Reservoir fracture prediction method and device

Abstract

The invention discloses a reservoir fracture prediction method and device, and the method comprises the steps: carrying out the preprocessing of seismic data of a to-be-predicted region, and determining a plurality of seismic attributes according to the preprocessed seismic data; preprocessing the logging data of the to-be-predicted area to obtain logging information; obtaining reservoir fracture density data of the to-be-predicted region according to the seismic attributes, the logging information and a pre-established discrete fracture network model; wherein the discrete fracture network model is formed by combining a large fracture discrete random network model, a medium fracture discrete random network model and a small fracture discrete random network model.

Description

Technical Field

[0001] The present invention relates to the field of geophysical exploration technology, and in particular to a reservoir fracture prediction method and device. Background Art

[0002] Fractures are important channels for oil and gas migration in low-permeability reservoirs and have a great impact on the development of oil and gas fields. Low-permeability reservoirs have poor porosity and permeability, and high displacement pressure. It is difficult for oil to undergo significant secondary migration in such reservoirs. The composite transport mode of reservoirs and fractures enables better connectivity between reservoirs and source rocks, and oil and gas can migrate and adjust along the fracture development zone. At the same time, during water injection development, fractures can easily cause uneven water absorption in injection wells, water channeling along the fracture direction, and rapid water content increase in oil wells in sandstone fracture development areas. The water drive degree of fracture lateral wells is low, and the oil well production capacity decreases significantly. Therefore, objectively understanding the distribution characteristics of reservoir fractures based on accurate identification is the key to efficient exploitation of low-permeability reservoirs.

[0003] In the prior art, seismic methods are widely used to detect fractures, which generally include the following aspects: ① fracture prediction based on longitudinal wave anisotropy; ② special technologies for special cases, such as identifying fracture-cavity reservoirs through seismic response characteristics of fracture-cavity reservoirs; ③ numerical simulation technology of tectonic stress; ④ fracture prediction research of logging constrained wave impedance inversion, etc.; for example, an invention patent discloses a method for predicting underground fractures, by obtaining the radial component R(t) and the lateral component T(t) of the converted shear wave; obtaining the time difference function Δt(t) between the fast shear wave component S1(t) and the slow shear wave component S2(t) according to the radial component R(t) and the lateral component T(t); derivating the time difference function Δt(t) between the fast shear wave component S1(t) and the slow shear wave component S2(t) to obtain the derivative function dt(t) of the time difference function; and using the derivative function dt(t) to predict the intensity of fracture development.

[0004] However, the above-mentioned fracture identification and prediction is performed on seismic data volumes, and the resolution and accuracy are affected to a certain extent; therefore, there is an urgent need for a reservoir fracture prediction method that can improve the resolution and accuracy. Summary of the invention

[0005] The present application provides a reservoir fracture prediction method and device, which predicts reservoir fractures based on seismic data and logging data; and, during the prediction, the fractures are divided into three levels of fractures, and different levels of fracture models are established according to the identification characteristics of different levels of fractures, and finally merged into a discrete fracture network model for predicting fracture density, thereby improving the prediction accuracy of fracture density.

[0006] In a first aspect, the present application provides a reservoir fracture prediction method, the method comprising:

[0007] Preprocessing the seismic data of the area to be predicted, and determining a plurality of seismic attributes according to the preprocessed seismic data;

[0008] Preprocess the logging data of the predicted area to obtain logging information;

[0009] Reservoir fracture density data of the area to be predicted is obtained according to the seismic attributes, the logging information and a pre-established discrete fracture network model; wherein the discrete fracture network model is composed of a large fracture discrete random network model, a medium fracture discrete random network model and a small fracture discrete random network model.

[0010] In an exemplary embodiment, the large fracture discrete random network model is established based on artificial earthquake fault interpretation information;

[0011] The medium crack discrete random network model is a model established based on the properties of ant bodies;

[0012] The small fracture discrete random network model is a model established based on the inter-well fracture probability body model.

[0013] In an exemplary embodiment, preprocessing of seismic data includes one or more of the following: resampling, de-emphasis processing, and median filtering.

[0014] In an exemplary embodiment, determining a plurality of seismic attributes based on the preprocessed seismic data includes:

[0015] Conduct stratigraphic structural interpretation based on pre-processed seismic data;

[0016] Extract seismic attributes based on the stratigraphic structure interpretation results.

[0017] In an exemplary embodiment, the seismic attributes include one or more of the following: variance volume, ant volume, structural dip, structural azimuth and curvature.

[0018] In an exemplary embodiment, the step of preprocessing the well logging data of the area to be predicted to obtain the well logging information includes:

[0019] De-noising the dual laterolog data in the prediction area;

[0020] The deep lateral logging data after denoising is subtracted from the shallow lateral logging data to obtain the amplitude difference of the dual lateral logging data;

[0021] The logging fracture density data is determined according to the amplitude difference of the dual laterolog data, and the logging fracture density data is used as the logging information.

[0022] In an exemplary embodiment, the process of establishing the small crack discrete random network model is as follows:

[0023] Step 1. Determine the correlation coefficient between the probability of fracture development and the seismic attribute, and establish a seismic attribute fusion body according to the correlation coefficient;

[0024] Step 2. Establish a three-dimensional model of formation brittleness based on the well logging mineral composition;

[0025] Step 3. Generate a fault distance attribute volume according to the large fracture discrete random network model;

[0026] Step 4. Establishing an inter-well fracture probability body model based on the seismic attribute fusion body, the formation brittleness three-dimensional model and the fault distance attribute body;

[0027] Step 5. Establish the small fracture discrete random network model according to the inter-well fracture probability model, small fracture geometric parameters, and logging fracture density in the logging information.

[0028] In an exemplary embodiment, establishing an inter-well fracture probability body model according to the seismic attribute fusion body, the formation brittleness three-dimensional model and the fault distance attribute body includes:

[0029] Based on the seismic attribute fusion body, the 3D formation brittleness model and the fault distance attribute body, the inter-well fracture probability body model is established by using the multivariate fusion method.

[0030] In an exemplary embodiment, the establishing of an inter-well fracture probability body model according to the seismic attribute fusion body, the formation brittleness three-dimensional model and the fault distance attribute body includes:

[0031] Setting the small fracture geometric parameters and the logging fracture density as the small fracture discrete random network model parameters;

[0032] The inter-well fracture probabilistic model is used as the objective function to generate the small fracture discrete random network model.

[0033] In an exemplary embodiment, determining the correlation coefficient between the fracture development probability and the seismic attribute, and establishing the seismic attribute fusion body according to the correlation coefficient includes:

[0034] Normalize the seismic attributes;

[0035] According to the correlation analysis between the probability of fracture development and each normalized seismic attribute, the correlation coefficient between the probability of fracture development and each seismic attribute is determined;

[0036] The correlation coefficient is used as a weight coefficient to perform weighted averaging on the normalized seismic attribute volume to obtain a seismic attribute fusion volume.

[0037] In an exemplary embodiment, the step of establishing a three-dimensional formation brittleness model based on well logging mineral composition includes:

[0038] Calculate the logging mineral composition using a multi-mineral model based on the logging data;

[0039] The brittleness index value of a single well is calculated using the mineral composition ratio method;

[0040] Under the control of lithofacies, a three-dimensional formation brittleness model is established based on the brittleness index value of a single well.

[0041] In a second aspect, an embodiment of the present invention provides a device for predicting reservoir fractures, the device comprising: a memory and a processor; the memory is used to store a program for predicting reservoir fractures, and the processor is used to read and execute the program for predicting reservoir fractures, and execute any one of the methods described in the above embodiments.

[0042] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a data processing program is stored, and the data processing program is executed by a processor to perform a reservoir fracture prediction method according to any one of the above embodiments.

[0043] Compared with the related art, the present application provides a reservoir fracture prediction method and device, the method comprising: preprocessing the seismic data of the predicted area, determining multiple seismic attributes according to the preprocessed seismic data; preprocessing the well logging data of the predicted area to obtain well logging information; obtaining the reservoir fracture density data of the predicted area according to the seismic attributes, the well logging information and the pre-established discrete fracture network model; wherein the discrete fracture network model is composed of a large fracture discrete random network model, a medium fracture discrete random network model and a small fracture discrete random network model. The present application predicts reservoir fractures based on seismic data and well logging data; and, when predicting, the fractures are divided into three levels of fractures, and different levels of fracture models are established according to the identification characteristics of different levels of fractures, and finally merged into a discrete fracture network model for predicting fracture density, thereby improving the prediction accuracy of fracture density.

[0044] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or be understood by implementing the present application. Other advantages of the present application can be realized and obtained by the schemes described in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The accompanying drawings are used to provide an understanding of the technical solution of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the technical solution of the present application and do not constitute a limitation on the technical solution of the present application.

[0046] Figure 1 This is a flow chart of a reservoir fracture prediction method according to an embodiment of the present application;

[0047] Figure 2 A schematic diagram of a reservoir fracture prediction device according to an embodiment of the present application;

[0048] Figure 3 A flow chart is established for a small crack discrete random network model in some exemplary embodiments. DETAILED DESCRIPTION

[0049] The present application describes multiple embodiments, but the description is exemplary rather than restrictive, and it is obvious to those skilled in the art that there may be more embodiments and implementations within the scope of the embodiments described in the present application. Although many possible feature combinations are shown in the drawings and discussed in the specific embodiments, many other combinations of the disclosed features are also possible. Unless specifically limited, any feature or element of any embodiment may be used in combination with any other feature or element in any other embodiment, or may replace any other feature or element in any other embodiment.

[0050] The present application includes and contemplates combinations of features and elements known to those of ordinary skill in the art. The embodiments, features and elements disclosed in the present application may also be combined with any conventional features or elements to form a unique invention scheme defined by the claims. Any features or elements of any embodiment may also be combined with features or elements from other invention schemes to form another unique invention scheme defined by the claims. Therefore, it should be understood that any feature shown and / or discussed in the present application may be implemented individually or in any appropriate combination. Therefore, except for the limitations made according to the attached claims and their equivalents, the embodiments are not subject to other restrictions. In addition, various modifications and changes may be made within the scope of protection of the attached claims.

[0051] In addition, when describing representative embodiments, the specification may have presented the method and / or process as a specific sequence of steps. However, to the extent that the method or process does not rely on the specific order of the steps described herein, the method or process should not be limited to the steps of the specific order described. As will be understood by those of ordinary skill in the art, other sequences of steps are also possible. Therefore, the specific sequence of the steps set forth in the specification should not be interpreted as a limitation to the claims. In addition, the claims for the method and / or process should not be limited to the steps of performing them in the order written, and those skilled in the art can easily understand that these sequences can be changed and still remain within the spirit and scope of the embodiments of the present application.

[0052] The embodiment of the present invention provides a reservoir fracture prediction method, such as Figure 1 As shown, the method includes steps S100-S120, which are specifically as follows:

[0053] Step S100: preprocessing the seismic data of the area to be predicted, and determining a plurality of seismic attributes according to the preprocessed seismic data;

[0054] Step S110: pre-processing the well logging data of the predicted area to obtain well logging information;

[0055] Step S120: Obtaining reservoir fracture density data of the area to be predicted based on the seismic attributes, the well logging information and a pre-established discrete fracture network model.

[0056] In this embodiment, the discrete fracture network model is composed of a large fracture discrete random network model, a medium fracture discrete random network model and a small fracture discrete random network model. The discrete fracture random network model defines the location, occurrence, size and other related parameters of the fracture as random variables and assigns them a certain probability distribution function, so as to describe the fracture system with discrete fracture data.

[0057] In an exemplary embodiment, the seismic data of the predicted area is preprocessed, and the preprocessing includes one or more of the following: resampling, strong axis removal, median filtering, and edge detection. In this embodiment, for each preprocessing step, the specific implementation method is not specifically limited, and the corresponding processing method can be selected by those skilled in the art.

[0058] For example, the resampling process can resample the original seismic data sampling time according to the reservoir thickness in the target area.

[0059] The strong axis removal process can be performed on the seismic reflection cycle through the wavelet demarcation technology. The specific implementation method is to decompose the strong reflection wavelet into wavelets with different amplitudes and frequencies, remove the wavelets with large amplitudes, and reconstruct them with wavelets with small amplitudes, so as to achieve the effect of suppressing the strong axis.

[0060] The median filter process is used to enhance the continuity of seismic reflection and reduce the impact of noise.

[0061] The detection of the edge of the seismic data body is mainly to find the discontinuity points in the data body, and to enhance this discontinuity with the help of attribute extraction techniques such as dip and azimuth attributes, chaos attributes and variance attributes; among them, the variance body technology uses the similarity between adjacent seismic signals to describe the lateral heterogeneity of strata, lithology, etc., and reveals the discontinuity information in the data body by calculating the variance value of the sample points, so as to identify faults and lithology. On regular layer planes, the amplitude along the layer does not change much and the variance is small; while near the fault, the amplitude changes greatly and the variance is also large.

[0062] In an exemplary embodiment, a plurality of seismic attributes are determined based on the preprocessed seismic data; a stratigraphic structural interpretation is performed based on the preprocessed seismic data; and seismic attributes are extracted based on the stratigraphic structural interpretation results.

[0063] The implementation process of extracting earthquake attributes in this embodiment includes:

[0064] Step 1: Seismic data horizon interpretation;

[0065] Before analyzing seismic attributes, it is necessary to interpret the seismic data in layers, including synthetic seismic records, layer calibration, and layer tracking. Synthetic seismic records and layer calibration are the basic work of seismic data interpretation and are the link between well logging data and seismic data.

[0066] Specifically, step 1 includes:

[0067] Step 1.1: Synthetic seismic record production;

[0068] The reflection coefficient sequence is calculated by using the acoustic time difference logging data and the density logging data. Then, the seismic wavelet is extracted from the sub-side channel and convolved with the reflection coefficient sequence to obtain the synthetic seismic record of a single well.

[0069] Step 1.2: Seismic layer calibration;

[0070] The synthetic seismic record of the single well obtained in step 1.1 is compared with the seismic trace near the well. First, it is roughly calibrated with the marker formation, and then finely calibrated with the wave group and wave system comparison until the correlation between the synthetic seismic record and the seismic trace near the well reaches a predetermined threshold.

[0071] Step 1.3: Layer tracing interpretation;

[0072] According to the seismic waveform characteristics and reflection characteristics of the seismic traces near the well, gradually accurate interpretation grids are used to track and compare on the seismic profile, establish an isochronous stratigraphic framework, and complete the stratigraphic interpretation of the target area.

[0073] Step 2: Extract seismic attributes based on the stratigraphic structure interpretation results;

[0074] Seismic attributes include one or more of the following: variance volume, ant volume, structural dip, structural azimuth, and curvature.

[0075] In an exemplary embodiment, based on logging, seismic and other data, the reservoir fractures are divided into three levels of fractures, namely large fractures, medium fractures and small fractures according to the fracture scale, the limit of the fracture identification method and the main controlling factors of fracture development, and the corresponding large fracture discrete random network model, medium fracture discrete random network model and small fracture discrete random network model are established respectively. Among them, the large fracture discrete random network model is established based on the artificial seismic fault interpretation information; the medium fracture discrete random network model is a model established based on the ant body attributes; the small fracture discrete random network model is a model established based on the inter-well fracture probability body model.

[0076] In an exemplary embodiment, a large fracture discrete random network model is established based on artificial earthquake fault interpretation information:

[0077] Based on the structural interpretation results of the layers and faults of the three-dimensional original seismic data, the plane distribution law of the fault interpretation corresponding to each layer can be obtained, and a discrete random network model of large fractures is established based on this.

[0078] In an exemplary embodiment, the medium fracture discrete random network model is a model established based on the ant body attributes. The medium fracture discrete random network model can be established using the ant body attributes, or by extracting a variety of three-dimensional seismic attribute bodies for fracture detection, preferably seismic attributes that are sensitive to fractures. The seismic attributes that are sensitive to fractures can be preferably selected by making along-layer attribute slices and interlayer attribute slices of the target layer, and performing structural and fault element analysis, from which the seismic attributes that are sensitive to fractures are selected to reveal the planar distribution law of fractures. For example: the structural dip or curvature attribute is preferably used to establish a medium fracture discrete random network model.

[0079] Taking the selection of ant body attributes to establish a medium fracture discrete random network model as an example, the process of generating ant bodies using three-dimensional original seismic data bodies is mainly divided into three steps: first, the original seismic data body is subjected to structural smoothing to reduce noise interference and enhance the continuity of seismic reflection wave event axes; second, the variance analysis technology is used to obtain the variance body of the seismic data body after structural smoothing to highlight the discontinuous information of seismic data; third, the ant tracking parameters are tested multiple times to determine the optimal parameter values ​​suitable for the study area, and the ant tracking technology is used to track the discontinuous traces in the variance body that meet the preset fracture conditions to generate the ant body. Among them, the ant body parameters are: defining seed points, offset, ant search step size, etc.

[0080] In an exemplary embodiment, the logging data mainly includes acoustic wave, density, natural gamma, natural potential, resistivity, well diameter, etc. The conventional logging curve is relatively complete. Due to different factors such as logging instruments, logging environment, and measurement time during field logging, the amplitude of each logging curve will still be interfered by non-formation measurement factors, and the logging data needs to be preprocessed. Specifically, the logging data preprocessing includes: logging data standardization, acoustic wave curve reconstruction, denoising, and calculation of logging fracture density data.

[0081] In an exemplary embodiment, before establishing the small fracture discrete random network model, it is necessary to pre-process the well logging data of the prediction area to obtain the well logging information, including:

[0082] Step 1. De-noising the dual laterolog data in the prediction area;

[0083] Step 2: Perform subtraction processing on the denoised deep lateral logging data and the shallow lateral logging data to obtain the amplitude difference of the dual lateral logging data;

[0084] Step 3: Determine logging fracture density data according to the amplitude difference of the dual laterolog data, and use the logging fracture density data as logging information.

[0085] In an exemplary embodiment, the process of establishing a small crack discrete random network model is as follows: Figure 3 As shown below:

[0086] Step S31. Determine the correlation coefficient between the probability of fracture development and the seismic attribute, and establish a seismic attribute fusion body according to the correlation coefficient;

[0087] In this step, the fracture development probability is obtained by statistically analyzing the occurrence data of the target study area; for example, the ratio of the thickness of a certain formation fracture development section to the formation thickness of multiple wells in the target study area is statistically calculated to obtain the fracture development probability;

[0088] The implementation process of establishing a seismic attribute fusion body according to the correlation coefficient includes:

[0089] Step 311: normalize the seismic attributes;

[0090] Step 312: Determine the correlation coefficient between the fracture development probability and each seismic attribute according to the correlation analysis between the fracture development probability and each normalized seismic attribute;

[0091] Step 313: Use the correlation coefficient as a weight coefficient to perform weighted averaging on the normalized seismic attribute volume to obtain a seismic attribute fusion volume.

[0092] The processing of this step can reduce the problem of multiple solutions for a single seismic attribute.

[0093] Step S32. Establishing a three-dimensional model of formation brittleness according to the well logging mineral composition;

[0094] In this step, a three-dimensional formation brittleness model is established based on the logging mineral composition, including:

[0095] Step 321. Calculate the logging mineral composition using a multi-mineral model based on the logging data;

[0096] Step 322. Calculate the single well brittleness index value using the mineral composition ratio method;

[0097] Step 323. Under the control of lithofacies, a three-dimensional formation brittleness model is established according to the brittleness index value of a single well.

[0098] Step S33. Generate a fault distance attribute body according to a large fracture discrete random network model;

[0099] Faults are one of the important controlling factors for the development of fractures. Generally speaking, the degree of fracture development is correlated with the distance to the fault, that is, the closer the distance to the fault, the more developed the fracture. Therefore, based on the large fracture discrete random network model, the distance attribute volume to the fault is generated and used as the constraint data for establishing the fracture probability volume model.

[0100] Step S34. Establishing an inter-well fracture probability body model according to the seismic attribute fusion body, the formation brittleness three-dimensional model and the fault distance attribute body;

[0101] In this step, a multivariate fusion method may be used to establish an inter-well fracture probability body model based on the seismic attribute fusion body, the formation brittleness three-dimensional model and the fault distance attribute body.

[0102] Step S35: Establish the small fracture discrete random network model according to the inter-well fracture probability body model, small fracture geometric parameters, and the logging fracture density in the logging information.

[0103] In this step, a well-to-well fracture probability body model is established according to the seismic attribute fusion body, the formation brittle three-dimensional model and the fault distance attribute body, including:

[0104] Step 351. Setting the small fracture geometry parameters and the logging fracture density as the small fracture discrete random network model parameters;

[0105] In this step, the geometric parameters of the small fractures include: fracture occurrence, fracture length, fracture aperture, and fracture permeability; the logging fracture density is obtained based on the logging preprocessing.

[0106] Step 352. Taking the inter-well fracture probabilistic model as the objective function, a small fracture discrete random network model is established using the Gaussian simulation method.

[0107] Based on the above technical solution, the reservoir fracture prediction method provided by this application has the following technical effects:

[0108] 1. The present invention uses a fusion of multiple data such as seismic data and logging data to predict reservoir fractures. In addition, during the prediction, the fractures are divided into three levels of fractures, namely large fractures, medium fractures and small fractures, and discrete random network models of large fractures, medium fractures and small fractures are established respectively, which are fused into a discrete fracture network model. According to the identification characteristics of fractures of different levels, a large fracture discrete random network model is established by a deterministic method of artificial seismic fault interpretation; a medium discrete fracture random network model is established by a deterministic method of ant body tracking; an inter-well fracture probability body model is established by integrating multiple information such as seismic information, geological information, logging information, etc., and a fracture density model is established based on this as a constraint. Then, a small fracture discrete random network model is established using the small fracture geometry and the fracture density model as constraint data, thereby greatly improving the prediction accuracy of fractures.

[0109] 2. When establishing a small fracture discrete random network model, the fracture density obtained from logging data and the fracture density model obtained from seismic data are used to perform double constraints on the small fracture discrete random network model, which greatly improves the accuracy of small fracture prediction.

[0110] The disclosed embodiment also provides a device for predicting reservoir fractures. Figure 2 As shown, it includes: a memory S200 and a processor S210; the memory S200 is used to store a program for reservoir fracture prediction, and the processor S210 is used to read and execute the program for reservoir fracture prediction, and execute any one of the methods in the above embodiments.

[0111] The present disclosure also provides a computer-readable storage medium, on which a data processing program is stored. The data processing program is used by a processor to execute the method described in any one of the above embodiments.

[0112] Example 1

[0113] This example shows a reservoir fracture prediction method. The specific process is as follows:

[0114] Step 1: preprocess the seismic data of the predicted area; wherein the preprocessing includes one or more of the following: resampling, strong axis removal, median filtering, and edge detection.

[0115] Step 2: preprocess the logging data of the predicted area;

[0116] This step preprocesses the logging data of 195 wells in the study area, where the logging data includes the following: acoustic wave, density, natural gamma, natural potential, resistivity, well diameter, etc. The preprocessing of these logging data includes: standardization of logging data, reconstruction of acoustic wave curves, etc. In order to predict fractures, the preprocessing of logging data also includes calculating logging fracture density data; the process of calculating logging fracture density data includes: denoising the dual lateral logging data in the prediction area; performing difference processing on the denoised deep lateral logging data and the shallow lateral logging data to obtain the amplitude difference of the dual lateral logging data; determining the logging fracture density data according to the amplitude difference of the dual lateral logging data.

[0117] Step 3, determining multiple seismic attributes according to the preprocessed seismic data;

[0118] Perform stratigraphic structural interpretation based on preprocessed seismic data; extract seismic attributes based on stratigraphic structural interpretation results.

[0119] Step 4: Establish a discrete crack network model based on the large crack discrete random network model, the medium crack discrete random network model and the small crack discrete random network model.

[0120] Based on logging, seismic and other data, the natural fractures in the reservoir are divided into three levels of fractures, namely large fractures, medium fractures and small fractures according to the fracture scale, the limit of fracture identification method and the main controlling factors of fracture development; a discrete fracture network model is established for each level of fractures.

[0121] 41. A large fracture discrete random network model is established based on the interpretation information of artificial earthquake faults.

[0122] 42. A medium-crack discrete random network model is established based on the properties of ant bodies.

[0123] 43. A probability model of inter-well fractures is established by integrating seismic information, geological information, logging information and other information, and a fracture density model is established based on this as a constraint. Then, a discrete random network model of small fractures is established using the target-based characteristic point process simulation method, with the small fracture geometry and fracture density model as constraint data.

[0124] 44. The discrete random network models of large cracks, medium cracks and small cracks are integrated into a discrete crack network model, and the equivalent physical property parameter model of the cracks is obtained through coarsening.

[0125] Step 5: Use the discrete fracture network model to predict reservoir fracture density.

[0126] In this example, dual lateral logging data are used to constrain the small fracture discrete random network model. In addition, when generating the small fracture discrete random network model, the logging fracture density obtained by the logging data and the fracture density model obtained by the seismic data are used to perform dual constraints on the small fracture discrete random network model, thereby improving the accuracy of small fracture prediction.

[0127] It will be appreciated by those skilled in the art that all or some of the steps, systems, and functional modules / units in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. In hardware implementations, the division between the functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed by several physical components in cooperation. Some or all components may be implemented as software executed by a processor, such as a digital signal processor or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or non-transitory medium) and a communication medium (or temporary medium). As known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, it is well known to those of ordinary skill in the art that communication media typically contain computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

Claims

1. A reservoir fracture prediction method, characterized in that: The method comprises: Preprocessing the seismic data of the area to be predicted, and determining a plurality of seismic attributes according to the preprocessed seismic data; Preprocess the logging data of the predicted area to obtain logging information; Reservoir fracture density data of the area to be predicted is obtained according to the seismic attributes, the logging information and a pre-established discrete fracture network model; wherein the discrete fracture network model is composed of a large fracture discrete random network model, a medium fracture discrete random network model and a small fracture discrete random network model.

2. The reservoir fracture prediction method according to claim 1, characterized in that: The large fracture discrete random network model is established based on artificial earthquake fault interpretation information; The medium crack discrete random network model is a model established based on the properties of ant bodies; The small fracture discrete random network model is a model established based on the inter-well fracture probability body model.

3. The reservoir fracture prediction method according to claim 1, characterized in that: The preprocessing of seismic data includes one or more of the following: resampling, de-emphasis processing and median filtering.

4. The reservoir fracture prediction method according to claim 1, characterized in that: The determining of a plurality of seismic attributes according to the preprocessed seismic data comprises: Conduct stratigraphic structural interpretation based on pre-processed seismic data; Extract seismic attributes based on the stratigraphic structure interpretation results.

5. The reservoir fracture prediction method according to claim 4, characterized in that: The seismic attributes include one or more of: variance volume, ant volume, structural dip, structural azimuth, and curvature.

6. The reservoir fracture prediction method according to claim 3, characterized in that: The preprocessing of the well logging data of the area to be predicted to obtain the well logging information includes: De-noising the dual laterolog data in the prediction area; The deep lateral logging data after denoising is subtracted from the shallow lateral logging data to obtain the amplitude difference of the dual lateral logging data; The logging fracture density data is determined according to the amplitude difference of the dual laterolog data, and the logging fracture density data is used as the logging information.

7. The reservoir fracture prediction method according to claim 6, characterized in that: The establishment process of the small crack discrete random network model is as follows: Step 1. Determine the correlation coefficient between the probability of fracture development and the seismic attribute, and establish a seismic attribute fusion body according to the correlation coefficient; Step 2. Establish a three-dimensional model of formation brittleness based on the well logging mineral composition; Step 3. Generate a fault distance attribute volume according to the large fracture discrete random network model; Step 4. Establishing an inter-well fracture probability body model based on the seismic attribute fusion body, the formation brittleness three-dimensional model and the fault distance attribute body; Step 5. Establish the small fracture discrete random network model according to the inter-well fracture probability model, small fracture geometric parameters, and logging fracture density in the logging information.

8. The reservoir fracture prediction method according to claim 7, characterized in that: The method of establishing an inter-well fracture probability body model according to the seismic attribute fusion body, the formation brittle three-dimensional model and the fault distance attribute body comprises: A multivariate fusion method is used to establish an inter-well fracture probability body model based on the seismic attribute fusion body, the formation brittle three-dimensional model and the fault distance attribute body.

9. The reservoir fracture prediction method according to claim 7, characterized in that: The method of establishing an inter-well fracture probability body model according to the seismic attribute fusion body, the formation brittle three-dimensional model and the fault distance attribute body comprises: Setting the small fracture geometric parameters and the logging fracture density as the small fracture discrete random network model parameters; The inter-well fracture probabilistic model is used as the objective function to generate the small fracture discrete random network model.

10. The reservoir fracture prediction method according to claim 7, characterized in that: The determining of the correlation coefficient between the fracture development probability and the seismic attribute, and establishing a seismic attribute fusion body according to the correlation coefficient, comprises: Normalize the seismic attributes; According to the correlation analysis between the probability of fracture development and each normalized seismic attribute, the correlation coefficient between the probability of fracture development and each seismic attribute is determined; The correlation coefficient is used as a weight coefficient to perform weighted averaging on the normalized seismic attribute volume to obtain a seismic attribute fusion volume.

11. The reservoir fracture prediction method according to claim 7, characterized in that: The method of establishing a three-dimensional formation brittleness model according to the well logging mineral composition includes: Calculate the logging mineral composition using a multi-mineral model based on the logging data; The brittleness index value of a single well is calculated using the mineral composition ratio method; Under the control of lithofacies, a three-dimensional formation brittleness model is established based on the brittleness index value of a single well.

12. A device for predicting reservoir fractures, characterized in that: The device comprises: a memory and a processor; the memory is used to store a program for reservoir fracture prediction, and the processor is used to read and execute the program for reservoir fracture prediction, and execute the method according to any one of claims 1-11.

13. A computer-readable storage medium having a data processing program stored thereon, wherein the data processing program is executed by a processor to perform the reservoir fracture prediction method according to any one of claims 1 to 11.

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