmatic rock-related oil and gas resource evaluation and metal mineral exploration.

[0029] The present invention is applicable to the prediction of underground magmatic rock bodies, and provides strong support for the evaluation of oil and gas resources and the exploration of metal minerals related to magmatic rocks. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 1. It is a flowchart of the working process in the embodiment;

[0031] Figure 2 2 is a schematic diagram of the inclination data volume of the analysis point in the embodiment;

[0032] Figure 3 2 is a schematic diagram of the azimuth data volume of the analysis point in the embodiment;

[0033] Figure 4 Schematic diagram of constructing a guided filtering data volume in an embodiment;

[0034] Figure 5 is a plan view of the sensitive attribute body of igneous rock in the embodiment;

[0035] Figure 6 is a cross-sectional view of a sensitive attribute body of igneous rock in the embodiment;

[0036] Figure 7 Schematic diagram of pattern recognition attributes in an embodiment;

[0037] Figure 8 Schematic diagram of waveform clustering attributes in an embodiment;

[0038] Figure 9 is a schematic diagram of a fusion attribute body in an embodiment;

[0039] Figure 10 is a three-dimensional visualization stereogram of the sensitive attribute body of igneous rock in the embodiment;

[0040] Figure 11 It is a three-dimensional stereoscopic image of the magmatic rock body in the example area in the embodiment. DETAILED DESCRIPTION

[0041] The present invention will be further described in detail below by way of specific examples. It should be understood that the described examples are only used to illustrate the present invention and are not intended to limit the present invention.

[0042] Example A method for predicting underground magma rock mass based on post-stack seismic data

[0043] This embodiment is a method for predicting underground magmatic rock mass based on post-stack seismic data. This embodiment is carried out in Shengli Oilfield in Bohai Bay Basin. The workflow of this embodiment is shown in the following figure: Figure 1 As shown, the method includes the following steps performed in sequence:

[0044] S1. Obtain the maximum similarity window as the inclination data volume, azimuth data volume and construction guidance filter data volume of the analysis point

[0045] S11. Collect post-stack seismic data, conduct multi-window dip scanning, use vertical calculation windows to estimate the dip and azimuth of the seismic reflection interface, select the window with the highest seismic waveform similarity as the dip data volume and azimuth data volume of the analysis point, the dip data volume is as follows: Figure 2 As shown, the azimuth data volume is as follows Figure 3 As shown;

[0046] S12. Use the information of the dip data volume and the azimuth data volume to locate the reflection interface of the post-stack seismic data, smooth the continuous reflection interface in the post-stack seismic data, amplify the waveform difference of the discontinuous reflection interface, and generate the structural guidance filter data volume, such as Figure 4As shown in the figure, structurally guided filtering doubles the signal-to-noise ratio, resolution, and fidelity of post-stack seismic data, improves seismic event continuity, and makes fault breaks clearer and more distinct. In the newly generated structurally guided filtered data, structural boundary features are clearer, faults and horizons of different levels are easier to identify, and geological body boundaries are more clearly reflected. This allows for better extraction of other geometric attributes.

[0047] S2. Get sensitive attributes

[0048] Using the dip data volume, azimuth data volume and structure-guided filtering data volume, sensitive attribute volumes that can identify igneous rocks are obtained. The sensitive attribute volumes include amplitude curvature attribute, variance texture attribute, amplitude difference attribute, image edge detection attribute and coherent energy amplitude gradient attribute.

[0049] The gradient of the coherence result of the seismic data is calculated by using the dip data volume and the constructed guided filter data volume to obtain the coherent energy amplitude gradient attribute volume; the lateral second-order derivative of the amplitude component of the post-stack seismic data is calculated by using the coherent energy amplitude gradient attribute volume to obtain the amplitude curvature attribute volume; the standard deviation of an element in the image matrix and its surrounding elements is calculated by constructing the guided filter data volume to obtain the variance texture attribute reflecting the degree of local data divergence; the amplitude or root mean square amplitude difference between the center point and the surrounding adjacent points is calculated by constructing the guided filter data volume to obtain the amplitude difference attribute volume; the edge and grayscale mutation part of the image of the constructed guided filter data volume are enhanced to obtain the image edge detection attribute volume;

[0050] S3. Process the attribute body separately

[0051] The above five types of sensitive attribute bodies are displayed in plane and section. The plane diagram of the igneous rock sensitive attribute body is as follows: Figure 5 As shown in the figure, the cross-section of the sensitive attribute body of igneous rock is as follows: Figure 6 As shown, check whether the magma body is complete in shape, with clear boundaries, strong contrast, and high fidelity. Select several attribute bodies with better effects and process them separately:

[0052] (1) The selected attribute body is used for magmatic rock body “pattern recognition”. First, sample points are picked up at the location of the magmatic rock body in the attribute body and summarized into a sample set. This is used as a classifier to search for other data with the same waveform characteristics in the attribute body and amplify its amplitude value to obtain a pattern recognition attribute body. The pattern recognition attribute is as follows: Figure 7 As shown, it is predicted as a magmatic body;

[0053] (2) The selected attribute bodies are subjected to waveform clustering processing. First, the training samples selected from the target layer segments of the selected attribute bodies are learned through a neural network. A model is constructed through multiple iterations to establish a quantity plate representing various waveforms in the seismic layer segment. Each type of waveform generates a fixed value. Next, the correlation between the actual attribute body data and the model is compared, and each type of waveform in the actual attribute body data is assigned a value to obtain a waveform clustering attribute body. The waveform clustering attribute body is as follows: Figure 8 As shown;

[0054] (3) The selected attribute bodies are subjected to data fusion processing to complement each other with relevant information of the magma body to form a fused attribute body. The fused attribute body is as follows: Figure 9 As shown in the figure, the amplitude energy difference between the igneous body and the surrounding rock formation is amplified after fusion, and the obtained igneous body information is richer and more accurate;

[0055] Among the above three methods, the data body that best reflects the magmatic rock body is selected as the result attribute body for three-dimensional visualization. The three-dimensional visualization stereogram of the magmatic rock sensitive attribute body is shown as follows: Figure 10 As shown, the morphological range and combination characteristics of igneous bodies at different locations and sizes can be viewed.

[0056] S4. Use drilling data, gravity, magnetic and electrical data, and seismic forward modeling results to verify the accuracy of the magmatic rock mass characteristics in the resulting attribute volume. If inaccurate, adjust the parameters of the above attribute processing until they match;

[0057] (1) In the result attribute body, the cross-section of an existing well and the lithologic data of the well are displayed. The magmatic rock mass in the well lithologic data is consistent with the magmatic rock mass prediction result of the result attribute body;

[0058] (2) Jointly display the cross-section, plane and three-dimensional visualization space in the existing gravity, magneto-electric data. The range of the magmatic rock body in the gravity, magneto-electric data is consistent with the magmatic rock body prediction results of the result attribute body.

[0059] (3) Based on the morphological range of the magmatic rock mass of the result attribute body, a digital geological model containing rock physical parameters is established to simulate the excitation, propagation and reception of seismic waves in the model to obtain simulated seismic data. The waveform characteristics within the predicted range of the magmatic rock mass are consistent with the actual post-stack seismic data;

[0060] S5. The results of the above three steps are consistent. In the three-dimensional visualization space, the differential amplitude of the magmatic rock body is extracted from the result attribute body to complete the three-dimensional characterization of the magmatic rock body. The three-dimensional characterization of the magmatic rock body in the example area is shown as follows: Figure 11 As shown, the conclusions on the morphological range and relative position of the underground magmatic bodies in the study area are summarized.

Claims

1. A method for predicting underground magma rock mass based on post-stack seismic data, characterized in that: The method comprises the following steps performed in sequence: S1. Obtain the maximum similarity window as the inclination data volume, azimuth data volume and construction guidance filter data volume of the analysis point; S11. Collect post-stack seismic data, perform multi-window dip scanning, and use vertical calculation windows to estimate the dip and azimuth of the seismic reflection interface. Select the window with the highest seismic waveform similarity as the dip and azimuth data volume of the analysis point. S12. Using information from the dip data volume and the azimuth data volume to locate reflection interfaces in the post-stack seismic data, smoothing continuous reflection interfaces in the post-stack seismic data, and amplifying waveform differences for discontinuous reflection interfaces to generate a structural guidance filter data volume; S2. Get sensitive attributes Using the dip data volume, azimuth data volume and structure-guided filtering data volume, a sensitive attribute volume capable of identifying igneous rocks is obtained; S3. Process the attribute body separately The sensitive attribute bodies are jointly displayed in plane, section and 3D visualization, and qualified attribute bodies are selected for processing to obtain result attribute bodies; S4. Complete the underground magma rock prediction In the three-dimensional visualization space, the differential amplitude of the magmatic body is extracted from the result attribute body to complete the three-dimensional characterization of the magmatic body, and the morphological range and relative position of the underground magmatic body in the study area are summarized to complete the underground magmatic body prediction.

2. The method for predicting underground magmatic rock mass based on post-stack seismic data according to claim 1, characterized in that: In step S2, the sensitive attribute body includes amplitude curvature attribute, texture attribute, amplitude difference attribute, image edge detection attribute, and coherent energy amplitude gradient attribute.

3. The method for predicting underground magmatic rock mass based on post-stack seismic data according to claim 1, characterized in that: In step S3, the selection criteria are that the magmatic body has a complete morphology, clear boundaries, a contrast ratio increased by 1 times, and is consistent with drilling data, gravity, magnetic and electrical data, and seismic forward modeling results.

4. The method for predicting underground magmatic rock mass based on post-stack seismic data according to claim 1, characterized in that: In step S3, the processing includes the following methods: S31. Use sensitive attribute bodies to identify magmatic rock patterns The sample set established by the determined information in the attribute body is used as a classifier to classify the data in the sensitive attribute body, thereby distinguishing the data related to the magma rock body and obtaining the pattern recognition attribute body; S32. Perform waveform cluster analysis on sensitive attribute bodies Based on the correlation between the overall change of the attribute body amplitude and the lithology, the sensitive attribute bodies are divided into several categories of waveform characteristics, and the attribute bodies representing the waveform characteristics of the magmatic rock body are selected to identify the scope and morphology of the magmatic rock body; S33. Data fusion of sensitive attributes The sensitive attribute bodies are fused to complement each other with relevant information of magma rock bodies to form a fused attribute body; S34. Select result attribute body From the attribute bodies obtained in S31, S32 and S33, the attribute body that best matches the characteristics of the magmatic rock body is selected as the result attribute body.

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