Well-to-seismic combined iterative fracture prediction method and device

Abstract

This application provides an iterative fracture prediction method and apparatus combining well and seismic data, belonging to the field of geophysical exploration. The method includes the following steps: acquiring 3D seismic data of several oil wells within a target area; processing the 3D seismic data to obtain multi-scale 3D seismic fracture attributes; acquiring the tensile fracture coefficient and shear fracture coefficient of the rock within the target area; generating a geological attribute data volume based on the multi-scale 3D seismic fracture attributes, tensile fracture coefficient, and shear fracture coefficient using an attribute ratio fusion method; performing mesh generation based on the geological attribute data volume and establishing independent cross-sections in conjunction with the 3D seismic data to generate a fracture prediction model; acquiring real-time well logging information of each oil well in the target area during production, and iteratively updating the fracture prediction model based on the well logging information. This application aims to improve the accuracy of fracture prediction.

Description

Technical Field

[0001] This application relates to the field of geophysical exploration interpretation, and more specifically, to an iterative fracture prediction method and apparatus that combines well and seismic analysis. Background Technology

[0002] Currently, there are two main types of crack prediction methods: one is based on post-stack seismic properties or optimization, mainly focusing on properties such as coherence, curvature, ant-like structures, and coherent enhancement. The other type is based on pre-stack P-wave anisotropy, whose core principle is that seismic properties differ in different azimuths during the propagation of seismic waves. Under certain conditions, we assume that these differences are caused by cracks, and predict crack characteristics by obtaining the azimuth anisotropy of seismic wave characteristics (amplitude, frequency, phase, and transformation properties).

[0003] Fracture prediction based on post-stack seismic attributes or optimization primarily addresses large-scale fractures and has achieved good results in conventional reservoirs. However, for unconventional reservoirs, microfractures are a significant concern in exploration and development, limiting the application of this method. Fracture prediction methods based on pre-stack P-wave anisotropy offer some improvement in microfracture identification, but they require high-quality data (ideal azimuth coverage). Furthermore, these methods mainly identify high-angle fractures and have shown good results in fracture-vuggy reservoirs such as carbonate rocks. However, unconventional reservoirs primarily consist of horizontal fractures, indicating a gap between this method and actual production needs.

[0004] The aforementioned two fracture prediction methods primarily rely on 3D seismic data, and their predicted fracture results mainly consist of seismic attributes. As long as the data remains unchanged, the prediction results will not vary significantly. With the deepening of unconventional oil and gas exploration and development and the widespread adoption of the integrated seismic-geological-engineering concept, there is a growing demand for fracture prediction results to serve subsequent engineering technologies and production in real time. However, at present, fracture prediction results are merely pure seismic attribute predictions, far behind the pace of unconventional oil and gas development. Summary of the Invention

[0005] This application provides an iterative fracture prediction method combining well and seismic analysis, aiming to continuously update fracture prediction results and improve prediction accuracy.

[0006] In a first aspect, embodiments of this application provide an iterative fracture prediction method combining well and seismic analysis, comprising the following steps:

[0007] Obtain 3D seismic data of several oil wells within the target area;

[0008] The three-dimensional seismic data is processed to obtain multi-scale three-dimensional seismic fracture attributes, which include coherence attributes, curvature attributes, and pre-stack fracture intensity.

[0009] Obtain the tensile fracture coefficient and shear fracture coefficient of the rock within the target area;

[0010] Based on the multi-scale three-dimensional seismic fracture attributes, the tensile fracture rupture coefficient, and the shear fracture rupture coefficient, a geological attribute data volume is generated using an attribute ratio fusion method.

[0011] Based on the geological attribute data volume, grid division is carried out, and independent faults are established in combination with the three-dimensional seismic data to generate a fracture prediction model; the fracture prediction model is used to characterize the spatial location, fault, and fault attitude of the independent faults.

[0012] The system acquires real-time logging information of each oil well in the target area during production, and iteratively updates the fracture prediction model based on the logging information. The logging information includes imaging logging, actual drilled fracture information, and microseismic monitoring results.

[0013] Optionally, processing the three-dimensional seismic data to obtain multi-scale three-dimensional seismic fracture attributes includes:

[0014] Obtain the P-wave generated by the shot receiver in the three-dimensional seismic data;

[0015] The pre-stack crack strength is obtained by utilizing the anisotropy of the P-wave.

[0016] The P-waves are superimposed and frequency-divided to obtain post-stack three-dimensional seismic data;

[0017] Based on the post-stack 3D seismic data, the coherence attribute and the curvature attribute are obtained.

[0018] Optionally, the step of utilizing the anisotropy of P-waves to obtain pre-stack crack intensity includes:

[0019] The seismic reflection characteristic values ​​of the P-wave in at least three directions are obtained; the seismic reflection characteristics include seismic amplitude, frequency, phase, wave impedance, and seismic properties.

[0020] Based on the seismic reflection characteristic values ​​of the P-wave in at least three directions, a system of equations is constructed to solve the small-scale crack characterization formula, and the major and minor axes of the ellipse characterized by the small-scale crack characterization formula are obtained.

[0021] The formula for characterizing small-scale cracks is as follows:

[0022] A(β) = A0 + α·cos2β;

[0023] In the formula, A represents the seismic reflection characteristics in different azimuths; A0 represents the azimuth-average seismic reflection characteristics; α represents the difference between the extreme azimuth seismic reflection characteristics and the azimuth-average reflection characteristics; and β represents the angle between the shot-receiver azimuth and the fracture direction.

[0024] in,

[0025] β = φ - θ;

[0026] In the formula, φ is the azimuth angle of the shot detection; θ is the azimuth angle of the crack direction;

[0027] The pre-stack crack strength is obtained based on the major and minor axes of the ellipse.

[0028] Alternatively, the pre-stack crack strength can be obtained using the following formula:

[0029]

[0030] In the formula, γ is the pre-stack crack strength; δ1 is the major axis of the ellipse; and δ2 is the minor axis of the ellipse.

[0031] Optionally, obtaining the tensile fracture coefficient and shear fracture coefficient of the rock within the target area includes:

[0032] Obtain a core sample from the target area, perform core testing on the core sample, and obtain the shear strength and internal friction coefficient of the core sample.

[0033] Obtain the maximum horizontal principal stress, intermediate horizontal principal stress, and minimum horizontal principal stress of the core sample in the target area;

[0034] Obtain the tensile stress and shear stress of the core sample in the target area;

[0035] The tensile fracture coefficient and the shear fracture coefficient can be calculated using the following formulas:

[0036]

[0037]

[0038] In the formula, K is the tensile crack rupture coefficient; R is the shear crack rupture coefficient; σ t It is tensile stress; |σ t | represents the shear strength of the core; τ is the shear stress; |τ| is the shear fracture strength of the core;

[0039] in,

[0040]

[0041] In the formula, σ1 is the maximum horizontal principal stress; σ2 is the intermediate horizontal principal stress; and σ3 is the minimum horizontal principal stress.

[0042] |τ|=S0-μ·σ;

[0043] In the formula, μ is the internal friction coefficient; S0 is the shear strength of the rock core.

[0044] Optionally, the tensile stress and shear stress of the core sample in the target area are obtained using the Griffith criterion and the Coulomb-Navi criterion.

[0045] Optionally, the geological attribute data volume can be calculated and generated using the following formula:

[0046] Fuse = aA + bB + cC + dD + eE;

[0047] In the formula, Fuse is the geological attribute data volume, a is the proportion of coherent attributes in the geological attribute data volume; b is the proportion of curvature attributes in the geological attribute data volume; c is the proportion of pre-stack fracture intensity in the geological attribute data volume; d is the proportion of tensile fracture rupture coefficient in the geological attribute data volume; e is the proportion of shear fracture rupture coefficient in the geological attribute data volume; and a+b+c+d+e=1; A is the coherent attribute, B is the curvature attribute, C is the pre-stack fracture intensity, D is the tensile fracture rupture coefficient; and E is the shear fracture coefficient.

[0048] Optionally, based on the well logging information, the fracture prediction model is iteratively updated, including:

[0049] The well logging information is processed to obtain the coherence attribute, curvature attribute, pre-stack fracture strength, tensile fracture fracture coefficient, and shear fracture fracture coefficient contained in the well logging information;

[0050] An interpolation method is used to interpolate the coherence attributes, curvature attributes, pre-stack fracture strength, tensile fracture rupture coefficient, and shear fracture rupture coefficient contained in the well logging information into the geological attribute data volume.

[0051] Optionally, the grid division based on the geological attribute data volume includes:

[0052] Obtain several shot points from the shot detector in the three-dimensional seismic data;

[0053] Connect the blast point to the nearest oil well to it, forming the blast line;

[0054] The grid is divided into sections, with the shot lines as the length and the line connecting two adjacent shot lines as the width.

[0055] Secondly, embodiments of this application provide an iterative fracture prediction device combining well and seismic analysis, comprising:

[0056] The seismic data acquisition module is used to acquire three-dimensional seismic data of several oil wells within the target area;

[0057] The seismic data processing module is used to process the three-dimensional seismic data and obtain multi-scale three-dimensional seismic fracture attributes, including coherence attributes, curvature attributes, and pre-stack fracture intensity.

[0058] The rock coefficient acquisition module is used to acquire the tensile fracture coefficient and shear fracture coefficient of the rock in the target area;

[0059] The proportional fusion module is used to generate a geological attribute data volume based on the multi-scale three-dimensional seismic fracture attributes, the tensile fracture rupture coefficient, and the shear fracture rupture coefficient using an attribute proportional fusion method.

[0060] The model generation module is used to perform grid division based on the geological attribute data volume, and to establish independent cross sections in combination with the three-dimensional seismic data to generate a fracture prediction model; the fracture prediction model is used to characterize the spatial location, fault, and fault attitude of the independent cross sections.

[0061] The iteration module is used to acquire the logging information of each oil well in the target area in real time during production, and to iteratively update the fracture prediction model based on the logging information; the logging information includes imaging logging, actual drilled fracture information and microseismic monitoring results.

[0062] Beneficial effects: By acquiring and processing 3D seismic data from several oil wells in the target area, the coherence, curvature, and pre-stack fracture strength of the 3D seismic data are extracted. Then, combined with the tensile fracture coefficient and shear fracture coefficient of the rock in the target area, a fracture prediction model is generated by proportional fusion. In subsequent production, well logging information is continuously acquired to iteratively update the fracture prediction model. By combining the fracture prediction results from the early stage with the data from the later stage of production, the fracture prediction results can be continuously updated, thereby improving the accuracy of fracture prediction. Attached Figure Description

[0063] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0064] Figure 1 This is a flowchart of the control method proposed in an embodiment of this application;

[0065] Figure 2It is an ellipse characterized by the small-scale crack characterization formula proposed in one embodiment of this application;

[0066] Figure 3 This is the basic data of the coherent attributes obtained in one embodiment of this application;

[0067] Figure 4 This is the basic data of curvature properties obtained in one embodiment of this application;

[0068] Figure 5 This is a crack prediction model proposed in one embodiment of this application;

[0069] Figure 6 This is a fracture prediction model optimized using imaging logging data, proposed in one embodiment of this application;

[0070] Figure 7 This is a functional block diagram of a prediction device proposed in an embodiment of this application. Detailed Implementation

[0071] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0072] In one embodiment, a flowchart of an iterative fracture prediction method combining well and seismic analysis is provided as follows: Figure 1 As shown, the prediction method may specifically include the following steps:

[0073] S101: Obtain 3D seismic data of several oil wells within the target area.

[0074] In this embodiment, a shot-detector method is used to obtain three-dimensional seismic data of the oil well. The three-dimensional seismic data is a waveform. The three-dimensional seismic data can provide feedback on the basic situation in the target area. By processing the three-dimensional seismic data, the basic situation of the fractures in the target area can be determined.

[0075] S102, The three-dimensional seismic data is processed to obtain multi-scale three-dimensional seismic fracture attributes, which include coherence attributes, curvature attributes and pre-stack fracture intensity.

[0076] The pre-stack fracture intensity is directly obtained from the 3D seismic data using the P-wave anisotropy within the data. The coherence and curvature properties are obtained from the post-stack 3D seismic data, which is acquired through stacking and frequency division of the 3D seismic data. In this embodiment, coherence, curvature, and pre-stack fracture intensity are used as the basic data.

[0077] S103, obtain the tensile fracture coefficient and shear fracture coefficient of the rock in the target area.

[0078] By conducting lithological tests on the rocks within the target area and then using the Griffith criterion and the Coulomb-Navi criterion to determine the tensile and shear stresses of the rocks, the tensile fracture coefficient and the shear fracture coefficient of the rocks within the target area can be obtained. Both the Griffith criterion and the Coulomb-Navi criterion are conventional techniques.

[0079] S104. Based on the multi-scale three-dimensional seismic fracture attributes, the tensile fracture rupture coefficient, and the shear fracture rupture coefficient, a geological attribute data volume is generated using an attribute ratio fusion method.

[0080] Combining the five data points obtained in steps S102 and S103—coherence attribute, curvature attribute, pre-stack fracture strength, tensile fracture rupture coefficient, and shear fracture rupture coefficient—the generated geological attribute data volume can reflect the basic characteristics of fractures within the target area.

[0081] S105, Based on the geological attribute data volume, grid division is carried out, and independent cross sections are established in combination with the three-dimensional seismic data to generate a fracture prediction model; the fracture prediction model is used to characterize the spatial location, fault, and fault attitude of the independent cross sections.

[0082] After generating a fracture prediction model by dividing the area into grids and establishing independent cross-sections, the distribution of fractures near each oil well in the target area can be seen more clearly when analyzing the target area using the fracture prediction model.

[0083] S106, real-time acquisition of well logging information of each oil well in the target area during production, and iterative update of the fracture prediction model based on the well logging information; the well logging information includes imaging logging, actual drilled fracture information and microseismic monitoring results.

[0084] By collecting and studying fracture information related to oil wells during the production process, the established fracture prediction model is combined with well logging data; the fracture prediction model is continuously optimized and updated. As exploration and development deepen, more and more well logging data and development and production data are generated, and the fracture prediction model is constantly updated, with prediction accuracy continuously improving, achieving the effect of real-time guidance for unconventional oil and gas development.

[0085] This embodiment acquires and processes 3D seismic data from several oil wells within the target area, extracting coherence attributes, curvature attributes, and pre-stack fracture intensity from the 3D seismic data. It then combines this data with the tensile fracture coefficient and shear fracture coefficient of the rock within the target area, generating a fracture prediction model through proportional fusion. In subsequent production, well logging information is continuously acquired to iteratively update the fracture prediction model. By combining early fracture prediction results with later production data, the fracture prediction results can be continuously updated, thereby improving the accuracy of fracture prediction.

[0086] In one embodiment, a flowchart of an iterative fracture prediction method combining well and seismic analysis is provided as follows: Figure 1 As shown:

[0087] S101: Obtain 3D seismic data of several oil wells within the target area.

[0088] Within the target area, at least three oil wells are selected as sample wells, and three-dimensional seismic data of these three oil wells are obtained using shot-receiver method; when selecting sample wells, the standard is to cover the largest target area.

[0089] S102, The three-dimensional seismic data is processed to obtain multi-scale three-dimensional seismic fracture attributes, which include coherence attributes, curvature attributes and pre-stack fracture intensity.

[0090] The processing of 3D seismic data mainly involves stacking and frequency division. In post-stack data, the primary goal is to obtain characteristics of large-scale fractures, such as coherence and curvature properties. In pre-stack data, the anisotropy of P-waves is used to obtain characteristics of small-scale fractures, i.e., pre-stack fracture intensity.

[0091] The process of processing the three-dimensional seismic data to obtain multi-scale three-dimensional seismic fracture attributes includes:

[0092] Obtain the P-wave generated by the shot receiver in the three-dimensional seismic data;

[0093] The pre-stack crack strength is obtained by utilizing the anisotropy of the P-wave.

[0094] Includes the following steps:

[0095] The seismic reflection characteristic values ​​of the P-wave in at least three directions are obtained; the seismic reflection characteristics include seismic amplitude, frequency, phase, wave impedance, and seismic properties.

[0096] Based on the seismic reflection characteristic values ​​of the P-wave in at least three directions, a system of equations is constructed to solve the small-scale crack characterization formula, and the major and minor axes of the ellipse characterized by the small-scale crack characterization formula are obtained.

[0097] In this embodiment, the constructed equation set has three unknowns, so seismic reflection characteristic values ​​in three directions are needed to solve the equation set and obtain the solution of the small-scale crack characterization formula with respect to the ellipse. Then, the pre-stack crack strength is obtained through this solution.

[0098] The formula for characterizing small-scale cracks is as follows:

[0099] A(β) = A0 + α·cos2β;

[0100] In the formula, A represents the seismic reflection characteristics in different azimuths; A0 represents the azimuth-average seismic reflection characteristics; α represents the difference between the extreme azimuth seismic reflection characteristics and the azimuth-average reflection characteristics; and β represents the angle between the shot-receiver azimuth and the fracture direction.

[0101] in,

[0102] β = φ - θ;

[0103] In the formula, φ is the azimuth angle of the shot detection; θ is the azimuth angle of the crack direction.

[0104] like Figure 2 , Figure 2 The ellipse represented by the formula for small-scale cracks is shown, where A0, α, and β correspond one-to-one with those in the above text.

[0105] The pre-stack crack strength is obtained based on the major and minor axes of the ellipse.

[0106] The pre-stack crack strength is obtained using the following formula:

[0107]

[0108] In the formula, γ is the pre-stack crack strength; δ1 is the major axis of the ellipse; and δ2 is the minor axis of the ellipse.

[0109] By solving the system of equations constructed above, we can obtain the solution of the ellipse represented by the small-scale crack characterization formula. Then, by using this solution, we can obtain the values ​​of the major axis and minor axis of the ellipse, and thus obtain the value of the pre-stack crack strength.

[0110] The P-waves are superimposed and frequency-divided to obtain post-stack three-dimensional seismic data;

[0111] Based on the post-stack 3D seismic data, the coherence attribute and the curvature attribute are obtained.

[0112] The obtained data are as follows: 3 and Figure 4 As shown, where, Figure 3 The basic data of the coherent attributes obtained in this embodiment are shown; Figure 4 The basic data of curvature attributes obtained in this embodiment are shown; in this embodiment, conventional techniques are used to obtain both coherence attributes and curvature attributes.

[0113] S103, obtain the tensile fracture coefficient and shear fracture coefficient of the rock in the target area;

[0114] The acquisition of the tensile fracture coefficient and shear fracture coefficient of the rock within the target area includes:

[0115] Obtain a core sample from the target area, perform core testing on the core sample, and obtain the shear strength and internal friction coefficient of the core sample.

[0116] Obtain the maximum horizontal principal stress, intermediate horizontal principal stress, and minimum horizontal principal stress of the core sample in the target area;

[0117] The maximum, intermediate, and minimum horizontal principal stresses of the core sample in the target area were obtained using a tectonic stress calculation formula based on curvature properties, which is a conventional technique.

[0118] Obtain the tensile stress and shear stress of the core sample in the target area;

[0119] The tensile stress and shear stress of the core sample in the target area were obtained using the Griffith criterion and the Coulomb-Navi criterion.

[0120] The tensile fracture coefficient and the shear fracture coefficient can be calculated using the following formulas:

[0121]

[0122]

[0123] In the formula, K is the tensile crack rupture coefficient; R is the shear crack rupture coefficient; σ t It is tensile stress; |σ t | represents the shear strength of the core; τ is the shear stress; |τ| is the shear fracture strength of the core;

[0124] in,

[0125]

[0126] In the formula, σ1 is the maximum horizontal principal stress; σ2 is the intermediate horizontal principal stress; and σ3 is the minimum horizontal principal stress.

[0127] |τ|=S0-μ·σ;

[0128] In the formula, μ is the internal friction coefficient; S0 is the shear strength of the rock core.

[0129] Both μ and S0 can be obtained through core testing of the target area. For example, when obtaining S0, a rock sample from the target area is taken and an increasing shear force is applied to the sample. When the sample breaks under the shear force, the magnitude of the shear force at the current moment is the shear strength of the target area.

[0130] S104. Based on the multi-scale three-dimensional seismic fracture attributes, the tensile fracture rupture coefficient, and the shear fracture rupture coefficient, a geological attribute data volume is generated using an attribute ratio fusion method.

[0131] The geological attribute data volume is generated using the following formula:

[0132] Fuse = aA + bB + cC + dD + eE;

[0133] In the formula, Fuse is the geological attribute data volume, a is the proportion of coherent attributes in the geological attribute data volume; b is the proportion of curvature attributes in the geological attribute data volume; c is the proportion of pre-stack fracture intensity in the geological attribute data volume; d is the proportion of tensile fracture rupture coefficient in the geological attribute data volume; e is the proportion of shear fracture rupture coefficient in the geological attribute data volume; and a+b+c+d+e=1; A is the coherent attribute, B is the curvature attribute, C is the pre-stack fracture intensity, D is the tensile fracture rupture coefficient; and E is the shear fracture coefficient.

[0134] The geological attribute data volume generated by the attribute ratio fusion method contains the features included in all the basic data, and can reflect the fracture characteristics of the target area, laying the foundation for the next step of building a fracture prediction model.

[0135] S105. Based on the geological attribute data volume, grid division is carried out, and independent cross sections are established in combination with the three-dimensional seismic data to generate a fracture prediction model; the fracture prediction model is used to characterize the spatial location, fault, and fault attitude of the independent cross sections.

[0136] When performing grid generation based on the geological attribute data volume, the following steps shall be followed:

[0137] Obtain several shot points from the shot detector in the three-dimensional seismic data;

[0138] The shot point is the location of the shot receiver during the acquisition of 3D seismic data.

[0139] Connect the blast point to the nearest oil well to it, forming the blast line;

[0140] By using the blast point and the nearest oil well as the blast line, it can be ensured that there is an oil well in each grid when dividing the grid.

[0141] The grid is divided into sections, with the shot lines as the length and the line connecting two adjacent shot lines as the width.

[0142] By connecting multiple shot lines, the geological attribute data volume is divided into multiple grids, which cover the target area. This division of the target area into multiple grids facilitates the location of the fracture within the target area in subsequent studies.

[0143] Figure 5 The crack prediction model obtained in this embodiment is shown. Figure 5 In the diagram, the thick, sloping line represents the fracture surface of the crack.

[0144] After gridding, the geological attribute data volume is dissected by combining it with 3D seismic data. The dissected data is then connected end to end to form an independent cross section. This independent cross section can more clearly represent the crack prediction model of the target area. By observing the independent cross section, cracks existing in the target area, as well as the direction and intensity of the cracks, can be quickly found on the crack prediction model.

[0145] S106, real-time acquisition of well logging information of each oil well in the target area during production, and iterative update of the fracture prediction model based on the well logging information; the well logging information includes imaging logging, actual drilled fracture information and microseismic monitoring results.

[0146] Figure 6 This embodiment illustrates a fracture prediction model optimized using imaging logging data, which allows observation of the direction and intensity of fractures in the target area.

[0147] This embodiment establishes a fracture prediction model based on preliminary basic data, and then iteratively updates the fracture prediction model by combining it with well logging information obtained in later production. The well logging information can reflect the actual fracture situation in the target area. Through continuous iterative updates, the fracture prediction model can continuously approach the reality, bringing more accurate and precise prediction results to the fracture prediction model.

[0148] The process of iteratively updating the fracture prediction model based on the well logging information includes:

[0149] The well logging information is processed to obtain the coherence attribute, curvature attribute, pre-stack fracture strength, tensile fracture fracture coefficient, and shear fracture fracture coefficient contained in the well logging information;

[0150] An interpolation method is used to interpolate the coherence attributes, curvature attributes, pre-stack fracture strength, tensile fracture rupture coefficient, and shear fracture rupture coefficient contained in the well logging information into the geological attribute data volume.

[0151] During interpolation, information obtained from well logging data that is not present in the fracture prediction model is inserted into the fracture prediction model. By continuously acquiring well logging data and interpolating, the fracture prediction model can get closer and closer to the actual situation. When the well logging data reaches a maximum value, the fracture prediction model can directly reflect the fracture situation in the target area.

[0152] This embodiment acquires and processes 3D seismic data from several oil wells within the target area, extracting coherence attributes, curvature attributes, and pre-stack fracture intensity from the 3D seismic data. It then combines this data with the tensile fracture coefficient and shear fracture coefficient of the rock within the target area, generating a fracture prediction model through proportional fusion. In subsequent production, well logging information is continuously acquired to iteratively update the fracture prediction model. By combining early fracture prediction results with later production data, the fracture prediction results can be continuously updated, thereby improving the accuracy of fracture prediction.

[0153] In one embodiment, a functional block diagram of an iterative fracture prediction device combining well and seismic analysis is provided as follows: Figure 7 As shown, it includes:

[0154] The seismic data acquisition module is used to acquire three-dimensional seismic data of several oil wells within the target area;

[0155] The seismic data processing module is used to process the three-dimensional seismic data and obtain multi-scale three-dimensional seismic fracture attributes, including coherence attributes, curvature attributes, and pre-stack fracture intensity.

[0156] The rock coefficient acquisition module is used to acquire the tensile fracture coefficient and shear fracture coefficient of the rock in the target area;

[0157] The proportional fusion module is used to generate a geological attribute data volume based on the multi-scale three-dimensional seismic fracture attributes, the tensile fracture rupture coefficient, and the shear fracture rupture coefficient using an attribute proportional fusion method.

[0158] The model generation module is used to perform grid division based on the geological attribute data volume, and to establish independent cross sections in combination with the three-dimensional seismic data to generate a fracture prediction model; the fracture prediction model is used to characterize the spatial location, fault, and fault attitude of the independent cross sections.

[0159] The iteration module is used to acquire the logging information of each oil well in the target area in real time during production, and to iteratively update the fracture prediction model based on the logging information; the logging information includes imaging logging, actual drilled fracture information and microseismic monitoring results.

[0160] As the device embodiment is basically similar to the method embodiment, the description is relatively simple, and relevant parts can be found in the description of the method embodiment.

[0161] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0162] Those skilled in the art will understand that embodiments of this application can be provided as methods, apparatus, or computer program products. Therefore, embodiments of this application can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, embodiments of this application can take the form of computer program products implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0163] This application describes embodiments with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0164] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0165] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal equipment, causing a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0166] Although preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they become aware of the basic inventive concepts. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0167] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or terminal device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or terminal device that includes the element.

[0168] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. An iterative fracture prediction method combining well and seismic analysis, characterized in that, Includes the following steps: Obtain 3D seismic data of several oil wells within the target area; The three-dimensional seismic data is processed to obtain multi-scale three-dimensional seismic fracture attributes, which include coherence attributes, curvature attributes, and pre-stack fracture intensity. Obtain the tensile fracture coefficient and shear fracture coefficient of the rock within the target area; Based on the multi-scale three-dimensional seismic fracture attributes, the tensile fracture rupture coefficient, and the shear fracture rupture coefficient, a geological attribute data volume is generated using an attribute ratio fusion method. Based on the geological attribute data volume, grid division is carried out, and independent faults are established in combination with the three-dimensional seismic data to generate a fracture prediction model; the fracture prediction model is used to characterize the spatial location, fault, and fault attitude of the independent faults. The system acquires real-time logging information of each oil well in the target area during production, and iteratively updates the fracture prediction model based on the logging information. The logging information includes imaging logging, actual drilled fracture information, and microseismic monitoring results.

2. The prediction method according to claim 1, characterized in that, The process of processing the three-dimensional seismic data to obtain multi-scale three-dimensional seismic fracture attributes includes: Obtain the P-wave generated by the shot receiver in the three-dimensional seismic data; The pre-stack crack strength is obtained by utilizing the anisotropy of the P-wave. The P-waves are superimposed and frequency-divided to obtain post-stack three-dimensional seismic data; Based on the post-stack 3D seismic data, the coherence attribute and the curvature attribute are obtained.

3. The prediction method according to claim 2, characterized in that, The method of utilizing the anisotropy of P-waves to obtain pre-stack crack strength includes: The seismic reflection characteristic values ​​of the P-wave in at least three directions are obtained; the seismic reflection characteristics include seismic amplitude, frequency, phase, wave impedance, and seismic properties. Based on the seismic reflection characteristic values ​​of the P-wave in at least three directions, a system of equations is constructed to solve the small-scale crack characterization formula, and the major and minor axes of the ellipse characterized by the small-scale crack characterization formula are obtained. The formula for characterizing small-scale cracks is as follows: A(β) = A0 + α·cos2β; In the formula, A represents the seismic reflection characteristics in different azimuths; A0 represents the azimuth-average seismic reflection characteristics; α represents the difference between the extreme azimuth seismic reflection characteristics and the azimuth-average reflection characteristics; and β represents the angle between the shot-receiver azimuth and the fracture direction. in, β = φ - θ; In the formula, φ is the azimuth angle of the shot detection; θ is the azimuth angle of the crack direction; The pre-stack crack strength is obtained based on the major and minor axes of the ellipse.

4. The prediction method according to claim 3, characterized in that, The pre-stack crack strength is obtained using the following formula: In the formula, γ is the pre-stack crack strength; δ1 is the major axis of the ellipse; and δ2 is the minor axis of the ellipse.

5. The prediction method according to claim 1, characterized in that, The acquisition of the tensile fracture coefficient and shear fracture coefficient of the rock within the target area includes: Obtain a core sample from the target area, perform core testing on the core sample, and obtain the shear strength and internal friction coefficient of the core sample. Obtain the maximum horizontal principal stress, intermediate horizontal principal stress, and minimum horizontal principal stress of the core sample in the target area; Obtain the tensile stress and shear stress of the core sample in the target area; The tensile fracture coefficient and the shear fracture coefficient can be calculated using the following formulas: In the formula, K is the tensile crack rupture coefficient; R is the shear crack rupture coefficient; σ t It is tensile stress; |σ t | represents the shear strength of the core; τ is the shear stress; |τ| is the shear fracture strength of the core; in, In the formula, σ1 is the maximum horizontal principal stress; σ2 is the intermediate horizontal principal stress; and σ3 is the minimum horizontal principal stress. |τ|=S0-μ·σ; In the formula, μ is the internal friction coefficient; S0 is the shear strength of the rock core.

6. The prediction method according to claim 5, characterized in that, The tensile stress and shear stress of the core sample in the target area were obtained using the Griffith criterion and the Coulomb-Navi criterion.

7. The iterative fracture prediction method combining wellbore and seismic analysis according to claim 1, characterized in that, The geological attribute data volume is generated using the following formula: Fuse = aA + bB + cC + dD + eE; In the formula, Fuse is the geological attribute data volume, a is the proportion of coherent attributes in the geological attribute data volume; b is the proportion of curvature attributes in the geological attribute data volume; c is the proportion of pre-stack fracture intensity in the geological attribute data volume; d is the proportion of tensile fracture rupture coefficient in the geological attribute data volume; e is the proportion of shear fracture rupture coefficient in the geological attribute data volume; and a+b+c+d+e=1; A is the coherent attribute, B is the curvature attribute, C is the pre-stack fracture intensity, D is the tensile fracture rupture coefficient; and E is the shear fracture coefficient.

8. The prediction method according to claim 1, characterized in that, Based on the well logging information, the fracture prediction model is iteratively updated, including: The well logging information is processed to obtain the coherence attribute, curvature attribute, pre-stack fracture strength, tensile fracture fracture coefficient, and shear fracture fracture coefficient contained in the well logging information; An interpolation method is used to interpolate the coherence attributes, curvature attributes, pre-stack fracture strength, tensile fracture rupture coefficient, and shear fracture rupture coefficient contained in the well logging information into the geological attribute data volume.

9. The prediction method according to claim 1, characterized in that, The grid division based on the geological attribute data volume includes: Obtain several shot points from the shot detector in the three-dimensional seismic data; Connect the blast point to the nearest oil well to it, forming the blast line; The grid is divided into sections, with the shot lines as the length and the line connecting two adjacent shot lines as the width.

10. An iterative fracture prediction device combining well and seismic testing, characterized in that, include: The seismic data acquisition module is used to acquire three-dimensional seismic data of several oil wells within the target area; The seismic data processing module is used to process the three-dimensional seismic data and obtain multi-scale three-dimensional seismic fracture attributes, including coherence attributes, curvature attributes, and pre-stack fracture intensity. The rock coefficient acquisition module is used to acquire the tensile fracture coefficient and shear fracture coefficient of the rock in the target area; The proportional fusion module is used to generate a geological attribute data volume based on the multi-scale three-dimensional seismic fracture attributes, the tensile fracture rupture coefficient, and the shear fracture rupture coefficient using an attribute proportional fusion method. The model generation module is used to perform grid division based on the geological attribute data volume, and to establish independent cross sections in combination with the three-dimensional seismic data to generate a fracture prediction model; the fracture prediction model is used to characterize the spatial location, fault, and fault attitude of the independent cross sections. The iteration module is used to acquire the logging information of each oil well in the target area in real time during production, and to iteratively update the fracture prediction model based on the logging information; the logging information includes imaging logging, actual drilled fracture information and microseismic monitoring results.

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