CN113158490A - Method for establishing tight sandstone reservoir permeability calculation model - Google Patents

Method for establishing tight sandstone reservoir permeability calculation model Download PDF

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CN113158490A
CN113158490A CN202110523448.1A CN202110523448A CN113158490A CN 113158490 A CN113158490 A CN 113158490A CN 202110523448 A CN202110523448 A CN 202110523448A CN 113158490 A CN113158490 A CN 113158490A
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刘景东
张存剑
蒋有录
刘华
李磊
何路露
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China University of Petroleum East China
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Abstract

The invention relates to a method for establishing a permeability calculation model of a compact sandstone reservoir, which is based on a laser scanning confocal microscope and image processing, utilizes the laser scanning confocal microscope to extract pores, then utilizes the image processing to extract the surface porosity, the pore shape factor, the equivalent pore diameter, the pore diameter sorting index and the fractal dimension parameter to represent the pore structure, and further establishes the permeability calculation model through the pore structure parameter. The permeability calculation model established by the method of the invention predicts the permeability of the compact sandstone reservoir, can accurately predict the permeability, can complete the permeability prediction of any position of the sample, solves the problem that the permeability of the compact sandstone reservoir is evaluated only by relying on the rock columnar sample or logging data in the prior art, enables the permeability evaluation of the reservoir to reflect the heterogeneity of the reservoir more, has more comprehensive evaluation results, and effectively improves the success rate of exploration and development.

Description

Method for establishing tight sandstone reservoir permeability calculation model
Technical Field
The invention belongs to the technical field of oil and gas geological exploration, and particularly relates to a method for establishing a tight sandstone reservoir permeability calculation model.
Background
The compact sandstone reservoir has a overburden density matrix permeability less than or equal to 0.1 multiplied by 10-3μm2(corresponding to an air permeability of 1X 10 or less-3μm2) Reservoirs with porosity less than 10% (crepe et al, 2013). Unlike conventional reservoirs, tight sandstone reservoirs have the characteristics of small pore diameter, poor porosity and permeability, complex pore structure and strong heterogeneity (Zhu Ruo Ka et al, 2016), and the pore structure of the tight sandstone reservoirs determines the porosity and permeability of the reservoirs and influences the reservoir performance and seepage capacity of the tight sandstone reservoirs (Wang national pavilion et al, 2013). Current pore structure characterization techniques are aimed at improving characterization dimensions and resolution (wu shuang et al, 2018). Common tight sandstone reservoir characterization methods can be divided into a quantitative characterization technology and a qualitative characterization technology. Wherein, the quantitative characterization technology comprises high-pressure mercury pressing (HPMI), constant-speed mercury pressing (RCP), Nuclear Magnetic Resonance (NMR) and the like; the qualitative characterization technology comprises cast slice observation, Scanning Electron Microscope (SEM), Laser Scanning Confocal Microscope (LSCM) and the like. However, high-pressure mercury intrusion can only represent the pore size distribution of connected pores and is seriously influenced by the shielding effect of small pores (Zhao et al, 2015; Li et al, 2015); constant-rate mercury intrusion is limited by the maximum mercury intrusion pressure (about 900psi) and cannot reflect pore development with throat radii less than 0.12 μm (Wang et al, 2018); nmr cannot characterize the fluid-free pore space and the complexity of the pore structure (Lai, et al, 2018; Li, et al, 2018). The observation of the casting body slice is the most intuitive basic research method, but the method has the defects of low resolution, large artificial operation error and the like; scanning electron microscopy has high resolution but small analysis view, which results in poor representativeness and inability to reflect the bulk pore-throat structural features of tight sandstone reservoirs (Liu, et al., 2017). Compared with the traditional optical microscope,the resolution ratio is improved by 30% -40%, light signal interference (Liushao waves and the like, 2016) outside a focus can be effectively eliminated, a reservoir space is accurately extracted, and a quasi-three-dimensional reservoir space distribution image (Guangao and the like, 2009) is formed. However, the research on compact reservoirs by using LSCM is limited to qualitative characterization of pore structures (Liu et al, 2017; Xu et al, 2020; Liuchun et al, 2017) and lacks quantitative characterization of pore structures.
In the existing evaluation method of the permeability of the tight sandstone reservoir, the permeability is generally obtained by performing experimental tests on columnar rock samples. However, experimental tests require a complete and regular columnar sample, and only the permeability of the whole sample can be macroscopically explained, so that the heterogeneity difference of the permeability in the rock sample cannot be reflected, the obtained permeability is poor in accuracy, and the understanding of the fluid flow rule in the tight sandstone reservoir is influenced.
Disclosure of Invention
Aiming at the problems that the prior art cannot reflect the difference of permeability heterogeneity inside a rock sample and the like, the invention provides the method for establishing the compact sandstone reservoir permeability calculation model.
In order to achieve the aim, the invention provides a method for establishing a compact sandstone reservoir permeability calculation model, which comprises the following specific steps:
s1, sample preparation
Preparing a rock sample into a laser scanning confocal rock slice with the thickness of 0.03mm and dyeing the rock slice by adopting a fluorescent agent;
s2, collecting images and extracting pore parameters
Collecting pore images through a laser scanning confocal microscope; sequentially carrying out gray scale, binarization and threshold segmentation on the pore image, and extracting the area of a visual field, the pore areas of all pores and the pore perimeters;
s3, calculating pore structure parameters
Calculating the surface porosity phi, the pore shape factor Fs and the pore weighted equivalent pore diameter d according to the view area, the pore area and the pore perimeterePore size sorting index taupAnd fractal dimension DwFive pore structure parameters;
s4, establishing a permeability prediction model
Selecting the face porosity phi, the pore shape factor Fs and the pore weighted equivalent pore diameter dePore size sorting index taupAnd fractal dimension DwAnd (3) five pore structure parameters are combined with the permeability of the corresponding rock sample, the relationship between the permeability and the pore structure parameters is fitted by using a least square method, and a permeability prediction model is established and expressed as follows: k phi + bFs + cde+dτp+eDwAnd f, wherein K is permeability, and a, b, c, d, e and f are weight coefficients obtained by fitting.
Preferably, in step S2, the method for acquiring the pore image by the laser scanning confocal microscope includes:
selecting a visual field of pore development on the rock slice under a low-power objective lens to obtain an integral image;
and moving the rock slice, sequentially scanning and collecting a plurality of adjacent pore images by using the high-power objective lens according to a set arrangement sequence, and splicing in mapping software to form an integral image under the low-power objective lens.
Preferably, in step S2, the aperture image is subjected to a gradation process to obtain a gradation image, the gradation image is subjected to an image enhancement process, and then the gradation image is subjected to a binarization process.
Preferably, in step S3, the face porosity Φ is a ratio of the total extracted aperture area within the view to the area of the view, and is expressed as:
Figure BDA0003064892910000031
in the formula, SiThe area of the ith pore is shown, S is the viewing area, and n is the number of pores;
the pore shape factor Fs is calculated using the following equations (2) and (3):
Figure BDA0003064892910000032
Figure BDA0003064892910000041
in the formula, FsiIs the shape factor of the ith pore, CiIs the perimeter of the i-th aperture, αiThe percentage of the ith pore area to all pore areas;
pore weighted equivalent pore diameter deThe following formula (4) and formula (5) are used for calculation:
Figure BDA0003064892910000042
Figure BDA0003064892910000043
in the formula (d)eiIs the equivalent pore size of the ith pore;
pore size sorting index taupCalculated using the following equations (6) and (7):
Figure BDA0003064892910000044
Figure BDA0003064892910000045
in the formula, τpiIs the pore size sorting index of the i-th pore, diIs the ith pore diameter, dmaxThe maximum pore diameter of all pores under the visual field;
fractal dimension DwObtained by solving the following equation (8):
Figure BDA0003064892910000046
wherein C is the pore perimeter and z is a constant.
Compared with the prior art, the invention has the advantages and positive effects that:
the method for establishing the permeability calculation model of the compact sandstone reservoir is based on a laser scanning confocal microscope and image processing, utilizes the laser scanning confocal microscope to extract pores, then utilizes the image processing to extract the surface porosity, the pore shape factor, the equivalent pore diameter, the pore diameter sorting index and the fractal dimension parameter to represent the pore structure, and further establishes the permeability calculation model through the pore structure parameter. The permeability calculation model established by the method is used for predicting the permeability of the compact sandstone reservoir, the permeability can be accurately predicted, the permeability prediction of any position of the sample can be completed, the defect that the permeability of the compact sandstone reservoir is evaluated only by relying on the rock columnar sample or logging data in the prior art is overcome, the heterogeneity of the reservoir is reflected by the evaluation of the permeability of the reservoir, the evaluation result is more comprehensive, and the success rate of exploration and development is effectively improved.
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Fig. 1 is a flow chart of a method for establishing a tight sandstone reservoir permeability calculation model according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of pore image stitching according to an embodiment of the present invention;
fig. 3 is a schematic view of a pore image of four segments of tight sandstone reservoirs in the region of the meta-dam of the sichuan basin under laser fluorescence according to the embodiment of the present invention;
fig. 4 is a schematic view of a pore image of four segments of tight sandstone reservoirs in the region of the element dam of the sichuan basin under a confocal laser scanning microscope according to an embodiment of the present invention.
Detailed Description
The invention is described in detail below by way of exemplary embodiments. It should be understood, however, that elements, structures and features of one embodiment may be beneficially incorporated in other embodiments without further recitation.
In the past, the evaluation of the pore structure and permeability of the compact sandstone reservoir depends too much on high-pressure mercury intrusion, nuclear magnetic resonance and permeability test technologies, the understanding of the pore structure and permeability is too macroscopic, the representation of heterogeneity such as pore structure difference and permeability in the compact sandstone reservoir is lacked, and the understanding of the fluid flow rule in the compact sandstone reservoir is influenced. The invention provides a method for establishing a permeability calculation model of a compact sandstone reservoir, which is based on a laser scanning confocal microscope and image processing, utilizes the laser scanning confocal microscope to extract pores, then utilizes the image processing to extract the surface porosity, the pore shape factor, the equivalent pore diameter, the pore diameter sorting index and the fractal dimension parameter to represent the pore structure, and further establishes the permeability calculation model through the pore structure parameter. The permeability calculation model established by the method is used for predicting the permeability of the compact sandstone reservoir, the permeability can be accurately predicted, the permeability prediction of any position of the sample can be completed, the defect that the permeability of the compact sandstone reservoir is evaluated only by relying on the rock columnar sample or logging data in the prior art is overcome, the heterogeneity of the reservoir is reflected by the evaluation of the permeability of the reservoir, the evaluation result is more comprehensive, and the success rate of exploration and development is effectively improved.
Referring to fig. 1, the invention provides a method for establishing a tight sandstone reservoir permeability calculation model, which comprises the following specific steps:
s1, sample preparation
The rock samples were made into 0.03mm thick laser scanning confocal rock slices and stained with fluorescent agent.
In this embodiment, the fluorescent agent is a rose fluorescent agent. Note that the fluorescent agent is not limited to the rose fluorescent agent, and other fluorescent agents such as an orange fluorescent agent and a yellow fluorescent agent may be used. The selection is based on the actual situation.
The rock slice is dyed by the fluorescent agent, so that pores and miscellaneous bases can be effectively distinguished, the observation precision is improved, and meanwhile, micro cracks which cannot be distinguished under a common optical microscope can be effectively identified.
S2, collecting images and extracting pore parameters
Collecting pore images through a laser scanning confocal microscope; and (4) sequentially carrying out gray scale, binarization and threshold segmentation on the pore image, and extracting the area of a visual field, the pore area of all pores and the pore perimeter.
Specifically, the method for acquiring the pore image through the laser scanning confocal microscope comprises the following steps:
selecting a visual field of pore development on the rock slice under a low-power objective lens to obtain an integral image;
and moving the rock slice, sequentially scanning and collecting a plurality of adjacent pore images by using the high-power objective lens according to a set arrangement sequence, and splicing in mapping software to form an integral image under the low-power objective lens. For example, referring to fig. 2, 9 pore images are collected in total, numbered (r-nini), and are spliced by coreldaw mapping software in a 3 x 3 arrangement mode to form an overall image under a low-power objective lens.
Specifically, on the basis of obtaining a pore image (namely an integral image under a formed low-power objective lens) of a Laser Scanning Confocal Microscope (LSCM), carrying out gray processing, binarization processing and threshold segmentation processing on the pore image, and firstly setting the pixel proportion of the LSCM pore image to be processed as a corresponding length proportion by using an image processing software scale tool; then, the LSCM pore image is converted into a gray image (8-bit); then, carrying out binarization processing on the LSCM pore image to obtain a black-and-white image with gray values of only 0 and 1, wherein the pores are white and other areas are black; and finally, carrying out threshold segmentation on the LSCM pore image to realize effective selection of all pore ranges, and finally extracting the field of view, the pore areas of all pores and the pore perimeters.
In one embodiment, in order to improve the image recognition effect, the pore image is subjected to grayscale processing to obtain a grayscale image, and after the grayscale image is subjected to image enhancement processing, the grayscale image is subjected to binarization processing.
S3, calculating pore structure parameters
Calculating the surface porosity phi, the pore shape factor Fs, the pore volume factor phi, the pore volume factor F, the pore volume factor phi and the pore perimeter,Pore weighted equivalent pore diameter dePore size sorting index taupAnd fractal dimension DwFive pore structure parameters.
Specifically, the face porosity Φ is the ratio of the total extracted pore area within the view to the area of the view, expressed as:
Figure BDA0003064892910000071
in the formula, SiIs the area of the ith aperture, S is the viewing area, and n is the number of apertures.
The pore shape factor Fs is calculated using the following equations (2) and (3):
Figure BDA0003064892910000072
Figure BDA0003064892910000073
in the formula, FsiIs the shape factor of the ith pore, CiIs the perimeter of the i-th aperture, αiThe ith pore area is a percentage of the total pore area.
Note that the pore shape factor reflects the roundness and roughness of the pores, and Fs is 1.0 when the pores are circular, and 0.785 when the pores are square.
Pore weighted equivalent pore diameter deThe following formula (4) and formula (5) are used for calculation:
Figure BDA0003064892910000081
Figure BDA0003064892910000082
in the formula (d)eiIs the equivalent pore size of the ith pore.
Pore size sorting index taupCalculated using the following equations (6) and (7):
Figure BDA0003064892910000083
Figure BDA0003064892910000084
in the formula, τpiIs the pore size sorting index of the i-th pore, diIs the ith pore diameter, dmaxThe maximum pore diameter of all pores under the visual field.
Note that the pore size sorting index τpiRanges from 0 to 1, with larger values indicating more concentrated pore sorting.
Fractal dimension DwObtained by solving the following equation (8):
Figure BDA0003064892910000085
wherein C is the pore perimeter and z is a constant.
It should be noted that the fractal dimension DwReflecting the heterogeneity of reservoir development, fractal dimension DwLarger indicates more heterogeneity in the reservoir. In planar geometry, fractal dimension DwIn the range of 1.0-2.0 (D)wNot preferably 1.0 or 2.0). When D is presentwA value of 2.0 indicates that the pores have a completely irregular shape or a rough surface, when DwA value of 1.0 indicates that the pore surface is completely smooth.
S4, establishing a permeability prediction model
Selecting the face porosity phi, the pore shape factor Fs and the pore weighted equivalent pore diameter dePore size sorting index taupAnd fractal dimension DwFive pore structure parameters are combined with the permeability of the corresponding rock sample, the relation between the permeability and the pore structure parameters is fitted by using the least square method, and a permeability prediction model is established to representComprises the following steps: k phi + bFs + cde+dτp+eDwAnd f, wherein K is permeability, and a, b, c, d, e and f are weight coefficients obtained by fitting.
The effectiveness of the permeability prediction model established by the method of the embodiment of the present invention is described below with reference to specific embodiments.
Taking the pore structure representation and permeability model establishment of four sections of compact sandstone reservoirs in the Yuan-Ba district of Sichuan basin as an example, the method specifically comprises the following steps:
s1, sample preparation
Four sections of 15 tight sandstone reservoir samples in the Yuanba area are selected to be made into a laser scanning confocal rock slice with the thickness of 0.03mm, and rose fluorescent agent (see the light color part in figure 3) is adopted for dyeing.
S2, collecting images and extracting pore parameters
And observing the development condition of pores and cracks of each sheet under the conditions of single polarization, orthogonal light and laser fluorescence to know the macroscopic characteristics of the pores. Then selecting a position for pore development, and scanning by using a low-power objective lens of the LSCM to obtain a pore image under the low-power objective lens; and then continuously moving the sheet, acquiring planar and continuous 9 LSCM pore images under the LSCM high-power objective lens, and splicing the acquired 9 LSCM pore images in CorelDRAW mapping software according to the pore images under the low-power objective lens to form an overall image under the low-power objective lens. According to the processed LSCM pore images (see fig. 3 and 4), the main storage space types of the four sections of compact sandstone reservoirs in the research area can be judged to be inter-granular solution pores and intra-granular solution pores, and micro cracks and casting holes are developed at the same time.
On the basis of obtaining the LSCM pore image, firstly, setting the pixel proportion of the LSCM pore image to be processed as a corresponding length proportion by using an image processing software scale tool; then, the LSCM pore image is converted into a gray image (8-bit) and image enhancement processing is carried out to improve the identification effect; then, carrying out binarization processing on the LSCM pore image to obtain a black-and-white image with gray values of only 0 and 1, wherein the pores are white and other areas are black; and finally, carrying out threshold segmentation on the LSCM pore image, selecting all pores, and extracting the view area, the pore area of all pores and the pore perimeter.
S3, calculating pore structure parameters
Through LSCM image extraction and analysis of four sections of compact sandstone reservoirs in the research area, the formula (1) to the formula (8) are adopted to carry out surface porosity phi, pore shape factor Fs and pore weighted equivalent pore diameter dePore size sorting index taupAnd fractal dimension DwFive pore structure parameters were calculated, and the calculation results are shown in table 1.
TABLE 1
Figure BDA0003064892910000101
S4, establishing a permeability prediction model
Selecting the face porosity phi, the pore shape factor Fs and the pore weighted equivalent pore diameter d of 11 samplesePore size sorting index taupAnd fractal dimension DwAnd (3) fitting the relation between the permeability and the pore structure parameters by using a least square method, and establishing a permeability prediction model to express as follows: k-0.0022 phi-0.05681 Fs +0.000157de-0.00965τp-0.02738Dw+0.0798. The accuracy of the permeability prediction model was tested using the pore structure parameters and permeability data for the remaining 4 samples. The formula correlation coefficient R20.93563, indicating better correlation between the pore structure parameter and the permeability. The error range of the permeability and the actually measured permeability calculated by the permeability prediction model is (0.002592-0.008716) multiplied by 10-3μm2The error rate is 3.89-10.71% (see table 2), and the whole is low, so that the permeability prediction model established by the method can accurately predict the permeability, and the effectiveness of the permeability prediction model is proved.
TABLE 2
Figure BDA0003064892910000111
The above-described embodiments are intended to illustrate rather than to limit the invention, and any modifications and variations of the present invention are possible within the spirit and scope of the claims.

Claims (4)

1. A method for establishing a tight sandstone reservoir permeability calculation model is characterized by comprising the following specific steps:
s1, sample preparation
Preparing a rock sample into a laser scanning confocal rock slice with the thickness of 0.03mm and dyeing the rock slice by adopting a fluorescent agent;
s2, collecting images and extracting pore parameters
Collecting pore images through a laser scanning confocal microscope; sequentially carrying out gray scale, binarization and threshold segmentation on the pore image, and extracting the area of a visual field, the pore areas of all pores and the pore perimeters;
s3, calculating pore structure parameters
Calculating the surface porosity phi, the pore shape factor Fs and the pore weighted equivalent pore diameter d according to the view area, the pore area and the pore perimeterePore size sorting index taupAnd fractal dimension DwFive pore structure parameters;
s4, establishing a permeability prediction model
Selecting the face porosity phi, the pore shape factor Fs and the pore weighted equivalent pore diameter dePore size sorting index taupAnd fractal dimension DwAnd (3) five pore structure parameters are combined with the permeability of the corresponding rock sample, the relationship between the permeability and the pore structure parameters is fitted by using a least square method, and a permeability prediction model is established and expressed as follows: k phi + bFs + cde+dτp+eDwAnd f, wherein K is permeability, and a, b, c, d, e and f are weight coefficients obtained by fitting.
2. The method for establishing the tight sandstone reservoir permeability calculation model of claim 1, wherein in the step S2, the method for acquiring the pore image through the laser scanning confocal microscope comprises the following steps:
selecting a visual field of pore development on the rock slice under a low-power objective lens to obtain an integral image;
and moving the rock slice, sequentially scanning and collecting a plurality of adjacent pore images by using the high-power objective lens according to a set arrangement sequence, and splicing in mapping software to form an integral image under the low-power objective lens.
3. The method for establishing the tight sandstone reservoir permeability calculation model as claimed in claim 1 or 2, wherein in step S2, the pore image is subjected to grayscale processing to obtain a grayscale image, and after the grayscale image is subjected to image enhancement processing, binarization processing is performed on the grayscale image.
4. The method for building a tight sandstone reservoir permeability calculation model according to claim 1, wherein in step S3, the face porosity Φ is the ratio of the total pore area extracted in the view to the view area, and is represented as:
Figure FDA0003064892900000021
in the formula, SiThe area of the ith pore is shown, S is the viewing area, and n is the number of pores;
the pore shape factor Fs is calculated using the following equations (2) and (3):
Figure FDA0003064892900000022
Figure FDA0003064892900000023
in the formula, FsiIs the shape factor of the ith pore, CiIs the perimeter of the i-th aperture, αiThe percentage of the ith pore area to all pore areas;
pore weighted equivalent pore diameter deAdopts the following formulaEquation (4) and equation (5):
Figure FDA0003064892900000024
Figure FDA0003064892900000025
in the formula (d)eiIs the equivalent pore size of the ith pore;
pore size sorting index taupCalculated using the following equations (6) and (7):
Figure FDA0003064892900000026
Figure FDA0003064892900000027
in the formula, τpiIs the pore size sorting index of the i-th pore, diIs the ith pore diameter, dmaxThe maximum pore diameter of all pores under the visual field;
fractal dimension DwObtained by solving the following equation (8):
Figure FDA0003064892900000031
wherein C is the pore perimeter and z is a constant.
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