CN113586019B - Fracturing optimization method and device for shale gas reservoir and computer storage medium - Google Patents
Fracturing optimization method and device for shale gas reservoir and computer storage medium Download PDFInfo
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Classifications
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- E—FIXED CONSTRUCTIONS
- E21—EARTH DRILLING; MINING
- E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B43/00—Methods or apparatus for obtaining oil, gas, water, soluble or meltable materials or a slurry of minerals from wells
- E21B43/25—Methods for stimulating production
- E21B43/26—Methods for stimulating production by forming crevices or fractures
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- E—FIXED CONSTRUCTIONS
- E21—EARTH DRILLING; MINING
- E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B49/00—Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells
Abstract
The disclosure provides a fracturing optimization method and device for a shale gas reservoir and a computer storage medium, and belongs to the field of shale gas exploration and development. The fracturing optimization method comprises the following steps: determining the bedding development strength of a shale gas reservoir section to be evaluated, wherein the bedding development strength refers to the degree of formation, development and existence of a layered structure which is generated by the change of rock along the vertical direction along the time change; determining the seam development strength of the shale gas reservoir section to be evaluated, wherein the seam development strength refers to the formation, development and existence degree of various seam changes with time due to uneven rock structure; and optimizing a fracturing scheme of the shale gas reservoir section to be evaluated based on the bedding development intensity and the bedding seam development intensity of the shale gas reservoir section to be evaluated.
Description
Technical Field
The disclosure relates to the field of shale gas exploration and development, in particular to a fracturing optimization method and device for shale gas reservoirs and a computer storage medium.
Background
In the petroleum field, fracturing (also called hydraulic fracturing) refers to a method for forming cracks in hydrocarbon reservoirs by utilizing the hydraulic action in the oil or gas extraction process. Fracturing is a means of stimulation modification of shale gas reservoirs. In order to obtain good reservoir fracturing effect and avoid blind fracturing, the fracturing process parameters (including the fracturing fluid consumption and the perforation number) of the shale gas reservoir must be explored first.
In the related art, fracturing process parameters of shale gas reservoirs are mostly analyzed based on logging data (including rock tensile strength, pore pressure, horizontal minimum ground stress, etc.).
Along with the strategic transition of shale gas exploration and development from shallow to deep, shale gas hydraulic fracturing faces the difficult problems of high stress, deep burial and other complex geological conditions, and the fracturing optimization method provided by the related technology has poor application effect under the complex geological conditions.
Disclosure of Invention
The embodiment of the disclosure provides a fracturing optimization method and device for a shale gas reservoir and a computer storage medium, which can adapt to the optimization of a fracturing scheme of the shale gas reservoir under complex geological conditions. The technical scheme is as follows:
in one aspect, a fracturing optimization method of a shale gas reservoir is provided, the fracturing optimization method comprising:
determining the bedding development strength of a shale gas reservoir section to be evaluated, wherein the bedding development strength refers to the degree of formation, development and existence of a layered structure which is generated by the change of rock along the vertical direction along the time change;
determining the seam development strength of the shale gas reservoir section to be evaluated, wherein the seam development strength refers to the formation, development and existence degree of various seam changes with time due to uneven rock structure;
And optimizing a fracturing scheme of the shale gas reservoir section to be evaluated based on the bedding development intensity and the bedding seam development intensity of the shale gas reservoir section to be evaluated.
Optionally, the determining the bedding development strength of the shale gas reservoir section to be evaluated includes:
acquiring the weight of physical property data relative to the bedding development intensity, wherein the physical property data refer to data related to the appearance and the property of an object;
acquiring physical property data of the shale gas reservoir section to be evaluated;
and calculating the bedding development intensity of the shale gas reservoir section to be evaluated based on the weight of the physical property data relative to the bedding development intensity and the physical property data of the shale gas reservoir section to be evaluated.
Optionally, the acquiring the weight of the physical property data relative to the bedding development intensity comprises:
acquiring images of rock slices of each coring well sample, wherein the coring well samples are rock samples in the coring wells of the wells where the shale gas reservoir sections to be evaluated are located;
determining the bedding development strength of each cored well sample based on the image of the rock laminate of each cored well sample;
obtaining physical property data of each coring well sample;
and determining the weight of the physical property data relative to the bedding development intensity based on the bedding development intensity of each core sample and the physical property data of each core sample.
Optionally, the determining the bedding development strength of each cored well sample based on the image of the rock slice of each cored well sample comprises:
determining the number of layers of each cored well sample based on the image of the rock laminate of each cored well sample;
determining a pixel mean value of the image of the rock slice of each cored well sample after graying based on the image of the rock slice of each cored well sample;
and calculating the bedding development intensity of each coring well sample based on the number of layers of each coring well sample and the pixel average value of the image of the rock slice of each coring well sample after graying.
Optionally, the determining the number of layers of each cored well sample based on the image of the rock slice of each cored well sample includes:
according to the size of the target grid, carrying out grid division on the images of the rock slices of each coring well sample;
graying the image of the rock slice of each cored well sample;
counting the number of unit grids with pixel values larger than a target threshold value in each row of unit grids after graying, wherein the unit grids in the same row are parallel to the extending direction of a single rock stratum;
and when the number of the unit grids with the pixel values of two adjacent rows larger than the target threshold value is larger than and smaller than the target number respectively, adding one to the number of the layers of the corresponding sample.
Optionally, the determining the seam development strength of the shale gas reservoir section to be evaluated includes:
acquiring the weight of the physical property data relative to the seam formation strength;
acquiring physical property data of the shale gas reservoir section to be evaluated;
and calculating the seam development intensity of the shale gas reservoir section to be evaluated based on the seam development intensity of the shale gas reservoir section to be evaluated, the weight of the physical property data relative to the seam development intensity and the physical property data of the shale gas reservoir section to be evaluated.
Optionally, the obtaining the weight of the physical property data relative to the seam formation strength of the layer includes:
acquiring images of rock slices of each coring well sample, wherein the coring well samples are rock samples in the coring wells of the wells where the shale gas reservoir sections to be evaluated are located;
determining the seam development intensity of each cored well sample based on the image of the rock slice of each cored well sample;
obtaining physical property data of each coring well sample;
and determining the weight of the physical data relative to the bedding seam development intensity based on the bedding seam development intensity of the shale gas reservoir section to be evaluated, the bedding seam development intensity of each coring well sample and the physical data of each coring well sample.
Optionally, the optimizing the fracturing scheme of the shale gas reservoir section to be evaluated based on the bedding development intensity and the bedding seam development intensity of the shale gas reservoir section to be evaluated includes:
calculating the natural weakness index of the shale gas reservoir section to be evaluated based on the bedding development intensity index and the bedding seam development intensity of the shale gas reservoir section to be evaluated;
when the natural weak surface index is lower than a first target value, enhancing the fracturing degree corresponding to the fracturing scheme of the shale gas reservoir section to be evaluated;
when the natural weak surface development intensity is higher than the first target value and lower than a second target value, maintaining the fracturing degree corresponding to the fracturing scheme of the shale gas reservoir section to be evaluated, wherein the first target value is smaller than the second target value;
and when the natural weak surface development intensity is higher than the second target value, weakening the fracturing degree corresponding to the fracturing scheme of the shale gas reservoir section to be evaluated.
In a second aspect, there is provided a fracture optimization apparatus for a shale gas reservoir, the fracture optimization apparatus comprising:
the first determining module is used for determining the bedding development strength of the shale gas reservoir section to be evaluated, wherein the bedding development strength refers to the degree of formation, development and existence of a layered structure change along the vertical direction generated by the rock change along the time;
The second determining module is used for determining the seam development intensity of the shale gas reservoir section to be evaluated, wherein the seam development intensity refers to the formation, development and existence degree of various seam changes with time due to uneven rock structure;
and the evaluation module is used for optimizing the fracturing scheme of the shale gas reservoir section to be evaluated based on the bedding development intensity and the bedding seam development intensity of the shale gas reservoir section to be evaluated.
In a third aspect, there is provided a fracture optimization apparatus for a shale gas reservoir, the fracture optimization apparatus comprising: a memory, a processor and a computer program stored in the memory and executable on the processor, the processor being configured to implement the aforementioned method of fracture optimization of a shale gas reservoir when the computer program is executed.
In a fourth aspect, a computer storage medium having stored therein at least one instruction loaded and executed by a processor to implement the foregoing method of fracture optimization of a shale gas reservoir is provided.
The technical scheme provided by the embodiment of the disclosure has the beneficial effects that:
the development intensity of the natural weak surface can be represented by determining the bedding development intensity and the bedding seam development intensity of the shale gas reservoir section to be evaluated, and the geological recognition of complex geological conditions (high stress, deep burial and the like) can be accurately realized because the natural weak surface is an important feature of geology; based on the bedding development intensity and the bedding seam development intensity of the shale gas reservoir section to be evaluated, the fracturing scheme of the shale gas reservoir section to be evaluated is optimized, and the fracturing operation efficiency and the yield increasing effect are improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings required for the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present disclosure, and other drawings may be obtained according to these drawings without inventive effort for a person of ordinary skill in the art.
FIG. 1 is a flow chart of a fracturing optimization method for a shale gas reservoir provided by an embodiment of the present disclosure;
FIG. 2 is a flow chart of a fracturing optimization method for a shale gas reservoir provided by an embodiment of the present disclosure;
FIG. 3 is a schematic diagram of an image processing flow provided by an embodiment of the present disclosure;
fig. 4 is a block diagram of a fracturing optimization apparatus for a shale gas reservoir provided in an embodiment of the present disclosure;
fig. 5 is a block diagram of a fracturing optimization apparatus for a shale gas reservoir provided in an embodiment of the present disclosure.
Detailed Description
For the purposes of clarity, technical solutions and advantages of the present disclosure, the following further details the embodiments of the present disclosure with reference to the accompanying drawings.
First, explanation of nouns involved in the embodiments of the present invention will be described.
The weak surface is a stress weak surface, and is a surface that is easily broken and slipped when subjected to stress.
Bedding refers to the layered structure produced by the variation of rock in the vertical direction.
The intensity of the development of a layer refers to the extent to which the layer is formed, developed and present over time.
The layer seams, also called inter-layer seams, refer to various intra-layer seams formed by the uneven rock structure.
The development strength of the layer seam refers to the degree of formation, development and presence of the layer seam over time.
Physical data, which refers to data related to the appearance and properties of the object, for example, physical data of shale gas reservoir sections includes siliceous mineral content, carbonate content, pyrite content, and total organic carbon (Total Organic Carbon, abbreviated TOC) content.
In order to meet the shale gas reservoir evaluation under complex geological conditions (high stress, deep burial and the like), high-precision geological knowledge is required to be realized, and the geological knowledge comprises various rules of determining development characteristics of natural weaknesses (including development strength of the natural weaknesses), fracture mechanics of intersection of natural cracks and hydraulic cracks and the like. Development characteristics of natural weaknesses have not been effectively predicted and identified. For example, as a natural weakness, no explicit numerical calculation method has yet achieved quantitative characterization, making analysis of the rules of the layer (morphology of the developmental intensity of the layer in the longitudinal direction) more delayed. Moreover, for natural fractures, current geophysical means only can predict natural fracture zones of 20m and above, but natural fractures of meter-scale or meter-scale below that actually intersect hydraulic fractures are almost ineffective in achieving prediction or characterization. Based on the above, in the fracturing optimization method of the shale gas reservoir, the development intensity (including the bedding development intensity and the bedding seam development intensity) of the natural weakness in the shale gas reservoir can be effectively represented, and the fracturing optimization method is significant in the aspects of fracturing design optimization and the like.
Fig. 1 is a flow chart of a fracturing optimization method for a shale gas reservoir provided by an embodiment of the present disclosure. Referring to fig. 1, the fracture optimization method flow comprises the following steps.
And 101, determining the bedding development strength of the shale gas reservoir section to be evaluated.
The shale gas reservoir segments to be evaluated may be respective shale gas reservoir segments of a horizontal segment in a shale gas horizontal well. Wherein fracturing is one of well modification means for horizontal sections in shale gas horizontal wells.
And 102, determining the seam development strength of the shale gas reservoir section to be evaluated.
And 103, optimizing a fracturing scheme for evaluating the shale gas reservoir section to be evaluated based on the bedding development intensity and the bedding seam development intensity of the shale gas reservoir section to be evaluated.
In the embodiment, the bedding development intensity and the bedding seam development intensity of the shale gas reservoir section to be evaluated are determined, so that the bedding development intensity and the bedding seam development intensity can represent the development intensity of a natural weak face, and the geological recognition of complex geological conditions (high stress, deep burial and the like) can be accurately realized because the natural weak face is an important feature of geology; based on the bedding development intensity and the bedding seam development intensity of the shale gas reservoir section to be evaluated, the fracturing scheme of the shale gas reservoir section to be evaluated is optimized, and the fracturing operation efficiency and the yield increasing effect are improved.
Fig. 2 is a flow chart of a fracturing optimization method for a shale gas reservoir provided by an embodiment of the present disclosure. Referring to fig. 2, the fracture optimization method flow includes the following steps.
And 201, acquiring physical property data of a shale gas reservoir section to be evaluated.
The physical property data of the shale gas reservoir section to be evaluated comprise siliceous mineral content, carbonate content, pyrite content and total organic carbon (Total Organic Carbon, abbreviated TOC) content of the shale gas reservoir section to be evaluated. The siliceous mineral content may be equal to the sum of the quartz content and the feldspar content and the carbonate content may be equal to the sum of the calcite content and the dolomite content.
Physical property data of the shale gas reservoir section to be evaluated can be obtained from logging interpretation data of the shale gas reservoir section to be evaluated.
And 202, obtaining the weight of the physical property data relative to the layer seam development intensity and the weight of the physical property data relative to the layer seam development intensity.
In this example, the weight is the importance of the physical property data with respect to the layer strength or the layer seam strength. Alternatively, the present embodiment does not limit the determination manner of the weight, and the weight may be preset in the computer by an engineer according to experience, or may be calculated by the computer through data of a large number of samples.
The embodiment provides a mode of calculating the weight of physical property data relative to the bedding development intensity and the weight of physical property data relative to the bedding seam development intensity through a large number of coring well samples, wherein the coring well samples can be the coring well rock samples of the well region where the shale gas reservoir section to be evaluated is located. Typically, there will be at least 1 well with 2-5 wells. The coring well samples are typically taken along the depth of the coring well, and different coring well samples may correspond to different depths.
The manner of calculating the weights by coring a well sample may include the following steps.
And step one, obtaining physical property data of each coring well sample.
Physical property data of the core well samples include siliceous mineral content, carbonate content, pyrite content, and total organic carbon content of the corresponding samples.
Physical property data of each core well sample can be obtained from the X-ray diffraction test data and the physical property test data of each core well sample. X-ray diffraction test data of each coring well sample comprise the proportion (content) of mineral components such as quartz, feldspar, calcite, dolomite, pyrite, clay and the like; physical property test data for each cored well sample includes porosity and total organic carbon content.
And a second step of acquiring images of rock laminates of each cored well sample.
In the second step, first, rock slices taken from each core well rock sample (sample) are collected, the slices being vertically sliced, i.e. vertical slices can be displayed, the slices after preparation ensuring that the lithology changes are identified in relation to the microcrack development characteristics (slices and depositions, each layer representing a formation of a geologic age, with the general fracture developing along the layer, the cutting in this direction being easily observable for the fracture).
Alternatively, a slice of each sample of at least one complete cored well may be selected for weight analysis, wherein a slice may be taken from one cored well sample.
And secondly, observing the vertical thin slice (any surface) by adopting a single polarization microscope, and unifying images to screen capture under the scale of 500 mu m to obtain the images of the corresponding rock slices.
For facilitating the identification of the number of strata and the unification of the pixel mean references hereinafter, the image size of each rock slice is unification of size, for example 2400 pixels x 1800 pixels, the long axis is the layering direction (the extending direction of the single strata) and the short axis is the vertical evolution direction of the rock. The image size can be preset under a lamella mirror.
And thirdly, determining the weight of the physical property data relative to the bedding development intensity based on the physical property data of each core sample and the image of the rock slice of each core sample.
The third step may include the following steps.
And step 1, determining the bedding development strength of each core well sample based on the image of the rock slice of each core well sample.
The bedding development strength of the coring well sample may characterize the bedding development strength of a shale gas reservoir at a depth at which the coring well sample is located.
Step 1 may include the following steps.
Step 11, determining the number of layers of each core sample based on the image of the rock slice of each core sample.
Step 11 may include the following steps.
Step 11a, according to the target mesh size, performing mesh division on the image of the rock slice of each coring well sample.
The mesh is divided to reduce the amount of calculation and improve the calculation efficiency. The single grid size may be 60 pixels by 60 pixels, with dimensions equivalent to 0.125mm by 0.125mm, for 2400 pixel by 1800 pixel images, the major and minor axes of each image include 40 and 30 grids, respectively.
Step 11b, graying the image of the rock slice of each cored well sample.
To reduce the effect of microcracks, the mirror-identified microcracks may be wiped off before graying the image of the rock slice of each cored well sample, and then filled with mineral tones that contact the original fracture walls. Then, the image is grayed.
And 11c, counting the number of the unit grids with pixel values larger than a target threshold value in each row of unit grids after graying, wherein the arrangement direction of the unit grids in the same row is parallel to the extending direction of the single rock stratum.
When the number of the unit grids of which the pixel values are larger than the target threshold value in the two adjacent rows is larger than and smaller than the target number (the number of the unit grids of which the pixel values are larger than the target threshold value in one of the two adjacent rows is larger than the target number, and the number of the unit grids of which the pixel values are larger than the target threshold value in the other row is smaller than the target number), respectively, the number of the layers of the corresponding sample is increased by one.
Step 11c includes:
first, the image after gradation is converted into a black-and-white image.
The conversion mode may include: setting 128 pixels as a target threshold, equivalent a grid with pixels larger than 128 after graying to a grid with pixels 255 (white), and equivalent a grid with pixels smaller than 128 after graying to a grid with pixels 0 (black), and acquiring an image with only black and white grids.
In the image after gradation, the pixel value of the bright white grid is 255, the pixel value of the black grid is 0, and the pixel value of the gray grid is an intermediate value between 0 and 255. When comparing with the target threshold value, the average value of all the pixels included in the cell grid may be compared with the target threshold value, or any one of the pixels may be acquired from the cell grid and the acquired pixel value may be compared with the target threshold value. Preferably, any one pixel is acquired from the cell grid and the acquired pixel value is compared with the target threshold value, so that the calculation efficiency can be improved.
Next, the number of black grids in each row of the black and white image is determined.
When the number of the long-axis black grids in a certain row is more than 20, the black carbon and clay layers are considered as the behavior, otherwise, the black carbon and clay layers are white brittle layers. When the adjacent rows of long-axis black grids are larger than 20, black carbon and clay layers are continuously deposited according to the rows, and otherwise, white brittle layers are formed. When two adjacent rows of long-axis black grids are respectively larger than 20 and smaller than 20, the number of read tattoos is increased by 1 layer according to the deposition environment change surface between the two rows, so that the number of tattoos in the longitudinal unit thickness (0.125 mm multiplied by 30=3.75 cm) of the picture is obtained as a unified unit, and finally the number of tattoos in the standard unit thickness (1 m) is integrated as a result.
Step 12, determining the pixel mean value of the image of the rock slice of each coring well sample after graying based on the image of the rock slice of each coring well sample.
The pixel average value is the pixel average value, and the higher the pixel average value is, the higher the proportion of the bright white layer is, namely the high content of brittle components, and the lower the proportion of the black layer is, namely the high content of carbon and clay components. Alternatively, after the image is grayed (step 11 b), the pixel values of each unit grid in the whole image may be read, and the average value thereof is taken as the average value of the pixels of the corresponding sample of the image.
And step 13, calculating the bedding development intensity of each coring well sample based on the number of layers of each coring well sample and the pixel average value of the image of the rock slice of each coring well sample after graying.
Multiplying the number of layers of each coring well sample by the pixel mean value of the image of the rock slice of the corresponding coring well sample after graying to obtain the bedding development intensity of each coring well sample.
And step 2, determining the weight of the physical property data relative to the bedding development intensity based on the bedding development intensity of each core well sample and the physical property data of each core well sample.
Weight relations between different physical property data and the bedding development intensity are established in advance so as to characterize the bedding development intensity. The weight relationship is shown in equation (1).
L o P o ＝a 1 f Si +b 1 f Ca +c 1 f Py +d 1 f Toc +f 1 (1)
Wherein:
L o -number of layers, layers/m;
P o -pixel mean value, 0-255;
f Si 、f Ca 、f Py 、f Toc -siliceous (quartz + feldspar) content, carbonate (calcite + dolomite) content, pyrite content, total organic carbon content,%;
a 1 、b 1 、c 1 、d 1 、f 1 -weight coefficients related to the intensity of bedding development.
The data of all the coring well rock slices collected by the well region are expressed according to equation (1), and the weight a is carried out by combining the equation (1) of each coring well rock slice 1 、b 1 、c 1 、d 1 、f 1 Is calculated by the computer.
And fourthly, determining the weight of the physical property data relative to the bedding seam development intensity based on the bedding development intensity of the shale gas reservoir section to be evaluated, the physical property data of each coring well sample and the image of the rock slice of each coring well sample.
The fourth step may include the following steps.
And step A, determining the seam formation strength of each core well sample based on the image of the rock slice of each core well sample.
The layer seam development strength comprises the number of cracks and the width of the cracks, and the step A can comprise the following steps: based on the image of the rock slice for each cored well sample, the number of cracks and the width of the cracks for each cored well sample are determined.
The vertical thin sections of the rock were observed under a single polarization microscope, the number of cracks per unit thickness (0.125 mm×30=3.75 cm) was counted for the observation of the width of the cracks (the cracks were clearly visible under the microscope due to the large magnification of the microscope) for the aforementioned pictures of untreated microcracks (without wiping off treatment), and the number of cracks was integrated into the number of cracks per unit thickness (1 m) as a result. Alternatively, the slit width is 20 to 50 μm at most under the mirror.
The number of cracks and the width of the cracks of each core well sample can be read in advance and set in a computer.
And B, determining the weight of the physical data relative to the bedding seam development intensity based on the bedding seam development intensity of the shale gas reservoir section to be evaluated, the bedding seam development intensity of each core-taking well sample and the physical data of each core-taking well sample.
And (3) pre-establishing weight relations among different physical property data, the bedding development intensity and the bedding seam development intensity to characterize the bedding seam development intensity, wherein the weight relations are shown in an equation (2).
F o W o ＝a 2 f Si +b 2 f Ca +c 2 f Py +d 2 f Toc +e 2 L o P o +f 2 (2)
Wherein:
F o -number of cracks, bars/m;
W o crack width, mm;
L o -number of layers, layers/m;
P o -pixel mean value, 0-255;
f Si 、f Ca 、f Py 、f Toc -siliceous (quartz + feldspar) content, carbonate (calcite + dolomite) content, pyrite content, total organic carbon content,%;
a 2 、b 2 、c 2 、d 2 、f 2 -a weight coefficient related to the strength of the layer seam development.
The data of all the coring well rock slices collected by the well region are expressed according to equation (2), and the weight a is performed by combining equation (2) of each coring well rock slice 2 、b 2 、c 2 、d 2 And f 2.
And 203, calculating the bedding development intensity of the shale gas reservoir section to be evaluated based on the weight of the physical property data relative to the bedding development intensity and the physical property data of the shale gas reservoir section to be evaluated.
And respectively substituting the weight of the physical property data relative to the bedding development intensity and the physical property data of the shale gas reservoir section to be evaluated into an equation (1), and calculating to obtain the bedding development intensity of the shale gas reservoir section to be evaluated.
And 204, calculating the seam development intensity of the shale gas reservoir section to be evaluated based on the weight of the physical property data relative to the seam development intensity of the layer and the physical property data of the shale gas reservoir section to be evaluated.
And respectively substituting the weight of the physical property data relative to the seam development intensity of the layers and the physical property data of the shale gas reservoir section to be evaluated into equation (2), and calculating to obtain the seam development intensity of the shale gas reservoir section to be evaluated.
And 205, calculating the natural weakness index of the shale gas reservoir section to be evaluated based on the bedding development intensity and the bedding seam development intensity of the shale gas reservoir section to be evaluated.
Step 205 may include the following steps.
And 205a, determining a bedding development intensity index based on the bedding development intensity of the shale gas reservoir section to be evaluated.
The bedding development intensity index is calculated according to the following equation (3).
Wherein:
L n -lamellar developmental intensity, dimensionless;
L o P o -the strength of the bedding development of the shale gas reservoir section to be evaluated, (L) o P o ) max 、(L o P o ) min -maximum, minimum value of the bedding development intensity calculated on the basis of the image of each rock slice, layer/m.
And 205b, determining a seam development intensity index based on the seam development intensity of the shale gas reservoir section to be evaluated.
The layer seam development strength index is calculated according to the following equation (4).
Wherein:
F n -layer seam development intensity, dimensionless;
F o W o -to evaluate the formation seam development strength of the shale gas reservoir section, (F) o W o ) max 、(F o W o ) min -maximum and minimum values of the seam development intensity of the layers calculated on the basis of the images of the individual rock lamellae, bars mm/m.
And 205c, calculating the natural weak surface index of the shale gas reservoir section to be evaluated based on the bedding development intensity index and the bedding seam development intensity.
The natural weakness index can be calculated as in equation (5) below.
Wherein:
I n -natural weak face index, dimensionless.
And 206, optimizing a fracturing scheme of the shale gas reservoir section to be evaluated based on the natural weakness index of the shale gas reservoir section to be evaluated.
Step 206 may include: when the natural weakness index is lower than a first target value, enhancing the fracturing degree corresponding to the fracturing scheme of the shale gas reservoir section to be evaluated; when the natural weak face development intensity is higher than a first target value and lower than a second target value, maintaining the fracturing degree corresponding to the fracturing scheme of the shale gas reservoir section to be evaluated, wherein the first target value is smaller than the second target value; and when the natural weak face development intensity is higher than the second target value, weakening the fracturing degree corresponding to the fracturing scheme of the shale gas reservoir section to be evaluated.
In addition, when the natural weakness index is equal to the first target value, the fracturing degree corresponding to the fracturing scheme of the shale gas reservoir section to be evaluated can be enhanced, and the fracturing degree corresponding to the fracturing scheme of the shale gas reservoir section to be evaluated can be maintained; when the natural weak face development strength is equal to the second target value, the fracturing degree corresponding to the fracturing scheme of the shale gas reservoir section to be evaluated can be maintained, and the fracturing degree corresponding to the fracturing scheme of the shale gas reservoir section to be evaluated can be weakened.
Alternatively, the fracturing scheme of the shale gas reservoir section to be evaluated refers to the shallow (< 3500 m) and deep (> 3500 m) shale gas reservoir fracturing bulk process. Shallow (< 3500 m) and deep (> 3500 m) shale gas reservoir fracturing body processes can be obtained from a single well fracturing design book that has been subjected to fracturing construction near the well.
Illustratively, the first target value is 0.4 and the second target value is 0.6, when I n <0.4, representing that the natural weak surface development intensity of the shale gas reservoir section is lower, increasing the liquid consumption of the fracturing fluid, increasing the number of perforation holes and implementing temporary plugging steering technology on the basis of the shale gas reservoir fracturing main process with corresponding depth to promote the maximum steering expansion of water conservancy cracks, so as to form a larger reservoir reconstruction volume; when 0.4 <I n <0.6, the development degree of the natural weak surface is high, and the shale gas reservoir fracturing main process corresponding to the depth can be matched at the moment to realize more sufficient transformation; when I n >When 0.6, the development degree of the natural weak surface is too high, the fluid loss behavior of the fracturing fluid towards the weak surface is increased, the construction risk is increased, the perforation hole number can be reduced on the basis of the shale gas reservoir fracturing main body process with the depth corresponding to the perforation hole number, the sand is carried by the high concentration of the glue solution and the sand is carried by the low concentration of the slickwater in a stage pumping manner, effective support and effective seam making are respectively realized, more fracturing fluid is ensured to be used for making new seams, the fluid efficiency is increased, and the sand blocking and pressure abnormal risks are reduced.
The shale gas reservoir fracturing main process corresponding to the depth of the shale gas reservoir section to be evaluated is described by taking a shallow (< 3500 m) shale gas reservoir fracturing main process as an example.
The main process of the shallow shale gas reservoir comprises the following steps: the length of a single section is 60-80 m, the number of clusters is 3 clusters, and the single-section fracturing fluid is 1800m 3 The low-viscosity slick water is taken as the main material, the single-section sand amount is 120t, wherein, the quartz sand with 70/140 meshes accounts for 36t, and the ceramsite with 40/70 meshes accounts for 84t, the construction discharge capacity is 12-14 m 3 And/min, wherein the number of perforation holes in a single section is 36-40 holes.
When I n <And when the first target value (such as 0.4) represents that the natural weak surface development intensity of the shale gas reservoir section is lower, the fracturing degree corresponding to the fracturing scheme of the shale gas reservoir section to be evaluated is enhanced. Illustratively, the large fracturing fluid usage is increased to 2000m 3 Increasing the number of perforation holes to 40-48 holes, and implementing temporary plugging steering technology to promote steering expansion of water conservancy cracks to the greatest extent, so as to form a larger reservoir reconstruction volume.
When the first target value (e.g. 0.4)<I n <And when the second target value (such as 0.6) represents that the natural weak face development degree is high, the fracturing degree corresponding to the fracturing scheme of the shale gas reservoir section to be evaluated is kept. Illustratively, fracturing is performed according to a shallow or deep shale gas reservoir body process.
When I n >And at a second target value (such as 0.6), representing that the natural weakness development degree is too high, weakening the fracturing degree corresponding to the fracturing scheme of the shale gas reservoir section to be evaluated. By way of example, the number of perforation holes is reduced to 30, sand is carried by the glue solution in high concentration and sand is carried by the slickwater in low concentration, and stage mixing and pumping are carried out, so that effective support and effective seam making are respectively realized, more fracturing fluid is ensured to be used for making new seams, the liquid efficiency is increased, and the risks of sand blockage and pressure abnormality are reduced.
The steps 205-206 realize that the fracturing scheme of the shale gas reservoir section to be evaluated is optimized based on the bedding development intensity and the bedding seam development intensity of the shale gas reservoir section to be evaluated.
An example application scenario of the fracturing optimization method of the shale gas reservoir provided by the embodiment is described below.
Taking an A well as an example, taking the A well as a coring well, acquiring X diffraction test data and physical property test data, wherein the method comprises the following steps: 1) The proportion of mineral components such as quartz, feldspar, calcite, dolomite, pyrite, clay and the like; 2) Total organic carbon content; and collecting the rock laminate.
And 2, sorting the sheets collected by the well A, grinding the sheets into longitudinal sections by high-density sampling, carrying out 2400-pixel x 1800-pixel image size processing, 60-pixel x 60-pixel unit grid division, micro-crack smearing processing, full mirror image gray level processing, unit grid pixel value reading, threshold judgment, dividing all grids into black and white, finally identifying the variable pictures of the layers, reading the number of the layers, and converting the number of the layers into the number of the layers with the thickness of 1 m. The processing procedure is shown in fig. 3 (the middle position of fig. 3 is marked with a broken line to rough mark the boundary of the tattoos in the image after graying).
And 3, calculating weights of all the data of the coring well rock slices collected in the research area, wherein the weight formula of the well area where the well A is located is shown as an equation (1). Alternatively, a is calculated 1 、b 1 、c 1 、d 1 、f 1 The method comprises the following steps of: 0.48, 2.07, 0.12, 0.32, 42.
And 4, taking an A well as an example, identifying the number of micro-cracks and the width of the cracks of the thin sheet which are ground by the well core, and carrying out 1m thickness calculation on the number of the micro-cracks, wherein the width of the cracks is 20-50 mu m under a mirror.
Step 5, characterizing the seam development intensity, weighting all the data of the coring well rock slices collected by the research area according to equation (2), optionally, calculating a 2 、b 2 、c 2 、d 2 、f 2 The method comprises the following steps: 0.34, 1.41, 0.07, 2.2, 0.32, 55.
And 6, establishing natural weak face evaluation indexes and optimizing fracturing process parameters. Taking the B well as an example, the B well is a well zone of the A well, and physical parameters of the B well are shown in the following table 1.
TABLE 1
| Fracturing segment (segment number) | Carbonate rock (%) | Siliceous mineral (%) | Pyrite (%) | TOC(％) |
| 1 | 11.2 | 58.6 | 0.2 | 2.5 |
| 2 | 12.6 | 52.6 | 0.4 | 3.2 |
| 3 | 12.8 | 53.4 | 0.6 | 4.4 |
| 4 | 13.5 | 51.5 | 0.5 | 3.5 |
| 5 | 14.6 | 65.5 | 0.4 | 3.4 |
| 6 | 15.4 | 61.6 | 0.2 | 3.2 |
| 7 | 16.1 | 61.4 | 0.3 | 2.8 |
| 8 | 13.1 | 58.4 | 0.3 | 3.6 |
| 9 | 12.8 | 62.3 | 0.5 | 2.7 |
| 10 | 12.9 | 66.5 | 0.5 | 2.5 |
| 11 | 11.2 | 62.5 | 0.6 | 3.1 |
| 12 | 10.5 | 63.5 | 0.4 | 2.8 |
| 13 | 9.7 | 61.5 | 0.4 | 3.4 |
| 14 | 8.2 | 54.4 | 0.3 | 4.1 |
| 15 | 7.7 | 56.2 | 0.5 | 4.6 |
| 16 | 6.5 | 57.8 | 0.6 | 4.2 |
| 17 | 4.3 | 56.3 | 0.4 | 4.5 |
| 18 | 5.7 | 55.4 | 0.5 | 5.2 |
| 19 | 6.2 | 61.6 | 0.6 | 4.8 |
| 20 | 6.1 | 60.5 | 0.5 | 5.4 |
| 21 | 6.4 | 57.7 | 0.4 | 6.1 |
| 22 | 6.1 | 56.9 | 0.5 | 6.3 |
| 23 | 7.3 | 57.6 | 0.6 | 5.7 |
| 24 | 6.4 | 55.5 | 0.6 | 5.9 |
| 25 | 6.2 | 56.2 | 0.6 | 5.6 |
The weights obtained for well a were applied to well B in combination with equations (1) - (5) to obtain the various calculated parameters, see table 2 below.
TABLE 2
According to the calculation comparison of each shale gas reservoir section of the well B, for sections 1 to 13, the natural weak face evaluation index is 0.4 to 0.6, so that the construction is carried out along with the fracturing design process parameters; for the sections 14 to 25, the natural weak face evaluation index is smaller than 0.4, so the development degree of the natural weak face is low, and the hydraulic fracture maximum steering expansion is promoted by increasing the liquid consumption of the fracturing fluid, increasing the number of perforation holes and implementing temporary plugging steering technology.
In the embodiment, the bedding development intensity and the bedding seam development intensity of the shale gas reservoir section to be evaluated are determined, so that the bedding development intensity and the bedding seam development intensity can represent the development intensity of a natural weak face, and the geological recognition of complex geological conditions (high stress, deep burial and the like) can be accurately realized because the natural weak face is an important feature of geology; based on the bedding development intensity and the bedding seam development intensity of the shale gas reservoir section to be evaluated, the fracturing scheme of the shale gas reservoir section to be evaluated is optimized, and the fracturing operation efficiency and the yield increasing effect are improved.
In addition, based on experimental analysis data and logging while drilling interpretation data obtained by conventional interpretation means, objective rules are respected, correlations and weight coefficients of various factors related to natural weak surface formation and development and natural weak surface development scale are summarized, a non-equivalent linear regression equation is established, evaluation and characterization of natural weak surface development intensity are realized, an optimized basis is provided for shale gas horizontal well fracturing schemes and designs, an original real-time adjustment means based on artificial experience is improved, natural weak surface development scale near a shaft can be accurately identified before fracturing, and optimization of fracturing scheme design has a quantitative effect.
Fig. 4 is a block diagram of a fracturing optimization apparatus for a shale gas reservoir provided in an embodiment of the disclosure, and referring to fig. 4, the fracturing optimization apparatus includes: a first determination module 401, a second determination module 402, and an evaluation module 403.
The first determining module 401 is used for determining that the bedding development of the shale gas reservoir section to be evaluated is strong.
A second determination module 402 is configured to determine a seam development strength of the shale gas reservoir interval to be evaluated.
The evaluation module 403 is configured to optimize a fracturing scheme of the shale gas reservoir section to be evaluated based on the bedding development intensity and the bedding seam development intensity of the shale gas reservoir section to be evaluated.
Optionally, the first determining module 401 is configured to obtain a weight of the physical property data relative to the bedding development intensity; acquiring physical property data of a shale gas reservoir section to be evaluated; and calculating the bedding development intensity of the shale gas reservoir section to be evaluated based on the weight of the physical data relative to the bedding development intensity and the physical data of the shale gas reservoir section to be evaluated.
Optionally, the first determining module 401 is configured to obtain an image of a rock slice of each coring well sample, where the coring well sample is a rock sample in a coring well of a well zone where the shale gas reservoir section to be evaluated is located; determining the bedding development strength of each cored well sample based on the image of the rock laminate of each cored well sample; obtaining physical property data of each coring well sample; and determining the weight of the physical property data relative to the bedding development intensity based on the bedding development intensity of each core sample and the physical property data of each core sample.
Optionally, the first determining module 401 is configured to determine the number of layers of each core sample based on the image of the rock slice of each core sample; determining a pixel mean value of the image of the rock slice of each cored well sample after graying based on the image of the rock slice of each cored well sample; and calculating the bedding development intensity of each coring well sample based on the number of layers of each coring well sample and the pixel average value of the image of the rock slice of each coring well sample after graying.
Optionally, the first determining module 401 is configured to grid the image of the rock slice of each cored well sample according to the target grid size; graying the image of the rock slice of each cored well sample; counting the number of unit grids with pixel values larger than a target threshold value in each row of unit grids after graying, wherein the unit grids in the same row are parallel to the extending direction of a single rock stratum; and when the number of the unit grids with the pixel values of two adjacent rows larger than the target threshold value is larger than and smaller than the target number respectively, adding one to the number of the layers of the corresponding sample.
Optionally, the second determining module 402 is configured to obtain a weight of the physical property data relative to the seam formation strength of the layer; acquiring physical property data of a shale gas reservoir section to be evaluated; and calculating the seam development strength of the shale gas reservoir section to be evaluated based on the seam development strength of the shale gas reservoir section to be evaluated, the weight of the physical property data relative to the seam development strength and the physical property data of the shale gas reservoir section to be evaluated.
Optionally, the second determining module 402 is configured to obtain an image of a rock slice of each cored well sample, where the cored well sample is a cored well rock sample in a well zone where the shale gas reservoir section to be evaluated is located; determining the seam development intensity of each cored well sample based on the image of the rock slice of each cored well sample; obtaining physical property data of each coring well sample; and determining the weight of the physical data relative to the bedding seam development intensity based on the bedding seam development intensity of the shale gas reservoir section to be evaluated, the bedding seam development intensity of each coring well sample and the physical data of each coring well sample.
Optionally, the second determining module 402 is configured to determine a number of cracks and a width of cracks for each of the cored well samples based on the image of the rock slice for each of the cored well samples; and calculating the bedding joint development strength of each core well sample based on the number of cracks and the width of the cracks of each core well sample.
Optionally, the evaluation module 403 is configured to calculate a natural weak surface index of the shale gas reservoir section to be evaluated based on the bedding development intensity index and the bedding seam development intensity of the shale gas reservoir section to be evaluated; when the natural weakness index is lower than a first target value, enhancing the fracturing degree corresponding to the fracturing scheme of the shale gas reservoir section to be evaluated; when the natural weak face development intensity is higher than a first target value and lower than a second target value, maintaining the fracturing degree corresponding to the fracturing scheme of the shale gas reservoir section to be evaluated, wherein the first target value is smaller than the second target value; and when the natural weak face development intensity is higher than the second target value, weakening the fracturing degree corresponding to the fracturing scheme of the shale gas reservoir section to be evaluated.
Fig. 5 shows a block diagram of a fracturing optimization apparatus for shale gas reservoirs according to an exemplary embodiment of the present invention. The fracture optimization device 300 of the shale gas reservoir may be a computer.
The fracturing optimization apparatus 300 includes a Central Processing Unit (CPU) 301, a system memory 304 including a Random Access Memory (RAM) 302 and a Read Only Memory (ROM) 303, and a system bus 305 connecting the system memory 304 and the central processing unit 301. The fracture optimization apparatus 300 also includes a basic input/output system (I/O system) 306 to facilitate the transfer of information between the various devices within the computer, and a mass storage device 307 for storing an operating system 313, application programs 314, and other program modules 315.
The basic input/output system 306 includes a display 308 for displaying information and an input device 309, such as a mouse, keyboard, etc., for user input of information. Wherein both the display 308 and the input device 309 are coupled to the central processing unit 301 via an input output controller 310 coupled to the system bus 305. The basic input/output system 306 may also include an input/output controller 310 for receiving and processing input from a number of other devices, such as a keyboard, mouse, or electronic stylus. Similarly, the input output controller 310 also provides output to a display screen, a printer, or other type of output device.
The mass storage device 307 is connected to the central processing unit 301 through a mass storage controller (not shown) connected to the system bus 305. The mass storage device 307 and its associated computer-readable media provide non-volatile storage for the fracture optimization apparatus 300. That is, the mass storage device 307 may include a computer readable medium (not shown) such as a hard disk or CD-ROM drive.
Computer readable media may include computer storage media and communication media without loss of generality. Computer storage 13 media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes RAM, ROM, EPROM, EEPROM, flash memory or other solid state memory technology, CD-ROM, DVD or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices. Of course, those skilled in the art will recognize that computer storage media are not limited to the ones described above. The system memory 304 and mass storage device 307 described above may be collectively referred to as memory.
According to various embodiments of the invention, the fracture optimization apparatus 300 may also be operated by a remote computer connected to a network, such as the Internet. That is, the fracture optimization apparatus 300 may be connected to the network 312 through a network interface unit 311 connected to the system bus 305, or alternatively, the network interface unit 311 may be used to connect to other types of networks or remote computer systems (not shown).
The memory also includes one or more programs, one or more programs stored in the memory and configured to be executed by the CPU. The one or more programs include instructions for performing the fracture optimization method provided by the embodiments of the present invention.
It should be noted that: the fracturing optimization device for the shale gas reservoir provided by the embodiment only performs illustration by dividing the functional modules when the fracturing optimization is performed on the shale gas reservoir, and in practical application, the functional allocation can be completed by different functional modules according to needs, namely, the internal structure of the device is divided into different functional modules so as to complete all or part of the functions described above. In addition, the fracturing optimization device of the shale gas reservoir provided by the embodiment and the fracturing optimization method embodiment of the shale gas reservoir belong to the same conception, and detailed implementation processes of the fracturing optimization device are detailed in the method embodiment and are not repeated here.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program for instructing relevant hardware, where the program may be stored in a computer readable storage medium, and the storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
The foregoing description of the preferred embodiments of the present disclosure is provided for the purpose of illustration only, and is not intended to limit the disclosure to the particular embodiments disclosed, but on the contrary, the intention is to cover all modifications, equivalents, alternatives, and alternatives falling within the spirit and principles of the disclosure.
Claims (7)
1. A fracturing optimization method of a shale gas reservoir, the fracturing optimization method comprising:
acquiring images of rock slices of all the coring well samples, wherein the coring well samples are rock samples in the coring well of the well region where the shale gas reservoir section to be evaluated is located, and the sizes of the images of the rock slices of all the coring well samples are the same;
according to the size of a target grid, carrying out grid division on images of rock slices of each coring well sample, wherein each image comprises a plurality of unit grids, each unit grid comprises a plurality of pixels, and the unit grids in the same row are parallel to the extending direction of a single rock stratum;
Graying the image of the rock slice of each coring well sample, adopting any pixel obtained from the unit grids, comparing the pixel value of any pixel with a target threshold value, counting the corresponding unit grid as a unit grid with the pixel value larger than the target threshold value when the pixel value of any pixel is larger than the target threshold value, and adding one to the number of tattoos of the corresponding sample when the number of the unit grids with the pixel values larger than the target threshold value of two adjacent rows is respectively larger than and smaller than the target number, so as to obtain the number of tattoos of each coring well sample;
determining a pixel mean value of the image of the rock slice of each cored well sample after graying based on the image of the rock slice of each cored well sample;
multiplying the number of layers of each coring well sample by the pixel mean value of the image of the rock slice of the corresponding coring well sample after graying to obtain the bedding development strength of each coring well sample, wherein the bedding development strength refers to the formation, development and existence degree of the layered structure change along the vertical direction;
acquiring physical property data of each coring well sample, wherein the physical property data refers to data related to the appearance and the property of an object;
Based on the bedding development intensity of each core well sample and the physical property data of each core well sample, the weight of the physical property data relative to the bedding development intensity is determined by adopting the following formula:
L o P o ＝a 1 f Si +b 1 f Ca +c 1 f Py +d 1 f Toc +f 1
wherein L is o The number of layers is the number of layers/m; p (P) o Is pixel average value, 0-255; f (f) Si 、f Ca 、f Py 、f Toc Respectively the siliceous content, the carbonate content, the pyrite content and the total organic carbon content,%; a, a 1 、b 1 、c 1 、d 1 、f 1 Is a weight coefficient related to the bedding development intensity;
acquiring physical property data of the shale gas reservoir section to be evaluated;
calculating the bedding development intensity of the shale gas reservoir section to be evaluated based on the weight of the physical data relative to the bedding development intensity and the physical data of the shale gas reservoir section to be evaluated;
determining the seam development strength of the shale gas reservoir section to be evaluated, wherein the seam development strength refers to the formation, development and existence degree of various seam changes with time due to uneven rock structure;
and optimizing a fracturing scheme of the shale gas reservoir section to be evaluated based on the bedding development intensity and the bedding seam development intensity of the shale gas reservoir section to be evaluated.
2. The fracturing optimization method of claim 1, wherein the determining the layer seam development strength of the shale gas reservoir section to be evaluated comprises:
Acquiring the weight of the physical property data relative to the seam formation strength;
acquiring physical property data of the shale gas reservoir section to be evaluated;
and calculating the seam development intensity of the shale gas reservoir section to be evaluated based on the seam development intensity of the shale gas reservoir section to be evaluated, the weight of the physical property data relative to the seam development intensity and the physical property data of the shale gas reservoir section to be evaluated.
3. The fracture optimization method of claim 2, wherein the obtaining the weight of the physical property data relative to the strength of the layer seam development comprises:
acquiring images of rock slices of each coring well sample, wherein the coring well samples are rock samples in the coring wells of the wells where the shale gas reservoir sections to be evaluated are located;
determining the seam development intensity of each cored well sample based on the image of the rock slice of each cored well sample;
obtaining physical property data of each coring well sample;
and determining the weight of the physical data relative to the bedding seam development intensity based on the bedding seam development intensity of the shale gas reservoir section to be evaluated, the bedding seam development intensity of each coring well sample and the physical data of each coring well sample.
4. The fracturing optimization method of claim 1, wherein optimizing the fracturing scheme of the shale gas reservoir section to be evaluated based on the bedding development strength and the bedding seam development strength of the shale gas reservoir section to be evaluated comprises:
Calculating the natural weakness index of the shale gas reservoir section to be evaluated based on the bedding development intensity index and the bedding seam development intensity of the shale gas reservoir section to be evaluated;
when the natural weak surface index is lower than a first target value, enhancing the fracturing degree corresponding to the fracturing scheme of the shale gas reservoir section to be evaluated;
when the natural weak surface development intensity is higher than the first target value and lower than a second target value, maintaining the fracturing degree corresponding to the fracturing scheme of the shale gas reservoir section to be evaluated, wherein the first target value is smaller than the second target value;
and when the natural weak surface development intensity is higher than the second target value, weakening the fracturing degree corresponding to the fracturing scheme of the shale gas reservoir section to be evaluated.
5. A fracture optimization device of a shale gas reservoir, the fracture optimization device comprising:
the first determining module is used for obtaining images of rock slices of all coring well samples, wherein the coring well samples are rock samples in a coring well of a well zone where a shale gas reservoir section to be evaluated is located; according to the size of a target grid, carrying out grid division on images of rock slices of each coring well sample, wherein each image comprises a plurality of unit grids, each unit grid comprises a plurality of pixels, and the unit grids in the same row are parallel to the extending direction of a single rock stratum; graying the image of the rock slice of each coring well sample, adopting any pixel obtained from the unit grids, comparing the pixel value of any pixel with a target threshold value, counting the corresponding unit grid as a unit grid with the pixel value larger than the target threshold value when the pixel value of any pixel is larger than the target threshold value, and adding one to the number of tattoos of the corresponding sample when the number of the unit grids with the pixel values larger than the target threshold value of two adjacent rows is respectively larger than and smaller than the target number, so as to obtain the number of tattoos of each coring well sample; determining a pixel mean value of the image of the rock slice of each cored well sample after graying based on the image of the rock slice of each cored well sample; multiplying the number of layers of each coring well sample by the pixel mean value of the image of the rock slice of the corresponding coring well sample after graying to obtain the bedding development strength of each coring well sample, wherein the bedding development strength refers to the formation, development and existence degree of the layered structure change along the vertical direction; acquiring physical property data of each coring well sample, wherein the physical property data refers to data related to the appearance and the property of an object; based on the bedding development intensity of each core well sample and the physical property data of each core well sample, the weight of the physical property data relative to the bedding development intensity is determined by adopting the following formula:
L o P o ＝a 1 f Si +b 1 f Ca +c 1 f Py +d 1 f Toc +f 1
Wherein L is o The number of layers is the number of layers/m; p (P) o Is pixel average value, 0-255; f (f) Si 、f Ca 、f Py 、f Toc Respectively the siliceous content, the carbonate content, the pyrite content and the total organic carbon content,%; a, a 1 、b 1 、c 1 、d 1 、f 1 Is a weight coefficient related to the bedding development intensity;
acquiring physical property data of the shale gas reservoir section to be evaluated; calculating the bedding development intensity of the shale gas reservoir section to be evaluated based on the weight of the physical data relative to the bedding development intensity and the physical data of the shale gas reservoir section to be evaluated;
the second determining module is used for determining the seam development intensity of the shale gas reservoir section to be evaluated, wherein the seam development intensity refers to the formation, development and existence degree of various seam changes with time due to uneven rock structure;
and the evaluation module is used for optimizing the fracturing scheme of the shale gas reservoir section to be evaluated based on the bedding development intensity and the bedding seam development intensity of the shale gas reservoir section to be evaluated.
6. A fracture optimization device of a shale gas reservoir, the fracture optimization device comprising: a memory, a processor and a computer program stored in the memory and executable on the processor, the processor being configured to implement the method of fracture optimization of a shale gas reservoir as claimed in any of claims 1 to 4 when the computer program is executed.
7. A computer storage medium having stored therein at least one instruction loaded and executed by a processor to implement the method of fracture optimization of a shale gas reservoir of any of claims 1-4.
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