Source rock prediction method and system used under volcanic rock background

Abstract

The invention discloses a hydrocarbon source rock prediction method and system used under the volcanic rock background, and the method comprises the steps: building an earthquake recognition mode representing the distribution of developed hydrocarbon source rocks under the volcanic rock background according to the logging and earthquake response characteristics of a target region; according to the seismic facies division result of the target area, in combination with sensitive seismic attributes for volcanic rock and hydrocarbon source rock distribution, qualitatively analyzing macroscopic distribution of hydrocarbon source rocks; taking an earthquake identification mode as guidance, and performing inversion on the hydrocarbon source rock macroscopic distribution map in a mode that a three-dimensional earthquake region with a high signal-to-noise ratio drives a two-dimensional earthquake region with a low signal-to-noise ratio to obtain a hydrocarbon source rock quantitative distribution prediction map; and extracting hydrocarbon source rock development size characteristic distribution information from the hydrocarbon source rock quantitative distribution prediction map by taking a hydrocarbon source rock impedance threshold value as a constraint so as to obtain a hydrocarbon source rock development plane distribution map. According to the method, distribution prediction is carried out on volcanic eruption intermittent deposition hydrocarbon source rocks in areas without wells or with few wells under the volcanic rock background.

Description

Technical Field

[0001] The present invention relates to the technical field of oil exploration and development, and particularly to a method and system for predicting source rocks under the background of volcanic rocks. Background Art

[0002] Source rocks are the basis of hydrocarbon-bearing systems and hydrocarbon accumulation. The prediction of source rocks plays a crucial role in oil and gas exploration. Usually, the distribution of source rocks is mainly analyzed by geochemical tests on core and cuttings samples in wells to obtain evaluation parameters such as organic carbon of source rocks, and then to clarify the vertical distribution and thickness of source rocks. In areas with few or no wells, the prediction of the thickness and distribution range of source rocks has always been a difficult point in source rock research.

[0003] Existing technologies have conducted a lot of explorations on the geophysical prediction of source rocks developed under the sedimentary background of marine and continental clastic rocks, and some methods have achieved certain effects, but most of them are single methods and have limitations. In the complex background of volcanic rock development, there is no relevant prediction technology for the distribution of source rocks deposited during the intermission of multiple volcanic eruptions. Therefore, it is necessary to develop a breakthrough in the prediction technology of source rocks under the background of volcanic rocks.

[0004] The prior art provides a patent document of a method for multi-parameter prediction of source rocks under the condition of few wells (publication number: CN201711315830.3). This solution mainly uses Fourier transform to determine the position of the target layer where the source rock is located based on the seismic volume data in the time domain; at the position of the target layer, seismic attribute information is extracted from the seismic volume data in the time domain; attribute fusion is performed according to the seismic attribute information, and the position of the source rock is determined in combination with the logging data; the lithology data in the logging data is statistically analyzed to determine the sedimentary facies model corresponding to the lithology data in each depth segment; the position of the organic carbon body data under different sedimentary facies models at the position of the source rock is determined by using the organic carbon parameter prediction curve and geostatistical inversion technology; segmentation is performed according to the position of the organic carbon body data to obtain organic carbon body data under multiple different sedimentary facies models; the organic carbon body data under multiple different sedimentary facies models are combined to obtain the predicted value of organic carbon, improving the accuracy of organic carbon prediction.

[0005] The prior art provides a patent document for a method for predicting the lithologic assemblage of clastic rocks in marine strata (publication number: CN201710232854.6). In areas without wells, based on the principle of sedimentation process-seismic response comparison, a template of lithologic assemblage and seismic response is established using the synthetic seismogram method; by analyzing the differences in seismic wave amplitude characteristics of reflected waves in different lithologies in the rock formation in 2D seismic data, forward calculation of the rock wave impedance characteristics is carried out to establish the relationship between marine clastic rocks and their seismic responses; the lithologic template combination established using the sedimentation process-seismic response technology and seismic wave waveform and amplitude analysis technology is used to predict the thickness of marine sandstone and mudstone and their lithologic assemblage; an artificial single-well column is established; lithology identification is carried out on the seismic section according to the seismic waveform characteristics and amplitude characteristics; and the mudstone source rock area is found according to the seismic waveform characteristics and amplitude characteristics, combined with statistical laws.

[0006] The prior art provides a patent document for a method for seismic reservoir prediction of shale oil and gas reservoirs (publication number: CN201610833059.8). The solution includes: 1) calculating rock physical parameters using well logging data; 2) performing crossplot analysis on the rock physical parameters to select effective parameters; 3) conducting forward simulation analysis in combination with the effective parameters to clarify the geophysical response characteristics of shale reservoirs and determine the seismic sensitive attributes that can be used to solve the underground shale reservoirs in the study area; 4) extracting the seismic sensitive attributes of shale reservoirs using post-stack seismic data and conducting spatial distribution prediction; 5) comprehensively predicting the distribution of sweet spots in shale reservoirs. Starting from the characteristics of shale oil and gas reservoirs, this method makes full use of various seismic prediction technical means to conduct target evaluation of sweet spots, which can provide a powerful means for shale reservoir description, fundamentally improve the exploration success rate of shale oil and gas reservoirs, and minimize the exploration and development risks.

[0007] The prior art provides a patent document for a method for predicting the development and distribution of high-quality lacustrine source rocks (publication number: CN201910675231.5). The solution includes: for the main source rock intervals in the target area, respectively using well logging data to determine the vertical distribution range of high-quality source rocks in each single well, that is, the vertical depth and thickness, and using the interpolation method to draw the isopach map of the plane distribution thickness of high-quality source rocks according to the determined vertical distribution range of high-quality source rocks in each single well to determine the vertical distribution range of high-quality source rocks; for the main source rock intervals in the target area, respectively using seismic data to determine the lateral distribution changes of high-quality source rocks; making a development map of high-quality source rocks for the target area; thus obtaining the specific distribution of high-quality lacustrine source rocks in the main source rock intervals of the target area, that is, the prediction result of the distribution of high-quality lacustrine source rocks.

[0008] The prior art provides a technical literature titled "Using Frequency Division Inversion to Predict Source Rocks". Among them, frequency division inversion is an advanced seismic inversion technology developed in recent years. This frequency division inversion scheme first conducts spectral analysis on seismic data to determine the effective frequency band range of the data; then uses wavelet frequency division technology to divide the original seismic data into low, medium, and high-frequency data volumes; then calculates the relationship between amplitude and frequency (AVF) at different thicknesses, introduces the AVF relationship as independent information into the inversion, thereby establishing a non-linear mapping relationship between the logging target curve and the seismic waveform, and obtaining the final inversion result. The advantage of this method is that by introducing AVF information, the inversion result is more reliable, and it solves the problems of thin reservoir identification and lateral discontinuity, improving the resolution of reservoir prediction. However, the disadvantage of this method is that using the concept of tuning to calculate the thickness of source rocks has a large error in areas with obvious thin interbeds and lithological horizontal heterogeneity.

[0009] The prior art provides a technical literature titled "Identifying High-Quality Source Rocks Using Seismic Facies". This literature uses artificial neural network analysis technology, statistical clustering hierarchical classification technology, and principal component analysis (PCA) technology. Referring to the shape changes of seismic traces, it converts the changes in the sample values of seismic data into changes in the shape of seismic traces. In actual operation, first, several typical shapes are divided, then each actual seismic trace is assigned the shape of a very similar model trace, and finally, it is calibrated with actual drilling information to give actual geological significance. The advantage of this literature is that it can make full use of seismic data information and has a good effect in areas with few or no wells. However, the disadvantage of this literature is that the vertical resolution is relatively low, and there is a serious problem of multiple solutions in the absence of pattern guidance and well calibration.

[0010] In addition, the prior art provides a technical literature titled "Application of Two-Dimensional and Three-Dimensional Joint Pseudo-Well Inversion Technology". This literature is based on the principle of constrained sparse pulse, makes full use of the two-dimensional survey lines and well data passing through the three-dimensional work area for constraint, and realizes the inversion work of the three-dimensional work area without wells. In fact, it is to perform inversion on the two-dimensional survey lines with well constraints, output the inversion results in the overlapping area of the two-dimensional and three-dimensional work areas, and use the inversion results as well data to constrain the inversion of the three-dimensional work area to achieve the purpose of improving the resolution of reservoir prediction. The advantage of this method is that it makes full use of the existing well data information and improves the vertical resolution of reservoir prediction in the three-dimensional area without wells; the limitation is that the signal-to-noise ratio and resolution of the two-dimensional data are relatively low, and using the inversion results of the two-dimensional data to constrain the inversion of the three-dimensional work area results in a decrease in resolution and an increase in multiple solutions.

[0011] In summary, the existing technologies usually start from drilling, combine seismic reflection characteristics, establish a seismic identification model for hydrocarbon source rocks, and then, guided by the model, mainly use seismic attributes, seismic inversion, and seismic facies analysis to predict the distribution range of hydrocarbon source rocks, which has certain effects. However, there are also obvious drawbacks. For example, seismic attributes, etc. are not constrained by existing wells, the information of existing wells is not fully utilized, and the vertical resolution is poor; while seismic inversion makes full use of well information and has high vertical resolution, but the lateral multi-solution problem is obvious. Due to the multi-solution nature of seismic data, especially in areas with few wells, the reliability of the prediction results by a single method is low, which will instead increase the exploration risk.

[0012] Therefore, the existing technologies need to study a prediction scheme for the distribution of hydrocarbon source rocks applicable to areas with no wells or few wells under volcanic rock backgrounds. Summary of the Invention

[0013] The purpose of the present invention is to provide a prediction scheme for the distribution of hydrocarbon source rocks applicable to areas with no wells or few wells under volcanic rock backgrounds.

[0014] To solve the above technical problems, an embodiment of the present invention provides a method for predicting hydrocarbon source rocks under volcanic rock backgrounds, including: establishing a seismic identification model characterizing the distribution of hydrocarbon source rocks developed under volcanic rock backgrounds according to the logging and seismic response characteristics of the target area; qualitatively analyzing the macroscopic distribution of hydrocarbon source rocks according to the seismic facies division results of the target area, in combination with sensitive seismic attributes for volcanic rocks and the distribution of hydrocarbon source rocks; guiding by the seismic identification model, inverting the macroscopic distribution map of hydrocarbon source rocks by means of driving low-signal-to-noise two-dimensional seismic areas with high-signal-to-noise three-dimensional seismic areas to obtain a quantitative distribution prediction map of hydrocarbon source rocks; extracting the distribution information of the development size characteristics of hydrocarbon source rocks from the quantitative distribution prediction map of hydrocarbon source rocks with the impedance threshold value of hydrocarbon source rocks as a constraint, so as to obtain a planar distribution map of the development of hydrocarbon source rocks.

[0015] Preferably, in the step of establishing a seismic identification model characterizing the distribution of hydrocarbon source rocks developed under volcanic rock backgrounds according to the logging and seismic response characteristics of the target area, it includes: obtaining a model characterizing the relationship between multiple logging curves under different lithologies based on the drilling data of the target area and its adjacent areas, and based on this, determining the rock type division results under volcanic rock backgrounds by analyzing sensitive logging curves of hydrocarbon source rocks; obtaining a seismic identification model for the development of hydrocarbon source rocks by using wave equation forward modeling verification according to the seismic response characteristics of different rock types, and the seismic identification model includes logging response characteristics, seismic inversion characteristics, and seismic attribute characteristics.

[0016] Preferably, the sensitive logging curves of hydrocarbon source rocks in the target area are acoustic time difference logging curves and density curves.

[0017] Preferably, in the step of qualitatively analyzing the macroscopic distribution of the source rock according to the seismic facies division results of the target area and in combination with sensitive seismic attributes for the distribution of volcanic rocks and source rocks, the following steps are included: performing seismic facies division processing, wavelet transform frequency division energy analysis, and multiple conventional seismic attribute response analyses on the target area respectively, and based on this, selecting the sensitive seismic response attributes of the target area; according to the seismic facies division results of the target area and in combination with the sensitive seismic response attributes, analyzing to obtain the macroscopic distribution map of the source rock.

[0018] Preferably, cluster analysis is used to divide the seismic facies of the target area, wherein the sensitive seismic response attributes are wavelet transform energy attributes and root mean square attributes.

[0019] Preferably, in the step of taking the seismic identification mode as a guide and inverting the macroscopic distribution map of the source rock by driving the two-dimensional seismic area with low signal-to-noise ratio by the three-dimensional seismic area with high signal-to-noise ratio to obtain the quantitative distribution prediction map of the source rock, the following steps are included: taking the seismic identification mode as a guide, establishing virtual wells in the three-dimensional high signal-to-noise ratio area in the macroscopic distribution map of the source rock; based on the virtual wells, according to the seismic identification mode, using the two-dimensional and three-dimensional joint seismic inversion method to invert the three-dimensional work area, the two-dimensional and three-dimensional overlapping work area, and the two-dimensional work area in the macroscopic distribution map of the source rock in sequence, and establishing the wave impedance distribution map of the two-dimensional and three-dimensional joint inversion of the target area to be used as the quantitative distribution prediction map of the source rock.

[0020] Preferably, in the step of taking the impedance threshold value of the source rock as a constraint and extracting the distribution information of the development size characteristics of the source rock from the quantitative distribution prediction map of the source rock to obtain the plane distribution map of the source rock development, the following steps are included: taking the impedance threshold value of the source rock as a constraint and taking the wave impedance distribution characteristics in the quantitative distribution prediction map of the source rock as a basis, extracting the T0 time domain including the source rock thickness; according to the velocity data volume of the target area, converting the T0 time domain to obtain the plane distribution map of the source rock thickness.

[0021] On the other hand, an embodiment of the present invention provides a computer-readable storage medium, which contains a series of instructions for executing the steps of the above-mentioned source rock prediction method.

[0022] In addition, the embodiment of the present invention further provides a hydrocarbon source rock prediction system for the volcanic rock background, including: a seismic pattern recognition module configured to establish a seismic recognition pattern characterizing the distribution of hydrocarbon source rocks developed under the volcanic rock background according to the well logging and seismic response characteristics of the target area; a macroscopic distribution analysis module configured to qualitatively analyze the macroscopic distribution of hydrocarbon source rocks according to the seismic facies division results of the target area and in combination with sensitive seismic attributes for the distribution of volcanic rocks and hydrocarbon source rocks; a quantitative distribution analysis module configured to invert the macroscopic distribution map of hydrocarbon source rocks by driving the two-dimensional seismic area with low signal-to-noise ratio by means of the three-dimensional seismic area with high signal-to-noise ratio under the guidance of the seismic recognition pattern to obtain a quantitative distribution prediction map of hydrocarbon source rocks; a development feature generation module configured to extract the distribution information of the development size characteristics of hydrocarbon source rocks from the quantitative distribution prediction map of hydrocarbon source rocks with the impedance threshold value of hydrocarbon source rocks as a constraint, so as to obtain a planar distribution map of the development of hydrocarbon source rocks.

[0023] Preferably, the development feature generation module is further configured to use the impedance threshold value of hydrocarbon source rocks as a constraint and based on the wave impedance distribution characteristics in the quantitative distribution prediction map of hydrocarbon source rocks, extract the T0 time domain containing the thickness of hydrocarbon source rocks, and then convert the T0 time domain according to the velocity data volume of the target area to obtain a planar distribution map of the thickness of hydrocarbon source rocks.

[0024] Compared with the prior art, one or more of the above embodiments may have the following advantages or beneficial effects:

[0025] The present invention proposes a method and system for predicting hydrocarbon source rocks under the volcanic rock background. The method and system clarify the distribution of hydrocarbon source rocks, lithological characteristics, and rock combination types in different sedimentary environments through the division of sedimentary system tracts under the volcanic rock background, and establish a seismic recognition pattern for hydrocarbon source rocks; carry out macroscopic qualitative prediction of hydrocarbon source rocks by combining seismic wavelet transform frequency division energy and seismic facies division of waveform clustering analysis; on this basis, carry out quantitative prediction of hydrocarbon source rocks based on two-dimensional to three-dimensional joint inversion constructed by genetic algorithm virtual wells; and carry out qualitative and quantitative multi-parameter fusion prediction of hydrocarbon source rocks through grouping clustering and principal component analysis. Thus, the present invention mainly aims at hydrocarbon source rocks deposited during the volcanic eruption intermission period under the volcanic rock background, conducts prediction of the distribution of hydrocarbon source rocks in areas with few or no wells, uses the application mode as a constraint, geology guides geophysical exploration, and effectively depicts by a multi-parameter prediction method combining qualitative and quantitative methods, so as to objectively predict the distribution and scale of hydrocarbon source rocks.

[0026] Thus, through geological guidance of geophysical exploration, pattern guidance of attributes, and multi-parameter fusion, the present invention combines methods such as seismic facies division by cluster analysis, frequency-divided energy based on wavelet transform, and two-dimensional to three-dimensional joint inversion, effectively depicting the spatial distribution of source rocks in the trench-arc basin. Moreover, it has been directly applied to the research of favorable source rock area evaluation and hydrocarbon reservoir scale assessment, effectively guiding the exploration deployment work. This technical method has strong practical application value and application effect, worthy of popularization and application, and has good application prospects.

[0027] Other features and advantages of the present invention will be described in the following specification, and part of them will become obvious from the specification, or be understood by implementing the present invention. The objectives and other advantages of the present invention can be achieved and obtained through the structures specifically pointed out in the specification, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention, and do not constitute a limitation to the present invention. In the drawings:

[0029] Figure 1 It is a schematic diagram of the steps of the method for predicting source rocks under the volcanic rock background according to the embodiment of the present application.

[0030] Figure 2 It is a schematic diagram of the specific process of the method for predicting source rocks under the volcanic rock background according to the embodiment of the present application.

[0031] Figure 3 It is an example diagram of the crossplot of natural gamma and acoustic travel time logging curves in the method for predicting source rocks under the volcanic rock background according to the embodiment of the present application.

[0032] Figure 4 It is an example diagram of the crossplot of natural gamma and acoustic impedance logging curves in the method for predicting source rocks under the volcanic rock background according to the embodiment of the present application.

[0033] Figure 5 It is an example diagram of the seismic identification pattern chart in the method for predicting source rocks under the volcanic rock background according to the embodiment of the present application.

[0034] Figure 6 It is an example diagram of the analysis result of the conventional seismic response attributes in the method for predicting source rocks under the volcanic rock background according to the embodiment of the present application.

[0035] Figure 7 It is an example diagram of the macroscopic distribution map of source rocks in the method for predicting source rocks under the volcanic rock background according to the embodiment of the present application.

[0036] Figure 8This is an example diagram of the quantitative distribution prediction map of the hydrocarbon source rock in the hydrocarbon source rock prediction method under the volcanic rock background in the embodiment of the present application.

[0037] Figure 9 This is a module block diagram of the hydrocarbon source rock prediction system in the embodiment of the present application under the volcanic rock background. Detailed implementation manners

[0038] The following will combine the drawings and embodiments to detail the implementation manners of the present invention, so as to fully understand how the present invention uses technical means to solve technical problems and achieve the implementation process of technical effects and implement accordingly. It should be noted that as long as there is no conflict, each embodiment in the present invention and each feature in each embodiment can be combined with each other, and the formed technical solutions are all within the protection scope of the present invention.

[0039] In addition, the steps shown in the flowchart of the drawings can be executed in a computer system such as a set of computer-executable instructions. And although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0040] The terms used here are only for describing specific embodiments and do not intend to limit the exemplary embodiments. Unless the context clearly indicates otherwise, the singular forms "a" and "an" used here also intend to include the plural. It should also be understood that the terms "including" and / or "comprising" used here specify the presence of the stated features, integers, steps, operations, units and / or components, and do not exclude the presence or addition of one or more other features, integers, steps, operations, units, components and / or their combinations.

[0041] To solve the technical problems in the above background technology, the embodiments of the present application propose a hydrocarbon source rock prediction method and system under the volcanic rock background. The method and system first divide the seismic facies through waveform clustering, make full use of the waveform information of seismic data to clarify the macroscopic distribution range of the hydrocarbon source rock, then analyze the frequency-divided energy through wavelet transform to improve the prediction accuracy of the hydrocarbon source rock. The virtual well construction makes full use of the three-dimensional seismic area with high signal-to-noise ratio to drive the two-dimensional seismic area with low signal-to-noise ratio to promote the reasonable use of existing data. Then, combined with the joint inversion of two-dimensional and three-dimensional seismic, make the maximum use of existing drilling and two-dimensional and three-dimensional seismic information, infer the unknown from the known, and through the comprehensive application of multiple technologies, minimize the ambiguity of the prediction results of a single geophysical technology. Improving the prediction accuracy is the only feasible way, which greatly improves the reliability of the prediction results of the hydrocarbon source rock distribution.

[0042] Example 1

[0043] Figure 1Schematic diagram of the steps of the method for predicting the distribution of source rocks under the volcanic rock background according to the embodiments of the present application. Figure 2 It is a schematic diagram of the specific process of the method for predicting the distribution of source rocks under the volcanic rock background according to the embodiments of the present application. The following combines Figure 1 and Figure 2 to illustrate the specific step process of the source rock prediction method described in the embodiments of the present invention.

[0044] In step S110, according to the logging response characteristics and seismic response characteristics of the target area to be evaluated (the target work area to be studied), a seismic identification model representing the distribution of source rocks developed under the volcanic rock background is established.

[0045] In step S110, first, according to the drilling data of the target area and its adjacent areas (the adjacent areas of the target work area to be studied), a model representing the relationship between multiple logging curves under different lithologies is obtained, and based on these crossplots, the rock type division results under the volcanic rock background are determined by analyzing the source rock sensitive logging curves; then, according to the seismic response characteristics of different rock types, forward modeling verification using the wave equation is carried out to obtain the seismic identification model of the development of source rocks under the volcanic rock background. In the embodiments of the present invention, the seismic identification model includes, but is not limited to: logging response characteristics, seismic inversion characteristics, and seismic attribute characteristics.

[0046] In one embodiment, the rock type division results are volcanic rocks and source rocks under the volcanic rock background.

[0047] In one embodiment, the source rock sensitive logging curves in the target area are acoustic time difference logging curves and density curves.

[0048] Make full use of the actual drilling data of the target work area to be studied and its adjacent areas, carry out the analysis of the rock-electric relationship of the target area, and use crossplots or histograms to establish a relationship model representing the pairwise relationship of each logging curve under different lithologies, so as to find the logging curves sensitive to lithology, especially the logging curves sensitive to source rocks, in order to use the source rock sensitive logging curves to divide the rock types under the volcanic rock background, so that the division results can form a significant difference between the source rocks and other types of rocks, facilitating the identification of source rocks. In the embodiments of the present invention, the acoustic time difference logging curve and the density logging curve can be selected as the source rock sensitive logging curves, because volcanic rocks generally have the characteristics of high velocity and high density, while the source rocks under the volcanic rock background generally have the characteristics of low velocity and low density.

[0049] Then, carry out the analysis of the seismic and seismic response characteristics of different rock types in the target area, summarize the seismic response characteristics of different types and different combined types of source rocks, and use forward modeling verification of the wave equation to establish the seismic identification model of source rocks under the volcanic rock background.

[0050] Example A

[0051] There are 2 three - dimensional areas and 40 two - dimensional survey lines in Area A in the northwest. There are a total of 3 drilling wells, belonging to the two - dimensional - three - dimensional mixed area with few wells.

[0052] In step S110, based on Example A, making full use of the actual drilling data of Work Area A and its adjacent areas, a comprehensive lithology - logging curve histogram is established to carry out the lithology classification of Research Area A. The lithology of Work Area A is divided into three major categories: volcanic rocks (such as tuff, basalt, andesite, etc.), sandstones, and mudstones (including dark mudstones, tuffaceous mudstones, and carbonaceous mudstones). Through the cross - plot analysis of various logging response curves under different lithologies (see Figure 3 and Figure 4 ), the electrical properties of source rocks such as volcanic rocks, mudstones, carbonaceous mudstones, and siltstone mudstones are clarified. As Figure 3 shown, the cross - plot of the acoustic travel - time logging curve and the natural gamma logging curve shows that the acoustic travel - time is sensitive to lithology: volcanic rocks have low travel - time, low GR, and high impedance; sandstones have medium travel - time, medium GR, and medium impedance; mudstones, carbonaceous mudstones, and tuffaceous mudstones have high travel - time, low impedance, and high GR, and the source rocks of volcanic rocks and mudstones can be clearly distinguished.

[0053] For the above - mentioned Example A, in step S110, through the seismic response characteristics of volcanic rocks and source rocks of mudstone type, using wave - equation forward modeling verification, a seismic identification model for source rocks developed under the background of volcanic rocks is established (see Figure 5 ). As Figure 5 shown, the logging response characteristics of source rocks are clarified as low velocity, low density, high gamma, and low wave impedance; the seismic reflection characteristics are layered reflection, medium - strong amplitude, medium - low frequency, and good lateral continuity; the seismic attribute characteristics are high energy, strong attenuation, and low impedance; the gravity and magnetic characteristics show low gravity, low magnetic force, and low - resistance anomalies. The logging response characteristics of volcanic rocks are high velocity, high density, low gamma, and high wave impedance; the seismic reflection characteristics are mound - shaped - medium - strong - weak amplitude, medium - high frequency, and weak continuity; the seismic attribute characteristics are low energy, low attenuation, and high impedance; the gravity and magnetic characteristics show low gravity, high magnetic force, and high - resistance anomalies.

[0054] In step S120, according to the seismic facies division results of the target area, combined with sensitive seismic attributes for the distribution of volcanic rocks and source rocks, the macroscopic distribution of source rocks is qualitatively analyzed.

[0055] In step S120, seismic facies division processing, wavelet transform frequency division energy analysis, and analysis of multiple conventional seismic attribute responses are respectively carried out on the target area. Based on the results of these processes and analyses, the sensitive seismic response attributes of the target area are optimized. Then, according to the seismic facies division results of the target area and in combination with the sensitive seismic response attributes, a macroscopic distribution map of the hydrocarbon source rock is analyzed and obtained.

[0056] In one embodiment, cluster analysis is used to divide the seismic facies of the target area.

[0057] In one embodiment, multiple conventional seismic attributes include but are not limited to: root mean square attribute, maximum wave trough attribute, maximum wave peak attribute, variance attribute, maximum energy attribute, and maximum frequency attribute, etc.

[0058] In one embodiment, the sensitive seismic response attributes are wavelet transform energy attribute and root mean square attribute.

[0059] Guided by the seismic identification pattern of the hydrocarbon source rock obtained in step S110, seismic facies division processing of the target interval is carried out by means of neural network cluster analysis. According to the seismic facies division results of the target area and in combination with the analysis results of wavelet transform frequency division energy and conventional seismic attributes of the target area, through information fusion means, based on one or more sensitive seismic response attributes, the macroscopic distribution of the hydrocarbon source rock is qualitatively analyzed.

[0060] For example: For the above-mentioned Area A, in step S120, seismic facies division processing based on cluster analysis, wavelet transform frequency division energy analysis, and conventional seismic attribute response analysis of the target area are carried out (for an example of the results of the conventional seismic attribute response analysis, see Figure 6 ). By using the principal component analysis method to optimize sensitive attributes, the sensitive seismic response attributes are clarified. Specifically, by using the principal component analysis method and through the comparative analysis of more than 10 seismic attributes in the study area, it is considered that the wavelet transform energy attribute and the root mean square attribute are the most sensitive to the distribution of the hydrocarbon source rock. Then, through information fusion means and in combination with these two attributes, the macroscopic distribution of the hydrocarbon source rock is qualitatively analyzed, see Figure 7 .

[0061] Figure 6 This is an example diagram of the analysis results of the conventional seismic response attributes in the hydrocarbon source rock prediction method for the volcanic rock background in the embodiments of the present application. Figure 6 The upper diagram in Figure 6 shows the cross-well seismic profile of the target area for wavelet transform frequency division energy analysis,

[0062] Step S130 is guided by the target area seismic recognition pattern obtained in step S110, and inversely calculates the macroscopic distribution map of source rocks by driving the two-dimensional seismic area with low signal-to-noise ratio through the three-dimensional seismic area with high signal-to-noise ratio, so as to obtain the predicted map of the quantitative distribution of source rocks, that is, the wave impedance distribution map based on two-dimensional-three-dimensional joint inversion is obtained.

[0063] In step S130, guided by the target area seismic recognition pattern obtained in step S110, a genetic algorithm is used to establish virtual wells in the three-dimensional high signal-to-noise ratio area in the macroscopic distribution map of source rocks; then, based on the established virtual wells, according to the seismic recognition pattern, a two-dimensional and three-dimensional joint seismic inversion method is adopted to inversely calculate the three-dimensional working area, the two-dimensional and three-dimensional overlapping working area and the two-dimensional working area in the macroscopic distribution map of source rocks in sequence, and a wave impedance distribution map of two-dimensional and three-dimensional joint inversion of the target area is established for use as the predicted map of the quantitative distribution of source rocks.

[0064] In the embodiment of the present invention, under the guidance of the seismic recognition pattern of source rocks, source rock inversion is carried out, the unknown is inferred from the known, and the three-dimensional seismic area with high signal-to-noise ratio drives the two-dimensional seismic area with low signal-to-noise ratio to improve the prediction resolution of source rocks. In addition, since the inversion result of source rocks is affected not only by the quality of seismic data, but also by the number of constrained wells and their planar distribution state, and the well-seismic matching degree, etc., therefore, in order to improve the inversion accuracy of source rocks in areas with no wells or few wells, the embodiment of the present invention will adopt a virtual well inversion method to improve the prediction accuracy, and for the two-dimensional and three-dimensional mixed area, the well-seismic matching degree is improved through two-dimensional-three-dimensional joint seismic inversion.

[0065] For example: for the above-mentioned working area A, in step S130, guided by the pattern, the unknown is inferred from the known, and the high signal-to-noise ratio area drives the low signal-to-noise ratio area to improve the prediction resolution of the reservoir. Since the inversion result is affected not only by the quality of seismic data, but also by the number and planar distribution of constrained wells, and the well-seismic matching degree. In order to improve the prediction accuracy of the two-dimensional areas in the east and north of working area A, the high-resolution area drives the low-resolution area, extracts the inversion result of the three-dimensional working area as a virtual well, and through two-dimensional-three-dimensional joint seismic inversion, the distribution range of source rocks in working area A is quantitatively described.

[0066] Step S140 takes the impedance threshold value of source rocks as a constraint, and extracts the distribution information of the development size characteristics of source rocks from the predicted map of the quantitative distribution of source rocks obtained in step S130, so as to obtain the planar distribution map of the development of source rocks.

[0067] In step S140, first, the impedance threshold value of source rocks is used as a constraint, and the wave impedance distribution characteristics in the predicted map of the quantitative distribution of source rocks are used as a basis to extract the T0 time domain containing the thickness of source rocks. Then, according to the velocity data volume of the target area, the currently extracted T0 time domain is converted to obtain the planar distribution map of the thickness of source rocks.

[0068] In the embodiment of the present invention, multiple logging curves in the target area are first intersected with the drilling lithology (with the lithology as the intersection reference, the vertical and horizontal coordinates being the values of different logging curves respectively, and the sample point colors and shapes representing the lithology), so as to analyze and obtain the hydrocarbon source rock wave impedance threshold value as a constraint. Then, based on the wave impedance distribution map obtained by two-dimensional-three-dimensional joint inversion in step S130, the T0 time domain including information such as the hydrocarbon source rock thickness is extracted, and finally, through the conversion of the velocity data volume in the target area, the planar distribution of the hydrocarbon source rock thickness is obtained. See Figure 8 .

[0069] The present invention predicts that the planar distribution of the hydrocarbon source rock thickness coincides with the drilled wells at the well points, and the inter-well distribution is consistent with the fused multi-parameter seismic attributes, verifying the reasonableness and accuracy of the prediction results, greatly improving the accuracy and reliability of the prediction of the Carboniferous hydrocarbon source rocks in a certain area, providing the hydrocarbon source rock distribution and thickness data for oil and gas exploration in this area, and further proposing favorable exploration directions.

[0070] Example 2

[0071] Based on the above hydrocarbon source rock prediction method, the embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored, and the computer program is executed to run a hydrocarbon source rock prediction method under the background of volcanic rocks. The computer program can run computer instructions, and the computer instructions include computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate forms, etc.

[0072] The computer-readable storage medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0073] It should be noted that the content included in the computer-readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, it is appropriately increased or decreased according to the requirements of legislation and patent practice. For example, in some jurisdictions, it is appropriately increased or decreased according to the requirements of legislation and patent practice. For example, in some jurisdictions, according to patent practice, the computer-readable storage medium does not include electrical carrier signals and telecommunication signals.

[0074] Example 3

[0075] Based on the above hydrocarbon source rock prediction method for volcanic rock background, an embodiment of the present invention further provides a hydrocarbon source rock prediction system for volcanic rock background. This hydrocarbon source rock prediction system is used to implement the above hydrocarbon source rock prediction method.

[0076] Figure 9 It is a block diagram of the modules of the hydrocarbon source rock prediction system for volcanic rock background in an embodiment of this application. As Figure 9 shown, the hydrocarbon source rock prediction system described in the embodiment of the present invention includes: a seismic pattern recognition module 91, a macroscopic distribution analysis module 92, a quantitative distribution analysis module 93, and a development feature generation module 94.

[0077] Specifically, the seismic pattern recognition module 91 is implemented according to the method described in step S110 above, and is configured to establish a seismic recognition pattern representing the distribution of hydrocarbon source rocks developed under volcanic rock background based on the logging and seismic response characteristics of the target area; the macroscopic distribution analysis module 92 is implemented according to the method described in step S120 above, and is configured to qualitatively analyze the macroscopic distribution of hydrocarbon source rocks based on the seismic facies division results of the target area and in combination with sensitive seismic attributes for the distribution of volcanic rocks and hydrocarbon source rocks; the quantitative distribution analysis module 93 is implemented according to the method described in step S130 above, and is configured to take the seismic recognition pattern as a guide and perform inversion on the macroscopic distribution map of hydrocarbon source rocks by driving the two-dimensional seismic area with low signal-to-noise ratio by the three-dimensional seismic area with high signal-to-noise ratio to obtain a quantitative distribution prediction map of hydrocarbon source rocks; the development feature generation module 94 is implemented according to the method described in step S140 above, and is configured to extract the distribution information of the development size characteristics of hydrocarbon source rocks from the quantitative distribution prediction map of hydrocarbon source rocks with the impedance threshold value of hydrocarbon source rocks as a constraint, so as to obtain a planar distribution map of hydrocarbon source rock development.

[0078] Furthermore, the development feature generation module 94 described in the embodiment of the present invention is further configured to take the impedance threshold value of hydrocarbon source rocks as a constraint and, based on the wave impedance distribution characteristics in the quantitative distribution prediction map of hydrocarbon source rocks, extract the T0 time domain containing the thickness of hydrocarbon source rocks, and then convert the T0 time domain according to the velocity data volume of the target area to obtain a planar distribution map of the thickness of hydrocarbon source rocks.

[0079] The present invention discloses a method and system for predicting source rocks under the background of volcanic rocks. Through the division of sedimentary system tracts under the background of volcanic rocks, the distribution of source rocks, lithological characteristics, and rock combination types in different sedimentary environments are clarified, and a seismic recognition pattern for source rocks is established; through the combination of seismic facies division by seismic wavelet transform frequency division energy and waveform clustering analysis, macroscopic qualitative prediction of source rocks is carried out; on this basis, two-dimensional to three-dimensional joint inversion based on genetic algorithm virtual wells is carried out for quantitative prediction of source rocks; qualitative and quantitative multi-parameter fusion prediction of source rocks is carried out through grouped clustering and principal component analysis. Thus, the present invention mainly aims at source rocks deposited during the volcanic eruption intermission period under the background of volcanic rocks, predicts the distribution of source rocks in areas with few or no wells, uses the application mode as a constraint, geology guides geophysical exploration, and effectively depicts source rocks by a multi-parameter prediction method combining qualitative and quantitative methods, so as to objectively predict the distribution and scale of source rocks.

[0080] Thus, through geology guiding geophysical exploration, pattern guiding attributes, and multi-parameter fusion, the present invention effectively depicts the spatial distribution of source rocks in trench-arc basins by combining methods such as seismic facies division by clustering analysis, frequency division energy based on wavelet transform, and two-dimensional to three-dimensional joint inversion. Moreover, it has been directly applied to the research of favorable area evaluation of source rocks and oil and gas reservoir scale assessment, effectively guiding the exploration deployment work. This technical method has strong practical application value and application effect, is worthy of popularization and application, and has good application prospects.

[0081] As mentioned above, the above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

[0082] In the description of the present invention, unless otherwise specified, the meaning of "a plurality of" is two or more; the orientation or positional relationships indicated by terms such as "upper", "lower", "left", "right", "inner", "outer", "front end", "rear end", "head", "tail", etc. are based on the orientation or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be construed as a limitation of the present invention. In addition, terms such as "first", "second", "third", etc. are only used for descriptive purposes and cannot be construed as indicating or implying relative importance.

[0083] In the description of the present invention, it should be noted that, unless otherwise clearly specified and defined, the terms "connected" and "connected to" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0084] It should be understood that the embodiments disclosed in the present invention are not limited to the specific structures, processing steps or materials disclosed herein, but should extend to equivalent alternatives of these features understood by those of ordinary skill in the relevant art. It should also be understood that the terms used herein are only for the purpose of describing specific embodiments and do not mean to limit.

[0085] The "one embodiment" or "embodiment" mentioned in the specification means that the specific features, structures or characteristics described in connection with the embodiment are included in at least one embodiment of the present invention. Therefore, the phrases "one embodiment" or "embodiment" that appear throughout the specification do not necessarily all refer to the same embodiment.

[0086] Although the embodiments disclosed in the present invention are as above, the content described above is only an embodiment adopted for the convenience of understanding the present invention and is not intended to limit the present invention. Any person skilled in the art within the technical field to which the present invention pertains can make any modifications and changes in the form of implementation and details without departing from the spirit and scope disclosed by the present invention. However, the scope of patent protection of the present invention shall still be subject to the scope defined by the appended claims.

Claims

1. A method for predicting source rocks under volcanic rock background, characterized in that, Including: Based on the logging and seismic response characteristics of the target area, establish a seismic identification model for characterizing the distribution of source rocks developed under the volcanic rock background; According to the seismic facies division results of the target area, combined with sensitive seismic attributes for the distribution of volcanic rocks and source rocks, qualitatively analyze the macroscopic distribution of source rocks; Guided by the seismic identification model, invert the macroscopic distribution map of source rocks by driving the two-dimensional seismic area with low signal-to-noise ratio by the three-dimensional seismic area with high signal-to-noise ratio, and obtain the quantitative distribution prediction map of source rocks; Using the impedance threshold value of source rocks as a constraint, extract the distribution information of the development size characteristics of source rocks from the quantitative distribution prediction map of source rocks, so as to obtain the plane distribution map of source rock development.

2. The hydrocarbon source rock prediction method according to claim 1, wherein In the step of establishing a seismic identification model for characterizing the distribution of source rocks developed under the volcanic rock background based on the logging and seismic response characteristics of the target area, it includes: According to the drilling data of the target area and its adjacent areas, obtain a model representing the relationship between multiple logging curves under different lithologies, and based on this, determine the rock type division results under the volcanic rock background by analyzing the sensitive logging curves of source rocks; According to the seismic response characteristics of different rock types, use wave equation forward modeling verification to obtain the seismic identification model for the development of source rocks, and the seismic identification model includes logging response characteristics, seismic inversion characteristics and seismic attribute characteristics.

3. The hydrocarbon source rock prediction method according to claim 2, characterized in that, The sensitive logging curves of source rocks in the target area are acoustic time difference logging curves and density curves.

4. The hydrocarbon source rock prediction method according to any one of claims 1 to 3, characterized in that, In the step of qualitatively analyzing the macroscopic distribution of source rocks according to the seismic facies division results of the target area, combined with sensitive seismic attributes for the distribution of volcanic rocks and source rocks, it includes: Carry out seismic facies division processing, wavelet transform frequency division energy analysis and multiple conventional seismic attribute response analysis on the target area respectively, and based on this, optimize the sensitive seismic response attributes of the target area; According to the seismic facies division results of the target area, combined with the sensitive seismic response attributes, analyze and obtain the macroscopic distribution map of the source rocks.

5. The hydrocarbon source rock prediction method according to claim 4, characterized in that, Use cluster analysis to divide the seismic facies of the target area, and among them, the sensitive seismic response attributes are wavelet transform energy attributes and root mean square attributes.

6. The hydrocarbon source rock prediction method according to any one of claims 1 to 5, characterized in that In the step of guiding by the seismic identification model, inverting the macroscopic distribution map of source rocks by driving the two-dimensional seismic area with low signal-to-noise ratio by the three-dimensional seismic area with high signal-to-noise ratio, and obtaining the quantitative distribution prediction map of source rocks, it includes: Guided by the seismic identification model, establish virtual wells in the three-dimensional high signal-to-noise ratio area in the macroscopic distribution map of source rocks; Based on the virtual wells, according to the seismic identification model, use a two-dimensional and three-dimensional joint seismic inversion method to invert the three-dimensional working area, the two-dimensional and three-dimensional overlapping working area and the two-dimensional working area in the macroscopic distribution map of source rocks in turn, and establish a wave impedance distribution map of two-dimensional and three-dimensional joint inversion for the target area, which is used as the quantitative distribution prediction map of source rocks.

7. The hydrocarbon source rock prediction method according to any one of claims 1 to 6, characterized in that, In the step of using the impedance threshold value of source rocks as a constraint, extracting the distribution information of the development size characteristics of source rocks from the quantitative distribution prediction map of source rocks, so as to obtain the plane distribution map of source rock development, it includes: Constrained by the impedance threshold value of the source rock and based on the wave impedance distribution characteristics in the quantitative distribution prediction map of the source rock, the T0 time domain containing the source rock thickness is extracted; Based on the velocity data volume of the target area, the T0 time domain is converted to obtain the plane distribution map of the source rock thickness.

8. A computer-readable storage medium, characterized in that, It includes a series of instructions for performing the steps of the source rock prediction method according to any one of claims 1 to 7.

9. A hydrocarbon source rock prediction system for volcanic rock background, characterized in that, It includes: A seismic pattern recognition module configured to establish a seismic recognition pattern characterizing the distribution of source rocks developed under the background of volcanic rocks according to the well logging and seismic response characteristics of the target area; A macroscopic distribution analysis module configured to qualitatively analyze the macroscopic distribution of source rocks based on the seismic facies division results of the target area and in combination with sensitive seismic attributes for the distribution of volcanic rocks and source rocks; A quantitative distribution analysis module configured to, guided by the seismic recognition pattern, invert the macroscopic distribution map of source rocks by driving the two-dimensional seismic area with low signal-to-noise ratio by the three-dimensional seismic area with high signal-to-noise ratio to obtain the quantitative distribution prediction map of source rocks; A development feature generation module configured to, constrained by the impedance threshold value of the source rock, extract the distribution information of the development size characteristics of the source rock from the quantitative distribution prediction map of the source rock, thereby obtaining the plane distribution map of the source rock development.

10. The source rock prediction system according to claim 9, wherein the development feature generation module is further configured to be constrained by the impedance threshold value of the source rock and based on the wave impedance distribution characteristics in the quantitative distribution prediction map of the source rock, extract the T0 time domain containing the source rock thickness, and then based on the velocity data volume of the target area, convert the T0 time domain to obtain the plane distribution map of the source rock thickness.

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