CN111754623A - Discrete fracture modeling method based on multi-point geostatistics - Google Patents
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Abstract
The invention provides a discrete crack modeling method based on multi-point geostatistics, which combines the multi-point geostatistics modeling and the discrete crack modeling method and comprises the steps of establishing a training image of multi-point geostatistics simulation consisting of discrete crack sheets; representing discrete fracture pieces by using a convex quadrilateral surface element, representing the spatial configuration relation of the fracture by using a data event formed by the fracture pieces, and calculating the similarity of the data event of the fracture pieces in a manner of overall scaling of the fracture pieces and weighting of the morphological parameters of the fracture pieces; and extracting a proper crack mode from the training image to an actual model by adopting a direct sampling mode according to a sequential method. The method can ensure the structural property of the spatial distribution of the cracks, reflect the scale of the cracks and objectively reproduce the spatial configuration relationship of the cracks with different underground scales.
Description
Technical Field
The invention relates to the field of oil and gas development, in particular to a discrete fracture modeling method based on multi-point geostatistics.
Background
The fractured oil reservoirs are widely distributed in the world, have important economic and strategic potentials, and also play an important role in development and production of oil and gas fields. The fracture geological modeling is a three-dimensional quantitative model reflecting the characterization parameters of the fractures in the fractured reservoir and the spatial distribution of the fractures, can reflect the distribution rules of the fractures, and can meet the requirements of reservoir engineering research.
The discrete fracture network model displays and represents each fracture by fracture slices with different shapes, sizes, azimuths and dip angles in a three-dimensional space, a plurality of fracture slices with consistent characteristics form a fracture group, and a plurality of fracture groups form a fracture system. The discrete fracture network model is rooted in random simulation, and the establishment of each fracture follows the following rules: the shape of the fracture piece is a convex polygon (rectangle, ellipse or more complex form); the size of the fracture patch conforms to a known distribution (e.g., negative index distribution); the position of the crack follows a spatial distribution function; the fracture orientation is obtained by extraction and even or Fisher distribution. The simulation process usually adopts an indicative point process method, firstly determines the position of a crack piece and determines the shape and the occurrence parameters of the crack piece.
At present, the main researches on the discrete fracture modeling method are as follows:
(1) the existing multi-point geostatistical modeling algorithms are all based on grid pixels, a training image represents a space distribution mode of a geologic body in a three-dimensional grid model mode, the spatial position relation among a plurality of grids can be simulated, the method is very beneficial to representing the space distribution of complex sedimentary facies types, the method is generally only used for representing a matrix part of a fractured oil reservoir, and the fractured part is also built by using a traditional discrete fracture modeling method, such as the document 'fracture-cave carbonate reservoir modeling and residual oil distribution research' (LihongKai et al, China geological university (Beijing) 2012.6), the matrix model is built by using multi-point geostatistical, the fracture model is built by using three directions by using the traditional discrete fracture modeling, and the multi-point geostatistical method is not used when the fracture model is used; in the literature, "the karst phased modeling method research in carbonate weathering karst areas" (Xinjiang petroleum geology, 2015.6), a karst phase model is established by multipoint geostatistics, a large-scale crack is established by a deterministic method, a crack model is established by random discrete crack modeling, and the multipoint geostatistical method is not used in the crack model. Neither method takes into account the complex spatial configuration of the fractures.
(2) From the aspect of discrete fracture modeling algorithm, the former people only improve part of links in a simulation frame, the simulation method firstly generates fracture development values according to a fracture density field and then generates fracture occurrence according to statistical fracture occurrence, then generating cracks according to artificially given crack forms, such as in the literature "random modeling method of discrete crack network" (Zhengsongqing et al, journal of Petroleum and Natural gas, 31, 4, 106, 110, 2009, 8 months), determining the position of a crack by adopting a space variable probability method, analyzing the fracture occurrence by adopting a cyclic statistical method, simulating the fracture occurrence and geometric parameters by adopting Fisher distribution and fractal distribution, respectively giving position probability and fracture geometric parameters to the cracks of different groups, wherein the distribution of the existing crack cannot be considered in the simulation process, but the method cannot reflect the space configuration relation of discrete cracks; in the literature, "constraining the spatial distribution of small-scale discrete fractures by using seismic data" (fault block oil and gas field, 2012.8), a fracture density model is converted into a probability model, the probability model is used as a simulation stopping condition, and the problem of matching simulated fracture density with well point data is solved, but the method cannot solve the problem of three-dimensional spatial configuration of multi-group system fractures.
(3) The existing scholars try to apply the thought of multipoint geostatistics to fracture modeling, and the simulation result can reflect the complex spatial configuration relationship of the fracture, for example, in a document 'fractured reservoir discrete fracture network model and numerical simulation' (academic newspaper of petroleum university, 2017.6), the fracture model is established by adopting the multipoint geostatistics, the spatial configuration relationship of the fracture can be considered, but the fracture is represented by adopting a grid method, the fracture which is similar to or smaller than the grid scale cannot be simulated, and the fracture density cannot be accurately depicted.
Therefore, the current state of the art in this field is: multi-point geostatistical modeling is typically used for three-dimensional geologic volume simulation of depositionally equal volume types in the attributes of a matrix; the discrete fracture modeling method only utilizes the statistical rules of parameters such as fracture density, attitude, morphology and the like to respectively simulate each group of fractures, and cannot consider the spatial configuration relationship of multi-scale fractures and multiple groups of system fractures; in the prior art, a pixel grid is adopted to represent a crack space distribution rule, an existing multi-point geostatistics method is adopted to establish a crack model, and the pixel grid is used to represent discrete cracks to limit parameters such as the size, the opening and the like of the cracks, so that the cracks with the size smaller than the grid size and the size similar to the grid size cannot be simulated, the multi-scale cracks cannot be simulated, and the crack simulation precision is seriously influenced.
Disclosure of Invention
In order to solve the technical problems, the invention provides a discrete fracture modeling method for a fractured reservoir, which adopts the thought of multi-point geological statistics, uses discrete fracture pieces to replace conventional pixel units, invents a novel discrete fracture network modeling method and reproduces the spatial complex configuration relationship of multiple scales and groups of fractures.
The invention discloses a discrete fracture modeling method based on multipoint geostatistics, which comprises the following steps of:
step 1, establishing a discrete fracture training image based on a multi-scale fractal theory;
and 2, performing multipoint geostatistical simulation based on the crack pieces.
Further, in the step 1, the method includes:
step 1.1, multi-scale fracture characteristic description;
step 1.2, representing the quantitative relation of crack scale characteristics;
step 1.3, establishing a multi-scale training image based on a multi-scale fractal theory;
further, in the step 2, the fracture piece-based multi-point geostatistical simulation comprises fracture piece characterization, fracture group system similarity calculation, fracture piece-based data event scaling, multi-scale fracture piece spatial distribution pattern similarity calculation and discrete fracture network simulation based on direct sampling.
Further, in step 1.1, the multi-scale fracture characteristic research comprises:
integrating multidimensional and multiscale data, and quantitatively describing the attitude parameters of the crack;
the method comprises the following steps of researching the geometric characteristics and the occurrence characteristics of cracks with different scales by means of seismic analysis, outcrop observation, imaging logging, core CT scanning and the like;
further, in the step 1.2, the fracture multi-scale feature quantitative relationship characterization includes analyzing spatial configuration features of fractures on different scales, and establishing a quantitative relationship between fracture group system, fracture density, geometric morphology and occurrence along with observation scale.
Further, in the step 1.3, the establishing of the multi-scale fracture training image based on the multi-scale fractal theory includes:
step 1.3.1, calculating a multi-scale fractal dimension based on the spatial configuration relationship of the cracks on different scales in the step 1.2;
and 1.3.2, establishing a multi-scale multi-group discrete fracture model according to a non-conditional simulation method based on the multi-scale fractal dimension obtained in the step 1.3.1, and establishing a training image of the multi-point geostatistical simulation.
Further, the multi-scale fractal dimension calculation comprises multi-scale fractal dimensions in respective characteristic dimension ranges of core CT scanning, imaging logging, outcrop observation and seismic analysis, and interpolation and splicing of the four multi-scale fractal dimensions, and finally the fractal dimension of continuous dimensions from the core dimension to the seismic dimension is established. The multi-scale fractal dimension of the invention is different from the single fractal dimension in the prior art, and the problem that the single fractal dimension cannot be calculated due to insufficient data is solved.
Further, the unconditional simulation of the multi-scale discrete fracture based on the multi-scale fractal dimension comprises the steps of calculating the change degree and the probability density of parameters such as fracture density, length, width, thickness, inclination angle and inclination by taking the multi-scale fractal dimension as a scale, and synchronously simulating multiple groups of fractures by taking the fracture density as probability control, wherein the simulation of a single group of fractures adopts a point process method of discrete fracture pieces. The point process method for dissociating the scattered crack pieces comprises the following steps: the position of the crack sheet is firstly determined, and then the crack sheet is generated according to the information of the length, the width, the thickness, the inclination angle, the tendency and the like of the crack sheet.
Further, the similarity calculation of the fracture group system comprises factors such as the size, density, inclination angle, inclination, thickness of the fracture piece and the change rate of each parameter.
The data event scaling method based on the fracture piece comprises data event establishment based on the fracture piece and data event scaling based on fracture piece contraction and extension, wherein the data event establishment based on the fracture piece is composed of known fracture pieces in space, and the fracture piece data event contracts and extends by taking the center of each fracture piece as an origin.
The similarity calculation of the multi-scale fracture piece spatial distribution mode comprises the establishment of the multi-scale fracture piece spatial distribution mode and the similarity calculation of two groups of multi-scale fracture piece spatial distribution modes, wherein the multi-scale fracture piece spatial distribution mode is formed by combining a limited number of fracture piece spaces, and the similarity calculation of the multi-scale fracture piece spatial distribution mode is based on the similarity calculation of the fracture group.
The direct sampling based discrete fracture distribution simulation includes generating a multi-grid system from the fracture multi-scale features and performing a discrete fracture distribution simulation on each of the multi-grids.
Further, the discrete fracture distribution simulation based on direct sampling comprises:
generating and setting the number of multiple grids according to the multi-scale features of the cracks, simulating from the coarsest grid to the finest grid, and constraining the crack density distribution of the finer grid according to the simulation result of the coarser grid;
simulating discrete crack distribution on a certain heavy and thick grid comprises the steps of simulating generation of a random simulation path, establishing a crack sheet data event along the random simulation path, and searching a crack space distribution relation which is most similar to the data event in a training image;
and assigning the searched crack distribution space distribution mode to an area to be simulated, updating a probability random path according to a simulation result, and executing next area point simulation along the probability random simulation path until the crack density probability distribution is met.
Compared with the prior art, the discrete fracture modeling method based on the multi-point geostatistics uses a multi-point geostatistics modeling thought in the fracture network modeling with the space discrete attribute, replaces the conventional pixel grid with the fracture pieces, redesigns the data event formed by the fracture pieces and the mode formed by the fracture pieces, and uses a corresponding similarity calculation method to realize a multi-point geostatistics modeling algorithm based on the fracture pieces and be used for the discrete fracture network modeling. The method can ensure the structural property of the spatial distribution of the cracks, reflect the scale of the cracks and objectively reproduce the spatial configuration relationship of the cracks with different underground scales.
The technical features described above can be combined in various technically feasible ways to produce new embodiments, as long as the object of the invention is achieved.
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The invention will be described in more detail hereinafter on the basis of non-limiting examples only and with reference to the accompanying drawings. Wherein:
FIG. 1 shows a flow chart of a discrete fracture modeling method based on multi-point geostatistices of the present invention;
FIG. 2 illustrates the difference in the representation of the fracture network between the prior art and the embodiment of the present invention;
FIG. 3 shows discrete fracture observations from an interpretation of an uphole fracture in an embodiment of the invention;
FIG. 4 shows XC gas field multi-scale fracture training images (the line segment width in the figure represents the fracture width);
FIG. 5 is a diagram illustrating a scaling of data events composed of fracture fragments in an embodiment of the present invention;
FIG. 6 shows the multi-point geostatistical discrete fracture simulation results in an embodiment of the invention.
Detailed Description
The invention will be described in further detail below with reference to the drawings and specific examples. It should be noted that, as long as there is no conflict, the embodiments and the features of the embodiments of the present invention may be combined with each other, and the technical solutions formed are within the scope of the present invention.
As shown in FIG. 1, the discrete fracture modeling method based on multi-point geostatistics comprises the following steps:
step 1, establishing a discrete fracture training image based on a multi-scale fractal theory; wherein, step 1 includes:
step 1.1, multi-scale fracture characteristic description;
step 1.2, representing the quantitative relation of crack scale characteristics;
and 1.3, establishing a multi-scale training image based on a multi-scale fractal theory.
And 2, performing multipoint geostatistical simulation based on the crack pieces.
Further, in the step 2, the fracture piece-based multipoint geostatistical simulation method includes a fracture piece characterization method, a fracture group system similarity calculation method, a fracture piece-based data event scaling method, a multiscale fracture piece spatial distribution pattern similarity calculation and a discrete fracture network simulation based on direct sampling.
Further, in step 1.1, the multi-scale fracture characteristic research comprises:
integrating multi-dimensional and multi-scale data, and quantitatively describing the attitude parameters such as crack tendency, dip angle, set system, density and the like;
the geometrical characteristics and the occurrence characteristics of the cracks with different scales are researched by means of seismic analysis, outcrop observation, imaging logging, core CT scanning and the like.
Further, in the step 1.2, the fracture multi-scale feature quantitative relationship characterization includes analyzing spatial configuration features of fractures on different scales, and establishing a quantitative relationship between fracture group system, fracture density, geometric morphology and occurrence along with observation scale.
Further, in the step 1.3, the establishing of the multi-scale fracture training image based on the multi-scale fractal theory includes:
step 1.3.1, calculating a multi-scale fractal dimension based on the spatial configuration relationship of the cracks on different scales in the step 1.2;
and 1.3.2, establishing a multi-scale multi-group discrete fracture model according to a non-conditional simulation method based on the multi-scale fractal dimension obtained in the step 1.3.1, and establishing a training image of the multi-point geostatistical simulation. The multi-scale fractal dimension is different from the single fractal dimension in the prior art, so that the problem that the single fractal dimension cannot be calculated due to insufficient data is solved.
Further, the multi-scale fractal dimension calculation comprises multi-scale fractal dimensions in respective characteristic dimension ranges of core CT scanning, imaging logging, outcrop observation and seismic analysis, and interpolation and splicing of the four multi-scale fractal dimensions, and finally the fractal dimension of continuous dimensions from the core dimension to the seismic dimension is established.
Further, the unconditional simulation of the multi-scale discrete fracture based on the multi-scale fractal dimension comprises the steps of calculating the change degree and the probability density of parameters such as fracture density, length, width, thickness, inclination angle and inclination by taking the multi-scale fractal dimension as a scale, and synchronously simulating multiple groups of fractures by taking the fracture density as probability control, wherein the simulation of a single group of fractures adopts a point process method of discrete fracture pieces. The point process method for dissociating the scattered crack pieces comprises the following steps: the position of the crack sheet is firstly determined, and then the crack sheet is generated according to the information of the length, the width, the thickness, the inclination angle, the tendency and the like of the crack sheet.
Furthermore, the crack sheet characterization method belongs to the prior art method, and a multipoint geostatistical simulation method based on direct sampling is also the prior art, but the prior art only aims at pixel grids, and the method aims at discrete crack simulation. The data event establishment and scaling based on the discrete fracture pieces are different from the data event establishment based on the pixel grid and the coarsening of the pixel grid, the similarity calculation method of the spatial distribution mode of the multi-scale fracture pieces is different from the similarity calculation method of the geological mode based on the pixel grid, and the discrete fracture piece simulation method based on the direct sampling is different from the pixel grid simulation method based on the direct sampling.
Further, the fracture piece characterization method comprises the step of representing the fracture piece by a convex quadrilateral on a three-dimensional space, wherein the attributes of the fracture piece comprise parameters such as inclination angle, inclination, width, length, thickness and central position.
Similarity calculations for the fracture family include factors such as size, density, dip, thickness of the fracture patch, and rate of change of various parameters.
The data event scaling method based on the fracture piece comprises data event establishment based on the fracture piece and data event scaling based on fracture piece contraction and extension, wherein the data event establishment based on the fracture piece is composed of known fracture pieces in space, and the fracture piece data event contracts and extends by taking the center of each fracture piece as an origin.
The similarity calculation of the multi-scale fracture piece spatial distribution mode comprises the establishment of the multi-scale fracture piece spatial distribution mode and the similarity calculation of two groups of multi-scale fracture piece spatial distribution modes, wherein the multi-scale fracture piece spatial distribution mode is formed by combining a limited number of fracture piece spaces, and the similarity calculation of the multi-scale fracture piece spatial distribution mode is based on the similarity calculation of the fracture group.
The direct sampling based discrete fracture distribution simulation includes generating a multi-grid system from the fracture multi-scale features and performing a discrete fracture distribution simulation on each of the multi-grids.
Further, the discrete fracture distribution simulation based on direct sampling comprises:
generating and setting the number of multiple grids according to the multi-scale features of the cracks, simulating from the coarsest grid to the finest grid, and constraining the crack density distribution of the finer grid according to the simulation result of the coarser grid;
simulating discrete crack distribution on a certain heavy and thick grid comprises the steps of simulating generation of a random simulation path, establishing a crack sheet data event along the random simulation path, and searching a crack space distribution relation which is most similar to the data event in a training image;
and assigning the searched crack distribution space distribution mode to an area to be simulated, updating a probability random path according to a simulation result, and executing next area point simulation along the probability random simulation path until the crack density probability distribution is met.
The difference between the method and the prior art is that a multi-point geostatistics method is adopted to directly simulate discrete cracks, instead of the cracks represented by pixel grids, or the cracks are simulated one by one in a group-by-group or crack piece-by-crack piece manner based on a point process point method; the training image is composed of discrete patches of cracks, rather than a grid of pixels; the data events consist of discrete patches of cracks, rather than a grid of pixels; the crack spatial distribution configuration relationship is represented by discrete crack sheets, rather than a pixel grid (as in fig. 2).
According to the discrete fracture modeling method based on the multipoint geostatistics, which is provided by the invention, the Xichuan depression XC gas field is taken as an example, and field implementation is carried out.
In the embodiment, the target interval of the XC gas field undergoes multi-phase construction movement, the natural fracture heterogeneity is strong, and the fracture prediction difficulty is large. The fine reservoir fracture modeling steps are as follows:
step 1, establishing a discrete fracture training image based on a multi-scale fractal theory. The method comprises the following steps of obtaining detailed fracture space parameters according to a plurality of means of field outcrop, well drilling rock core, CT scanning and imaging logging earthquake explanation, and specifically comprises the following steps:
step 1.1. multi-scale fracture characterization: according to field outcrop, well core, CT scanning, imaging logging and seismic data, the attitude parameters of the fractures, such as dip angles, tendencies, densities and the like, are described on corresponding scales, the reservoir fractures in the area mainly comprise low-angle fractures, and simultaneously develop oblique intersection seams, high-angle seams, reticular seams and the like, and the fracture trend mainly has three directions of NEE, NE and SEE.
Step 1.2, fracture scale characteristic quantitative relation characterization: on the basis of the statistical description of the fracture parameters obtained in the step 1.1, analyzing the spatial configuration relation of the fractures on different scales, establishing a quantitative relation of a fracture group system, fracture density, geometric morphology and attitude along with an observation scale, if certain kind of data is missing, carrying out interpolation according to the characteristic scale, and sequencing the different data reflecting the fracture characteristic scale from large to small, wherein the different data are seismic scale, field outcrop scale, imaging logging scale, drilling core scale and CT scanning scale.
Step 1.3, establishing a multi-scale multi-group system fracture model by adopting a multi-scale fractal theory according to an unconditional simulation method, wherein the multi-scale multi-group system fracture model is used as a training image for multi-point geostatistical simulation and comprises the following steps:
step 1.3.1. multi-scale fractal dimension calculation: and (3) calculating a multi-scale fracture fractal dimension according to the space configuration relation of the fractures on different scales obtained in the step (1.2), wherein the multi-scale fractal dimension calculation comprises the multi-scale fractal dimension in the characteristic scale range of core CT scanning, imaging logging, outcrop observation and seismic analysis, and the interpolation and splicing of the four multi-scale fractal dimensions, and finally establishing the fractal dimension of continuous scale from the core scale to the seismic scale.
Step 1.3.2, multi-scale discrete fracture unconditional simulation based on multi-scale fractal dimension: the variation degree and probability density of parameters such as crack density, length, width, thickness, inclination angle, inclination and the like are calculated by taking the multi-scale fractal dimension as a scale, and a plurality of groups of cracks are synchronously simulated by taking the crack density as probability control, wherein the simulation of a single group of cracks adopts a point process method of a discrete crack sheet. The point process method of the discrete fracture piece can firstly determine the position of the fracture piece, then generate the fracture piece according to the information of the length, the width, the thickness, the inclination angle, the inclination and the like of the fracture piece, and finally form the discrete fracture piece as shown in fig. 4 to form the training image model.
And 2, representing the discrete fracture pieces by using convex quadrilateral surface elements distributed in three-dimensional space, representing the spatial configuration relation of the fracture by using data events formed by the fracture pieces, and calculating the similarity of the data events of the fracture pieces in a manner of overall scaling of the fracture pieces and weighting of the morphological parameters of the fracture pieces.
And 3, extracting a proper crack mode from the training image to an actual model by adopting a direct sampling method according to a sequential method until the space to be simulated meets the requirement of the predicted crack density. In particular, the method comprises the following steps of,
setting the number of multiple grids to be three, respectively executing discrete crack distribution simulation on each grid, and from the coarsest grid system to the finest grid system, constraining the simulation result of the finer grid system by the simulation result of the coarser grid system;
the discrete crack distribution simulation on a certain regridding system comprises the generation of a random simulation path, the establishment of a crack piece data event on a random simulation path, the search of a crack space configuration mode which is most similar to the data event on a training image, the search process considers the space scaling of the data event (as shown in figure 5), the searched crack distribution mode is assigned to a space to be simulated, the probability random path is updated according to the simulation result, and the simulation of the next area is executed along the probability random simulation path until the crack density probability distribution is met. In this example, the final simulation result is shown in fig. 6.
The difference between the method and the prior art is that a multi-point geostatistics method is adopted to directly simulate discrete cracks, instead of the cracks represented by pixel grids, or the cracks are simulated one by one in a group-by-group or crack piece-by-crack piece manner based on a point process point method; the training image is composed of discrete patches of cracks, rather than a grid of pixels; the data events consist of discrete patches of cracks, rather than a grid of pixels; the fracture space distribution configuration relation is represented by discrete fracture pieces instead of a pixel grid, multipoint geostatistics is applied to Discrete Fracture Network (DFN) modeling, the reservoir model can be simultaneously or sequentially realized and contains data from various sources, the form and seepage characteristics of each fracture are reflected, the spatial distribution of the fractures with different scales and the configuration relation among the fractures can be ensured, and the conventional fracture modeling method is difficult to realize.
Parts which are not described in the invention can be realized by adopting or referring to the prior art.
Although the embodiments of the present invention have been described above, the above descriptions are only for the convenience of understanding the present invention, and are not intended to limit the present invention. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A discrete fracture modeling method based on multi-point geostatistics is characterized by comprising the following steps:
step 1, establishing a discrete fracture training image based on a multi-scale fractal theory;
and 2, performing multipoint geostatistical simulation based on the crack pieces.
2. The method for discrete fracture modeling based on multi-point geostatistical as claimed in claim 1, wherein in step 1, comprising:
step 1.1, multi-scale fracture characteristic description;
step 1.2, representing the quantitative relation of crack scale characteristics;
and 1.3, establishing a multi-scale training image based on a multi-scale fractal theory.
3. The method of claim 2, wherein in step 2, the fracture patch-based multi-point geostatistical simulation comprises fracture patch characterization, fracture group similarity calculation, fracture patch-based data event scaling, multi-scale fracture patch spatial distribution pattern similarity calculation, and direct sampling-based discrete fracture network simulation.
4. The multi-point geostatistical-based discrete fracture modeling method of claim 2 or 3, wherein in step 1.1, multi-scale fracture characteristic study comprises integrating multiple scales and multiple types of data to quantitatively describe the occurrence and geometric characteristics of different-scale fractures.
5. The multi-point geostatistical-based discrete fracture modeling method of claim 4, wherein in step 1.2, fracture multi-scale feature quantitative relationship characterization includes analyzing spatial configuration features of fractures at different scales, and establishing quantitative relationships of fracture group, fracture density, geometric morphology, attitude with observation scale.
6. The discrete fracture modeling method based on multi-point geostatistics of claim 5, characterized in that, in the step 1.3, the establishment of the multi-scale fracture training image based on the multi-scale fractal theory comprises:
step 1.3.1, calculating a multi-scale fractal dimension based on the spatial configuration relationship of the cracks on different scales in the step 1.2;
and 1.3.2, establishing a multi-scale multi-group discrete fracture model according to a non-conditional simulation method based on the multi-scale fractal dimension obtained in the step 1.3.1, and establishing a training image of the multi-point geostatistical simulation.
7. The discrete fracture modeling method based on multi-point geostatistics of claim 6, characterized in that in step 1.3.1, the multi-scale fractal dimension calculation includes multi-scale fractal dimensions in respective characteristic scale ranges of core CT scanning, imaging logging, outcrop observation and seismic analysis, and interpolation and splicing of four multi-scale fractal dimensions, and finally, a continuous-scale fractal dimension from a core scale to a seismic scale is established.
8. The multi-point geostatistical-based discrete fracture modeling method of claim 7, wherein in step 1.3.2, the multi-scale discrete fracture unconditional simulation based on the multi-scale fractal dimension comprises calculating the variation degree and probability density of parameters such as fracture density, length, width, thickness, inclination angle, inclination and the like by taking the multi-scale fractal dimension as a scale, and performing multi-group system fracture synchronous simulation controlled by the fracture density as a probability, wherein the simulation of a single group of fractures adopts a point process method of discrete fracture pieces.
9. The multi-point geostatistical-based discrete fracture modeling method of claim 3, wherein the similarity calculation considerations for a fracture family include the size, density, dip, thickness of fracture patch, and rate of change of each parameter;
the data event scaling method based on the fracture piece comprises the steps of data event establishment based on the fracture piece and data event scaling based on fracture piece contraction and extension, wherein the data event establishment based on the fracture piece is composed of known fracture pieces in space, and the data event of the fracture piece contracts and extends by taking the center of each fracture piece as an origin;
the similarity calculation of the multi-scale fracture piece spatial distribution mode comprises the establishment of the multi-scale fracture piece spatial distribution mode and the similarity calculation of two groups of multi-scale fracture piece spatial distribution modes, wherein the multi-scale fracture piece spatial distribution mode is formed by combining a plurality of limited fracture piece spaces, and the similarity calculation of the multi-scale fracture piece spatial distribution mode is based on the similarity calculation of the fracture group;
the direct sampling based discrete fracture distribution simulation includes generating a multi-grid system from the fracture multi-scale features and performing a discrete fracture distribution simulation on each of the multi-grids.
10. The multi-point geostatistical-based discrete fracture modeling method of claim 9, wherein the direct sampling-based discrete fracture distribution simulation comprises:
generating and setting the number of multiple grids according to the multi-scale features of the cracks, simulating from the coarsest grid to the finest grid, and constraining the crack density distribution of the finer grid according to the simulation result of the coarser grid;
simulating discrete crack distribution on a certain heavy and thick grid comprises the steps of simulating generation of a random simulation path, establishing a crack sheet data event along the random simulation path, and searching a crack space distribution relation which is most similar to the data event in a training image;
and assigning the searched crack distribution space distribution mode to an area to be simulated, updating a probability random path according to a simulation result, and executing next area point simulation along the probability random simulation path until the crack density probability distribution is met.
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