Method and device for quantitatively predicting thickness of intrusive rock

Abstract

The invention discloses a method and device for quantitatively predicting the thickness of an intrusive rock, and the method comprises the steps: analyzing the geological data, seismic data and logging data of a research region, and determining the characteristics of the intrusive rock; determining seismic attributes according to the intrusive rock features, and determining a distribution range of the intrusive rock according to the seismic attributes; performing pre-stack elastic wave impedance inversion within the distribution range of the intrusive rock, and predicting the thickness of the intrusive rock by using the inversion attribute; and correcting the thickness of the intrusive rock by using drilling data to obtain the final thickness of the intrusive rock.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of petroleum geological exploration interpretation, and in particular to a method and device for quantitatively predicting the thickness of intrusive rock. BACKGROUND

[0002] Igneous rock, also known as magmatic rock, is formed by magma eruption onto the earth's surface or intrusion into the crust and cooling and solidification. The deep crust and the upper part of the upper mantle are mainly composed of igneous rock, accounting for about 65% of the total volume of the crust and 95% of the total mass. Many igneous rocks are associated with clastic rock strata, and there are also a large number of igneous rocks mixed in carbonate rocks in rift basins. In most study areas, two types of igneous rocks, intrusive rocks and eruptive rocks, are mainly developed. Intrusive rocks are mostly late intrusive, and have a greater destructive effect on early-formed reservoirs and oil and gas reservoirs, such as baking reservoirs to make them denser, occupying reservoir space, and damaging oil and gas reservoirs. In oil and gas exploration, it is urgently needed to accurately predict the vertical and horizontal distribution of intrusive rocks, so as to take targeted measures in exploration and development deployment.

[0003] Intrusive rock, carbonate rock and mudstone have overlapping on conventional logging curves such as resistivity, density and acoustic time difference, and it is difficult to effectively distinguish the three by a single or multiple parameters, thus causing difficulties in seismic lithology identification, and failing to meet the needs of deep-sea oil and gas efficient exploration. Therefore, it is urgently needed to propose a new seismic identification method to realize quantitative prediction of intrusive rock, so as to accurately identify igneous rocks in complex carbonate reservoirs under salt and improve the interpretation accuracy. SUMMARY

[0004] The present application provides a method and device for quantitatively predicting the thickness of intrusive rock. The method uses a method combining seismic multi-attribute intrusive rock qualitative prediction and pre-stack inversion to quantitatively predict intrusive rock, and can obtain higher precision of intrusive rock thickness.

[0005] In a first aspect, the present application provides a method for quantitatively predicting the thickness of intrusive rock, the method comprising:

[0006] analyzing the geological data, seismic data and logging data of the study area to determine the characteristics of intrusive rock;

[0007] determining the seismic attributes according to the characteristics of the intrusive rock, and determining the distribution range of the intrusive rock according to the seismic attributes;

[0008] performing pre-stack elastic wave impedance inversion in the distribution range of the intrusive rock, and predicting the thickness of the intrusive rock by using the inversion attributes;

[0009] correcting the thickness of the intrusive rock by using drilling data to obtain the final thickness of the intrusive rock.

[0010] Optionally, the intrusive rock features include seismic reflection features and logging features.

[0011] The seismic attributes include single-frequency body amplitude, velocity and Poisson impedance.

[0012] Optionally, the method further comprises:

[0013] extracting single-frequency body amplitude attributes of the target layer from the seismic data;

[0014] determining a corresponding relationship between the intrusive rock development layer interpreted on the well log and the single-frequency body amplitude attributes according to the well-to-seismic calibration;

[0015] determining an amplitude threshold value corresponding to the intrusive rock on the single-frequency body amplitude attributes according to the corresponding relationship;

[0016] determining a first distribution range of the intrusive rock using the single-frequency body amplitude attributes according to the determined amplitude threshold value.

[0017] Optionally, the method further comprises:

[0018] extracting velocity attributes of the target layer from the seismic data;

[0019] establishing lithology and velocity probability distribution histograms according to the interval transit time curve in the well log data and the seismic processing velocity volume;

[0020] determining a threshold value of the velocity according to the lithology and velocity probability distribution histograms;

[0021] determining a second distribution range of the intrusive rock using the velocity attributes and the determined threshold value.

[0022] Optionally, the method further comprises:

[0023] setting weight coefficients for the first distribution range of the intrusive rock predicted by the single-frequency body amplitude attributes and the second distribution range of the intrusive rock predicted by the velocity attributes respectively;

[0024] obtaining a comprehensive attribute distribution range of the intrusive rock using the weight coefficients, the first distribution range and the second distribution range.

[0025] Optionally, the method further comprises:

[0026] determining the intrusive rock inversion attribute to be Poisson impedance attribute through rock physics analysis;

[0027] In the distribution range of the comprehensive attribute of the intrusive rock, a pre-stack elastic wave impedance inversion is performed on the target layer to extract a Poisson impedance attribute;

[0028] A threshold value of the Poisson impedance attribute is determined, and the thickness of the intrusive rock is determined according to the threshold value.

[0029] Optionally, the determination of the threshold value of the Poisson impedance attribute and the determination of the thickness of the intrusive rock according to the threshold value include:

[0030] According to drilling core data, intrusive rock lithology data in the target layer is counted;

[0031] A histogram is established according to the Poisson impedance attribute value and the lithology data of the drilling;

[0032] A lithology probability threshold value is determined according to a peak value of the Poisson impedance attribute corresponding to the intrusive rock in the histogram;

[0033] The thickness of the intrusive rock is determined according to the lithology probability threshold value and a preset time window range.

[0034] Optionally, the thickness of the intrusive rock is corrected by using the drilling data to obtain a final thickness of the intrusive rock, including:

[0035] In the time window range, the intrusive rock thickness value predicted by the Poisson impedance attribute is compared with the intrusive rock thickness value in the drilling;

[0036] If the difference between the two values meets a preset threshold value, the intrusive rock thickness value predicted by the Poisson impedance attribute is taken as the final thickness of the intrusive rock;

[0037] If the difference between the two values does not meet the preset threshold value, the lithology probability threshold value or the time window range is adjusted to re-predict the thickness of the intrusive rock.

[0038] In a second aspect, an embodiment of the present application further provides a device for quantitatively predicting the thickness of the intrusive rock, the device including a memory and a processor; the memory is used to save a program for quantitatively predicting the thickness of the intrusive rock, and the processor is used to read and execute the program for quantitatively predicting the thickness of the intrusive rock, and execute the method in any one of the above embodiments.

[0039] In a third aspect, an embodiment of the present application further provides a computer readable storage medium, and the computer readable storage medium stores a data processing program, and the data processing program is executed by a processor to execute the method for quantitatively predicting the thickness of the intrusive rock in any one of the above embodiments.

[0040] Compared with the related art, the application provides a method and device for quantitatively predicting the thickness of intrusive rock, which comprises the following steps: analyzing geological data, seismic data and logging data of a study area to determine the characteristics of intrusive rock; determining seismic attributes according to the characteristics of the intrusive rock, and determining the distribution range of the intrusive rock according to the seismic attributes; performing pre-stack elastic wave impedance inversion in the distribution range of the intrusive rock, and predicting the thickness of the intrusive rock by using the inversion attributes; and correcting the thickness of the intrusive rock by using drilling data to obtain the final thickness of the intrusive rock. The method combining qualitative prediction of seismic multi-attribute intrusive rock with quantitative prediction of pre-stack inversion can determine the thickness of the intrusive rock with high precision.

[0041] Other features and advantages of the application will be set forth in the following description, and in part will become apparent to those skilled in the art from the description, or can be learned by practice of the application. Other advantages of the application will be realized and attained by the embodiments of the application particularly pointed out in the specification. BRIEF DESCRIPTION OF DRAWINGS

[0042] The accompanying drawings are included to provide a further understanding of the application, and constitute a part of the specification, illustrate embodiments of the application, and are used to explain the technical solutions of the application, and do not constitute a limitation on the technical solutions of the application.

[0043] Figure 1 The flowchart of the method for quantitatively predicting the thickness of intrusive rock in the embodiments of the application;

[0044] Figure 2 The schematic diagram of the device for quantitatively predicting the thickness of intrusive rock in the embodiments of the application;

[0045] Figure 3 The histogram of Poisson impedance of intrusive rock and carbonate rock in the middle of the Aptian stage in a block of the Santos basin in an exemplary embodiment;

[0046] Figure 4 The amplitude plane distribution map of single frequency body in the middle of the Aptian stage in a block of the Santos basin in an exemplary embodiment;

[0047] Figure 5 The seismic Poisson impedance profile in the middle of the Aptian stage in a block of the Santos basin in an exemplary embodiment;

[0048] Figure 6 The drilling correction schematic diagram of the distribution of intrusive rock in the middle of the Aptian stage in a block of the Santos basin in an exemplary embodiment;

[0049] Figure 7 The prediction map of the plane thickness distribution of intrusive rock in the middle of the Aptian stage in a block of the Santos basin in an exemplary embodiment. DETAILED DESCRIPTION

[0050] The present application describes a number of embodiments, but the description is exemplary rather than limiting and it will be apparent to those of ordinary skill in the art that numerous more embodiments and implementations are possible within the scope of the embodiments described in the present application. Although a number of possible combinations of features have been set forth in the accompanying figures and discussed above, many other combinations will be possible. Any feature of any embodiment can be used in combination with any other feature or element from any other embodiment or in place thereof, unless specifically restricted or un-combinable. Moreover, except for any limitation of this disclosure that will be expressly recited herein, neither the description nor the claims should be construed as meaning that the application is limited to those steps or combinations of steps recited.

[0051] The present application includes and contemplates combinations of features and elements known to those of ordinary skill in the art. The embodiments, features and elements disclosed herein can also be combined with any conventional feature or element to form a unique application defined by the claims. Any feature or element of any embodiment can also be combined with features or elements from other application to form another unique application defined by the claims. Therefore, it is to be understood that any feature shown and / or discussed in the present application can be realized alone or in any appropriate combination. Thus, except as otherwise restricted by the appended claims or their equivalents, the embodiments are not restricted by other limitations. Moreover, various modifications and changes can be made within the scope of the appended claims.

[0052] Furthermore, in describing representative embodiments, the specification can have presented the method and / or process as a particular sequence of steps. However, to the extent that the method or process depends on more than one step, the method or process should not be construed as limited to the particular order of the steps presented herein. Other sequences of steps can also be possible. Therefore, the particular order of the steps set forth in the specification should not be construed as a limitation on the claims. Furthermore, the claims should not be limited to the steps of the method and / or process in the order presented but can include any number of additional or different steps not presented in the specification.

[0053] The embodiments of the present application provide a method for quantitatively predicting the thickness of intrusive rocks, as shown in the formula (1): Figure 1 The method comprises steps S100-S130:

[0054] S100: analyzing the geological data, seismic data and logging data of the study area to determine the characteristics of intrusive rocks;

[0055] S110: determining the seismic attributes according to the characteristics of the intrusive rocks, and determining the distribution range of the intrusive rocks according to the seismic attributes;

[0056] S120: In the distribution range of the intrusive rock, pre-stack elastic wave impedance inversion is performed, and the thickness of the intrusive rock is predicted by using the inversion attribute;

[0057] S130: The thickness of the intrusive rock is corrected by using the drilling data to obtain the final thickness of the intrusive rock.

[0058] In an example embodiment, the specific implementation process of determining the characteristics of the intrusive rock by analyzing the geological data, seismic data and logging data of the study area is as follows:

[0059] By analyzing the relevant geological data of the study area, the macro characteristics of the development of the sedimentary stratum and the intrusive rock are determined. By drilling, logging and well logging data, the specific geological horizon, depth, thickness and lithological characteristics of the development of the intrusive rock are determined.

[0060] Further, by two-dimensional or three-dimensional seismic data interpretation, the reflection characteristics of the intrusive rock in the study area are determined. The overall seismic reflection characteristics of the intrusive rock are low frequency and strong amplitude.

[0061] Further, by drilling, logging and well logging data, the electrical characteristics of the intrusive rock are determined, and the electrical characteristics are high speed, high density and high impedance.

[0062] In an example embodiment, the seismic attribute is determined according to the characteristics of the intrusive rock.

[0063] The characteristics of the intrusive rock include seismic reflection characteristics and logging characteristics; wherein the seismic reflection characteristics are low frequency and strong amplitude; and the logging characteristics are high speed, high density and high impedance.

[0064] According to the above seismic reflection characteristics, the single frequency body amplitude attribute in the seismic attribute is determined as the first sensitive seismic attribute; and according to the electrical characteristics of high speed, high density and high impedance, the velocity attribute is determined as the second sensitive seismic attribute.

[0065] In an example embodiment, the distribution range of the intrusive rock is determined according to a plurality of seismic attributes, including:

[0066] First, the distribution range of the intrusive rock is determined by using the first sensitive seismic attribute;

[0067] According to the low frequency and strong amplitude characteristics of the intrusive rock, the single frequency body amplitude attribute is used for lithology identification to determine the distribution range of the intrusive rock.

[0068] According to the well-to-seismic calibration, the position of the interpreted intrusive rock development layer on the logging and the corresponding amplitude on the seismic are determined, and the amplitude threshold value of the intrusive rock on the single frequency body is determined according to the lateral distribution characteristics, such as the amplitude value greater than 4000 in Example One to identify the intrusive rock.

[0069] Second step, determining the distribution range of the intrusive rock by using the second sensitive seismic attribute;

[0070] Third step, obtaining the distribution range of the comprehensive attribute of the intrusive rock determined based on the single-frequency body amplitude attribute and the velocity attribute by using the preset weight coefficient.

[0071] In the embodiment, the distribution range of the intrusive rock is determined by using the first sensitive seismic attribute, i.e., the distribution range of the intrusive rock is determined by using the single-frequency body attribute of the target layer extracted from the seismic data, and the specific implementation process is as follows:

[0072] According to the well-seismic calibration, the corresponding relationship between the interpreted intrusive rock development layer on the well logging and the single-frequency body attribute is determined;

[0073] According to the corresponding relationship, the amplitude threshold corresponding to the intrusive rock on the single-frequency body attribute is determined;

[0074] The first distribution range of the intrusive rock predicted by the single-frequency body attribute is determined by using the determined amplitude threshold.

[0075] In the embodiment, the distribution range of the intrusive rock is determined by using the second sensitive seismic attribute, i.e., the distribution range of the intrusive rock is determined by using the velocity attribute, and the specific implementation process is as follows:

[0076] First step, establishing the lithology and velocity probability distribution histogram according to the acoustic travel time curve in the well logging data and in combination with the seismic processing velocity body;

[0077] Second step, determining the threshold of the velocity according to the velocity probability distribution histogram;

[0078] Third step, determining the second distribution range of the intrusive rock by using the determined threshold according to the velocity attribute body of the intrusive rock. In the embodiment, first, the well logging data lithology interpretation is carried out by collecting the drilling and logging data of the research area, and the development and distribution characteristics of various lithologies such as sandstone, mudstone, carbonate rock, intrusive rock, and eruption rock on the well logging are determined. Second, the velocity distribution range of the intrusive rock is determined according to the acoustic travel time and other well logging data, and the threshold of the velocity attribute is determined by referring to the seismic velocity body and the velocity probability distribution histogram. Finally, the velocity distribution range of the intrusive rock is determined by using the determined threshold according to the velocity attribute body of the intrusive rock.

[0079] In the embodiment, the seismic attribute is determined according to the characteristics of the intrusive rock, and the distribution range of the intrusive rock is determined according to the seismic attribute, which includes:

[0080] The weight coefficient is set for the first distribution range of the intrusive rock predicted by the single-frequency body amplitude attribute and the second distribution range of the intrusive rock predicted by the velocity attribute, respectively;

[0081] The distribution range of the comprehensive attributes of intrusive rocks is obtained using the weighting coefficients, the first distribution range, and the second distribution range. For example, the preset weighting coefficients are: 0.6 for single-frequency volume and 0.4 for velocity attribute. The distribution range of the comprehensive attributes of intrusive rocks, obtained using the first and second sensitive seismic attributes and the weighting coefficients, is used to characterize the approximate distribution range of intrusive rocks. Finally, the geological data is compared with the characterized distribution range of intrusive rocks to determine if they match the development characteristics of igneous rocks. If they do not match, the attribute threshold values ​​and corresponding weighting values ​​are readjusted to ensure that the final distribution range of the comprehensive attributes of intrusive rocks roughly matches geological understanding and geological laws.

[0082] Based on the above process, the first distribution prediction of intrusive rocks was obtained by utilizing low-frequency strong amplitude characteristics and the second distribution prediction of intrusive rocks was obtained based on high-velocity properties. Combined with geological understanding, the development characteristics of intrusive rocks were identified and characterized by a combination of horizontal and vertical profiles, thus realizing a comprehensive qualitative prediction of the longitudinal and lateral distribution of intrusive rocks.

[0083] In one exemplary embodiment, within the distribution range of the intrusive rock, pre-stack elastic wave impedance inversion is performed to determine the thickness of the intrusive rock, including:

[0084] The first step is to determine the sensitive inversion property of the intrusive rock—seismic Poisson impedance—through rock physics analysis.

[0085] like Figure 3 As shown, in the Poisson impedance histogram of the Aptian stage in a certain block of the Santos Basin, it can be clearly seen that the main peak areas of the intrusive rocks and porous limestone are clearly separated and distinguishable.

[0086] The second step is to perform pre-stack elastic wave impedance inversion on the target layer within the range of the comprehensive properties of the intrusive rock to obtain the Poisson impedance properties.

[0087] The third step is to use the Poisson impedance property to determine the threshold value and obtain the thickness of the intrusive rock.

[0088] In one exemplary embodiment, determining the threshold value and obtaining the thickness of the intrusive rock using the Poisson impedance property includes:

[0089] The threshold value of the Poisson impedance property is determined based on the Poisson impedance histogram of intrusive rocks;

[0090] The thickness of the intrusive rock is obtained using Poisson impedance properties based on the determined threshold value;

[0091] The obtained intrusive rock thickness is statistically analyzed, and the statistical results are corrected using the drilled well data to obtain the final intrusive rock thickness.

[0092] In an example embodiment, the obtained intrusive rock thickness is statistically processed, and the statistical result is corrected by using the drilled well data to obtain the final intrusive rock thickness:

[0093] The intrusive rock thickness is predicted according to the determined threshold value, and the intrusive rock thickness of the target layer is extracted for statistical processing;

[0094] The statistical result is corrected by using the drilled well data to obtain the final result of the thickness distribution prediction of the intrusive rock of the target layer.

[0095] Specifically, for example, for a well, the interpreted intrusive rock thickness is a, and the predicted data is b, and the error between them is within an acceptable range, such as within 10%, that is, the calculation basically matches, otherwise the threshold value is adjusted and the prediction is re-performed, and then the well is corrected.

[0096] For example: 20 new drilled wells in the study area are verified, and the drilled intrusive rock results of 18 wells are consistent with the prediction before drilling, the qualitative distribution prediction accuracy of the intrusive rock is 95%, and the thickness prediction error is less than 5%.

[0097] According to the qualitative prediction of the longitudinal and horizontal distribution of the intrusive rock and the quantitative prediction result data volume and drawing of the pre-stack simultaneous inversion, the drilled well data of the developed intrusive rock are combined to compare and correct the prediction result of the intrusive rock from the plane distribution range, the longitudinal distribution layer, and the plane thickness. The threshold value, time window, and other aspects are adjusted to ensure that the prediction result matches the drilled well result.

[0098] In a second aspect, an embodiment of the present application also provides a device for quantitatively predicting the thickness of intrusive rock, as shown in Figure 2 The device includes a memory S200 and a processor S210; the memory is used to save a program for quantitatively predicting the thickness of intrusive rock, and the processor is used to read and execute the program for quantitatively predicting the thickness of intrusive rock, and execute the method of any one of the above embodiments.

[0099] In a third aspect, an embodiment of the present application also provides a computer readable storage medium, and the computer readable storage medium stores a data processing program, and the data processing program is executed by a processor to execute the method for quantitatively predicting the thickness of intrusive rock in any one of the above embodiments.

[0100] The method for quantitatively predicting the thickness of intrusive rock implemented by the embodiment has the following technical effects:

[0101] (1) The existing intrusive rock prediction method mainly uses post-stack inversion or seismic attributes, and the method of the present application comprehensively uses seismic attributes and inversion, qualitatively predicts the distribution of intrusive rock through seismic attribute analysis, and quantitatively predicts the thickness of intrusive rock through pre-stack inversion, thereby avoiding the problem of low prediction accuracy of post-stack inversion;

[0102] (2) The method of the present application can be applied based on well-seismic data, is easy to use, and can develop qualitative and quantitative prediction of intrusive rocks;

[0103] (3) The method is not only suitable for various strata in which intrusive rocks develop, such as carbonate rocks and clastic rocks, but also has wide application value in oil and gas fields in which igneous rocks are reservoirs.

[0104] Example one

[0105] This example takes a block in the Santos Basin as the research object to show the implementation process of quantitative prediction of the thickness of intrusive rocks, as follows:

[0106] Step 1, obtain seismic data and related geological data of the research area, and determine the target layer.

[0107] The 3D seismic data of a block in the Santos Basin are obtained, with a length of 2000 km 2 , 33 wells, and multiple comprehensive research reports of the research area, it can be determined that the intrusive rocks in the research area mainly develop in the middle of the Lower Cretaceous Aptian and are located in the upper part of the carbonate reservoir.

[0108] Step 2, extract seismic attributes and qualitatively predict the distribution of intrusive rocks.

[0109] Multiple attributes such as velocity volume, single-frequency volume, root mean square amplitude, seismic facies classification, coherence volume, and trace integration are extracted, combined with the corresponding characteristics of the intrusive rock seismic facies calibrated by wells, and the velocity and amplitude attributes are optimized for intrusive rock distribution prediction.

[0110] As shown in Figure 4 , the single-frequency volume amplitude plane distribution map of the middle of the Aptian in the Santos Basin block; the single-frequency volume amplitude plane distribution map predicted above can qualitatively predict the distribution of intrusive rocks, with red representing intrusive rocks and blue representing non-igneous rocks (carbonate rocks).

[0111] Step 3, use the petrophysical analysis of the drilled wells in the research area to determine Poisson impedance as a sensitive parameter reflecting intrusive rocks.

[0112] Step 4, perform prestack wave impedance inversion to obtain the Poisson impedance of intrusive rocks.

[0113] As shown in Figure 5 , a prestack inversion Poisson impedance profile of intrusive rocks in the middle of the Aptian in the Santos Basin block is shown, with red representing intrusive rocks and blue representing carbonate rocks.

[0114] Step 5, use the Poisson impedance attribute to determine the threshold value and obtain the thickness of the intrusive rocks.

[0115] Step 51, determine the threshold value of the Poisson impedance IP attribute according to the Poisson impedance histogram;

[0116] Step 52: Obtain the intrusive rock thickness using the Poisson impedance IP property based on the determined threshold value;

[0117] Within a pre-set time frame, the cumulative thickness of the intrusive rock is calculated based on a determined threshold value.

[0118] Step 6: Statistically analyze the obtained intrusive rock thickness using the drilled well data, and correct the statistical results using the drilled well data to obtain the final intrusive rock thickness.

[0119] This step involves the following steps:

[0120] Step 61: Predict the thickness of intrusive rocks based on the determined threshold value, and extract the thickness of intrusive rocks in the target layer for statistical analysis.

[0121] Step 62: Correct the statistical results with the drilling data to obtain the final prediction result of the thickness distribution of the intrusive rock in the target layer.

[0122] like Figure 6 The diagram shows a well calibration diagram of the distribution of intrusive rocks in the middle Aptian stage of a block in the Santos Basin. Calibration was performed using drilling data of encountered intrusive rocks and statistical predictions. After verification with 20 new wells in the study area, the intrusive rock encounter results in 18 wells were consistent with pre-drilling predictions. The accuracy rate of qualitative distribution prediction for intrusive rocks was 90%, and the thickness prediction error was less than 5%.

[0123] like Figure 7 As shown in the predicted thickness distribution map of the intrusive rocks in the middle of the Aptian stage in a certain block of the Santos Basin, it can be clearly seen that the accuracy of the prediction was verified by new wells. Among the 20 new wells drilled, the seismic prediction results of 18 wells were consistent with the actual drilling results (10 wells predicted the presence of intrusive rocks, and 10 wells actually encountered intrusive rocks; 10 wells predicted the absence of intrusive rocks, and 8 wells did not encounter intrusive rocks, 2 wells encountered intrusive rocks, and 2 wells were inaccurate in their predictions (wells 25 and 30, which predicted the presence of igneous rocks, but did not actually contain igneous rocks), with an accuracy rate of 90%.

[0124] This example demonstrates pre-stack inversion within the longitudinal and lateral distribution range of eruptive rocks defined by seismic attributes. The Poisson impedance obtained from the inversion is compared with drilled data to statistically analyze the thickness of intrusive rock profiles and determine the planar distribution range of the intrusive rocks. Combined with geological understanding of the igneous rock development characteristics in the study area, from... Figure 7 As can be seen, the comprehensive analysis and prediction results are highly accurate.

[0125] Those of ordinary skill in the art will realize and understand that all or some of the steps in the methods disclosed above and the functional modules / units in the systems and devices can be implemented as software, firmware, hardware, and appropriate combinations thereof. In hardware implementation, the division between the functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, one physical component can have multiple functions, or one function or step can be performed by several physical components in cooperation. Some or all of the components can be implemented as software executed by a processor, such as a digital signal processor or a microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on computer-readable media, which can include computer storage media (or non-transitory media) and communication media (or transitory media). As is well known to those of ordinary skill in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computer. Furthermore, it is common and well understood by those of ordinary skill in the art that communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and can include any information delivery media.

Claims

1. A method of quantitatively predicting the thickness of an invaded rock, characterized by, The method comprises: analyzing geological data, seismic data and logging data of a study area to determine intrusive rock characteristics; determining seismic attributes according to the intrusive rock characteristics and determining a distribution range of the intrusive rock according to the seismic attributes; performing pre-stack elastic wave impedance inversion within the distribution range of the intrusive rock and predicting a thickness of the intrusive rock by using the inversion attributes; correcting the thickness of the intrusive rock by using drilling data to obtain a final thickness of the intrusive rock.

2. The method for quantitatively predicting the thickness of intrusive rock according to claim 1, wherein the intrusive rock characteristics comprise seismic reflection characteristics and logging characteristics; the seismic attributes comprise single-frequency body amplitude, velocity and Poisson impedance.

3. The method for quantitatively predicting the thickness of intrusive rock according to claim 2, wherein determining seismic attributes according to the intrusive rock characteristics and determining a distribution range of the intrusive rock according to the seismic attributes comprises: extracting single-frequency body amplitude attributes of a target layer from seismic data; determining a corresponding relationship between the intrusive rock development layer interpreted on a well log and the single-frequency body amplitude attributes according to well-to-seismic calibration; determining an amplitude threshold value corresponding to the intrusive rock on the single-frequency body amplitude attributes according to the corresponding relationship; determining a first distribution range of the intrusive rock by using the single-frequency body amplitude attributes according to the determined amplitude threshold value.

4. The method for quantitatively predicting the thickness of intrusive rock according to claim 3, wherein determining seismic attributes according to the intrusive rock characteristics and determining a distribution range of the intrusive rock according to the seismic attributes comprises: extracting velocity attributes of a target layer from seismic data; establishing a lithology and velocity probability distribution histogram according to the interval transit time curve in the logging data and the seismic processing velocity volume; determining a threshold value of the velocity according to the lithology and velocity probability distribution histogram; determining a second distribution range of the intrusive rock by using the velocity attributes and the determined threshold value.

5. The method for quantitatively predicting the thickness of intrusive rock according to claim 4, wherein determining seismic attributes according to the intrusive rock characteristics and determining a distribution range of the intrusive rock according to the seismic attributes comprises: setting a weight coefficient for the first distribution range of the intrusive rock predicted by the single-frequency body amplitude attributes and the second distribution range of the intrusive rock predicted by the velocity attributes respectively; obtaining a comprehensive attribute distribution range of the intrusive rock by using the weight coefficient, the first distribution range and the second distribution range.

6. The method for quantitatively predicting the thickness of intrusive rock according to claim 1, wherein performing pre-stack elastic wave impedance inversion within the distribution range of the intrusive rock and predicting a thickness of the intrusive rock by using the inversion attributes comprises: determining that the inversion attribute of the intrusive rock is a Poisson impedance attribute through rock physics analysis; extracting a Poisson impedance attribute by performing pre-stack elastic wave impedance inversion for a target layer within the comprehensive attribute distribution range of the intrusive rock; determining a threshold value of the Poisson impedance attribute and determining a thickness of the intrusive rock according to the threshold value.

7. The method for quantitatively predicting the thickness of intrusive rock according to claim 6, wherein determining a threshold value of the Poisson impedance attribute and determining a thickness of the intrusive rock according to the threshold value comprises: According to the drilling core data, the lithology data of the intrusive rock in the target layer is counted; According to the histogram of the Poisson impedance attribute value and the lithology data of the drilling, a histogram is established; According to the peak value of the Poisson impedance attribute corresponding to the eruption rock in the histogram, a lithology probability threshold value is determined; According to the lithology probability threshold value and the preset time window range, the thickness of the intrusive rock is determined.

8. The method of quantitatively predicting the thickness of the intrusive rock according to claim 7, wherein the thickness of the intrusive rock is corrected by using the drilling data to obtain the final thickness of the intrusive rock, comprising: In the time window range, the intrusive rock thickness value predicted by the Poisson impedance attribute is compared with the intrusive rock thickness value in the drilling; If the difference between the two satisfies the preset threshold value, the intrusive rock thickness value predicted by the Poisson impedance attribute is taken as the final intrusive rock thickness; If the difference between the two does not satisfy the preset threshold value, the lithology probability threshold value or the time window range is adjusted to re-predict the thickness of the intrusive rock. The device comprises a memory and a processor; the memory is used to save the program for quantitatively predicting the thickness of the intrusive rock, and the processor is used to read and execute the program for quantitatively predicting the thickness of the intrusive rock, and execute the method of any one of claims 1-8.

9. An apparatus for quantitatively predicting the thickness of an invaded rock, characterized by, 10. A computer readable storage medium, wherein a data processing program is stored on the computer readable storage medium, and the data processing program is executed by a processor to execute the method of quantitatively predicting the thickness of the intrusive rock according to any one of claims 1-8. ​

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