Method, device and equipment for determining reservoir body of faulted fracture-cave type reservoir

Abstract

The application discloses a method, device and equipment for determining fractured and controlled fracture-vug type reservoirs, wherein the seismic reflection characteristics of a fracture development area are determined according to a seismic imaging profile of the fracture development area, and the external contour of the fracture broken zone is depicted according to the seismic reflection characteristics; a plurality of sensitive seismic attributes are extracted according to pre-stack depth migration data of the fracture broken zone; the plurality of sensitive seismic attributes are input into a trained self-organizing neural network model; and the initial reservoir type of the fracture development area is output. According to the method, the different seismic attribute information reflecting the seismic facies of the fracture development area can be comprehensively represented as an attribute data body which effectively represents the reservoir structure by using the self-organizing neural network model, and the quantitative representation of different types of reservoirs by using the seismic facies is realized.

Description

Technical Field

[0001] This invention relates to the field of petroleum exploration technology, and in particular to a method, apparatus, and equipment for determining fault-controlled fracture-vuggy oil reservoirs. Background Technology

[0002] The reservoir-controlling effect of faults has been verified in studies of several large basins in China. Currently, scholars both domestically and internationally have conducted extensive research on seismic characterization methods for fault-controlled fracture-vuggy reservoirs. Among related technologies, by combining the results of fine structural interpretation of the reservoir development zone, it has been found that the profile structure of the development zone exhibits a typical "three-part" structural feature, which can be divided into a bedrock section, a fault-dissolved body rim, and a fault-dissolved body core. The degree of reservoir development varies at different structural locations within the reservoir.

[0003] Existing technologies employ a contour-internal hierarchical characterization approach, utilizing seismic attributes such as tensors, energy, and AFE (Advanced Fault Enhancement) to characterize the contours and internal structure of fracture zones in fault-dissolved reservoirs. This approach achieves a detailed depiction of the internal structure of fault-dissolved reservoirs, aiming to precisely analyze the dominant reservoir development areas within the fault-dissolved reservoirs and guide drilling trajectory design in production. However, existing technologies do not address how to comprehensively characterize the seismic facies of fault-controlled fracture-vuggy reservoirs using multiple seismic attributes, or how to determine the reservoir type of such reservoirs. Summary of the Invention

[0004] The technical problem to be solved by this invention is: how to use multiple seismic attributes to comprehensively characterize the seismic facies of fault-controlled fracture-vuggy reservoirs and determine the reservoir type of fault-controlled fracture-vuggy reservoirs.

[0005] To address the aforementioned technical problems, a first aspect of the present invention provides a method for determining fault-controlled fracture-vuggy reservoirs, comprising:

[0006] The seismic reflection characteristics of the fault development area are determined based on the seismic imaging profile of the fault development area, and the outer contour of the fault fracture zone is delineated based on the seismic reflection characteristics.

[0007] Multiple sensitive seismic attributes were extracted based on the pre-stack depth migration data of the fracture zone;

[0008] Input the multiple sensitive seismic attributes into the trained self-organizing neural network model;

[0009] Output the initial reservoir type of the fractured region.

[0010] In some embodiments, multiple sensitive seismic attributes are extracted based on the pre-stack depth migration data of the fracture zone, including:

[0011] The pre-stack depth migration data is processed using the structure tensor algorithm to obtain structure tensor eigenvalues; and,

[0012] The relative wave impedance difference is calculated based on the pre-stack depth offset data.

[0013] In some embodiments, the relative wave impedance difference is calculated based on the pre-stack depth migration data, including:

[0014] The relative impedance is obtained by inverting the pre-stack depth offset data;

[0015] The relative impedance is subjected to low-pass filtering, wherein the cutoff frequency is 10-15 Hz;

[0016] The relative wave impedance difference is obtained by subtracting the relative impedance from the low-pass filtered relative impedance.

[0017] In some embodiments, the sensitive seismic properties include at least two of the following for identifying cluttered reflections: variance, edge detection, energy gradient, structural tensor eigenvalues, and relative wave impedance difference.

[0018] In some embodiments, the method further includes:

[0019] Obtain logging data for the fractured area;

[0020] The logging data is interpreted to obtain the standard reservoir type of the fracture development area;

[0021] Compare the initial storage group type with the standard storage group;

[0022] When the initial reservoir type is inconsistent with the standard reservoir type, the classification parameters of the self-organizing neural network model are updated.

[0023] In some embodiments, when the initial reservoir type is inconsistent with the standard reservoir type, after updating the classification parameters of the self-organizing neural network model, the method further includes:

[0024] The reservoir type of the fracture development region is obtained using the updated self-organizing neural network model.

[0025] A second aspect of the present invention provides a device for determining fractured-vuggy oil reservoirs, comprising:

[0026] The determination module is used to determine the seismic reflection characteristics of the fault development area based on the seismic imaging profile of the fault development area, and to delineate the outer contour of the fault fracture zone based on the seismic reflection characteristics.

[0027] The extraction module is used to extract multiple sensitive seismic attributes based on the pre-stack depth migration data of the fracture zone.

[0028] An input module is used to input multiple of the sensitive seismic attributes into a trained self-organizing neural network model;

[0029] The output module is used to output the initial reservoir type of the fracture development region.

[0030] In some embodiments, the fault-controlled fracture-vuggy reservoir determination device further includes an update module, which is used for:

[0031] Obtain logging data for the fractured area;

[0032] The logging data is interpreted to obtain the standard reservoir type of the fracture development area;

[0033] Compare the initial storage group type with the standard storage group;

[0034] When the initial reservoir type is inconsistent with the standard reservoir type, the classification parameters of the self-organizing neural network model are updated.

[0035] A third aspect of the present invention provides a storage medium storing a computer program that, when executed by a processor, implements the method for determining fault-controlled fracture-vuggy reservoirs as described in any of the preceding claims.

[0036] In a fourth aspect, the present invention provides an apparatus comprising a memory and a processor, wherein the memory stores a computer program that, when executed by the processor, implements the method for determining fault-controlled fracture-vuggy reservoirs as described in any of the preceding claims.

[0037] Compared with the prior art, one or more embodiments of the above solutions may have the following advantages or beneficial effects:

[0038] The method for determining fault-controlled fracture-vuggy reservoirs provided by this invention determines the seismic reflection characteristics of the fault-developed area based on seismic imaging profiles and delineates the external contour of the fault fracture zone based on these characteristics. Multiple sensitive seismic attributes are extracted from the pre-stack depth migration data of the fault fracture zone. These attributes are then input into a trained self-organizing neural network model, outputting the initial reservoir type for the fault-developed area. This method utilizes a self-organizing neural network model to comprehensively represent different seismic attribute information reflecting seismic facies in the fault-developed area into an effective attribute data volume characterizing the reservoir structure, and achieves quantitative characterization of different reservoir types using seismic facies. Attached Figure Description

[0039] The scope of this disclosure can be better understood by reading the following detailed description of exemplary embodiments in conjunction with the accompanying drawings. The accompanying drawings are:

[0040] Figure 1 A flowchart illustrating a method for determining a fracture-vuggy oil reservoir according to an embodiment of the present invention is shown.

[0041] Figure 2 A flowchart illustrating another method for determining fracture-vuggy oil reservoirs provided in an embodiment of the present invention is shown.

[0042] Figure 3 A schematic diagram of pre-stack depth migration data profile of fault-controlled fracture-vuggy reservoirs is shown.

[0043] Figure 4 A schematic diagram of the structural tensor eigenvalue profile of a fractured-vuggy reservoir is shown.

[0044] Figure 5 A schematic diagram of the relative impedance difference profile of a fault-controlled fracture-vuggy reservoir is shown.

[0045] Figure 6 A schematic diagram of the seismic facies classification results for fault-controlled fracture-vuggy reservoirs is shown.

[0046] Figure 7(1) shows a schematic diagram of the fracture interpretation of a fracture-controlled fracture-vuggy reservoir; Figure 7(2) shows the seismic facies division results of the same profile as in Figure 7(1);

[0047] Figure 8 A comparison diagram of the interpretation results of fault zone core and seismic facies identification results for fault-controlled fractured-vuggy reservoirs is shown;

[0048] Figure 9 This diagram illustrates the structure of a fault-controlled fracture-vuggy oil reservoir determination device according to an embodiment of the present invention.

[0049] Figure 10 A schematic diagram of the structure of a device provided in an embodiment of the present invention is shown. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of the present invention clearer, the implementation method of the present invention will be described in detail below with reference to the accompanying drawings and embodiments, so that the process of how the present invention uses technical means to solve technical problems and achieve technical effects can be fully understood and implemented accordingly.

[0051] The reservoir-controlling effect of faults has been verified in studies of several large basins in China. Currently, scholars both domestically and internationally have conducted extensive research on seismic characterization methods for fault-controlled fracture-vuggy reservoirs. Among related technologies, by combining the results of fine structural interpretation of the reservoir development zone, it has been found that the profile structure of the development zone exhibits a typical "three-part" structural feature, which can be divided into a bedrock section, a fault-dissolved body rim, and a fault-dissolved body core. The degree of reservoir development varies at different structural locations within the reservoir.

[0052] Existing technologies employ a contour-internal hierarchical characterization approach, utilizing seismic attributes such as tensors, energy, and AFE (Advanced Fault Enhancement) to characterize the contours and internal structure of fracture zones in fault-subsidence reservoirs. This approach achieves a detailed depiction of the internal structure of fault-subsidence reservoirs, aiming to precisely analyze the dominant reservoir development areas within the fault-subsidence reservoir and guide drilling trajectory design in production. However, existing technologies do not address how to comprehensively characterize the seismic facies of fault-controlled fracture-vuggy reservoirs using multiple seismic attributes, or how to determine the reservoir type of such reservoirs.

[0053] In view of this, the present invention provides a method for determining fault-controlled fracture-vuggy reservoirs. This method determines the seismic reflection characteristics of the fault-developed area based on seismic imaging profiles and delineates the external contour of the fault fracture zone based on these characteristics. Multiple sensitive seismic attributes are extracted from the pre-stack depth migration data of the fault fracture zone. These attributes are then input into a trained self-organizing neural network model, outputting the initial reservoir type of the fault-developed area. This method utilizes a self-organizing neural network model to comprehensively represent different seismic attribute information reflecting seismic facies in the fault-developed area into an effective attribute data volume characterizing the reservoir structure, and achieves quantitative characterization of different reservoir types using seismic facies.

[0054] Example 1

[0055] See Figure 1 As shown, Figure 1 This diagram illustrates a flowchart of a method for determining a fault-controlled fracture-vuggy reservoir according to an embodiment of the present invention, which may include:

[0056] Step S101: Determine the seismic reflection characteristics of the fault development area based on the seismic imaging profile of the fault development area, and delineate the outer contour of the fault fracture zone based on the seismic reflection characteristics.

[0057] Step S102: Extract multiple sensitive seismic attributes based on the pre-stack depth migration data of the fault fracture zone;

[0058] Step S103: Input multiple sensitive seismic attributes into the trained self-organizing neural network model;

[0059] Step S104: Output the initial reservoir type of the fracture development region.

[0060] Among them, fault-developed areas are the dominant development zones for fracture-vuggy reservoirs of different scales. When the distribution of fault-controlled fracture-vuggy reservoir development is reflected on seismic profiles, typical fault development imaging shows a good correlation with reservoir seismic reflection anomalies. Anomalous reflection characteristics such as "beaded" reflections, random reflections, and weak reflections are mainly located on and around fault zones in terms of spatial distribution. However, seismic reflection anomalies decrease significantly away from fault zones, and are more often manifested as medium-intensity continuous seismic reflections from the carbonate matrix.

[0061] Given the unique characteristics of fault-controlled fracture-vuggy reservoirs, step S101 can specifically involve identifying relatively easily identifiable anomalous seismic reflection features from the seismic imaging profile of the fault-developed area, and delineating the external contour of the fault fracture zone based on these features. In some embodiments, anomalous seismic reflection features may include "beaded" reflections, random reflections, or weak reflections around the phase axis that are discontinuous or disturbed.

[0062] In embodiments of the present invention, sensitive seismic attributes can be used to characterize the geological features of fault-controlled fracture-vuggy reservoirs. In some embodiments, attributes that are more effective in characterizing fault-controlled fracture-vuggy reservoirs can be used as sensitive seismic attributes. In some embodiments, sensitive seismic attributes may include at least two of the following for identifying cluttered reflections: variance, edge detection, energy gradient, structural tensor features, and relative acoustic impedance difference.

[0063] In some embodiments, step S102 may specifically be:

[0064] The pre-stack depth migration data is processed using the structure tensor algorithm to obtain the structure tensor eigenvalues; and the relative wave impedance difference is calculated based on the pre-stack depth migration data.

[0065] In some embodiments, when using the structural tensor algorithm to process pre-stack depth migration data and extract structural tensor feature values, extraction parameters can be set based on the fractured zones depicted on the seismic imaging profile. These extraction parameters can include the range and time window of the main horizontal survey line, connecting survey lines, and longitudinal time sampling. As an example, the size of the extraction parameters can be set according to the development range and intensity of anomalous reflection features. In this embodiment of the invention, for fault-controlled fracture-vuggy reservoirs with large development depths and wide distribution ranges of anomalous reflection features, a relatively large range and window control for longitudinal time sampling can be set.

[0066] In some embodiments, the relative wave impedance difference can be calculated through the following steps:

[0067] The relative impedance is obtained by inverting the pre-stack depth migration data;

[0068] The relative impedance is subjected to low-pass filtering, with a cutoff frequency of 10–15 Hz.

[0069] The relative wave impedance difference is obtained by subtracting the relative impedance from the low-pass filtered relative impedance.

[0070] In this embodiment of the invention, the relative impedance is calculated from the pre-stack depth migration data, and the pre-stack depth migration data is converted from reflection interface information to stratigraphic lithology information by inversion of the pre-stack depth migration data.

[0071] In this embodiment of the invention, in order to highlight the outlier characteristics in the relative impedance, the relative impedance can be subjected to low-pass filtering within a certain range and time window to retain low-frequency signals below the cutoff frequency, thereby obtaining low-frequency trend information reflecting the spatial transformation of the relative wave impedance, so as to more effectively characterize the seismic phase.

[0072] In some embodiments, step S103 may specifically involve inputting the structural tensor eigenvalues ​​and relative wave group robustness as input neurons into a trained self-organizing neural network model to perform seismic phase clustering analysis. The self-organizing neural network model can be trained through the following steps:

[0073] Establish learning sample sets for different types of reservoirs, and each learning sample set may include multiple groups of sensitive seismic attributes;

[0074] Set the number of storage group classifications and conduct learning training for the learning sample set corresponding to each type of storage group, determine the neural network weights and the initial self-organizing neural network model corresponding to the storage group type;

[0075] The initial output of the initial self-organizing neural network model is compared with the reservoir type interpreted by well logging. If the comparison is inconsistent, the classification parameters are adjusted to optimize the initial self-organizing neural network model. Once the comparison is consistent, the correlation between seismic facies and reservoir type can be established, and a well-trained self-organizing neural network model can be obtained.

[0076] In this embodiment of the invention, step S104 can be to use a trained self-organizing neural network model to classify the input sensitive seismic attributes based on the correlation between seismic phases and reservoir types.

[0077] In some embodiments, the reservoir development of fault-controlled fracture-vuggy reservoirs typically revolves around a "three-part structure" consisting of a bedrock section, a fault-dissolved body edge, and a fault-dissolved body core around the main controlling fault surface. By determining the initial reservoir type of fault-controlled fracture-vuggy reservoirs, technical analysis methods can be further provided for the "three-part structure," giving corresponding geological meaning to the seismic facies classification results.

[0078] The above describes a method for determining fault-controlled fracture-vuggy reservoirs according to an embodiment of the present invention. The method determines the seismic reflection characteristics of the fault-developed area based on seismic imaging profiles and delineates the external contour of the fault fracture zone based on these characteristics. Multiple sensitive seismic attributes are extracted from the pre-stack depth migration data of the fault fracture zone. These attributes are then input into a trained self-organizing neural network model, outputting the initial reservoir type for the fault-developed area. This method utilizes a self-organizing neural network model to comprehensively represent different seismic attribute information reflecting seismic facies in the fault-developed area into an effective attribute data body representing the reservoir structure, and achieves quantitative characterization of different reservoir types using seismic facies.

[0079] Example 2

[0080] See Figure 2 As shown, Figure 2 This diagram illustrates a flowchart of another method for determining fault-controlled fracture-vuggy reservoirs provided by an embodiment of the present invention, which may include:

[0081] Step S201: Determine the seismic reflection characteristics of the fault development area based on the seismic imaging profile of the fault development area, and delineate the outer contour of the fault fracture zone based on the seismic reflection characteristics.

[0082] Step S202: Extract multiple sensitive seismic attributes based on the pre-stack depth migration data of the fault fracture zone;

[0083] Step S203: Input multiple sensitive seismic attributes into the trained self-organizing neural network model;

[0084] Step S204: Output the initial reservoir type of the fracture development region;

[0085] Step S205: Obtain logging data for the fractured area;

[0086] Step S206: Interpret the logging data to obtain the standard reservoir type in the fractured area;

[0087] Step S207: Compare the initial storage group type with the standard storage group;

[0088] Step S208: When the initial reservoir type is inconsistent with the standard reservoir type, update the classification parameters of the self-organizing neural network model;

[0089] Step S209: Obtain the reservoir type of the fracture development region using the updated self-organizing neural network model.

[0090] Among them, fault-developed areas are the dominant development zones for fracture-vuggy reservoirs of different scales. When the distribution of fault-controlled fracture-vuggy reservoir development is reflected on seismic profiles, typical fault development imaging shows a good correlation with reservoir seismic reflection anomalies. Anomalous reflection characteristics such as "beaded" reflections, random reflections, and weak reflections are mainly located on and around fault zones in terms of spatial distribution. However, seismic reflection anomalies decrease significantly away from fault zones, and are more often manifested as medium-intensity continuous seismic reflections from the carbonate matrix.

[0091] Given the unique characteristics of fault-controlled fracture-vuggy reservoirs, step S201 can specifically involve identifying relatively easily identifiable anomalous seismic reflection features from the seismic imaging profile of the fault-developed area, and delineating the external contour of the fault fracture zone based on these features. In some embodiments, anomalous seismic reflection features may include "beaded" reflections, random reflections, or weak reflections around the phase axis that are discontinuous or disturbed.

[0092] In embodiments of the present invention, sensitive seismic attributes can be used to characterize the geological features of fault-controlled fracture-vuggy reservoirs. In some embodiments, attributes that are more effective in characterizing fault-controlled fracture-vuggy reservoirs can be used as sensitive seismic attributes. In some embodiments, sensitive seismic attributes may include at least two of the following for identifying cluttered reflections: variance, edge detection, energy gradient, structural tensor features, and relative acoustic impedance difference.

[0093] In some embodiments, step S202 may specifically be:

[0094] The pre-stack depth migration data is processed using the structure tensor algorithm to obtain the structure tensor eigenvalues; and the relative wave impedance difference is calculated based on the pre-stack depth migration data.

[0095] In some embodiments, when using the structural tensor algorithm to process pre-stack depth migration data and extract structural tensor feature values, extraction parameters can be set based on the fractured zones depicted on the seismic imaging profile. These extraction parameters can include the range and time window of the main horizontal survey line, connecting survey lines, and longitudinal time sampling. As an example, the size of the extraction parameters can be set according to the development range and intensity of anomalous reflection features. In this embodiment of the invention, for fault-controlled fracture-vuggy reservoirs with large development depths and wide distribution ranges of anomalous reflection features, a relatively large range and window control for longitudinal time sampling can be set.

[0096] In some embodiments, the relative wave impedance difference can be calculated through the following steps:

[0097] The relative impedance is obtained by inverting the pre-stack depth migration data;

[0098] The relative impedance is subjected to low-pass filtering, with a cutoff frequency of 10–15 Hz.

[0099] The relative wave impedance difference is obtained by subtracting the relative impedance from the low-pass filtered relative impedance.

[0100] In this embodiment of the invention, the relative impedance is calculated from the pre-stack depth migration data, and the pre-stack depth migration data is converted from reflection interface information to stratigraphic lithology information by inversion of the pre-stack depth migration data.

[0101] In this embodiment of the invention, in order to highlight the outlier characteristics in the relative impedance, the relative impedance can be subjected to low-pass filtering within a certain range and time window to retain low-frequency signals below the cutoff frequency, thereby obtaining low-frequency trend information reflecting the spatial transformation of the relative wave impedance, so as to more effectively characterize the seismic phase.

[0102] In some embodiments, step S203 may specifically involve inputting the structural tensor eigenvalues ​​and relative wave group robustness as input neurons into a trained self-organizing neural network model to perform seismic phase clustering analysis. The self-organizing neural network model can be trained through the following steps:

[0103] Establish learning sample sets for different types of reservoirs, and each learning sample set may include multiple groups of sensitive seismic attributes;

[0104] Set the number of storage group classifications and conduct learning training for the learning sample set corresponding to each type of storage group, determine the neural network weights and the initial self-organizing neural network model corresponding to the storage group type;

[0105] The initial output of the initial self-organizing neural network model is compared with the reservoir type interpreted by well logging. If the comparison is inconsistent, the classification parameters are adjusted to optimize the initial self-organizing neural network model. Once the comparison is consistent, the correlation between seismic facies and reservoir type can be established, and a well-trained self-organizing neural network model can be obtained.

[0106] In this embodiment of the invention, step S204 can be to use a trained self-organizing neural network model to classify the input sensitive seismic attributes based on the correlation between seismic phases and reservoir types.

[0107] In some embodiments, the reservoir development of fault-controlled fracture-vuggy reservoirs typically exhibits a "three-part structure" around the main controlling fault surface, consisting of a bedrock segment, a fault-dissolved body rim, and a fault-dissolved body core. Determining the initial reservoir type of fault-controlled fracture-vuggy reservoirs can further provide technical analysis methods for the "three-part structure" and assign corresponding geological meaning to the seismic facies classification results.

[0108] In step S205, logging data of the fractured area can be obtained using methods conventional in the art.

[0109] In some embodiments, step S208 may specifically involve updating the classification parameters in the self-organizing neural network model when the initial storage group type is inconsistent with the standard storage group, until the comparison results are consistent, thereby updating the self-organizing neural network model and improving the effectiveness of storage group classification. In some embodiments, the classification parameters may include the number of classifications, the calculation error, and the maximum number of calculation loops. The calculation error can be used to represent the error allowed during training; for example, the calculation error can be set to 0.1. The maximum number of calculation loops can be the number of times the self-organizing neural network model calculates attribute correlations; for example, the maximum number of calculation loops can be set to 200.

[0110] In some embodiments, step S209 may specifically involve using an updated self-organizing neural network model to obtain the reservoir type of the fracture development region, thereby effectively improving the effectiveness of reservoir type identification.

[0111] The above is a method for determining fault-controlled fracture-vuggy reservoirs provided in Embodiment 2 of the present invention. It can achieve the same beneficial effects as Embodiment 1. In addition, by comparing the output initial reservoir type with the standard reservoir type and updating the classification parameters of the self-organizing neural network model when they are inconsistent, the updated self-organizing neural network model is used to determine the reservoir type in the fracture development area, which helps to improve the accuracy of the self-organizing neural network model and improve the effectiveness of reservoir type prediction.

[0112] Specific examples

[0113] The method for determining fracture-vuggy reservoirs provided in Embodiment 2 of the present invention is applied to a certain oilfield. See [link to relevant documentation]. Figure 3 As shown, Figure 3 A schematic diagram of the pre-stack depth migration data profile of a fault-controlled fracture-vuggy reservoir is shown. Figure 3 The fracture imaging is very clear, and the continuity of the strata and the reflected energy around the fracture change, which is a typical seismic profile feature of fault-controlled fracture-vuggy reservoirs.

[0114] See Figure 4 As shown, Figure 4 A schematic diagram of the structural tensor eigenvalue profile of a fault-controlled fracture-vuggy reservoir is shown. In this example, a large longitudinal time sampling range and time window were selected when calculating the structural tensor eigenvalues. The longitudinal time sampling range and time window can be set to 7 times the seismic trace spacing and 15 times the longitudinal time sampling rate. It can be seen that the structural tensor eigenvalues ​​calculated under these parameter conditions can clearly characterize the fault zone and its surrounding chaotic reflection range, and the range characterized by these eigenvalues ​​can be considered the reservoir boundary.

[0115] See Figure 5 As shown, Figure 5A schematic diagram of the relative impedance difference profile of a fault-controlled fracture-vuggy reservoir is shown. The relative impedance difference is obtained after targeted detrending processing based on the relative impedance, and its low outlier indicates that the reservoir is relatively well-developed. Figure 5 The abnormal body shown in the middle and Figure 3 The amplitude anomalies shown have a good correspondence. At the same time, since the wave impedance eliminates the sidelobe effect of the wavelet in the longitudinal direction, it has a higher longitudinal resolution, so the location focusing of the fracture-vuggy reservoir is more accurate.

[0116] See Figure 6 As shown, Figure 6 A schematic diagram of the seismic facies division results for fault-controlled fracture-vuggy reservoirs is shown. The self-organizing neural network method is used to further analyze the attached... Figure 4 The structural tensor eigenvalue properties and associated... Figure 5 The relative impedance difference properties shown are used to classify seismic facies. The self-organizing neural network method can achieve good identification of the internal structure of fault-controlled fracture-vuggy reservoirs. Different seismic facies results represent differences in the development degree of the reservoir.

[0117] Referring to Figures 7(1) and 7(2), Figure 7(1) shows a schematic diagram of the fracture interpretation of a fault-controlled fracture-vuggy reservoir. It shows the original pre-stack depth migration profile and the fractures it interprets. It can be seen that the fault displacements of the middle and left sides are relatively clear, and the seismic reflection anomalies caused by the fractures (generally considered to be the response of the fracture-vuggy reservoir) are also relatively clear. Figure 7(2) shows the seismic facies division results of the same profile as in Figure 7(1). It can be seen that Class I and Class II fracture-vuggy reservoirs are mainly located in the area between the middle fault and the left main fracture surface. Class I fracture-vuggy reservoirs are less distributed between the middle fault and the right side fault, and Class III fracture-vuggy reservoirs are the main type. This difference in reservoir distribution characteristics is related to factors such as the period and intensity of fault activity. Based on these seismic facies division results, a relevant basis can be provided for the three-part structural division of this type of reservoir.

[0118] See Figure 8 As shown, Figure 8 A comparison diagram showing the interpretation of fault zone cores and seismic facies identification results for fault-controlled fractured-vuggy reservoirs is presented. Among them, Figure 8 The left image in the diagram is a schematic diagram of the fracture zone core of a fault-controlled fracture-vuggy reservoir. Figure 8The right image in the diagram is a schematic representation of the seismic facies division results compared to the profile in the left image. In this profile, the seismic fault fracturing is more complex, but two large fault surfaces can be identified. Seismic reflection anomalies (reservoir response) are mainly generated under the influence of these fault surfaces, forming the entire reservoir development zone. From the dashed lines in the right image, it can be seen that Class I and Class II fracture-cavities, based on the seismic facies division results, mainly develop in the area between the two fault surfaces. This location is the core of the fault zone, where the reservoir is most developed. Class III fracture-cavities, on the other hand, mainly develop around the core zone, with a larger development area, spatially forming the edge of the fault zone. The probability of reservoir development on the outer edge of this edge is lower, representing the target bedrock segment. The three-part structural division based on seismic facies division results is more intuitive than the original seismic profile and considers multiple seismic attributes, making its basis more comprehensive.

[0119] Example 3

[0120] See Figure 9 As shown, Figure 9 This diagram illustrates a structural schematic of a fault-controlled fracture-vuggy oil reservoir determination device according to an embodiment of the present invention, which may include:

[0121] The determination module 91 is used to determine the seismic reflection characteristics of the fault development area based on the seismic imaging profile of the fault development area, and to delineate the outer contour of the fault fracture zone based on the seismic reflection characteristics.

[0122] Extraction module 92 is used to extract multiple sensitive seismic attributes based on pre-stack depth migration data of the fault fracture zone;

[0123] Input module 93 is used to input multiple sensitive seismic attributes into a trained self-organizing neural network model;

[0124] Output module 94 is used to output the initial reservoir type of the fracture development region.

[0125] In some embodiments, the extraction module 92 is specifically used for:

[0126] The pre-stack depth migration data is processed using the structure tensor algorithm to obtain the structure tensor eigenvalues; and the relative wave impedance difference is calculated based on the pre-stack depth migration data.

[0127] The relative wave impedance difference calculated based on pre-stack depth migration data can include:

[0128] The relative impedance is obtained by inverting the pre-stack depth migration data;

[0129] The relative impedance is subjected to low-pass filtering, with a cutoff frequency of 10–15 Hz.

[0130] The relative wave impedance difference is obtained by subtracting the relative impedance from the low-pass filtered relative impedance.

[0131] In other embodiments, the sensitive seismic properties may include at least two of the following for identifying cluttered reflections: variance, edge detection, energy gradient, structural tensor eigenvalues, and relative wave impedance difference.

[0132] In some embodiments, the fault-controlled fracture-vuggy reservoir determination device further includes an update module 95, which is used for:

[0133] Obtain logging data in fractured areas;

[0134] Well logging data were interpreted to determine the standard reservoir type in the fractured area;

[0135] Compare the initial storage group type with the standard storage group;

[0136] When the initial reservoir type is inconsistent with the standard reservoir type, the classification parameters of the self-organizing neural network model are updated.

[0137] In some embodiments, the update module 95 can also be used to obtain the reservoir type of the fracture development region using the updated self-organizing neural network model.

[0138] The above describes a fault-controlled fracture-vuggy reservoir determination device provided by an embodiment of the present invention. It includes a determination module 91, used to determine the seismic reflection characteristics of the fault-developed area based on seismic imaging profiles, and to delineate the external contour of the fault fracture zone based on the seismic reflection characteristics; an extraction module 92, used to extract multiple sensitive seismic attributes based on pre-stack depth migration data of the fault fracture zone; an input module 93, used to input the multiple sensitive seismic attributes into a trained self-organizing neural network model; and an output module 94, used to output the initial reservoir type of the fault-developed area. This device, by utilizing a self-organizing neural network model, can comprehensively characterize different seismic attribute information reflecting seismic facies in the fault-developed area into an effective attribute data body characterizing the reservoir structure, and achieves quantitative characterization of different types of reservoirs using seismic facies.

[0139] Example 4

[0140] In another aspect, the present invention provides a storage medium storing a computer program, which, when executed by a processor, implements the method for determining fault-controlled fracture-vuggy reservoirs as described in Embodiment 1 or Embodiment 2 above.

[0141] The processes, functions, methods, and / or software described above may be recorded, stored, or fixed in one or more computer-readable storage media, which include program instructions that will be implemented by a computer to cause a processor to execute the program instructions. The storage medium may also individually include program instructions, data files, data structures, etc., or a combination thereof. The storage medium or program instructions may be specifically designed and understood by those skilled in the art of computer software, or the storage medium or instructions may be known and available to those skilled in the art of computer software. Examples of computer-readable media include: magnetic media, such as hard disks, floppy disks, and magnetic tapes; optical media, such as CD-ROMs and DVDs; magneto-optical media, such as optical discs; and hardware devices specifically configured to store and execute program instructions, such as read-only memory (ROM), random access memory (RAM), flash memory, etc. Examples of program instructions include machine code (e.g., code generated by a compiler) and files containing high-level code that can be executed by a computer using an interpreter. The described hardware devices may be configured to function as one or more software modules to perform the operations and methods described above, and vice versa. In addition, computer-readable storage media can be distributed across networked computer systems, allowing for the storage and execution of computer-readable code or program instructions in a decentralized manner.

[0142] Example 5

[0143] In another aspect, the present invention also provides an apparatus, see [link to apparatus]. Figure 10 As shown, Figure 10 A schematic diagram of the structure of a device provided in an embodiment of the present invention is shown.

[0144] The device may include a memory 101 and a processor 102. The memory 101 stores a computer program, which, when executed by the processor 102, implements the method for determining fault-controlled fracture-vuggy reservoirs as described in Embodiment 1 or Embodiment 2 above.

[0145] It should be noted that the device may include one or more memories 101 and processors 102, which can be connected via a bus or other means. The memory 101, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules. The processor 102 executes various functional applications and data processing of the device by running the non-volatile software programs, instructions, and modules stored in the memory, thereby realizing the method for determining fault-controlled fracture-vuggy reservoirs as described above.

[0146] While the embodiments disclosed in this invention are as described above, the content is merely for the purpose of facilitating understanding of the invention and is not intended to limit the invention. Any person skilled in the art to which this invention pertains may make any modifications and variations in form and detail of the implementation without departing from the spirit and scope disclosed herein; however, the scope of protection of this invention shall still be determined by the scope defined in the appended claims.

Claims

1. A method for determining fault-controlled fracture-vuggy oil reservoirs, characterized in that, include: The seismic reflection characteristics of the fault development area are determined based on the seismic imaging profile of the fault development area, and the outer contour of the fault fracture zone is delineated based on the seismic reflection characteristics. Multiple sensitive seismic attributes were extracted based on the pre-stack depth migration data of the fracture zone; Input the multiple sensitive seismic attributes into the trained self-organizing neural network model; Output the initial reservoir type of the fracture development region; Several sensitive seismic attributes were extracted based on the pre-stack depth migration data of the fracture zone, including: The pre-stack depth migration data is processed using the structure tensor algorithm to obtain structure tensor eigenvalues; and, The relative wave impedance difference is calculated based on the pre-stack depth offset data. The relative wave impedance difference is calculated based on the pre-stack depth migration data, including: The relative impedance is obtained by inverting the pre-stack depth offset data; The relative impedance is subjected to low-pass filtering, wherein the cutoff frequency is 10~15 Hz; The relative wave impedance difference is obtained by subtracting the relative impedance from the low-pass filtered relative impedance.

2. The method according to claim 1, characterized in that, The sensitive seismic properties include at least two of the following for cluttered reflection identification: variance, edge detection, energy gradient, structural tensor eigenvalues, and relative wave impedance difference.

3. The method according to claim 1, characterized in that, The method further includes: Obtain logging data for the fractured area; The logging data is interpreted to obtain the standard reservoir type of the fracture development area; Compare the initial storage group type with the standard storage group type; When the initial reservoir type is inconsistent with the standard reservoir type, the classification parameters of the self-organizing neural network model are updated.

4. The method according to claim 3, characterized in that, When the initial reservoir type is inconsistent with the standard reservoir type, after updating the classification parameters of the self-organizing neural network model, the method further includes: The reservoir type of the fracture development region is obtained using the updated self-organizing neural network model.

5. A device for determining the reservoir of a fractured-vuggy oil reservoir, characterized in that, include: The determination module is used to determine the seismic reflection characteristics of the fault development area based on the seismic imaging profile of the fault development area, and to delineate the outer contour of the fault fracture zone based on the seismic reflection characteristics. The extraction module is used to extract multiple sensitive seismic attributes based on the pre-stack depth migration data of the fracture zone. An input module is used to input multiple of the sensitive seismic attributes into a trained self-organizing neural network model; The output module is used to output the initial reservoir type of the fracture development region; The extraction module is specifically used for: The pre-stack depth migration data is processed using the structure tensor algorithm to obtain structure tensor eigenvalues; and, The relative wave impedance difference is calculated based on the pre-stack depth offset data. The relative wave impedance difference is calculated based on the pre-stack depth migration data, including: The relative impedance is obtained by inverting the pre-stack depth offset data; The relative impedance is subjected to low-pass filtering, wherein the cutoff frequency is 10-15 Hz; The relative impedance difference is obtained by subtracting the relative impedance from the low-pass filtered relative impedance.

6. The apparatus according to claim 5, characterized in that, It also includes an update module, which is used for: Obtain logging data for the fractured area; The logging data is interpreted to obtain the standard reservoir type of the fracture development area; Compare the initial storage group type with the standard storage group type; When the initial reservoir type is inconsistent with the standard reservoir type, the classification parameters of the self-organizing neural network model are updated.

7. A storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the method for determining fault-controlled fracture-vuggy reservoirs as described in any one of claims 1 to 4.

8. A device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, it implements the method for determining fault-controlled fracture-vuggy reservoirs as described in any one of claims 1 to 4.

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