Volcanic reservoir prediction method, device, equipment and storage medium
By combining gradient structure tensor filtering and multi-attribute clustering analysis with a neural network model, the problem of low prediction accuracy for volcanic reservoirs was solved, and high-precision prediction of the distribution of volcanic reservoirs was achieved.
Patent Information
- Application Number
- CN202211403963.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2022-11-10
- Publication Date
- 2025-09-26
- Estimated Expiration
- 2042-11-10
AI Technical Summary
Volcanic reservoir prediction is difficult and existing methods have low accuracy, especially in the deep volcanic gas reservoirs in the Junggar Basin. The signal-to-noise ratio of seismic data is low, and conventional inversion cannot effectively predict high-quality fracture-pore reservoirs.
Gradient structure tensor filtering is used to process raw seismic data. Combining multi-attribute prediction and seismic multi-attribute clustering, the development characteristics, lithofacies and fractures of volcanic structures are analyzed, and the distribution of volcanic reservoirs is comprehensively predicted through a neural network model.
It improves the seismic lateral signal-to-noise ratio, significantly enhances the ability to characterize faults and fractures, provides multi-directional information, provides an important basis for volcanic reservoir prediction, and improves prediction accuracy.
Smart Images
Figure CN118050788B_ABST
Abstract
Description
Technical Field
[0001] The embodiments of the present application relate to the technical field of oil and gas seismic exploration, and in particular to a volcanic reservoir prediction method, apparatus, equipment, and storage medium. Background Art
[0002] With the increasing discovery of volcanic oil and gas reservoirs, volcanic rocks have become a new frontier for oil and gas exploration. However, due to their deep burial depth and the influence of factors such as volcanic eruption periods and tectonic deformation, the internal strata and stratigraphic sequences of volcanic rocks are very complex. This results in seismic data being mostly chaotic reflections, unclear boundaries between different volcanic facies and lithologic combinations, severe overlap in wave impedance characteristics, and high ambiguity in the prediction of volcanic structures and lithofacies distribution patterns. Therefore, volcanic reservoir prediction is difficult. Predicting high-quality volcanic reservoirs has become a major challenge in the efficient development of volcanic gas reservoirs. The accuracy and precision of reservoir predictions determine the benefits of volcanic gas reservoir development. Currently, the industry's volcanic reservoir prediction methods are relatively simple, and the prediction results of single methods are low in accuracy.
[0003] At present, the macroscopic exploration methods of volcanic rocks are limited to searching for volcanic rock bodies. Gravity exploration, magnetic exploration, acoustic magnetic field method, amplitude, phase and frequency analysis of composite traces and other technologies are mainly used to study the thickness distribution, lithofacies and physical properties of underground volcanic rocks. The microscopic research on the petrological characteristics of volcanic rocks, diagenesis and its influence on reservoir physical properties is more detailed. However, the underground geological conditions of deep volcanic gas reservoirs in the Junggar Basin are complex, with characteristics such as deep burial depth, strong heterogeneity, rapid changes in lithology and lithofacies, large changes in reservoir physical properties, and complex seismic reflection characteristics. Therefore, there are the following main problems in predicting volcanic reservoirs: (1) The quality of deep data. Affected by the absorption attenuation of the shallow surface layer, the high-frequency information of the seismic data is severely attenuated, the low-frequency part is missing, and the overall signal-to-noise ratio of the data is low, making it difficult to characterize the volcanic structure and subsequently predict volcanic reservoirs; (2) The quantitative prediction accuracy of volcanic reservoirs is low. Conventional reservoir prediction methods are mostly based on phase-controlled inversion for quantitative prediction of volcanic rocks. However, for fracture-pore type volcanic reservoirs, the rock composition and pore structure of volcanic rocks of different lithofacies vary greatly, and the relationship between the change of reservoir physical parameters and elastic parameters is also different. Conventional inversion cannot effectively predict high-quality reservoirs. Summary of the Invention
[0004] The present invention provides a method, apparatus, device, and storage medium for predicting volcanic reservoirs. The technical solutions provided by the present invention are as follows.
[0005] According to one aspect of an embodiment of the present application, a volcanic reservoir prediction method is provided, the method comprising:
[0006] Obtaining original seismic data;
[0007] Performing filtering processing based on the gradient structure tensor on the original seismic data to obtain processed seismic data;
[0008] Performing multi-attribute prediction based on the processed seismic data to determine volcanic structure development characteristics, wherein the volcanic structure development characteristics are used to characterize the development location of the crater and volcanic channel;
[0009] Analyze the logging response characteristics and seismic response characteristics of typical wells drilled in different volcanic rock facies belts, and use seismic multi-attribute clustering to predict volcanic rock facies to obtain volcanic rock facies prediction results. The volcanic rock facies prediction results are used to characterize the planar distribution range of volcanic rock facies;
[0010] performing multi-scale fracture prediction on the volcanic rock according to the processed seismic data to obtain fracture prediction results, wherein the fracture prediction results are used to characterize fracture information of the volcanic rock;
[0011] The distribution range of the volcanic reservoir is comprehensively predicted based on the development characteristics of the volcanic structure, the volcanic rock lithofacies prediction results and the fracture prediction results.
[0012] According to one aspect of an embodiment of the present application, a volcanic reservoir prediction device is provided, the device comprising:
[0013] Data acquisition module, used to obtain original seismic data;
[0014] A data filtering module, configured to perform filtering processing on the original seismic data based on the gradient structure tensor to obtain processed seismic data;
[0015] a structure determination module, configured to perform multi-attribute prediction based on the processed seismic data to determine the volcanic structure development characteristics, wherein the volcanic structure development characteristics are used to characterize the development location of the crater and volcanic channel;
[0016] The lithofacies prediction module is used to analyze the logging response characteristics and seismic response characteristics of typical wells drilled in different volcanic rock facies zones, and to predict the volcanic rock facies by combining seismic multi-attribute clustering to obtain volcanic rock facies prediction results; the volcanic rock facies prediction results are used to characterize the planar distribution range of the volcanic rock facies;
[0017] a fracture prediction module, configured to perform multi-scale fracture prediction on the volcanic rock based on the processed seismic data to obtain fracture prediction results, wherein the fracture prediction results are used to characterize fracture information of the volcanic rock;
[0018] A reservoir prediction module, configured to comprehensively predict the distribution range of volcanic reservoirs based on the volcanic structure development characteristics, the volcanic rock lithofacies prediction results, and the fracture prediction results;
[0019] According to one aspect of an embodiment of the present application, a computer device is provided, comprising a processor and a memory, wherein a computer program is stored in the memory, and the computer program is loaded and executed by the processor to implement the above-mentioned volcanic reservoir prediction method.
[0020] According to one aspect of an embodiment of the present application, a computer-readable storage medium is provided, in which a computer program is stored. The computer program is loaded and executed by a processor to implement the above-mentioned volcanic reservoir prediction method.
[0021] According to one aspect of an embodiment of the present application, a computer program product is provided, comprising a computer program stored in a computer-readable storage medium. A processor of a computer device reads the computer program from the computer-readable storage medium and executes the computer program, causing the computer device to perform the volcanic reservoir prediction method described above.
[0022] The technical solutions provided in the embodiments of the present application can bring the following beneficial effects:
[0023] By filtering the raw seismic data to generate processed seismic data, the lateral signal-to-noise ratio (SNR) is improved. The processed seismic data significantly enhances the ability to depict faults, facilitating the study of faults and fractures. Based on the processed seismic data, structural development characteristics are determined, and volcanic rock lithofacies and fractures are predicted, resulting in lithofacies and fracture prediction results. Based on these structural development characteristics, lithofacies predictions, and fracture predictions, the distribution range of volcanic reservoirs is determined. This multi-faceted information provides an important basis for volcanic reservoir prediction. BRIEF DESCRIPTION OF THE DRAWINGS
[0024] Figure 1 is a schematic diagram of a volcanic reservoir prediction method provided by one embodiment of the present application;
[0025] Figure 2 This is a flow chart of a volcanic reservoir prediction method provided by one embodiment of the present application;
[0026] Figure 3 This is a schematic diagram of the distribution range of volcanic reservoirs provided by one embodiment of the present application;
[0027] Figure 4 This is a block diagram of a volcanic reservoir prediction device provided by one embodiment of the present application;
[0028] Figure 5 This is a structural block diagram of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION
[0029] In order to make the objectives, technical solutions and advantages of this application clearer, the implementation methods of this application will be further described in detail below with reference to the accompanying drawings.
[0030] In the volcanic reservoir prediction method provided in the embodiments of the present application, each step may be performed by a computer device, which refers to an electronic device capable of data calculation, processing, and storage. For example, the computer device may be a PC (Personal Computer), a server, or the like.
[0031] like Figure 1 As shown, the present embodiment provides a volcanic reservoir prediction method that performs dip-enhanced processing on raw seismic data to obtain processed seismic data. Based on the processed seismic data, the volcanic structure distribution, volcanic lithofacies distribution, and predicted volcanic distribution are obtained. Furthermore, based on drilling and logging data, the volcanic structure distribution (volcanic structure development characteristics), volcanic lithofacies distribution (volcanic lithofacies prediction results), and volcanic fracture distribution (fracture prediction results), a multivariate comprehensive prediction is performed using a neural network model to output a prediction of favorable volcanic reservoir areas (volcanic reservoir distribution range). This prediction can then be used to determine well locations based on the volcanic reservoir distribution range.
[0032] The volcanic reservoir prediction method provided in the embodiment of the present application may include at least one of the following steps 110 to 160.
[0033] Step 110: Obtain original seismic data.
[0034] Raw seismic data refers to data obtained through seismic exploration at sampling points. Sampling points refer to the exact locations where seismic exploration is conducted in the area where volcanic reservoir prediction is required.
[0035] Step 120 , performing filtering processing based on the gradient structure tensor on the original seismic data to obtain processed seismic data.
[0036] The processed seismic data includes drilling data and logging data of the sampling points.
[0037] In some embodiments, GST (Gradient Structure Tensor) is used to filter the raw seismic data.
[0038] In some embodiments, filtering raw seismic data involves calculating the similarity of adjacent traces based on changes in dip and azimuth, thereby improving the lateral signal-to-noise ratio of seismic data. By calculating dip and azimuth, the resulting data significantly enhances fault characterization, facilitating subsequent research on faults and fractures.
[0039] In some embodiments, raw seismic data is filtered, fully accounting for dip variations at each sampling point during the calculation process. A comparison window is selected centered around the sample point, and a corresponding calculation weight is defined for each sampling point involved in the calculation. The dip calculation method primarily uses a small analysis window to identify the direction of maximum seismic data variation to determine the dip of the formation. Gradient vectors are then used to describe the dip and azimuth of the geological body.
[0040] A geological body is a product of geological action that occupies a certain space in the earth's crust, has its own inherent components, and can be distinguished from the surrounding materials.
[0041] Step 130: Perform multi-attribute prediction based on the processed seismic data to determine the development characteristics of the volcanic structure. The development characteristics of the volcanic structure are used to characterize the development location of the crater and volcanic channel.
[0042] The volcanic structure, also known as the volcanic body or volcanic deposit, is the collective term for the various components that make up a volcano. This includes the cone above the surface and the channels of magma underground. The volcanic structure is the final result of the interaction between the two opposing forces of construction and transformation.
[0043] Step 140 , performing well logging response and seismic response analysis on typical wells that encounter different volcanic rock facies belts, and combining seismic multi-attribute clustering to perform volcanic rock lithofacies prediction to obtain volcanic rock lithofacies prediction results. The volcanic rock lithofacies prediction results are used to characterize the planar distribution range of the volcanic rock lithofacies.
[0044] Lithic facies are rocks or rock combinations formed in a certain sedimentary environment, and are the main components of sedimentary facies.
[0045] Step 150 , performing multi-scale fracture prediction on the volcanic rock based on the processed seismic data to obtain fracture prediction results, which are used to characterize the fracture information of the volcanic rock.
[0046] Fracture prediction refers to the prediction of the occurrence, scale, opening degree, mechanical properties and other contents of fractures in volcanic rock reservoirs.
[0047] Fractures are an important factor affecting the migration, accumulation, enrichment and high yield of oil and gas, and are also the key to restricting the exploration of volcanic oil and gas in the study area. Therefore, the prediction of volcanic rock fractures is also an important part of volcanic rock reservoir prediction.
[0048] Step 160 : Comprehensively predict the distribution range of the volcanic reservoir based on the volcanic structure development characteristics, the volcanic rock lithofacies prediction results, and the fracture prediction results.
[0049] In some embodiments, a comprehensive evaluation is performed based on the development characteristics of volcanic structures, volcanic rock facies prediction results, and fracture prediction results to predict the distribution range of volcanic rock reservoirs and classify the volcanic rock reservoirs.
[0050] In some embodiments, volcanic reservoirs can be classified and evaluated to determine the most favorable volcanic rock development areas, and well location deployment recommendations can be obtained, with the well locations used for oil and gas development.
[0051] A well location refers to the specific location of an oil or gas well. A well location can be used for either oil or gas development, and this application does not limit this.
[0052] The technical solution provided in the embodiments of this application improves the lateral signal-to-noise ratio by filtering raw seismic data to produce processed seismic data. This significantly enhances the fault characterization capability of the processed seismic data, facilitating the study of faults and fractures. Based on the processed seismic data, structural development characteristics are determined, and volcanic rock lithofacies and fractures are predicted, resulting in lithofacies and fracture prediction results. Based on these structural development characteristics, lithofacies prediction results, and fracture prediction results, the distribution range of the volcanic reservoir is determined. This multi-faceted information provides an important basis for volcanic reservoir prediction.
[0053] Please refer to Figure 2 , which shows a flow chart of a volcanic reservoir prediction method provided by another embodiment of the present application. The method may include at least one of the following steps 210 to 280.
[0054] Step 210: Obtain original seismic data.
[0055] Step 220 , based on the original seismic data, a spatial combination method is used to obtain the characteristic vector of the seismic three-dimensional data volume according to the Gaussian function method.
[0056] In some embodiments, the following formula is used to obtain the eigenvector of the seismic 3D data volume:
[0057]
[0058] Among them, G(x, σ g ) is the Gaussian window function, σ g is the scale parameter, u(x) represents the displacement function, x i represents the position of the i-th particle, g i Represents the eigenvector of the seismic 3D data volume, i∈{1, 2, 3}.
[0059] In some embodiments, the smoothed gradient tensor is a symmetric tensor, all of whose eigenvalues are greater than or equal to 0.
[0060] Different from the traditional GST which only uses single-point calculation, the above method considers the spatial combination and adopts Gaussian function to improve the resolution and signal-to-noise ratio of the data.
[0061] Step 230 : constructing a gradient structure tensor based on the eigenvector of the three-dimensional seismic data volume and the gradient vector. The gradient structure tensor is used to characterize the dip and azimuth of the volcanic rock.
[0062] In some embodiments, the gradient structure tensor is constructed using the following formula:
[0063]
[0064] Among them, M refers to the characteristic matrix (gradient structure tensor), g is the characteristic vector of amplitude, g T is the transposed matrix of g, g x 、g y 、g z Represent the amplitude vector along the X, Y, and Z directions respectively.
[0065] Step 240: Obtain the dip attribute according to the gradient structure tensor; obtain processed seismic data according to the dip attribute and the original seismic data.
[0066] In some embodiments, the processed seismic data is the original seismic data after dip enhancement, and the process of obtaining the processed seismic data is the process of obtaining dip attributes.
[0067] In some embodiments, the gradient structure tensor can be expressed as follows:
[0068]
[0069] Among them, λ1, λ2, and λ3 are three non-negative eigenvalues of M, which can be obtained through the characteristic matrix M (gradient structure tensor) and satisfy λ1≥λ2≥λ3≥0; v1, v2, and v3 are the eigenvectors corresponding to λ1, λ2, and λ3 respectively.
[0070] In some embodiments, v1, v2, and v3 may constitute a local orthogonal coordinate system, where v1 is the direction with the maximum contrast in the local area, and v2 and v3 constitute a local plane perpendicular to v1.
[0071] In some embodiments, processed seismic data (dip attributes) can be calculated based on v1:
[0072]
[0073] Step 250: Perform multi-attribute prediction based on the processed seismic data to determine the development characteristics of the volcanic structure.
[0074] In some embodiments, the processed seismic data is subjected to frequency division scanning processing to obtain dominant frequency band data, which is dominant frequency band data for volcanic rock imaging; the amplitude attributes of the processed seismic data and the dominant frequency band data are extracted; the processed seismic data is subjected to layer flattening processing to obtain data flattened volume slices, which are used to screen the processed seismic data; the coherence attributes of the processed seismic data are extracted, which are used to characterize the correlation characteristics of the processed seismic data in different time domains; the development characteristics of the volcanic structure are determined based on the amplitude attributes, data flattened volume slices and coherence attributes of the processed seismic data and the dominant frequency band data.
[0075] In some embodiments, the structural development characteristics include at least one of the following: crater location, stratigraphic occurrence, and crater morphology.
[0076] In some embodiments, the processed seismic data is subjected to frequency division scanning analysis to determine the central frequency range of the dominant imaging frequency band of volcanic rocks (dominant frequency band data). Within the dominant frequency band, the imaging accuracy of volcanic rocks is higher and the internal reflection characteristics are clearer. At the same time, the amplitude attributes of the processed seismic data full frequency band and dominant frequency band data are extracted, and the crater location is identified by seismic data flattening volume slices, along-layer amplitude attributes and coherence attributes. After the volcanic eruption, it is affected by the later tectonic movement. On conventional seismic sections, the volcanic structure is difficult to identify. The use of layer flattening (volcanic rock top interface) seismic data volume can better reflect the stratigraphic occurrence after the volcanic eruption. During the volcanic eruption and volcanic rock condensation process, collapse or collapse will occur near the volcanic channel, which generally manifests as chaotic reflection characteristics on the seismic. The amplitude attributes and coherence attributes can be used to characterize the crater morphology. In order to reduce the multi-solution of the above methods, multiple information and multiple methods are mutually verified to further accurately characterize the crater and volcanic structure development area.
[0077] Step 260 , performing well logging response characteristics and seismic response characteristics analysis on typical wells that encounter different volcanic rock phases, and performing volcanic rock lithofacies prediction in combination with seismic multi-attribute clustering to obtain volcanic rock lithofacies prediction results.
[0078] In some embodiments, based on the fine calibration of well seismic data, the seismic response characteristics of each facies belt of volcanic rocks are analyzed, and the multi-attribute clustering and fusion methods are determined to perform volcanic lithofacies prediction and obtain volcanic lithofacies prediction results.
[0079] In some embodiments, the volcanic rock facies prediction result is obtained by the following formula:
[0080] m=αm1+(1-α)m2
[0081] Wherein, m is the lithofacies prediction result (frequency domain seismic phase attribute), m1 is the first amplitude attribute after filtering, m2 is the first spectrum attribute after filtering, and α is the weighting value.
[0082] Step 270 , performing multi-scale fracture prediction on the volcanic rock based on the processed seismic data to obtain fracture prediction results.
[0083] For the fracture prediction methods at various scales, parameter optimization and debugging are performed to determine the fracture prediction method for volcanic rock reservoirs; volcanic rock fracture prediction is performed based on the fracture prediction method for volcanic rock reservoirs and the processed seismic data to obtain fracture prediction results; among them, for medium and large scale fractures, prediction and identification are performed based on coherence attributes and curvature attributes to determine the overall distribution characteristics of the fractures; for small scale fractures, a joint attribute prediction is constructed based on the disorderly attribute and the GST similarity attribute. The disorderly attribute is used to characterize the disorderly development characteristics of volcanic rock fractures, and the GST similarity attribute is used to characterize the similar geometric properties of the gradient structure tensor of the dip angle of volcanic rock.
[0084] In some embodiments, different methods of improving the signal-to-noise ratio are first tested on the inner strata of volcanic rocks to enhance the recognition of faults. The main technical idea is to use the discontinuity of the event axis to highlight the geometric properties guided by fault identification, and perform normalization processing based on the enhanced filtering of the dip angle and the increase of faults, which can highlight small-scale cracks while retaining the information of large-scale faults.
[0085] In some embodiments, a disorderly attribute is calculated based on the eigenvalues of the gradient structure tensor; an overlapping plane group is determined based on the eigenvalues of the gradient structure tensor, and the reflector parameters of the overlapping plane group fall within a set value range; an edge feature is determined based on the overlapping plane group, and the edge feature is used to characterize the phase zone boundary of the volcanic rock; a GST similarity attribute is determined based on the edge feature; and a joint attribute prediction is constructed based on the GST similarity attribute and the disorderly attribute. In some embodiments, the disorderly attribute is calculated using the following formula:
[0086]
[0087] In some embodiments, all eigenvalues are sorted, λ i >λ i+1 , where all variations are defined by the first eigenvector. For example, if λ1>>λ2=λ2≈0, then the messy attribute value C=-1; if the three eigenvectors are equal, λ1=λ2=λ2, then C=1; if there is a boundary, λ1=λ2>>λ2=0, then C=0, and C is the messy attribute.
[0088] In some embodiments, the overlapping plane group is determined by the following formula:
[0089]
[0090] In the filter tilt estimation, a set of overlapping windows is determined to ensure an optimal planar value. Where r ranges between r = 1 / 2 (for random reflectors) and r = 1 (for planar reflectors).
[0091] In some embodiments, an overlapping plane that satisfies a condition is determined in the overlapping plane group based on the eigenvalue of the gradient structure tensor, and an edge feature is determined based on the overlapping plane that satisfies the condition. The overlapping plane that satisfies the condition may also be referred to as an optimal plane value.
[0092] In some embodiments, the optimal plane value can be determined by the following formula:
[0093]
[0094] In some embodiments, until the random reflector is Then the edge feature can be simplified as:
[0095]
[0096] Wherein, f is the edge feature, and its range is set as follows: for λ1≈λ2>>λ2≈0, the boundary f=1; for λ1>>λ2≈λ2 or λ1≈λ2≈λ2, the boundary f=0, and the range of f is between 0 and 1.
[0097] In some embodiments, in order to better represent the crack prediction results (GST similarity geometric attributes), r and f are combined to define the crack prediction result S:
[0098]
[0099] Among them, f is the edge feature, is the optimal plane value.
[0100] In some embodiments, based on fracture prediction methods at different scales, the optimal fracture prediction method for volcanic reservoirs is selected through the optimization and debugging of different parameters. For medium- and large-scale fractures, coherence and curvature attributes are used for prediction and identification to determine the overall distribution characteristics of the fractures. For small-scale fractures, a combined attribute prediction method constructed by the clutter attribute (C) and the GST similarity geometry attribute (S) is used.
[0101] Step 280 : Comprehensively predict the distribution range of the volcanic reservoir based on the volcanic structure development characteristics, the volcanic rock lithofacies prediction results, and the fracture prediction results.
[0102] In some embodiments, the distribution range of volcanic reservoirs is predicted and the volcanic reservoirs are classified based on drilling data, logging data, structural development characteristics, lithofacies prediction results, and fracture prediction results.
[0103] In some embodiments, a neural network model is used to predict the distribution range of volcanic reservoirs and classify volcanic reservoirs based on the development characteristics of volcanic structures, volcanic rock lithofacies prediction results, and fracture prediction results.
[0104] This application does not limit the specific structure of the neural network model.
[0105] In some embodiments, a neural network model is used to cluster and fuse the volcanic structure development characteristics, volcanic rock facies prediction results, and fracture prediction results to predict the distribution range of volcanic rock reservoirs and classify volcanic rock reservoirs.
[0106] In some embodiments, volcanic reservoirs may be classified and evaluated to determine the most favorable volcanic rock development areas and obtain well location deployment recommendations.
[0107] In some embodiments, as Figure 3 As shown, according to the distribution range 310 of the volcanic reservoir, the volcanic reservoir is classified to obtain the well location 320, where 330 represents a fracture.
[0108] The technical solution provided in the embodiments of the present application filters the original seismic data to obtain processed seismic data, achieves a fault strengthening effect, and uses multi-attribute to characterize the crater structure based on the processed seismic data to determine the structure development characteristics. Secondly, by analyzing the seismic response characteristics of different volcanic rock phases, multi-attribute clustering is optimized to predict volcanic rock phases. Then, based on a multi-scale and multi-information fracture prediction method, fracture prediction is performed in volcanic rock reservoirs. Finally, based on multiple information such as structure development characteristics, lithofacies prediction results, and fracture prediction results, multi-information clustering and fusion are carried out to predict the distribution range of volcanic rock reservoirs and conduct classification and evaluation of volcanic rock reservoirs, providing reliable support for subsequent volcanic rock reservoir prediction, etc., and has very important guiding significance for future volcanic rock seismic exploration technology.
[0109] The following are device embodiments of the present application, which can be used to implement the method embodiments of the present application. For details not disclosed in the device embodiments of the present application, please refer to the method embodiments of the present application.
[0110] Please refer to Figure 4 , which shows a block diagram of a volcanic reservoir prediction device provided by one embodiment of the present application. This device has the functionality to implement the aforementioned volcanic reservoir prediction method. This functionality can be implemented by hardware or by hardware executing corresponding software. This device can be a computer device or be incorporated into a computer device. The device 400 may include: a data acquisition module 410, a data filtering module 420, a mechanism determination module 430, a lithofacies prediction module 440, a fracture prediction module 450, and a reservoir prediction module 460.
[0111] The data acquisition module 410 is used to acquire original seismic data.
[0112] The data filtering module 420 is used to filter the original seismic data to obtain processed seismic data, where the processed seismic data is dip-enhanced seismic data.
[0113] The structure determination module 430 is used to perform filtering processing based on the gradient structure tensor on the original seismic data to obtain processed seismic data.
[0114] The lithofacies prediction module 440 is used to analyze the logging response characteristics and seismic response characteristics of typical wells drilled in different volcanic rock facies zones, and combine seismic multi-attribute clustering to predict volcanic rock facies to obtain volcanic rock facies prediction results; the volcanic rock facies prediction results are used to characterize the planar distribution range of the volcanic rock facies.
[0115] The fracture prediction module 450 is used to perform multi-scale fracture prediction on the volcanic rock based on the processed seismic data to obtain fracture prediction results, and the fracture prediction results are used to characterize the fracture information of the volcanic rock.
[0116] The reservoir prediction module 460 is used to comprehensively predict the distribution range of volcanic reservoirs based on the volcanic structure development characteristics, the volcanic rock lithofacies prediction results and the fracture prediction results.
[0117] In some embodiments, the data filtering module 420 is used to obtain the characteristic vector of the seismic three-dimensional data volume based on the original seismic data by adopting a spatial combination method and a Gaussian function method; construct a gradient structure tensor based on the characteristic vector of the seismic three-dimensional data volume through a gradient vector, and the gradient structure tensor is used to characterize the dip and azimuth of volcanic rocks; obtain the dip attribute based on the gradient structure tensor; and obtain the processed seismic data based on the dip attribute and the original seismic data.
[0118] In some embodiments, the structure determination module 430 is used to perform frequency division scanning processing on the processed seismic data to obtain dominant frequency band data, which is dominant frequency band data of volcanic rocks; extract the amplitude attributes of the processed seismic data and the dominant frequency band data; perform layer flattening processing on the processed seismic data to obtain data flattened volume slices, which are used to screen the processed seismic data; extract coherence attributes of the processed seismic data, which are used to characterize the correlation characteristics of the processed seismic data in different time domains; determine the development characteristics of the volcanic structure based on the amplitude attributes of the processed seismic data and the dominant frequency band data, the data flattened volume slices and the coherence attributes; the development characteristics of the volcanic structure include at least one of the following: crater location, stratigraphic attitude, and crater morphology.
[0119] In some embodiments, the lithofacies prediction module 440 is used to analyze the seismic facies markers of each volcanic rock phase belt based on the well-seismic fine calibration, determine the multi-attribute clustering and fusion method, perform volcanic rock phase prediction, and obtain the volcanic rock phase prediction result.
[0120] In some embodiments, the fracture prediction module 450 is used to optimize and debug parameters for fracture prediction methods at various scales, determine a fracture prediction method for volcanic rock reservoirs; perform volcanic rock fracture prediction based on the fracture prediction method for volcanic rock reservoirs and the processed seismic data to obtain the fracture prediction results; wherein, for medium and large scale fractures, prediction and identification are performed based on coherence attributes and curvature attributes to determine the overall distribution characteristics of the fractures; for small scale fractures, a joint attribute prediction is constructed based on the disorderly attribute and the GST similarity attribute, the disorderly attribute is used to characterize the disorderly development characteristics of the fractures in the volcanic rock, and the GST similarity attribute is used to characterize the similar geometric attributes of the gradient structure tensor of the dip angle of the volcanic rock.
[0121] In some embodiments, the crack prediction module 450 is used to calculate the messy attribute based on the eigenvalue of the gradient structure tensor; determine the overlapping plane group based on the eigenvalue of the gradient structure tensor, and the reflector parameters of the overlapping plane group belong to the set value range; determine the edge characteristics based on the overlapping plane group, and the edge characteristics are used to characterize the phase zone boundary of the volcanic rock; determine the GST similarity attribute based on the edge characteristics; and construct a joint attribute prediction based on the GST similarity attribute and the messy attribute.
[0122] In some embodiments, the crack prediction module 450 is configured to determine an overlapping plane that meets a condition in the overlapping plane group according to the eigenvalue of the gradient structure tensor; and determine the edge feature according to the overlapping plane that meets the condition.
[0123] The technical solution provided in the embodiments of this application improves the lateral signal-to-noise ratio by filtering raw seismic data to produce processed seismic data. This significantly enhances the fault characterization capability of the processed seismic data, facilitating the study of faults and fractures. Based on the processed seismic data, structural development characteristics are determined, and volcanic rock lithofacies and fractures are predicted, resulting in lithofacies and fracture prediction results. Based on these structural development characteristics, lithofacies prediction results, and fracture prediction results, the distribution range of the volcanic reservoir is determined. This multi-faceted information provides an important basis for volcanic reservoir prediction.
[0124] Please refer to Figure 5 , which shows a schematic diagram of the structure of a computer device provided in one embodiment of the present application. The computer device can be any electronic device with data calculation, processing, and storage functions. The computer device can be used to implement the volcanic reservoir prediction method provided in the above embodiment. Specifically:
[0125] The computer device 500 includes a central processing unit (CPU, central processing unit), GPU (graphics processing unit), and FPGA (field programmable gate array) 501, a system memory 504 including RAM (random-access memory) 502 and ROM (read-only memory) 503, and a system bus 505 connecting the system memory 504 and the central processing unit 501. The computer device 500 also includes a basic input / output system (I / O system) 506 for facilitating information transmission between various components within the server, and a mass storage device 505 for storing an operating system 513, application programs 514, and other program modules 515.
[0126] In some embodiments, the basic input / output system 506 includes a display 508 for displaying information and an input device 509, such as a mouse or keyboard, for user input. Both the display 508 and the input device 509 are connected to the central processing unit 501 via an input / output controller 510 connected to the system bus 505. The basic input / output system 506 may also include an input / output controller 510 for receiving and processing input from a variety of other devices, such as a keyboard, mouse, or electronic stylus. Similarly, the input / output controller 510 also provides output to a display screen, printer, or other types of output devices.
[0127] The mass storage device 507 is connected to the central processing unit 501 via a mass storage controller (not shown) connected to the system bus 505. The mass storage device 506 and its associated computer-readable media provide non-volatile storage for the computer device 500. In other words, the mass storage device 507 may include a computer-readable medium (not shown) such as a hard disk or a CD-ROM (Compact Disc Read-Only Memory) drive.
[0128] Without loss of generality, the computer-readable medium may include computer storage media and communication media. Computer storage media include volatile and non-volatile, removable and non-removable media implemented by any method or technology for storing information such as computer-readable instructions, data structures, program modules or other data. Computer storage media include RAM, ROM, EPROM (Erasable Programmable Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), flash memory or other solid-state storage technology, CD-ROM, DVD (Digital Video Disc) or other optical storage, tape cassettes, magnetic tape, disk storage or other magnetic storage devices. Of course, those skilled in the art will appreciate that the computer storage media is not limited to the above-mentioned ones. The above-mentioned system memory 504 and mass storage device 507 can be collectively referred to as memory.
[0129] According to an embodiment of the present application, the computer device 500 can also be connected to a remote computer on a network such as the Internet for operation. That is, the computer device 500 can be connected to the network 512 via the network interface unit 511 connected to the system bus 505. Alternatively, the network interface unit 511 can be used to connect to other types of networks or remote computer systems (not shown).
[0130] The memory stores a computer program, which is loaded and executed by the processor to implement the above-mentioned volcanic reservoir prediction method.
[0131] In an exemplary embodiment, a computer-readable storage medium is further provided, wherein a computer program is stored in the computer-readable storage medium. The computer program is loaded and executed by a processor to implement the above-mentioned volcanic reservoir prediction method.
[0132] Optionally, the computer-readable storage medium may include: ROM (Read-Only Memory), RAM (Random-Access Memory), SSD (Solid State Drives), or an optical disk, etc. Among them, the random access memory may include ReRAM (Resistance Random Access Memory) and DRAM (Dynamic Random Access Memory).
[0133] In an exemplary embodiment, a computer program product is also provided, which includes a computer program stored in a computer-readable storage medium. A processor reads and executes the computer program from the computer-readable storage medium to implement the above-mentioned volcanic reservoir prediction method.
[0134] It should be understood that the term "plurality" used herein refers to two or more. "And / or" describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can mean: A exists alone, A and B exist simultaneously, or B exists alone. The character " / " generally indicates an "or" relationship between the associated objects.
[0135] The above description is merely an exemplary embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.
Claims
1. A volcanic reservoir prediction method, characterized in that: The method comprises: Obtaining original seismic data; Performing filtering processing based on the gradient structure tensor on the original seismic data to obtain processed seismic data; Performing frequency division scanning processing on the processed seismic data to obtain dominant frequency band data, wherein the dominant frequency band data is dominant imaging frequency band data of volcanic rocks; extracting amplitude attributes of the processed seismic data and the dominant frequency band data; performing layer flattening processing on the processed seismic data to obtain data flattened volume slices, wherein the data flattened volume slices are used to screen the processed seismic data; Extracting coherent attributes of the processed seismic data, wherein the coherent attributes are used to characterize the correlation characteristics of the processed seismic data in different time domains; Determining volcanic structure development characteristics based on the processed seismic data and the amplitude attributes of the dominant frequency band data, the data flattened volume slice, and the coherence attributes, wherein the volcanic structure development characteristics are used to characterize the development location of the crater and volcanic channel; the volcanic structure development characteristics include at least one of the following: crater location, stratigraphic occurrence, and crater morphology; Analyze the logging response characteristics and seismic response characteristics of typical wells drilled in different volcanic rock facies belts, and use seismic multi-attribute clustering to predict volcanic rock facies to obtain volcanic rock facies prediction results. The volcanic rock facies prediction results are used to characterize the planar distribution range of volcanic rock facies; performing multi-scale fracture prediction on the volcanic rock according to the processed seismic data to obtain fracture prediction results, wherein the fracture prediction results are used to characterize fracture information of the volcanic rock; The distribution range of the volcanic reservoir is comprehensively predicted based on the development characteristics of the volcanic structure, the volcanic rock lithofacies prediction results and the fracture prediction results.
2. The method according to claim 1, characterized in that The filtering process based on the gradient structure tensor is performed on the original seismic data to obtain processed seismic data, including: According to the original seismic data, a spatial combination method is used to obtain a eigenvector of a three-dimensional seismic data volume according to a Gaussian function method; According to the eigenvector of the three-dimensional seismic data volume, a gradient structure tensor is constructed through a gradient vector, wherein the gradient structure tensor is used to characterize the dip and azimuth of the volcanic rock; Obtaining a dip attribute according to the gradient structure tensor; The processed seismic data is obtained according to the dip attribute and the original seismic data.
3. The method according to claim 1, characterized in that The logging response characteristics and seismic response characteristics of typical wells drilled in different volcanic rock phases are analyzed, and volcanic rock lithofacies prediction is performed in combination with seismic multi-attribute clustering to obtain volcanic rock lithofacies prediction results, including: Based on the fine calibration of well seismic data, the seismic response characteristics of each facies belt of volcanic rocks are analyzed, and the multi-attribute clustering and fusion methods are determined to perform volcanic lithofacies prediction and obtain the volcanic lithofacies prediction results.
4. The method according to claim 1, wherein The performing multi-scale fracture prediction on the volcanic rock according to the processed seismic data to obtain seismic data after the fracture prediction results are processed includes: Optimize and debug parameters for fracture prediction methods at various scales to determine fracture prediction methods for volcanic reservoirs; Predicting volcanic rock fractures according to the volcanic rock reservoir fracture prediction method and the processed seismic data to obtain the fracture prediction result; Among them, for medium and large scale faults, prediction and identification are carried out based on coherence attributes and curvature attributes to determine the overall distribution characteristics of the faults; For small-scale fractures, a joint attribute prediction is constructed based on the disorderly attribute and the GST similarity attribute. The disorderly attribute is used to characterize the disorderly development characteristics of the fractures of the volcanic rock, and the GST similarity attribute is used to characterize the similar geometric attributes of the gradient structure tensor of the dip angle of the volcanic rock.
5. The method according to claim 4, characterized in that For small-scale faults, a joint attribute prediction is constructed based on the messy attribute and the GST similarity attribute, including: The messy attribute is calculated according to the eigenvalue of the gradient structure tensor; determining an overlapping plane group according to an eigenvalue of the gradient structure tensor, wherein reflector parameters of the overlapping plane group belong to a set value range; determining edge features based on the set of overlapping planes, wherein the edge features are used to characterize the facies boundary of the volcanic rock; Determining the GST similarity attribute based on the edge features; A joint attribute prediction is constructed based on the GST similarity attribute and the messy attribute.
6. The method according to claim 5, characterized in that Determining edge features according to the overlapping plane group includes: Determining an overlapping plane that meets a condition in the overlapping plane group according to the eigenvalue of the gradient structure tensor; The edge feature is determined according to the overlapping planes that meet the conditions.
7. A volcanic reservoir prediction device, characterized in that: The device comprises: Data acquisition module, used to obtain original seismic data; A data filtering module, configured to perform filtering processing on the original seismic data based on the gradient structure tensor to obtain processed seismic data; a structure determination module for performing frequency division scanning on the processed seismic data to obtain dominant frequency band data, wherein the dominant frequency band data is dominant imaging frequency band data of volcanic rocks; extracting amplitude attributes of the processed seismic data and the dominant frequency band data; performing layer flattening on the processed seismic data to obtain data flattened volume slices, wherein the data flattened volume slices are used to screen the processed seismic data; extracting coherence attributes of the processed seismic data, wherein the coherence attributes are used to characterize the correlation characteristics of the processed seismic data in different time domains; determining volcanic structure development characteristics based on the amplitude attributes of the processed seismic data and the dominant frequency band data, the data flattened volume slices, and the coherence attributes, wherein the volcanic structure development characteristics are used to characterize the development location of the crater and volcanic channel; the volcanic structure development characteristics include at least one of the following: crater location, stratigraphic occurrence, and crater morphology; The lithofacies prediction module is used to analyze the logging response characteristics and seismic response characteristics of typical wells drilled in different volcanic rock facies zones, and to predict the volcanic rock facies by combining seismic multi-attribute clustering to obtain volcanic rock facies prediction results; the volcanic rock facies prediction results are used to characterize the planar distribution range of the volcanic rock facies; a fracture prediction module, configured to perform multi-scale fracture prediction on the volcanic rock based on the processed seismic data to obtain fracture prediction results, wherein the fracture prediction results are used to characterize fracture information of the volcanic rock; The reservoir prediction module is used to comprehensively predict the distribution range of the volcanic reservoir based on the development characteristics of the volcanic structure, the volcanic rock lithofacies prediction results and the fracture prediction results.
8. A computer device, characterized in that: The computer device includes a processor and a memory, wherein a computer program is stored in the memory, and the computer program is loaded and executed by the processor to implement the volcanic reservoir prediction method according to any one of claims 1 to 6.
9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which is loaded and executed by a processor to implement the volcanic reservoir prediction method according to any one of claims 1 to 6.