CN116203635B - A well-seismic combined iterative fracture prediction method and device - Google Patents
A well-seismic combined iterative fracture prediction method and deviceInfo
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Abstract
本申请实施例在于提供一种井震结合的迭代裂缝预测方法及装置，属于地球物理勘探领域。包括以下步骤：获取目标区域内若干口采油井的三维地震资料；对三维地震资料进行处理，获取多尺度三维地震裂缝属性；获取目标区域内岩石的张裂缝破裂系数和剪切裂缝破裂系数；基于多尺度三维地震裂缝属性、张裂缝破裂系数和剪切裂缝破裂系数，采用属性比例融合方式，生成地质属性数据体；基于地质属性数据体，开展网格划分，并结合三维地震资料建立独立断面，生成裂缝预测模型；实时获取目标区域中各个采油井在生产中的测井信息，并基于测井信息，对裂缝预测模型进行迭代更新。本申请旨在提高裂缝预测的精度。
The embodiment of the present application is to provide an iterative fracture prediction method and device combining well and seismic data, which belongs to the field of geophysical exploration. It includes the following steps: obtaining three-dimensional seismic data of several oil wells in the target area; processing the three-dimensional seismic data to obtain multi-scale three-dimensional seismic fracture attributes; obtaining the tensile fracture rupture coefficient and the shear fracture rupture coefficient of the rock in the target area; based on the multi-scale three-dimensional seismic fracture attributes, the tensile fracture rupture coefficient and the shear fracture rupture coefficient, using the attribute ratio fusion method to generate a geological attribute data body; based on the geological attribute data body, grid division is carried out, and independent sections are established in combination with three-dimensional seismic data to generate a fracture prediction model; real-time acquisition of logging information of each oil well in the target area during production, and based on the logging information, iterative update of the fracture prediction model. This application aims to improve the accuracy of fracture prediction.
Description
Technical Field
The embodiment of the application relates to the field of geophysical exploration explanation, in particular to a well-seismic combined iterative fracture prediction method and device.
Background
The crack prediction method at the present stage mainly comprises two types, namely a crack prediction method based on post-stack seismic attribute or optimization, and mainly comprises the attribute of coherence attribute, curvature attribute, ant body, coherence reinforcement and the like. The core principle of the crack prediction method based on pre-stack P wave anisotropy is that seismic waves have differences in seismic properties of different directions in the propagation process of the seismic waves in different directions, under certain conditions, the differences are assumed to be caused by cracks, and the crack characteristics are predicted by solving the direction anisotropy of the seismic wave characteristics (amplitude, frequency, phase and conversion properties).
Crack prediction based on post-stack seismic attributes or optimization mainly solves large-scale cracks, achieves a good effect in conventional reservoirs, but for unconventional reservoirs, micro-cracks are a concern for exploration and development, and the application of the method is limited. The crack prediction method based on prestack P wave anisotropy has certain improvement on micro crack identification, but has certain requirement on data quality (the direction required as wide as possible), meanwhile, the crack prediction method based on prestack P wave anisotropy mainly identifies high-angle cracks, the method obtains better effect in fracture-cavity type reservoirs such as carbonate, and the unconventional reservoirs mainly adopt horizontal cracks, and the method has a gap from actual production requirements.
The two crack prediction methods are mainly three-dimensional seismic data, and the obtained crack prediction results are mainly seismic attributes, so long as the data are unchanged, the prediction results do not change greatly. With the deep development of unconventional oil and gas exploration and the popularization of earthquake and geological engineering integration concepts, the crack prediction result is required to be used for the subsequent engineering technology and production service in real time. But the crack prediction results at the present stage are only pure seismic attribute crack prediction results, and are far from following the unconventional oil and gas development steps.
Disclosure of Invention
The embodiment of the application provides an iterative fracture prediction method for well-seismic combination, which aims to achieve the effects of continuously updating fracture prediction results and improving prediction accuracy.
In a first aspect, an embodiment of the present application provides a method for predicting an iterative fracture in a well-seismic combination, including the following steps:
acquiring three-dimensional seismic data of a plurality of oil recovery wells in a target area;
Processing the three-dimensional seismic data to obtain multi-scale three-dimensional seismic fracture attributes, wherein the multi-scale three-dimensional seismic fracture attributes comprise coherence attributes, curvature attributes and prestack fracture strength;
Acquiring a fracture coefficient and a fracture coefficient of the rock in the target area;
Based on the multi-scale three-dimensional seismic fracture attribute, the fracture coefficient of the fracture and the fracture coefficient of the shear fracture, adopting an attribute proportion fusion mode to generate a geological attribute data volume;
based on the geological attribute data volume, carrying out grid division, and establishing an independent section by combining the three-dimensional seismic data to generate a crack prediction model, wherein the crack prediction model is used for representing the spatial homing, faults and fault occurrence of the independent section;
Logging information of each oil well in the target area in production is obtained in real time, and based on the logging information, the fracture prediction model is iteratively updated, wherein the logging information comprises imaging logging, real drilling fracture information and microseism monitoring results.
Optionally, the processing the three-dimensional seismic data to obtain a multi-scale three-dimensional seismic fracture attribute includes:
P waves generated by offset in the three-dimensional seismic data are obtained;
Acquiring the prestack crack strength by utilizing the anisotropy of the P wave;
Performing superposition frequency division processing on the P waves to obtain post-superposition three-dimensional seismic data;
and acquiring the coherence attribute and the curvature attribute based on the post-stack three-dimensional seismic data.
Optionally, the acquiring the pre-stack fracture strength by using the anisotropy of the P-wave includes:
acquiring seismic reflection characteristic values of the P wave in at least 3 directions, wherein the seismic reflection characteristic values comprise seismic amplitude, frequency, phase, wave impedance and seismic attribute;
Based on the seismic reflection characteristic values of the P waves in at least 3 directions, constructing an equation set to solve a small-scale crack characterization formula, and obtaining a major axis and a minor axis of the small-scale crack characterization formula to characterize ellipse;
wherein, the small-scale crack characterization formula is:
A(β)=A0+α·cos2β;
wherein A is the seismic reflection characteristics of different azimuth, A 0 is the azimuth average seismic reflection characteristic, alpha is the difference value between the azimuth extremum seismic reflection characteristic and the azimuth average reflection characteristic, and beta is the included angle between the offset azimuth and the crack trend;
wherein, the
β=φ-θ;
Wherein phi is the azimuth angle of offset observation, and theta is the azimuth angle of crack strike;
And acquiring the prestack fracture strength based on the major axis and the minor axis of the ellipse.
Alternatively, the pre-stack fracture strength is obtained by the following formula:
where γ is the pre-stack fracture strength, δ 1 is the major axis of the ellipse, and δ 2 is the minor axis of the ellipse.
Optionally, the acquiring the fracture coefficient and the fracture coefficient of the rock in the target area includes:
obtaining a core of the target area, performing core testing on the core, and obtaining the shear strength and the internal friction coefficient of the core;
Obtaining the maximum horizontal main stress, the middle horizontal main stress and the minimum horizontal main stress of the core of the target area;
obtaining the tensile stress and the shearing stress of a core in a target area;
The fracture coefficient of the fracture and the fracture coefficient of the shear fracture are calculated by the following formula:
Wherein K is a fracture coefficient of a tensile fracture, R is a fracture coefficient of a shear fracture, sigma t is a tensile stress, sigma t is a shear strength of the core, tau is a shear stress, tau is a shear fracture strength of the core;
wherein, the
Wherein, sigma 1 is the maximum horizontal main stress, sigma 2 is the middle horizontal main stress, and sigma 3 is the minimum horizontal main stress;
|τ|=S0-μ·σ;
Where μ is the internal coefficient of friction and S 0 is the shear strength of the core.
Optionally, the tensile stress and the shear stress of the core of the obtained target area adopt a Griffins criterion and a Coulomb-Navie criterion.
Optionally, the geological attribute data volume is computationally generated by the following formula:
Fuse=aA+bB+cC+dD+eE;
Wherein Fuse is a geological attribute data volume, a is the proportion of a coherent attribute in the geological attribute data volume, B is the proportion of a curvature attribute in the geological attribute data volume, C is the proportion of pre-stack fracture strength in the geological attribute data volume, D is the proportion of a fracture coefficient in the geological attribute data volume, E is the proportion of a shear fracture coefficient in the geological attribute data volume, a+b+c+d+e=1, a is the coherent attribute, B is the curvature attribute, C is the pre-stack fracture strength, D is the fracture coefficient, and E is the shear fracture coefficient.
Optionally, based on the logging information, iteratively updating the fracture prediction model includes:
processing the well logging information to obtain coherent attribute, curvature attribute, pre-stack fracture strength, fracture coefficient and fracture coefficient contained in the well logging information;
and interpolating the coherence attribute, curvature attribute, pre-stack fracture strength, fracture coefficient and fracture coefficient contained in the well logging information into a geological attribute data volume by adopting an interpolation method.
Optionally, the developing meshing based on the geological attribute data volume includes:
acquiring a plurality of shot points of offset in the three-dimensional seismic data;
Connecting the shot point with the oil extraction well closest to the shot point as a shot line;
And taking the gun lines as the length of the grid and taking the connecting line between two adjacent gun lines as the width of the grid to obtain the divided grid.
In a second aspect, an embodiment of the present application provides a well-shock-combined iterative fracture prediction apparatus, including:
the earthquake data acquisition module is used for acquiring three-dimensional earthquake data of a plurality of oil recovery wells in the target area;
the seismic data processing module is used for processing the three-dimensional seismic data to obtain multi-scale three-dimensional seismic crack attributes, wherein the multi-scale three-dimensional seismic crack attributes comprise coherence attributes, curvature attributes and prestack crack strength;
The rock coefficient acquisition module is used for acquiring the fracture coefficient and the fracture coefficient of the shear fracture of the rock in the target area;
The proportion fusion module is used for generating a geological attribute data volume by adopting an attribute proportion fusion mode based on the multi-scale three-dimensional seismic fracture attribute, the fracture coefficient of the fracture and the fracture coefficient of the shear fracture;
The model generation module is used for developing grid division based on the geological attribute data body and establishing an independent section by combining the three-dimensional seismic data to generate a crack prediction model, wherein the crack prediction model is used for representing the spatial homing, faults and fault occurrence of the independent section;
the iteration module is used for acquiring logging information of each oil well in the target area in production in real time, and carrying out iterative updating on the crack prediction model based on the logging information, wherein the logging information comprises imaging logging, real drilling crack information and microseism monitoring results.
The method has the advantages that the three-dimensional seismic data of a plurality of oil recovery wells in a target area are obtained and processed, the coherence attribute, the curvature attribute and the prestack fracture strength in the three-dimensional seismic data are obtained, then the fracture prediction model is generated in a proportional fusion mode by combining the fracture coefficient of the fracture in the target area and the fracture coefficient of the fracture, in subsequent production, logging information is continuously obtained to iteratively update the fracture prediction model, and the fracture prediction result in the earlier stage is combined with the data in the later stage, so that the fracture prediction result can be continuously updated, and the fracture prediction precision is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments of the present application will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart illustrating steps of a control method according to an embodiment of the present application;
FIG. 2 is an ellipse characterized by a small-scale fracture characterization formula in accordance with one embodiment of the present application;
FIG. 3 is basic data of coherence properties acquired by an embodiment of the present application;
FIG. 4 is a diagram of the underlying data of curvature attributes obtained in accordance with one embodiment of the present application;
FIG. 5 is a crack prediction model according to an embodiment of the present application;
FIG. 6 is a fracture prediction model optimized using imaging log data according to an embodiment of the present application;
fig. 7 is a functional block diagram of a prediction apparatus according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
In one embodiment, a flowchart of the steps of a method for providing iterative fracture prediction for well-shock coupling is shown in fig. 1, and the method may specifically include the steps of:
s101, acquiring three-dimensional seismic data of a plurality of oil recovery wells in a target area.
In the embodiment, the three-dimensional seismic data of the oil production well is obtained by adopting a shot detection mode, the three-dimensional seismic data is a waveform, the basic condition in the target area can be fed back through the three-dimensional seismic data, and the basic condition of the crack of the target area can be judged through the processing of the three-dimensional seismic data.
S102, processing the three-dimensional seismic data to obtain multi-scale three-dimensional seismic fracture attributes, wherein the multi-scale three-dimensional seismic fracture attributes comprise coherence attributes, curvature attributes and prestack fracture strength;
The method comprises the steps of obtaining pre-stack crack strength directly on three-dimensional seismic data by utilizing P wave anisotropy in the three-dimensional seismic data, obtaining coherence attribute and curvature attribute on post-stack three-dimensional seismic data, and obtaining the post-stack three-dimensional seismic data by carrying out superposition frequency division processing on the three-dimensional seismic data. In the present embodiment, coherence properties, curvature properties, and prestack fracture strength are taken as the base data.
S103, acquiring the fracture coefficient and the fracture coefficient of the rock in the target area.
And (3) carrying out lithology test on the rock in the target area, and then solving the tensile stress and the shear stress of the rock by utilizing the Griffith criterion and the Coulomb-Navie criterion, so that the fracture coefficient of the tensile crack and the fracture coefficient of the shear crack of the rock in the target area can be obtained. Wherein, griffith and Coulomb-Navie criteria are both conventional techniques.
S104, based on the multi-scale three-dimensional seismic fracture attribute, the fracture coefficient of the fracture and the fracture coefficient of the shear fracture, adopting an attribute proportion fusion mode to generate a geological attribute data volume.
Combining the five data obtained in the step S102 and the step S103, the coherence attribute, the curvature attribute, the prestack fracture strength, the fracture coefficient of the fracture and the fracture coefficient of the shear fracture, and the generated geological attribute data can reflect the basic characteristics of the fracture in the target area.
S105, based on the geological attribute data volume, carrying out grid division, and establishing an independent section by combining the three-dimensional seismic data to generate a crack prediction model, wherein the crack prediction model is used for representing the spatial homing, faults and fault occurrence of the independent section;
After the fracture prediction model is generated by meshing and establishing independent sections, the fracture distribution condition near each oil extraction well in the target area in the fracture prediction model can be seen more clearly when the target area is analyzed through the fracture prediction model.
S106, logging information of each oil well in the target area in production is obtained in real time, and based on the logging information, the fracture prediction model is iteratively updated, wherein the logging information comprises imaging logging, real drilling fracture information and microseism monitoring results.
And in the production process, collecting and researching the crack information related to the oil extraction well, combining the established crack prediction model with logging data, and continuously optimizing and updating the crack prediction model. Along with deep exploration and development, logging data and development and production data are more and more, a fracture prediction model is updated continuously, prediction accuracy is improved continuously, and the effect of guiding unconventional oil gas development in real time is achieved.
According to the method, the three-dimensional seismic data of a plurality of oil recovery wells in a target area are obtained and processed, the coherence attribute, the curvature attribute and the prestack fracture strength in the three-dimensional seismic data are obtained, then the fracture prediction model is generated in a proportional fusion mode by combining the fracture coefficient of the fracture in the target area and the fracture coefficient of the fracture, in subsequent production, logging information is continuously obtained to iteratively update the fracture prediction model, and the fracture prediction result in the earlier stage is combined with the data in the later stage, so that the fracture prediction result can be continuously updated, and the fracture prediction precision is improved.
In one embodiment, a step flow diagram of a method for iterative fracture prediction for well-shock coupling is provided as shown in FIG. 1:
s101, acquiring three-dimensional seismic data of a plurality of oil recovery wells in a target area.
And in the target area, at least 3 oil extraction wells are selected as sample wells, three-dimensional seismic data of the 3 oil extraction wells are acquired in a shot-detection mode, and when the sample wells are selected, the range capable of covering the maximum target area is taken as a standard.
S102, processing the three-dimensional seismic data to obtain multi-scale three-dimensional seismic fracture attributes, wherein the multi-scale three-dimensional seismic fracture attributes comprise coherence attributes, curvature attributes and prestack fracture strength;
The processing of the three-dimensional seismic data is mainly superposition frequency division processing, and in post-stack data, the processing is mainly used for acquiring characteristics of large-scale cracks, such as coherence properties and curvature properties. On pre-stack data, the characteristic of the small-scale crack, namely the pre-stack crack strength, is obtained by utilizing the anisotropy of the P wave.
The processing the three-dimensional seismic data to obtain the multi-scale three-dimensional seismic crack attribute comprises the following steps:
P waves generated by offset in the three-dimensional seismic data are obtained;
Acquiring the prestack crack strength by utilizing the anisotropy of the P wave;
the method comprises the following steps:
acquiring seismic reflection characteristic values of the P wave in at least 3 directions, wherein the seismic reflection characteristic values comprise seismic amplitude, frequency, phase, wave impedance and seismic attribute;
And constructing an equation set to solve a small-scale crack characterization formula based on the seismic reflection characteristic values of the P waves in at least 3 directions, and obtaining the major axis and the minor axis of the ellipse characterized by the small-scale crack characterization formula.
In this embodiment, the constructed equation set has 3 unknowns, so that the equation set can be solved by using the seismic reflection eigenvalues in three directions, so as to obtain a solution of the characterization formula of the small-scale fracture with respect to the ellipse, and then the pre-stack fracture strength is obtained by using the solution.
Wherein, the small-scale crack characterization formula is:
A(β)=A0+α·cos2β;
wherein A is the seismic reflection characteristics of different azimuth, A 0 is the azimuth average seismic reflection characteristic, alpha is the difference value between the azimuth extremum seismic reflection characteristic and the azimuth average reflection characteristic, and beta is the included angle between the offset azimuth and the crack trend;
wherein, the
β=φ-θ;
Wherein phi is the azimuth angle of offset observation, and theta is the azimuth angle of crack strike.
As in fig. 2, fig. 2 shows ellipses characterized by a small scale fracture characterization formula, where a 0, α, and β are in one-to-one correspondence with the above.
And acquiring the prestack fracture strength based on the major axis and the minor axis of the ellipse.
The pre-stack fracture strength was obtained by the following formula:
where γ is the pre-stack fracture strength, δ 1 is the major axis of the ellipse, and δ 2 is the minor axis of the ellipse.
By solving the constructed equation set, the solution of the ellipse represented by the small-scale fracture representation formula can be obtained, and the values of the major axis of the ellipse and the minor axis of the ellipse can be obtained through the solution, so that the value of the pre-stack fracture strength can be obtained.
Performing superposition frequency division processing on the P waves to obtain post-superposition three-dimensional seismic data;
and acquiring the coherence attribute and the curvature attribute based on the post-stack three-dimensional seismic data.
The obtained data are shown in fig. 3 and fig. 4, wherein fig. 3 shows basic data of the coherence attribute obtained in the present embodiment, fig. 4 shows basic data of the curvature attribute obtained in the present embodiment, and in the present embodiment, conventional technical means are adopted for obtaining the coherence attribute and the curvature attribute.
S103, acquiring a fracture coefficient and a fracture coefficient of the rock in the target area;
the acquiring the fracture coefficient and the fracture coefficient of the rock in the target area comprises the following steps:
obtaining a core of the target area, performing core testing on the core, and obtaining the shear strength and the internal friction coefficient of the core;
Obtaining the maximum horizontal main stress, the middle horizontal main stress and the minimum horizontal main stress of the core of the target area;
The maximum horizontal main stress, the middle horizontal main stress and the minimum horizontal main stress of the core of the target area are obtained by adopting a structural stress calculation formula based on curvature attributes, and the method is a conventional technical means.
Obtaining the tensile stress and the shearing stress of a core in a target area;
And the tensile stress and the shearing stress of the core of the obtained target area adopt a Griffins criterion and a Coulomb-Navie criterion.
The fracture coefficient of the fracture and the fracture coefficient of the shear fracture are calculated by the following formula:
Wherein K is a fracture coefficient of a tensile fracture, R is a fracture coefficient of a shear fracture, sigma t is a tensile stress, sigma t is a shear strength of the core, tau is a shear stress, tau is a shear fracture strength of the core;
wherein, the
Wherein, sigma 1 is the maximum horizontal main stress, sigma 2 is the middle horizontal main stress, and sigma 3 is the minimum horizontal main stress;
|τ|=S0-μ·σ;
Where μ is the internal coefficient of friction and S 0 is the shear strength of the core.
Mu and S 0 can be obtained through core testing of a research target area, for example, when S 0 is obtained, a rock sample of the target area is taken, a continuously increasing shearing force is applied to the rock sample, and when the rock sample breaks under the shearing force, the shearing force at the current moment is the shearing strength of the target area.
S104, generating a geological attribute data volume by adopting an attribute proportion fusion mode based on the multi-scale three-dimensional seismic fracture attribute, the fracture coefficient of the fracture and the fracture coefficient of the shear fracture;
the geological attribute data volume is computationally generated by the following formula:
Fuse=aA+bB+cC+dD+eE;
Wherein Fuse is a geological attribute data volume, a is the proportion of a coherent attribute in the geological attribute data volume, B is the proportion of a curvature attribute in the geological attribute data volume, C is the proportion of pre-stack fracture strength in the geological attribute data volume, D is the proportion of a fracture coefficient in the geological attribute data volume, E is the proportion of a shear fracture coefficient in the geological attribute data volume, a+b+c+d+e=1, a is the coherent attribute, B is the curvature attribute, C is the pre-stack fracture strength, D is the fracture coefficient, and E is the shear fracture coefficient.
The geological attribute data volume generated by the attribute proportion fusion mode has the characteristics contained in all basic data, can reflect the crack characteristics of a target area, and is used for making a foundation for constructing a crack prediction model in the next step.
S105, based on the geological attribute data volume, conducting grid division, and establishing independent sections by combining the three-dimensional seismic data to generate a crack prediction model, wherein the crack prediction model is used for representing spatial homing, faults and fault occurrence of the independent sections.
When grid division is performed based on the geological attribute data volume, the method comprises the following steps of:
acquiring a plurality of shot points of offset in the three-dimensional seismic data;
The shot point is the position where the shot is located when the three-dimensional seismic data is acquired.
Connecting the shot point with the oil extraction well closest to the shot point as a shot line;
By using the oil extraction well with the closest shot point and the closest shot point as shot lines, when grid division is performed, one oil extraction well can be ensured in each grid.
And taking the gun lines as the length of the grid and taking the connecting line between two adjacent gun lines as the width of the grid to obtain the divided grid.
The geological attribute data volume is divided into a plurality of grids by connecting a plurality of gun lines, the grids are covered on the target area, and the target area is divided into a plurality of grids so as to be convenient for positioning which piece of crack is on the target area in the subsequent study.
Fig. 5 shows a fracture prediction model obtained in this example, and in fig. 5, the inclined thick line is the fracture surface of the fracture.
After the grids are divided, the geological attribute data volume is split by combining the three-dimensional seismic data, and an independent section is formed by connecting the two sections end to end after the geological attribute data volume is split, the independent section can clearly represent a crack prediction model of a target area, and cracks existing in the target area, the trend and the strength of the cracks can be rapidly found on the crack prediction model through observation of the independent section.
S106, logging information of each oil well in the target area in production is obtained in real time, and based on the logging information, the fracture prediction model is iteratively updated, wherein the logging information comprises imaging logging, real drilling fracture information and microseism monitoring results.
Fig. 6 shows a fracture prediction model optimized by imaging logging data, through which the trend and strength of a fracture in a target area can be observed in this embodiment.
According to the embodiment, the crack prediction model is built through the earlier-stage basic data, and then the well logging information acquired in the production at the later stage is combined to carry out iterative updating on the crack prediction model, wherein the well logging information can reflect the real underground crack condition of the target area, and the continuous iterative updating enables the crack prediction model to be continuously close to the real condition, so that a more accurate and more accurate prediction result is brought to the prediction of the crack prediction model.
Based on the logging information, iteratively updating the fracture prediction model includes:
processing the well logging information to obtain coherent attribute, curvature attribute, pre-stack fracture strength, fracture coefficient and fracture coefficient contained in the well logging information;
and interpolating the coherence attribute, curvature attribute, pre-stack fracture strength, fracture coefficient and fracture coefficient contained in the well logging information into a geological attribute data volume by adopting an interpolation method.
When interpolation is carried out, part of information which is acquired from logging information and is not in a fracture prediction model is inserted into the fracture prediction model, the fracture prediction model can be more and more close to the real situation through continuous acquisition of the logging information and interpolation, and when the logging information reaches a maximum value, the fracture prediction model can directly reflect the fracture situation of a target area.
According to the method, the three-dimensional seismic data of a plurality of oil recovery wells in a target area are obtained and processed, the coherence attribute, the curvature attribute and the prestack fracture strength in the three-dimensional seismic data are obtained, then the fracture prediction model is generated in a proportional fusion mode by combining the fracture coefficient of the fracture in the target area and the fracture coefficient of the fracture, in subsequent production, logging information is continuously obtained to iteratively update the fracture prediction model, and the fracture prediction result in the earlier stage is combined with the data in the later stage, so that the fracture prediction result can be continuously updated, and the fracture prediction precision is improved.
In one embodiment, a functional block diagram of a well-shock bonded iterative fracture prediction apparatus is provided as shown in fig. 7, comprising:
the earthquake data acquisition module is used for acquiring three-dimensional earthquake data of a plurality of oil recovery wells in the target area;
the seismic data processing module is used for processing the three-dimensional seismic data to obtain multi-scale three-dimensional seismic crack attributes, wherein the multi-scale three-dimensional seismic crack attributes comprise coherence attributes, curvature attributes and prestack crack strength;
The rock coefficient acquisition module is used for acquiring the fracture coefficient and the fracture coefficient of the shear fracture of the rock in the target area;
The proportion fusion module is used for generating a geological attribute data volume by adopting an attribute proportion fusion mode based on the multi-scale three-dimensional seismic fracture attribute, the fracture coefficient of the fracture and the fracture coefficient of the shear fracture;
The model generation module is used for developing grid division based on the geological attribute data body and establishing an independent section by combining the three-dimensional seismic data to generate a crack prediction model, wherein the crack prediction model is used for representing the spatial homing, faults and fault occurrence of the independent section;
the iteration module is used for acquiring logging information of each oil well in the target area in production in real time, and carrying out iterative updating on the crack prediction model based on the logging information, wherein the logging information comprises imaging logging, real drilling crack information and microseism monitoring results.
For the device embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and reference is made to the description of the method embodiments for relevant points.
In this specification, each embodiment is described in a progressive manner, and each embodiment is mainly described by differences from other embodiments, and identical and similar parts between the embodiments are all enough to be referred to each other.
It will be apparent to those skilled in the art that embodiments of the present application may be provided as a method, apparatus, or computer program product. Accordingly, embodiments of the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the application may take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
Embodiments of the present application are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may 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, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiment and all such alterations and modifications as fall within the scope of the embodiments of the application.
Finally, it is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising one does not exclude the presence of other like elements in a process, method, article or terminal device comprising the element.
While the principles and embodiments of the present application have been described in detail in this application, the foregoing embodiments are provided to facilitate understanding of the principles and concepts of the application and are further provided by one of ordinary skill in the art to which the application pertains.
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