Multi-scale fault prediction, crack prediction and comprehensive prediction method and device

Abstract

The invention discloses a multi-scale fault prediction, crack prediction and comprehensive prediction method and device. The method comprises the steps of obtaining quasi-coherence body data by using a pre-established quasi-coherence calculation model based on obtained tectonic curvature body data of a prediction work area; based on the quasi-coherence body data, performing quasi-ant tracking to obtain quasi-ant body data; performing data optimization processing on the basis of the ant-like body data to obtain a final fault prediction data body; acquiring data for crack prediction from the post-stack seismic data volume of the prediction work area based on a predetermined optimal scanning time window in a direction corresponding to the true dip angle of the stratum, and performing intrinsic coherence calculation on the data for crack prediction to obtain a coherence calculation result; based on the coherent calculation result, using a pre-established crack prediction model to obtain a crack prediction data volume; and merging the final fault prediction data volume and the fracture prediction data volume to obtain a fault-fracture fusion data volume. And faults and cracks can be accurately predicted.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of oil and gas field development, and particularly relates to a multi-scale fault prediction, fracture prediction and comprehensive prediction method and device. BACKGROUND

[0002] Faults and fractures are widely distributed in the earth's crust, and are formed under the action of geological forces such as long-term geological tectonic movement, thus forming a class of fault and fracture type reservoirs and oil and gas reservoirs. At present, the oil and gas production of fault and fracture type reservoirs accounts for more than half of the total oil and gas production in the world. In recent years, more and more fault and fracture oil and gas reservoirs have been discovered in China, and almost every large oil region has fault and fracture oil and gas reservoirs, which shows that fault and fracture type oil and gas reservoir exploration is becoming an important energy strategy replacement exploration field in China. At the same time, faults and fractures play an important role in oil and gas accumulation, and they are channels for oil and gas migration and are conducive to oil and gas accumulation, and are water invasion channels that can destroy the oil and gas production of the formed oil and gas reservoirs, so predicting faults and fractures occupies a very important position in oil and gas exploration and development.

[0003] At present, post-stack seismic data is usually used to predict faults. Compared with pre-stack seismic data, post-stack seismic data has high signal-to-noise ratio, small data volume, small calculation amount, short calculation period and high work efficiency, and can better adapt to the needs of fast-paced exploration and development production, so the prediction method based on post-stack seismic data is widely used to predict faults. Generally, the widely used methods include curvature method, coherence method and ant tracking method. The curvature method can predict faults of a relatively wide range of scales, the coherence method can predict large-scale faults with large vertical fault gaps, and the ant tracking method can also predict faults. These methods are used alone to predict faults. SUMMARY

[0004] The present application inventors have found that although the curvature method can predict faults of a relatively wide range of scales, the magnitude of the data of various scales of the predicted faults has a small difference, the large and small faults are mixed together on the plane, the fault trend is poor, and the incoherent noise is heavy, which is not conducive to clear interpretation of the faults; the coherence method can only predict large-scale faults with large vertical fault gaps, and cannot predict small-scale faults with small vertical fault gaps, which cannot meet the needs of today's higher maturity exploration and development of exploration areas; although the ant tracking method can better predict faults, it needs high-quality and high-precision coherence or variance data volume as input, otherwise the fault prediction effect is poor.

[0005] In addition, there is no method for predicting fractures using stacked seismic data, that is, there is no method for predicting small-scale fractures caused by the up-and-down faulting of seismic events on the seismic profile that cannot be visually identified using stacked seismic data. At the same time, there is no method for grading and predicting faults and fractures to construct a whole fault-fracture system.

[0006] In summary, conventional post-stack prediction methods cannot better solve the practical difficulties and problems faced by exploration and development, such as "poor fault prediction effect, difficulty in fault prediction, and difficulty in fracture prediction", for example, Figure 1 As shown in the seismic profile, the blue dashed line in the figure is a fault with a small fault throw, and there are many faults and fractures with smaller scales between the blue dashed lines. It is difficult to predict these faults and fractures using conventional methods and ideas.

[0007] In view of the above problems, the present application is proposed to provide a multi-scale fault prediction, fracture prediction and comprehensive prediction method and device to overcome the above problems or at least partially solve the above problems.

[0008] In a first aspect, an embodiment of the present application provides a multi-scale fault prediction method, comprising:

[0009] Based on the obtained structure curvature volume data of the prediction work area, a pre-established pseudo-coherence calculation model is used to obtain pseudo-coherence volume data; the structure curvature volume is obtained based on the structure curvature analysis of the post-stack seismic data volume of the prediction work area;

[0010] Based on the pseudo-coherence volume data, pseudo-ant tracking is performed to obtain pseudo-ant volume data;

[0011] Based on the pseudo-ant volume data, data optimization processing is performed to obtain final fault prediction data volume.

[0012] In some optional embodiments, the process of obtaining the structure curvature volume based on the structure curvature analysis of the post-stack seismic data volume of the prediction work area comprises:

[0013] Obtain the range of the post-stack seismic data volume of the prediction work area;

[0014] Using the seismic data volume in the range of the post-stack seismic data volume as input data, multi-window dip scanning calculation is performed to obtain a stratigraphic dip angle volume and an azimuth volume;

[0015] Using the stratigraphic dip angle volume, the azimuth volume and the original seismic data volume as input data, structure curvature analysis calculation is performed to obtain structure curvature volume data.

[0016] In some optional embodiments, the process of obtaining the range of the post-stack seismic data volume of the prediction work area comprises:

[0017] determine a plane range of the post-stack seismic data volume based on the determined target area range of the prediction work to be carried out;

[0018] cut a seismic interpretation horizon within the plane range of the post-stack seismic data volume according to the plane range of the post-stack seismic data volume, and determine a time range of the seismic data volume.

[0019] In some optional embodiments, the obtaining of the pseudo-coherence data based on the structural curvature data of the prediction work area includes:

[0020] extracting the most positive curvature from each curvature attribute included in the structural curvature data of the prediction work area;

[0021] establishing a pseudo-coherence calculation model based on the predetermined pseudo-coherence parameters, and performing pseudo-coherence calculation on the most positive curvature based on the pseudo-coherence calculation model to obtain the pseudo-coherence data volume.

[0022] In some optional embodiments, the pseudo-coherence calculation model is:

[0023] A=C1-C0*Kpos;

[0024] wherein C0 and C1 are pseudo-coherence parameters, Kpos is the most positive curvature, and A is the pseudo-coherence data volume.

[0025] In some optional embodiments, the data optimization processing based on the pseudo-ant data includes:

[0026] performing at least one of Kuwahara filtering, edge detection, and edge protection smoothing filtering processing based on the pseudo-ant data to obtain the final fault prediction data volume.

[0027] In some optional embodiments, the Kuwahara filtering based on the pseudo-ant data includes:

[0028] performing multi-window scanning based on the pseudo-ant data, calculating the variance of the sample points in all time windows, selecting a sliding time window with the smallest variance, and taking the median of the data in the time window with the smallest variance as the pseudo-ant data after Kuwahara filtering;

[0029] The edge detection processing based on the pseudo-ant data includes:

[0030] performing convolution on the pseudo-ant data based on the Roberts cross edge detection principle to obtain the gray level change rate of the pseudo-ant data as the pseudo-ant data after edge detection processing.

[0031] The embodiments of the present application also provide a crack prediction method, including:

[0032] Based on the best scanning time window of the stratum true dip angle corresponding azimuth, the data for fracture prediction is obtained from the post-stack seismic data volume of the prediction work area, and the coherent calculation result is obtained by performing eigen-coherent calculation on the data for fracture prediction.

[0033] Based on the coherent calculation result, the fracture prediction data volume is obtained by using the pre-established fracture prediction model.

[0034] In some optional embodiments, based on the best scanning time window of the stratum true dip angle corresponding azimuth, the data for fracture prediction is obtained from the post-stack seismic data volume of the prediction work area, and the coherent calculation result is obtained by performing eigen-coherent calculation on the data for fracture prediction, comprising:

[0035] The range of the post-stack seismic data volume of the prediction work area is obtained;

[0036] The seismic data volume in the range of the post-stack seismic data volume is subjected to multi-window dip angle scanning calculation to obtain the best scanning time window of the stratum true dip angle corresponding azimuth;

[0037] The seismic data in the best scanning time window is taken as the data for fracture prediction, and the coherent calculation result is obtained by performing eigen-coherent calculation based on the data for fracture prediction.

[0038] In some optional embodiments, based on the coherent calculation result, the fracture prediction data volume is obtained by using the pre-established fracture prediction model, comprising:

[0039] The fracture prediction model is pre-established as follows:

[0040] D = 1 - (B - C2) * C3,

[0041] Wherein, C2 and C3 are fracture prediction parameters, B is the coherent calculation result, and D is the fracture prediction data volume.

[0042] The coherent calculation result is input into the established fracture prediction model to obtain the fracture prediction data volume.

[0043] The embodiment of the application also provides a multi-scale fault and fracture comprehensive prediction method, comprising:

[0044] The final fault prediction data volume of the prediction work area is obtained by using the multi-scale fault prediction method as described above;

[0045] The fracture prediction data volume of the prediction work area is obtained by using the fracture prediction method as described above;

[0046] The final fault prediction data volume and the fracture prediction data volume are merged to obtain a fault-fracture fusion data volume.

[0047] In some optional embodiments, the final fault prediction data volume and the fracture prediction result data volume are merged to obtain a fault-fracture fusion data volume, including:

[0048] A fault-fracture fusion prediction model is established as follows:

[0049] E=C4*A+C5*D,

[0050] Wherein, C4 and C5 are fault-fracture fusion prediction parameters, A is the final fault prediction data volume, D is the fracture prediction data volume, and E is the fault-fracture fusion data volume.

[0051] The final fault prediction data volume and the fracture prediction data volume are input into the established fault-fracture fusion prediction model to obtain the fault-fracture fusion data volume.

[0052] In a second aspect, the embodiments of the present application provide a multi-scale fault prediction device, including:

[0053] A pseudo-coherence volume acquisition module is configured to use a pre-established pseudo-coherence calculation model to obtain pseudo-coherence volume data based on the obtained structural curvature volume data of the prediction work area.

[0054] A pseudo-ant volume acquisition module is configured to obtain pseudo-ant volume data by performing pseudo-ant tracking based on the pseudo-coherence volume data.

[0055] A fault prediction data volume acquisition module is configured to obtain a final fault prediction data volume by performing data optimization processing based on the pseudo-ant volume data.

[0056] The embodiments of the present application also provide a fracture prediction device, including:

[0057] A coherence calculation result acquisition module is configured to obtain a coherence calculation result by performing intrinsic coherence calculation on the obtained post-stack seismic data volume of the prediction work area.

[0058] A fracture prediction data volume acquisition module is configured to obtain a fracture prediction data volume by using a pre-established fracture prediction model based on the coherence calculation result.

[0059] The embodiments of the present application also provide a multi-scale fault and fracture comprehensive prediction device, including:

[0060] The multi-scale fault prediction device described above is configured to obtain a final fault prediction data volume of the prediction work area.

[0061] The fracture prediction device described above is configured to obtain a fracture prediction data volume of the prediction work area.

[0062] A fault-fracture fusion prediction device is configured to merge the final fault prediction data volume and the fracture prediction result data volume to obtain a fault-fracture fusion data volume.

[0063] The embodiment of the present application also provides a computer storage medium, which stores computer executable instructions, and the computer executable instructions are executed by a processor to implement at least one of the multi-scale fault prediction method, the fracture prediction method and the fault fracture comprehensive prediction method.

[0064] The embodiment of the present application also provides a terminal device, which comprises a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor executes the program to implement at least one of the multi-scale fault prediction method, the fracture prediction method and the fault fracture comprehensive prediction method.

[0065] The embodiment of the present application provides the above technical solution, and the beneficial effects at least include:

[0066] The multi-scale fault prediction method provided by the embodiment of the present application uses a pre-established pseudo-coherent calculation model to process the obtained structure curvature volume data of a prediction work area, to obtain pseudo-coherent volume data; performs pseudo-ant tracking based on the pseudo-coherent volume data, to obtain pseudo-ant volume data; and performs data optimization processing based on the pseudo-ant volume data, to obtain final fault prediction data volume. The method preferably uses structure curvature volume data as basic data for subsequent fracture prediction, and compared with the results of other conventional fault prediction methods, the structure curvature method has better fault prediction effect; the structure curvature volume data is processed based on the pseudo-coherent calculation model to obtain pseudo-coherent volume data, the obtained pseudo-coherent volume data is high-quality and high-precision coherent data compared with conventional input data, the pseudo-coherent volume data is used as input data of the pseudo-ant tracking method, and since the pseudo-coherent data meets the input parameter requirements of the pseudo-ant tracking method, high-quality pseudo-ant volume data can be obtained, the obtained pseudo-ant volume is used to predict faults, different scale faults can be predicted, the predicted faults have higher resolution, stronger and clearer levels, stronger trend, the prediction result is more in line with the actual situation, and the prediction result is more conducive to reliable interpretation of the faults, so that the interpretation accuracy of the faults is improved; the pseudo-ant volume data is optimized, so that the prediction result is more accurate, reliable and regular.

[0067] The fracture prediction method provided by the embodiment of the present application comprises the following steps: acquiring data for fracture prediction from post-stack seismic data of a prediction work area based on a pre-determined best scanning time window corresponding to a true dip angle of a stratum on an azimuth; performing intrinsic coherence calculation based on the acquired data for fracture prediction to obtain a coherence calculation result; and obtaining a fracture prediction data volume by using a pre-established fracture prediction model based on the coherence calculation result. In the method, the seismic data for fracture prediction is circumscribed by the pre-determined best scanning time window, so that the transverse micro-change of a seismic waveform of the post-stack seismic data caused by a fracture can be detected, that is, the best azimuth micro-signal in the prediction work area can be obtained. Based on the best azimuth micro-signal, the post-stack seismic micro-signal response caused by a fracture is used to perform fracture prediction, and the fractures existing in the stratum in the prediction work area can be accurately predicted.

[0068] The multi-scale fault and fracture comprehensive prediction method provided by the embodiment of the present application adopts the ant-like tracking method to predict faults of different scales, adopts the best azimuth micro-signal coherence method to predict fractures, and then the respective hierarchical prediction results are combined to complete the prediction of the entire fault-fracture system of the strata such as carbonate rock, sandstone and shale, so that the faults and fractures in the work area can be accurately predicted, higher-precision prediction data for the exploration and development of oil and gas reservoirs is provided, and the prediction effect of the development of the oil and gas reservoirs is improved.

[0069] Other features and advantages of the present 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 present application. The objects and other advantages of the present application can be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.

[0070] The technical solutions of the present application will be further described in detail below with reference to the drawings and embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0071] The accompanying drawings are included to provide a further understanding of the present application, and constitute a part of the specification, illustrate the present application and explain the present application together with the embodiments of the present application, and do not constitute a limitation on the present application. In the drawings:

[0072] Figure 1 It is a pre-stack time migration seismic profile of a certain area 1 well in the present application;

[0073] Figure 2 It is a flow chart of the multi-scale fault prediction method in the embodiment of the present application;

[0074] Figure 3a It is a reflection layer dip angle discrete scanning schematic diagram in the embodiment of the present application;

[0075] Figure 3b It is a best coherence window search schematic diagram in the embodiment of the present application;

[0076] Figure 4a Figure 1 is a schematic diagram of three-dimensional curved surface curvature in the first embodiment of the present application;

[0077] Figure 4b Figure 2 is a schematic diagram of a 3*3 horizon grid in the first embodiment of the present application;

[0078] Figure 5 Figure 3 is a schematic diagram of the principle of ant tracking in the first embodiment of the present application;

[0079] Figure 6 Figure 4 is a flow chart of directional ant colony algorithm applied to fault tracking and automatic identification in the first embodiment of the present application;

[0080] Figure 7 Figure 5 is a schematic diagram of the principle of two-dimensional Kuwahara filtering in the first embodiment of the present application;

[0081] Figure 8 Figure 6 is a flow chart of the fracture prediction method in the second embodiment of the present application;

[0082] Figure 9 Figure 7 is a flow chart of the multi-scale fault and fracture comprehensive prediction method in the third embodiment of the present application;

[0083] Figure 10 Figure 8 is a specific implementation flow chart of the multi-scale fault and fracture comprehensive prediction method in the fourth embodiment of the present application;

[0084] Figure 11 Figure 9 is a plan view of the size of the working target area range;

[0085] Figure 12a Figure 10 is a full work area top T0 map;

[0086] Figure 12b Figure 11 is a full work area bottom T0 map;

[0087] Figure 12c Figure 12 is a working target area top T0 map;

[0088] Figure 12d Figure 13 is a working target area bottom T0 map;

[0089] Figure 13a Figure 14 is a pre-stack time migration seismic profile of a main survey line 5010 of a certain working target area;

[0090] Figure 13b Figure 15 is a shadow group fault profile of original curvature prediction of the main survey line 5010 of the working target area;

[0091] Figure 13c Figure 16 is a top fault plan of original curvature prediction of the working target area;

[0092] Figure 14aDengyingzhi fault profile map for the working target area, predicted by the semblance coherence, through the main survey line 5010;

[0093] Figure 14b Dengding fault plane map for the working target area, predicted by the semblance coherence;

[0094] Figure 15a Dengyingzhi fault profile map for the working target area, predicted by the ant-like tracking, through the main survey line 5010;

[0095] Figure 15b Dengding fault plane map for the working target area, predicted by the ant-like tracking;

[0096] Figure 16a Dengyingzhi fault profile map for the working target area, processed by the salt-and-pepper filtering, through the main survey line 5010;

[0097] Figure 16b Dengding fault plane map for the working target area, processed by the salt-and-pepper filtering;

[0098] Figure 17a Dengyingzhi fault profile map for the working target area, processed by the edge detection, through the main survey line 5010;

[0099] Figure 17b Dengding fault plane map for the working target area, processed by the edge detection;

[0100] Figure 18a Fault interpretation pre-stack time migration seismic profile for the working target area, through the main survey line 5808;

[0101] Figure 18b Dengyingzhi fault interpretation profile map for the working target area, processed by the edge protection and smoothing filtering, through the main survey line 5010;

[0102] Figure 18c Dengyingzhi fault profile map for the working target area, processed by the edge protection and smoothing filtering, through the main survey line 5010;

[0103] Figure 18d Dengding fault plane map for the working target area, processed by the edge protection and smoothing filtering;

[0104] Figure 19a Original fracture profile map for the working target area, through the main survey line 5010, for the Dengsi target layer;

[0105] Figure 19b Original fracture plane map for the working target area, for the Dengding target layer;

[0106] Figure 20a Proportionally weighted fault profile map for the working target area, through the main survey line 5010, for the Dengsi target layer;

[0107] Figure 20bThe top proportionally weighted fault plane map of the working target area light;

[0108] Figure 21a The four purpose layer proportionally weighted fracture profile map of the working target area light through the main survey line 5010;

[0109] Figure 21b The top proportionally weighted fracture plane map of the working target area light;

[0110] Figure 22a The fault and fracture superimposed display profile map of the working target area light through the main survey line 5010;

[0111] Figure 22b The fault-fracture-seismic superimposed display profile map of the working target area light through the main survey line 5010;

[0112] Figure 23a The fault-fracture fusion profile map of the working target area light through the main survey line 5010;

[0113] Figure 23b The top fault-fracture fusion plane map of the working target area light;

[0114] Figure 24 The structural schematic diagram of the multi-scale fault prediction device in the embodiment of the present application;

[0115] Figure 25 The structural schematic diagram of the fracture prediction device in the embodiment of the present application;

[0116] Figure 26 The structural schematic diagram of the multi-scale fault fracture comprehensive prediction device in the embodiment of the present application. DETAILED DESCRIPTION

[0117] Exemplary embodiments of the present disclosure will be described in greater detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it is understood that the present disclosure can be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that the present disclosure can be more thoroughly understood, and the scope of the present disclosure can be accurately conveyed to those skilled in the art.

[0118] In order to solve the problems of poor fault prediction effect, great difficulty and difficult fracture prediction in the prior art, the embodiments of the present application provide a multi-scale fault prediction method, a fracture prediction method and a multi-scale fault fracture comprehensive prediction method, which can make the prediction effect of faults and fractures more accurate and reliable, and improve the exploration and development accuracy and effect of oil and gas reservoirs.

[0119] Embodiment one

[0120] The embodiment one of the present application provides a multi-scale fault prediction method, a flow thereof is as shown in the figure Figure 2 , and comprises the following steps.

[0121] Step S101: based on the obtained structure curvature volume data of the predicted work area, a pseudo-coherent calculation model established in advance is used to obtain pseudo-coherent volume data; wherein the structure curvature volume is obtained based on the post-stack seismic data volume of the predicted work area.

[0122] In some optional embodiments, the process of obtaining the structure curvature volume based on the post-stack seismic data volume of the predicted work area comprises:

[0123] 1) obtaining the range of the post-stack seismic data volume of the predicted work area.

[0124] The seismic data volume range comprises the plane range and the time range of the seismic data volume; based on the determined target area range of the work to be carried out, the plane range of the post-stack seismic data volume is determined, and the plane range comprises the Inline range and the Crossline range; according to the plane range of the post-stack seismic data volume, the seismic interpretation horizon within the range is cut, and the time range of the seismic data volume is determined.

[0125] 2) taking the seismic data volume within the range of the post-stack seismic data volume as input data, performing multi-window dip scanning calculation to obtain a stratum dip angle volume and an azimuth volume;

[0126] The stratum dip angle and azimuth volume are the data basis for subsequent structure curvature analysis and calculation. Generally, the stratum dip angle and azimuth are estimated by using a vertical window, which can provide more stable results than the estimation at the picked seismic interpretation horizon. In actual application, multi-window dip scanning can be used to estimate the stratum dip angle and azimuth.

[0127] The process of multi-window dip scanning comprises: for the selected analysis point, 360-degree omnidirectional dip scanning is performed to obtain the relative changes of the dip angle and the azimuth of the analysis point, as shown in the figure Figure 3a which is a schematic diagram of dip angle discrete scanning for the reflection horizon of the seismic stratum. The center point is taken as the analysis point, and the minimum dip angle test and the maximum dip angle test are performed. The dip angle of the determined maximum coherence value is taken as the true dip angle of the stratum; and the azimuth volume corresponding to the seismic data volume is obtained based on the true dip angle of the stratum. Figure 3b which is the search process of the maximum coherence value window. The black point in the figure is the analysis point, and the window refers to the gray small squares in the figure. The size of the window is not limited to four small squares, and can be selected according to the needs. By scanning and analyzing nine adjacent windows of the analysis point, the coherence value of each window is compared to obtain the best coherence window, and the best coherence window is applied to the fracture prediction method of the embodiment two.

[0128] The dip angle body, the corresponding azimuth angle body and the original seismic data body obtained by the dip angle scanning are taken as the input data of the next step, and the true dip angle of the stratum and the corresponding azimuth angle obtained by the dip angle scanning are taken as the input data in order to better predict the fault.

[0129] 3) Taking the stratum dip angle body, the azimuth angle body and the original seismic data body as the input data, the structural curvature analysis and calculation are carried out to obtain the structural curvature body data.

[0130] The explanation of the structural curvature is as shown in the following figure: Figure 4a As shown in the following figure, in the three-dimensional case, there is a normal vector N passing through any point P on the curved surface, and there are infinite planes passing through the normal vector N and cutting the curved surface, and there is a curvature along any cutting line at the P point. These curvatures are called normal curvatures. Among these normal curvatures, the average of two orthogonal normal curvatures is defined as the average curvature (Kmean), the normal curvature with the largest absolute value is defined as the maximum curvature (Kmax), the curvature orthogonal to the maximum curvature is defined as the minimum curvature (Kmin), and the product of the maximum curvature and the minimum curvature is defined as the Gaussian curvature (Kgauss). As a reference, the following figure shows the layer plane below which a grid bottom map is added, and nine grid nodes (1, 2, 3, …, 9) are marked, which represent the 3*3 grid unit to be used in the curvature calculation, as shown in the following figure: Figure 4a Figure 4b

[0131] Based on the above explanation, a 3*3 horizon grid is used to fit a quadratic surface, and the obtained structural surface can be represented by a quadratic equation as shown in the following formula (1). The coefficients a, b, c, d, e and f of the equation (1) are estimated by using the following formulas (2)-(7) through the grid node values.

[0132] f(x,y)=ax 2 +by 2 +cxy+dx+ey+f (1)

[0133]

[0134] Through the curvature calculation of the determined coefficients a, b, c, d, e and f of the equation (1), various curvature attribute values can be obtained, for example, the formula for calculating the maximum positive curvature through the coefficients a, b and c of the equation (1) is as follows:

[0135] Kpos=(a+b)+[(a-b) 2 +c 2 ] 1 / 2 ;

[0136] ​​In this step, the input stratum dip volume and corresponding azimuth volume are used to constrain the range of the original seismic data volume, and the curvature attribute is calculated based on the selected seismic data volume according to the curvature analysis calculation principle described above. For example, the calculation formula of the maximum positive curvature can be used to calculate the structural curvature data based on the seismic data volume. The structural curvature data can include the maximum positive curvature or other curvatures.

[0137] Since the maximum positive curvature is more easily interpreted in relation to the geometry of the fold, in this step, the structural curvature volume data of the predicted work area is obtained, and a pseudo-coherence calculation model is used to obtain pseudo-coherence volume data, including:

[0138] 1) Extract the maximum positive curvature from each curvature attribute included in the structural curvature volume data of the predicted work area.

[0139] 2) Establish a pseudo-coherence calculation model based on pre-determined pseudo-coherence parameters, and perform pseudo-coherence calculation on the maximum positive curvature based on the pseudo-coherence calculation model to obtain a pseudo-coherence volume.

[0140] The pseudo-coherence calculation model is:

[0141] A = C1 - C0 * Kpos

[0142] Where C0 and C1 are pseudo-coherence parameters, which can be constants, Kpos is the maximum positive curvature, and A is the pseudo-coherence volume.

[0143] This calculation model makes the obtained pseudo-coherence volume A and the conventional coherence value of the same order of magnitude, and the A value and the physical meaning of the conventional coherence value are similar, which can represent the similarity between seismic traces. The larger the value, the more similar the adjacent seismic traces. The smaller the value, the greater the difference between adjacent seismic traces, i.e., the stratum has greater discontinuity, the stratum is broken, and faults are developed. The conventional coherence value is obtained by using the conventional coherence method. Compared with the conventional coherence value, the pseudo-coherence volume is more sensitive to stratum deformation, and can predict faults of multiple scales, including large-scale faults with large up-and-down displacement on the seismic event, medium and small-scale faults with small up-and-down displacement on the seismic event, and smaller-scale faults with slight up-and-down displacement on the seismic event. It has higher and more accurate resolution for fault identification, and can predict faults with clearer strength, more abundant levels, clearer trends, and more reliable results, which is more conducive to reliable interpretation of faults, thereby improving the interpretation accuracy of faults.

[0144] Step S102: Perform pseudo-ant tracking based on the pseudo-coherence volume data to obtain pseudo-ant volume data.

[0145] The existing ant tracking can better predict faults, but needs high-quality and high-precision coherent or variance data bodies as input, but the existing conventional coherent or variance data cannot provide data of high enough quality and high enough precision, so the fault prediction effect is relatively poor, so in the present application, pseudo-ant tracking is used to obtain a pseudo-ant body data for fault prediction. The difference between the pseudo-ant tracking and the existing ant tracking method lies in that the pseudo-ant tracking is performed on a pseudo-coherent data body, and compared with the ant tracking performed on conventional input data, the pseudo-ant body data obtained has higher quality and higher precision. The pseudo-ant tracking refers to a method of performing ant tracking on a pseudo-coherent data body as input data, and the result is called a pseudo-ant body. The pseudo-ant body data can identify the development degree of a fault, and the greater the value, the more developed the stratum fracture.

[0146] The principle of ant tracking is shown in Figure 5 As can be seen from the figure, (a) two ants start from the nest at the same time; (b) the ant that selects the shortest path will reach before the ant that selects the longer path. In the process of ants returning from the nest to the food, the pheromone on the longer path will gradually volatilize over time, resulting in a decrease in concentration, while the pheromone on the shorter path will be strengthened during the return, so the pheromone on the shorter path is stronger per unit time, so more ants will choose the shorter path under the influence of the pheromone. The ant tracking algorithm is inspired by the path selection behavior of ants foraging. The ant tracking algorithm applied to fault identification can better predict faults. The fault attribute body exhibits local maximum or minimum of attribute values at the fault, such as the local minimum of coherent values at the fault. Based on this feature, the ant tracking algorithm can be used for fault tracking and identification of coherent profiles or horizontal slices. The flowchart of the method of applying the ant tracking algorithm to fault tracking and automatic identification is shown in Figure 6 As shown in the figure, it mainly includes:

[0147] 1) Ant tracking algorithm initialization: determine the initial distribution of ant tracking, and determine the number of ants to be arranged and the search direction of the ants.

[0148] 2) Tracking control strategy: mainly includes tracking step control, tracking abnormal step control, tracking normal step control and tracking allowed deviation; the attribute data representing the fault is discrete data, and in the tracking process, it is necessary to determine the tracking step, whether to track point by point or to track every few points; the attribute value error between effective points is small or the similarity is high, and the attribute value error between effective points and ineffective points is large or the similarity is poor, which are all the tracking allowed deviation; the tracking within the tracking allowed deviation range is the tracking normal step tracking, and the attribute data is selected; the tracking outside the tracking allowed deviation range is the tracking abnormal step tracking, and the attribute data is discarded.

[0149] 3) Extended search point selection: search points are determined based on the search direction of the ant and the tracking control strategy of the ant.

[0150] 4) Pheromone update: the fault point is tracked according to the above steps, the effective position of the fault is marked, and the signal is released.

[0151] Other ants affected by the signal in the area concentrate on tracking the fault point area, set the next ant distribution position from the effective position, re-expand the search point, track according to the tracking control strategy, and repeat the above steps until the tracking and identification of the fault area are completed.

[0152] According to the method of applying the ant tracking algorithm to fault tracking and identification, in this step, the pseudo-coherent data is taken as the input data, and the pseudo-ant tracking is performed according to the principle of the ant tracking algorithm to obtain the pseudo-ant data.

[0153] Step S103: Based on the pseudo-ant data, data optimization processing is performed to obtain the final fault prediction data volume.

[0154] In this step, at least one optimization method of Kuwahara filtering, edge detection and edge protection smoothing filtering processing can be used to optimize the pseudo-ant data. That is, at least one of Kuwahara filtering, edge detection and edge protection smoothing filtering processing is performed based on the pseudo-ant data to obtain the final fault prediction data volume. Among them:

[0155] 1) Kuwahara filtering based on pseudo-ant data, including:

[0156] Based on the preset sliding time window, the pseudo-ant data is scanned by multiple windows to determine the variance of the sample points in all sliding time windows, the sliding time window with the smallest variance is selected, and the median value of the data in the sliding time window with the smallest variance is used to generate the pseudo-ant data after Kuwahara filtering.

[0157] Kuwahara filtering can smooth the image while preserving meaningful edge information of the image. The principle of Kuwahara filtering is shown in Figure 3b The principle of maximum coherence value window search is similar, where Figure 3b is a plan view, and the three-dimensional schematic diagram of the filtering principle is shown in Figure 7 , Figure 7 The black ellipse part in the figure is the analysis point, which corresponds to the black point of each time window shown in Figure 3b The variance of the sample points in all time windows is calculated by multi-window scanning of the analysis point, the sliding time window with the smallest variance is selected, and the median value of the data in the time window with the smallest variance is the output of the Kuwahara filter.

[0158] 2) edge detection processing based on the ant-like body data, including:

[0159] Based on the Roberts cross edge detection principle, the ant-like body data is convolved to obtain the gray level change rate of the ant-like body data as the ant-like body data after edge detection processing.

[0160] Edge detection processing is mainly used to enhance the edges and gray level mutation parts of the image, so that the gray contrast is enhanced to facilitate edge picking and improve the clarity and recognition of the image profile or edge. Because the profile or edge is the place with the largest gray level change rate in the image, they are closely related to the changes of underground structure and lithology. If the image smoothing is understood as the integral action and low-pass filtering, the edge detection of the image corresponds to the differential action and high-pass filtering, which reduces the blur in the image by enhancing the high-frequency components.

[0161] In some optional embodiments, edge detection processing is performed based on Roberts cross edge detection, which is to use Roberts operator to convolve with the image data. Roberts operator is an operator that uses local difference to find edges, which contains two sets of 2x2 matrices representing horizontal and vertical directions, which are convolved with the image. If A represents the original image data, Gx and Gy represent the image gray values after horizontal and vertical edge detection, respectively, and the formula is as follows:

[0162]

[0163] The gray level formula of the image is:

[0164] The calculation formula of the gray direction in the image is:

[0165] Based on the gray level formula and the gray direction of the image, the gray level change rate of the image can be determined, and the edge detection of the image can be realized according to the gray level change rate of the image.

[0166] 3) edge protection smoothing filtering processing based on the ant-like body data, including:

[0167] The ant-like body data is filtered to obtain the ant-like body data after edge protection smoothing filtering processing.

[0168] Based on the above data optimization processing operation, at least one processing of the ant-like body data is performed to obtain the final fault prediction data body.

[0169] The method of the embodiment is preferably configured to construct the curvature body data as the basic data for subsequent fracture prediction, perform the quasi-coherent model calculation processing, the quasi-ant tracking processing and the data optimization processing, take the result data body obtained in the processing as the input data body of the next step in the processing, and obtain the final fracture prediction data body, which can predict fractures of different scales, and the predicted fractures have higher resolution, clearer strength and level, stronger trend, and are more in line with the actual situation and more conducive to reliable interpretation of the fractures, thereby improving the interpretation accuracy of the fractures.

[0170] Embodiment Two

[0171] Embodiment Two of the present application provides a fracture prediction method, the flowchart of which is shown in Figure 8 The method comprises the following steps:

[0172] Step S201: Based on the best scanning time window in the direction corresponding to the true dip angle of the stratum determined in advance, obtain the data for fracture prediction from the post-stack seismic data body of the prediction work area, and perform the eigen-coherent calculation on the data for fracture prediction to obtain the coherent calculation result.

[0173] This step mainly comprises the following steps: 1) obtaining the range of the post-stack seismic data body of the prediction work area;

[0174] Based on the target area range determined to be predicted, the planar range of the post-stack seismic data body is determined, and the planar range comprises the Inline range and the Crossline range.

[0175] According to the planar range of the post-stack seismic data body, the seismic interpretation horizon in the range is cut, and the time range of the seismic data body is determined.

[0176] 2) performing multi-window dip angle scanning calculation on the seismic data body in the range of the post-stack seismic data body to obtain the best scanning time window in the direction of the true dip angle of the stratum;

[0177] In Embodiment One, the multi-window dip angle scanning calculation is introduced, and based on the multi-window dip angle scanning calculation, the true dip angle of the stratum is obtained, and the best scanning time window in the direction of the true dip angle of the stratum is searched.

[0178] The specific steps are as follows: the underground stratum is scanned in 360 degrees in all directions, and a three-dimensional time window is taken along the time axis direction in the scanning direction, and the seismic data is cut along the time axis in the longitudinal direction to analyze the dip angle of the stratum, and finally the direction of the true dip angle of the stratum and the corresponding best scanning time window, also called kuwahara scanning window, are obtained.

[0179] 3) using the seismic data in the best scanning time window in the direction corresponding to the true dip angle of the stratum as input, performing intrinsic coherence calculation to obtain a coherence calculation result.

[0180] Step S202: based on the coherence calculation result, using a pre-established fracture prediction model to obtain a fracture prediction data volume.

[0181] The following fracture prediction model is pre-established:

[0182] D = 1 - (B - C2) * C3,

[0183] In the formula, the fracture prediction parameters C2 and C3 are constants, B is the coherence calculation result, and D is the fracture prediction data volume.

[0184] The coherence calculation result is input into the established fracture prediction model to obtain the fracture prediction data volume.

[0185] In this embodiment, the intrinsic coherence calculation is performed using the seismic data in the best scanning time window in the direction corresponding to the true dip angle, which can detect the lateral micro changes in the seismic waveform of the post-stack seismic data caused by the fractures, that is, the post-stack seismic micro signal response caused by the fractures can be used for fracture prediction, and thus the prediction of the fractures in the work area is completed.

[0186] Embodiment Three

[0187] Embodiment Three provides a multi-scale fault and fracture comprehensive prediction method, and the flow thereof is as shown in Figure 9 The method comprises the following steps:

[0188] The multi-scale fault prediction method described in Embodiment One is used to obtain the final fault prediction data volume of the predicted work area.

[0189] The fracture prediction method described in Embodiment Two is used to obtain the fracture prediction data volume of the predicted work area.

[0190] The final fault prediction data volume and the fracture prediction result data volume are merged to obtain a fault-fracture fusion data volume.

[0191] The following fault-fracture fusion prediction model is pre-established:

[0192] E = C4 * A + C5 * D,

[0193] In the formula, the fault-fracture fusion prediction parameters C4 and C5 are constants, A is the final fault prediction data volume, D is the fracture prediction data volume, and E is the fault-fracture fusion data volume.

[0194] The final fault prediction data volume and the fracture prediction data volume are input into the established fault-fracture fusion prediction model to obtain the fault-fracture fusion data volume.

[0195] This embodiment combines the final fault prediction data volume and the fracture prediction data volume, which can comprehensively depict the fracture development of underground strata and improve the exploration and development effect of oil and gas reservoirs.

[0196] Example 4

[0197] Embodiment 4 of this invention provides a specific implementation process of a multi-scale fault fracture comprehensive prediction method in a certain work area, the process of which is as follows: Figure 10 As shown, it includes the following steps:

[0198] Step S401: Determine the planar extent of the post-stack seismic data volume of the prediction work area.

[0199] Define the size of the target area for multi-scale fault prediction work on a plane, such as... Figure 11 The area selected by the black line is a planar map showing the size of the target area. Based on this, the planar range of the post-stack seismic data volume can be defined as Inline2700~Inline7400 and Crossline6600~Crossline12199.

[0200] Step S402: Determine the time range of the post-stack seismic data volume based on the determined planar range of the post-stack seismic data volume.

[0201] Based on the defined plane range of the post-stack seismic data volume, the seismic interpretation horizons within this range are manually edited and extracted, such as... Figure 12a This is the T0 diagram for the entire work area's light fixtures. Figure 12b This is the T0 diagram of the entire work area's light base. Figure 12c For manually editing the target area T0 diagram of the light fixture top, which has been cut out. Figure 12d For manually editing the target area of ​​the work, the T0 diagram of the light source is shown. According to... Figure 12c , Figure 12d The time axis on the right determines the time range of the post-stack seismic data volume to be 2000 ms to 4000 ms (T2000 to T4000). Thus, according to steps S401 and S402, the seismic data volume range of the target working area can be determined to be Inline2700 to Inline7400, Crossline6600 to Crossline12199, and T2000 to T4000.

[0202] Step S403: Based on the data volume within the defined target area seismic data volume range, perform multi-window dip angle scanning calculations to obtain the stratigraphic dip volume and azimuth volume.

[0203] Based on the seismic data volume within the defined target area, multi-window dip angle scanning calculations are performed using this data as input to obtain dip and azimuth volumes, which are then used as input data for the next step.

[0204] Step S404: Based on the stratigraphic dip volume, the azimuth volume and the related original seismic data volume, the structural curvature analysis is performed to obtain the structural curvature volume data.

[0205] As shown in Figure 13a is a pre-stack time migration seismic profile of the working target area through the main line 5010, based on the structural curvature analysis calculation of the Figure 13a seismic data, the structural curvature volume data is obtained. The obtained structural curvature volume data can also display faults, for example: Figure 13b is a fault profile of the Dengying Formation predicted by the curvature method in the working target area through the main line 5010, as shown in Figure 13c is a fault plane map of the Dengding Formation predicted by the curvature method in the working target area, in which black and dark gray are faults.

[0206] Step S405: Based on the structural curvature volume data, the pseudo-coherence calculation model is used to obtain the pseudo-coherence volume data.

[0207] The pseudo-coherence calculation model has been introduced in Embodiment One. The structural curvature volume data is input into the pseudo-coherence calculation model to obtain the pseudo-coherence volume data. The obtained pseudo-coherence volume data can also display faults, for example: Figure 14a is a fault profile of the Dengying Formation predicted by the pseudo-coherence volume in the working target area through the main line 5010, Figure 14b is a fault plane map of the Dengding Formation predicted by the pseudo-coherence volume in the working target area, in which black and dark gray are faults.

[0208] Step S406: Based on the pseudo-coherence volume data, the pseudo-ant tracking is performed to obtain the pseudo-ant volume data.

[0209] Based on the pseudo-coherence volume data, the pseudo-ant tracking is performed by using the existing ant tracking software module to obtain the pseudo-ant volume data. The obtained pseudo-ant volume data can also display faults, for example:

[0210] As shown in Figure 15a is a fault profile of the Dengying Formation predicted by the pseudo-ant tracking in the working target area through the main line 5010, Figure 15b is a fault plane map of the Dengding Formation predicted by the pseudo-ant tracking in the working target area, in which black and dark gray are faults. It can be seen that, compared with the fault prediction result of the curvature method (see Figure 13b , Figure 13c ), the predicted fault resolution is higher, the strength is more distinct, the hierarchy is clearer, the trend is stronger, the prediction result is more in line with the actual situation, and it is more conducive to the reliable interpretation of the fault, thereby improving the interpretation accuracy of the fault.

[0211] Step S407: Based on the pseudo-ant volume data, the data optimization processing is performed to obtain the final fault prediction volume data.

[0212] Example 1 describes the process of performing at least one of Kuwahara filtering, edge detection, and edge-protected smoothing filtering on the pseudo-ant body data to obtain the final fault prediction data volume.

[0213] In this step, the pseudo-ant body data is first subjected to Kuwahara filtering to obtain Kuwahara-filtered pseudo-ant body data; then, edge detection processing is performed on the Kuwahara-filtered pseudo-ant body data to obtain edge-detected pseudo-ant body data; further, edge protection smoothing filtering is performed on the edge-detected pseudo-ant body data to obtain edge-protected smoothing filtered pseudo-ant body data, which serves as the final fault prediction data volume.

[0214] Optionally, the order of optimization is not limited and can be adjusted.

[0215] Figure 16a This is a fault profile of the Dengying Group, which is processed by salt and pepper filtering along the main survey line 5010, representing the target area of ​​the work. Figure 16b The salt-and-pepper filtered tomographic plan view of the target area shows that the salt-and-pepper filtered tomographic results are comparable to the ant-tracking tomographic prediction results (see...). Figure 15a , Figure 15b With less "salt and pepper noise" and smoother results, the predicted fault strength is more distinct, the layers are clearer, and the trend is stronger. The prediction results are more conducive to the reliable interpretation of faults, thereby improving the accuracy of fault interpretation.

[0216] Figure 17a This is a fault profile of the Dengying Group, which is processed for edge detection of the target area along the main survey line 5010. Figure 17b This is a plan view of the lamp top fault after edge detection processing of the target area. Black and dark gray represent faults. It can be seen that the edge detection method processes faults better than the salt-and-pepper filtering method (see...). Figure 16a , Figure 16b The former has higher resolution and can predict faults at smaller scales. The former has more distinct fault prediction results, clearer hierarchy, and stronger trend than the latter. The prediction results are more conducive to the reliable interpretation of faults, thereby improving the accuracy of fault interpretation.

[0217] like Figure 18a Interpretation of pre-stack time-migrated seismic profiles along the 5010 fault line in the target area. Figure 18bThe light shadow group fault interpretation profile processed by the edge protection smoothing filtering of the main measuring line 5010 in the working target area is shown in the figure, wherein black and black gray are faults, it can be seen that the vertical fault distance of the largest scale fault is about 7.41 milliseconds of one-way travel time, the vertical fault distance of the smallest scale fault is about 0.2 milliseconds of one-way travel time, and the fault distance of the fault in the middle is between the two. The predicted fault profile has a good one-to-one correspondence with the seismic profile, which shows that the fault method can predict faults of different scales, and the prediction result is accurate and reliable, and the small fault prediction resolution is high.

[0218] As Figure 18c The light shadow group fault profile processed by the edge protection smoothing filtering of the main measuring line 5010 in the working target area is shown in the figure, Figure 18d The light top fault plane processed by the edge protection smoothing filtering of the working target area is shown in the figure, wherein black and black gray are faults, it can be seen that the edge protection smoothing filtering method is more smooth, more beautiful and more trend than the edge detection method (see Figure 17a 、 Figure 17b ) result, the prediction result is more conducive to reliable interpretation of the fault, thereby improving the interpretation accuracy of the fault; the fault processed by the edge protection smoothing filtering method has higher resolution than the fault predicted by the curvature method (such as Figure 13b 、 Figure 13c ) can predict smaller scale faults, and the former is more obvious than the latter in fault prediction result, the level is more clear, the trend is stronger, and the prediction result is more conducive to reliable interpretation of the fault, thereby improving the interpretation accuracy of the fault.

[0219] Step S408: Based on the best scanning time window corresponding to the predetermined true dip angle of the stratum, data used for fracture prediction is obtained from the post-stack seismic data volume of the predicted working area, and the data used for fracture prediction is subjected to intrinsic coherence calculation to obtain a coherence calculation result.

[0220] Step S409: Based on the coherence calculation result, a fracture prediction model established in advance is used to obtain a fracture prediction data volume.

[0221] The fracture prediction model has been introduced in Embodiment Two, and the coherence calculation result is input into the fracture prediction model to obtain the fracture prediction data volume.

[0222] As Figure 19a The light four original fracture profile of the working target area through the main measuring line 5010 is shown in the figure, Figure 19b The light top original fracture plane of the working target area is shown in the figure, wherein red, yellow and dark green are fractures.

[0223] The processes of predicting faults in steps S401-S407 and the processes of predicting fractures in steps S408-409 can be executed in any order, simultaneously or sequentially.

[0224] Step S410: merging the final fault prediction data volume and the fracture prediction data volume to obtain a fault-fracture fusion data volume.

[0225] Based on the final fault prediction data volume and the fracture prediction data volume, a pre-established fault-fracture fusion prediction model is used to obtain a fault-fracture fusion data volume.

[0226] The fault-fracture fusion prediction model has been described in Embodiment Three. The final fault prediction data volume and the fracture prediction data volume are input into the fault-fracture fusion prediction model to obtain a fault-fracture fusion data volume.

[0227] As shown in FIG. 5, the fault prediction data volume and the fracture prediction data volume are obtained by using the fault prediction model and the fracture prediction model respectively. Figure 20a As shown in FIG. 5, the fault prediction data volume and the fracture prediction data volume are obtained by using the fault prediction model and the fracture prediction model respectively. 20b The diagram shown in FIG. 6 represents the fault-related schematic diagram of the target layer of the lamp four purposes: Figure 20a FIG. 6A is a proportionally weighted fault profile diagram of the target layer of the lamp four purposes through the main survey line 5010 in the working target area, Figure 20b FIG. 6B is a proportionally weighted fault plan diagram of the lamp top in the working target area;

[0228] As shown in FIG. 7, the fault prediction data volume and the fracture prediction data volume are obtained by using the fault prediction model and the fracture prediction model respectively. Figure 21a As shown in FIG. 7, the fault prediction data volume and the fracture prediction data volume are obtained by using the fault prediction model and the fracture prediction model respectively. 21b The diagram shown in FIG. 8 represents the fracture-related schematic diagram of the target layer of the lamp four purposes: Figure 21a FIG. 8A is a proportionally weighted fracture profile diagram of the target layer of the lamp four purposes through the main survey line 5010 in the working target area, Figure 21b FIG. 8B is a proportionally weighted fracture plan diagram of the lamp top in the working target area;

[0229] As shown in FIG. 9, the fault prediction data volume and the fracture prediction data volume are obtained by using the fault prediction model and the fracture prediction model respectively. Figure 22a As shown in FIG. 9, the fault prediction data volume and the fracture prediction data volume are obtained by using the fault prediction model and the fracture prediction model respectively. 22b The diagram shown in FIG. 10 represents the fault-fracture superimposed display-related schematic diagram of the target layer of the lamp four purposes: Figure 22a FIG. 10A is a fault-fracture superimposed display profile diagram of the target layer of the lamp four purposes through the main survey line 5010 in the working target area, Figure 22b FIG. 10B is a fault-fracture-seismic superimposed display profile diagram of the target layer of the lamp four purposes through the main survey line 5010 in the working target area, in which black and dark gray represent faults, and red, yellow and dark green represent fractures;

[0230] As shown in FIG. 11, the fault prediction data volume and the fracture prediction data volume are obtained by using the fault prediction model and the fracture prediction model respectively. Figure 23a As shown in FIG. 11, the fault prediction data volume and the fracture prediction data volume are obtained by using the fault prediction model and the fracture prediction model respectively. 23b The diagram shown in FIG. 12 represents the fault-fracture fusion display-related schematic diagram of the target layer of the lamp four purposes: Figure 23a FIG. 12A is a fault-fracture fusion display profile diagram of the target layer of the lamp four purposes through the main survey line 5010 in the working target area, Figure 23b FIG. 12B is a fault-fracture fusion display plan diagram of the lamp top in the working target area, in which red, yellow and dark green represent fractures.

[0231] In the method of each of the above embodiments, the ant-like tracking method can be used to better predict faults of different scales, the best azimuth micro-signal coherence method is used to predict fractures, and then the results of each hierarchical prediction are combined, so that the development of underground stratum fractures can be comprehensively described, and the exploration and development effect of the oil and gas reservoir can be improved.

[0232] Based on the same inventive concept, the embodiment of the present application also provides a multi-scale fault prediction device 10, which can be arranged in a computer device with data processing capability, and the structure of the device is as shown in Figure 24 The device comprises a pseudo-coherence volume acquisition module 11, a pseudo-ant volume acquisition module 12 and a final fault prediction data volume acquisition module 13.

[0233] The pseudo-coherence volume acquisition module 11 is configured to obtain pseudo-coherence volume data based on the obtained structural curvature volume data of the prediction work area, and use a pre-established pseudo-coherence calculation model to obtain the pseudo-coherence volume data.

[0234] The pseudo-ant volume acquisition module 12 is configured to perform ant-like tracking based on the pseudo-coherence volume data to obtain pseudo-ant volume data.

[0235] The fault prediction data volume acquisition module 13 is configured to perform data optimization processing based on the pseudo-ant volume data to obtain a final fault prediction data volume.

[0236] The embodiment of the present application also provides a fracture prediction device 20, which can be arranged in a computer device with data processing capability, and the structure of the device is as shown in Figure 25 The device comprises a coherence calculation result acquisition module 21 and a fracture prediction data volume acquisition module 22.

[0237] The coherence calculation result acquisition module 21 is configured to perform intrinsic coherence calculation on the obtained post-stack seismic data volume of the prediction work area to obtain a coherence calculation result.

[0238] The fracture prediction data volume acquisition module 22 is configured to use a pre-established fracture prediction model to obtain a fracture prediction data volume based on the coherence calculation result.

[0239] In addition, the embodiment of the present application also provides a multi-scale fault and fracture comprehensive prediction device, which can be arranged in a computer device with data processing capability, and the structure of the device is as shown in Figure 26 The device comprises a multi-scale fault prediction device 10, a fracture prediction device 20 and a fault-fracture fusion prediction device 30.

[0240] The multi-scale fault prediction device 10 described above is configured to obtain a final fault prediction data volume of the prediction work area.

[0241] The fracture prediction device 20 described above is configured to obtain a fracture prediction data volume of the prediction work area.

[0242] The fault-crack fusion prediction device 30 is configured to combine the final fault prediction data volume and the crack prediction result data volume to obtain a fault-crack fusion data volume.

[0243] The embodiment of the present application further provides a computer storage medium, which stores computer executable instructions, and the computer executable instructions are executed by a processor to realize at least one of the multi-scale fault prediction method, the crack prediction method and the fault-crack comprehensive prediction method.

[0244] The embodiment of the present application further provides a terminal device, which comprises a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor executes the program to realize at least one of the multi-scale fault prediction method, the crack prediction method and the fault-crack comprehensive prediction method.

[0245] As to the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be described in detail here.

[0246] The above method and device of the embodiment of the present application can better predict faults and cracks of different scales, comprehensively depict the rupture development condition of the underground stratum, and improve the exploration and development effect of the oil and gas reservoir.

[0247] Unless specifically stated otherwise, terms such as processing, computing, calculating, determining, displaying, and the like, can refer to an action and / or process of one or more processing or computing systems, or similar devices, that manipulate and / or transform data represented as physical (e.g., electronic) quantities within the processing system's registers and / or memories into other data similarly represented as physical quantities within the processing system's memories, registers or other such information storage, transmission or display devices. Information and signals can be represented using any of a variety of different technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols, and chips that can be referenced throughout the above description can be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.

[0248] It should be apparent that the specific order and hierarchy of steps in the processes disclosed are examples only. Based on the description herein, the specific order and hierarchy of steps in the processes could be rearranged or otherwise modified without departing from the spirit of the disclosure. The accompanying method claims present elements of the various steps in a sample order, and are not meant to be limited to the specific order or hierarchy presented.

[0249] In the detailed description above, various features are grouped together in single embodiments for the purpose of streamlining the disclosure. This method of disclosure should not be interpreted as reflecting a necessity to disclose features in any single patent. Rather, according to the inventive concept, features can be combined in any single patent in one or more claims. Thus, the disclosure hereof is to be understood as being illustrative of the inventive concept and not a limitation thereof. For example, not every aspect of the creative process is described with every embodiment. It is contemplated that the creative process is a dynamic process that will necessitate implementation of new techniques by those skilled in the art. Those skilled in the art will appreciate that, in the development of this creative process, numerous implementation-specific decisions can be made. These implementation-specific decisions can vary from one implementation to another, and from one environment to another. Those skilled in the art will appreciate that such a development effort might be time-consuming, but that, otherwise, such efforts would not be a contribution to the art of the present disclosure, and would form no part of this disclosure.

[0250] Those skilled in the art will further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein can be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans can implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

[0251] The steps of a method or algorithm described in connection with the embodiments disclosed herein can be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module can reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such that the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium can be integral to the processor. The processor and the storage medium can reside in an ASIC. The ASIC can reside in a user terminal. In the alternative, the processor and the storage medium can reside as discrete components in a user terminal.

[0252] For a software implementation, the techniques described herein can be implemented with modules (e.g., procedures, functions, and so on) that perform the functions described herein. The software codes can be stored in memory units and executed by processors. The memory unit can be implemented within the processor or external to the processor, in which case it can be communicatively coupled to the processor via various means as is well known in the art.

[0253] The above description includes examples of one or more embodiments. Of course, not all possible combinations of components or methods described above can be claimed as an embodiment, but one of ordinary skill in the art will recognize that many such further combinations and permutations of the embodiments described are possible. Accordingly, the described embodiments are intended to embrace all such alterations, modifications and variations which fall within the scope of the appended claims. Additionally, where the description or the claims recite "a", "an" or a "the" one or more of elements, this does not exclude multiple numbers for this element. Further, where the description or the claims recite that elements are each independently recited, this does not exclude multiple elements from being the same. Further, where the description or the claims recite "comprising", "containing", "including" or "having" this does not exclude other elements. Further, where the description or the claims recite "or" this does not exclude "and". Further, where the description or the claims recite "about" or "substantially" this does not exclude other elements.

Claims

1. A multi-scale fault prediction method, characterized by, The method comprises the following steps: Based on the obtained structural curvature data of the predicted work area, a pseudo-coherent calculation model is used to obtain pseudo-coherent data; The structural curvature data is obtained by structural curvature analysis based on the post-stack seismic data volume of the predicted work area; Based on the pseudo-coherent data, pseudo-ant tracking is performed to obtain pseudo-ant data; Based on the pseudo-ant data, data optimization processing is performed to obtain the final fault prediction data volume.

2. The method of claim 1, wherein, The process of obtaining the structural curvature data based on the post-stack seismic data volume of the predicted work area comprises the following steps: Obtain the range of the post-stack seismic data volume of the predicted work area; Using the seismic data volume in the range as input data, multi-window dip scanning calculation is performed to obtain a stratigraphic dip angle volume and an azimuth angle volume; Using the stratigraphic dip angle volume, the azimuth angle volume and the original seismic data volume as input data, structural curvature analysis calculation is performed to obtain the structural curvature data.

3. The method of claim 2, wherein, The process of obtaining the range of the post-stack seismic data volume of the predicted work area comprises the following steps: Based on the determined target area range to be predicted, the planar range of the post-stack seismic data volume is determined; According to the planar range of the post-stack seismic data volume, the seismic interpretation horizon in the range is cut to determine the time range of the seismic data volume.

4. The method of claim 1, wherein, The process of obtaining the pseudo-coherent data based on the obtained structural curvature data of the predicted work area using the pre-established pseudo-coherent calculation model comprises the following steps: From each curvature attribute included in the structural curvature data of the predicted work area, the most positive curvature is extracted; Based on the pre-determined pseudo-coherent parameters, a pseudo-coherent calculation model is established, and the most positive curvature is calculated based on the pseudo-coherent calculation model to obtain the pseudo-coherent data volume.

5. The method of claim 4, wherein, The pseudo-coherent calculation model is: A=C1-C0*Kpos; Wherein, C0, C1 are pseudo-coherent parameters, Kpos is the most positive curvature, and A is the pseudo-coherent data volume.

6. The method of claim 1, wherein, The process of obtaining the final fault prediction data volume based on the pseudo-ant data comprises the following steps: Based on the pseudo-ant data, at least one of Kuwahara filtering, edge detection and edge protection smoothing filtering processing is performed to obtain the final fault prediction data volume.

7. The method of claim 6, wherein, The process of Kuwahara filtering based on the pseudo-ant data comprises the following steps: Based on the pseudo-ant data, multi-window scanning is performed to calculate the variance of all time windows, and the sliding time window with the smallest variance is selected, and the median value of the data in the time window with the smallest variance is taken as the pseudo-ant data after Kuwahara filtering; The process of edge detection based on the pseudo-ant data comprises the following steps: Based on the Roberts cross edge detection principle, the pseudo-ant data is convolved to obtain the gray level change rate of the pseudo-ant data, which is taken as the pseudo-ant data after edge detection processing.

8. A method of fracture prediction, characterized by, The method comprises the following steps: Based on the pre-determined best scanning time window corresponding to the true stratigraphic dip angle, the data for fracture prediction is obtained from the post-stack seismic data volume of the predicted work area, and the intrinsic coherence calculation result is obtained by performing intrinsic coherence calculation on the data for fracture prediction; Based on the coherence calculation result, a pre-established fracture prediction model is used to obtain a fracture prediction data volume.

9. The method of claim 8, wherein, Based on the best scanning time window of the true dip angle corresponding azimuth, the data for fracture prediction is obtained from the post-stack seismic data volume of the prediction area, and the coherent calculation result is obtained by performing intrinsic coherent calculation on the data for fracture prediction, including: obtaining the range of the post-stack seismic data volume of the prediction area; performing multi-window dip angle scanning calculation on the seismic data volume in the range of the post-stack seismic data volume to obtain the best scanning time window of the true dip angle corresponding azimuth; taking the seismic data in the best scanning time window as the data for fracture prediction, and performing intrinsic coherent calculation based on the data for fracture prediction to obtain the coherent calculation result.

10. The method of claim 8, wherein, Based on the coherent calculation result, the fracture prediction data volume is obtained by using the pre-established fracture prediction model, including: pre-establishing the following fracture prediction model: D = 1 - (B - C2) * C3, wherein C2 and C3 are fracture prediction parameters, B is the coherent calculation result, and D is the fracture prediction data volume; inputting the coherent calculation result into the established fracture prediction model to obtain the fracture prediction data volume.

11. A multi-scale fault-fracture integrated prediction method, characterized in that, including: using the multi-scale fault prediction method of any one of claims 1-7 to obtain the final fault prediction data volume of the prediction area; using the fracture prediction method of any one of claims 8-10 to obtain the fracture prediction data volume of the prediction area; merging the final fault prediction data volume and the fracture prediction data volume to obtain the fault-fracture fusion data volume.

12. The method of claim 11, wherein, Merging the final fault prediction data volume and the fracture prediction result data volume to obtain the fault-fracture fusion data volume, including: pre-establishing the following fault-fracture fusion prediction model: E = C4 * A + C5 * D, wherein C4 and C5 are fault-fracture fusion prediction parameters, A is the final fault prediction data volume, D is the fracture prediction data volume, and E is the fault-fracture fusion data volume; inputting the final fault prediction data volume and the fracture prediction data volume into the established fault-fracture fusion prediction model to obtain the fault-fracture fusion data volume.

13. A multi-scale fault prediction apparatus characterized by comprising: including: a pseudo-coherent volume acquisition module configured to obtain pseudo-coherent volume data by using a pre-established pseudo-coherent calculation model based on the obtained structural curvature volume data of the prediction area; a pseudo-ant volume acquisition module configured to obtain pseudo-ant volume data by performing pseudo-ant tracking based on the pseudo-coherent volume data; a fault prediction data volume acquisition module configured to obtain the final fault prediction data volume by performing data optimization processing based on the pseudo-ant volume data.

14. A fracture prediction apparatus characterized by comprising: including: a coherent calculation result acquisition module configured to obtain the coherent calculation result by performing intrinsic coherent calculation on the obtained post-stack seismic data volume of the prediction area; a fracture prediction data volume acquisition module configured to obtain the fracture prediction data volume by using a pre-established fracture prediction model based on the coherent calculation result.

15. A multi-scale fault-fracture integrated prediction device, characterized in that, including: the multi-scale fault prediction device of claim 13, configured to obtain the final fault prediction data volume of the prediction area; the fracture prediction device of claim 14, configured to obtain the fracture prediction data volume of the prediction area; a fault-fracture fusion prediction device configured to merge the final fault prediction data volume and the fracture prediction result data volume to obtain the fault-fracture fusion data volume.

16. A computer storage medium, comprising, The computer storage medium stores computer executable instructions, and the computer executable instructions are executed by the processor to implement at least one of the multi-scale fault prediction method in any one of claims 1-7, the fracture prediction method in any one of claims 8-10, and the fault fracture comprehensive prediction method in any one of claims 11-12.

17. A terminal device, comprising: Comprise: a memory, a processor, and a computer program stored in the memory and capable of running on the processor, and the processor implements at least one of the multi-scale fault prediction method in any one of claims 1-7, the fracture prediction method in any one of claims 8-10, and the fault fracture comprehensive prediction method in any one of claims 11-12 when executing the program.

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