CN115272156A - Oil and gas reservoir high-resolution wellbore imaging characterization method based on cyclic generation countermeasure network - Google Patents
Oil and gas reservoir high-resolution wellbore imaging characterization method based on cyclic generation countermeasure network Download PDFInfo
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Abstract
The invention discloses an oil and gas reservoir high-resolution wellbore imaging characterization method based on a circularly generated confrontation network, which relates to the technical field of oil and gas resource geological exploration, development and evaluation and comprises the following steps: s1, preprocessing a resistivity imaging image; s2, preprocessing an image of the outer surface of the full-diameter rock core; s3, mapping the resistivity imaging image to an image of the outer surface of the full-diameter core; and S4, carrying out high-resolution wellbore imaging characterization on the oil-gas reservoir. The method realizes the high-resolution shaft imaging characterization of the oil and gas reservoir, effectively fuses the advantages of logging information and petrophysical information, overcomes the problem of insufficient representativeness of petrophysical experiments, improves the longitudinal resolution of logging images by at least 10 times, improves the dividing capacity of oil and gas reservoir thin layers, has low cost of cylindrical core common light scanning experiments on the outer surface, reduces the cost of the high-resolution shaft imaging characterization, is easy to be applied and popularized in different oil and gas fields, and lays a foundation for fine evaluation of rock physical parameters of the oil and gas reservoir and construction of three-dimensional digital shafts.
Description
Technical Field
The invention relates to the technical field of geological exploration, development and evaluation of oil and gas resources, in particular to an oil and gas reservoir high-resolution shaft imaging characterization method based on a cyclic generation countermeasure network.
Background
High-precision representation of the stratum is always a key difficult point for fine exploration and development of oil and gas reservoirs at home and abroad. Although the current micro-nano rock physical experiments, such as the CT technology, the scanning electron microscope technology, the laser confocal technology and the like, can better represent the characteristics of mineral components and pore structures in rocks, the cost is higher, the experimental period is longer, the representativeness of strata with strong heterogeneity is lacked, and the massive rock physical experiments for each well are difficult to realize. The logging technology can acquire formation physical field parameters of the whole well in a continuous depth manner, but the resolution is low, even the resolution of the resistivity imaging logging image with the highest longitudinal resolution is only 2.0mm at present, the thin layer with high oil-gas content cannot be effectively identified, and the evaluation precision is lower than the result of a rock physical experiment.
The deep learning technology is a technology which can solve the problem of good nonlinear physical response relation, is applied to multi-scale digital rock core image construction and automatic mineral component segmentation in micro-nano scale rock physical experiments, and can also be applied to logging information to realize different lithofacies division, porosity calculation and the like. However, related results of applying deep learning to effectively combine logging technology and micro-nano-scale rock physics experiments are not seen at present. In order to fully combine the advantages of high precision of large-scale and micro-nano-scale rock physical experiments of well logging, deep learning is needed to realize high-resolution shaft imaging representation of oil and gas reservoirs. The conventional countermeasure network model requires that the resistivity imaging image corresponds to the high-resolution rock physical experiment image one to one, the requirement is strict, and the pore structure characteristics cannot be unified due to the fact that the drilling tool has a certain thickness. The recently developed cyclic generation countermeasure network algorithm is not limited by the corresponding relation, can be used as a method for imaging representation of a high-resolution shaft of an oil and gas reservoir, reduces the cost of high-precision representation of a stratum, overcomes the defect of low longitudinal resolution of a logging technology, improves the thin layer dividing capability of the oil and gas reservoir, and lays a foundation for fine evaluation of rock physical parameters of the oil and gas reservoir and construction of a three-dimensional digital shaft.
Disclosure of Invention
In order to solve the technical problem, the invention discloses an oil and gas reservoir high-resolution wellbore imaging characterization method based on a cyclic generation countermeasure network.
In order to achieve the purpose, the invention adopts the following technical scheme:
a high-resolution wellbore imaging characterization method for an oil and gas reservoir based on a cycle generation countermeasure network comprises the following steps:
s1, preprocessing a resistivity imaging image;
s2, preprocessing an image of the outer surface of the full-diameter rock core;
s3, mapping the resistivity imaging image to a full-diameter core outer surface image;
and S4, carrying out high-resolution wellbore imaging characterization on the oil-gas reservoir.
Optionally, in S1, the resistivity imaging image preprocessing step includes:
collecting data collected by a resistivity imaging logging instrument, generating a resistivity imaging dynamic image through a correction and image enhancement method, filling a blank band of the resistivity imaging dynamic image by combining a statistical algorithm, removing an abnormal value of the resistivity, replacing the abnormal value with an average value in an A multiplied by A range surrounding the point, converting the resistivity into the porosity by utilizing a Siemens degree formula of a argillaceous rock model to obtain a resistivity imaging image representing the porosity, and converting the resistivity imaging image into an 8-bit resistivity imaging gray image distributed in a [0,255] interval.
Optionally, in S2, the step of preprocessing the image of the outer surface of the full-diameter core includes:
collecting full-diameter cores collected by drilling, using a charge-coupled device in a core scanning analyzer to carry out common light scanning on the cylindrical cores on the outer surfaces of the cores at 360 degrees to obtain images with micron-sized resolution, distinguishing pores, clay minerals and different framework components according to the shape and geometric characteristics of rock components, and converting the images into 8-bit full-diameter core outer surface gray level images distributed in intervals of [0,255 ].
Optionally, in S3, the step of mapping the resistivity imaging image to the full-diameter core outer surface image includes:
s3.1: dividing the resistivity imaging gray level image obtained in the step S1 into K resistivity imaging gray level images with M multiplied by M pixels, compressing the full-diameter core outer surface gray level image obtained in the step S2 to Q times of the resolution of the resistivity imaging image, and dividing the full-diameter core outer surface gray level image into K Zhang Quan diameter core outer surface gray level images with (M multiplied by Q) x (M multiplied by Q) pixels, wherein Q is an integer;
s3.2: and taking the K resistivity imaging gray level images segmented by the S3.1 as an X domain, taking the segmented K Zhang Quan diameter core outer surface gray level images as a Y domain, and learning a mapping function from the X domain to the Y domain by using an antagonistic loss function of an antagonistic network algorithm generated in a circulating manner to realize the mapping from the resistivity imaging images to the full-diameter core outer surface images.
Optionally, in S4, the step of performing high resolution wellbore imaging characterization on the hydrocarbon reservoir includes:
and comparing image effects generated under different training times in the S3.2, optimizing the cycle iteration times, applying the optimal mapping model to the K resistivity imaging gray level images obtained in the S3.1, and finally splicing the images according to the sampling depth to realize the high-resolution shaft imaging representation of the oil and gas reservoir.
The method has the advantages that the method realizes high-resolution shaft imaging representation of the oil and gas reservoir, effectively fuses the advantages of logging information and petrophysical information, makes up for the problem of insufficient representativeness of petrophysical experiments, improves the longitudinal resolution of logging images by at least 10 times, improves the dividing capacity of oil and gas reservoir thin layers, has low cost of cylindrical core plain scanning experiments on the outer surface, reduces the cost of high-resolution shaft imaging representation, is easy to apply and popularize in different oil and gas fields, and lays a foundation for fine evaluation of rock physical parameters of the oil and gas reservoir and construction of three-dimensional digital shafts.
Drawings
FIG. 1 is a flow chart of a high-resolution wellbore imaging characterization method for an oil and gas reservoir based on a cycle generation countermeasure network according to the invention;
fig. 2 is a comparison graph of the resistivity imaging image and the processed image of the invention, (a) is an original dynamic resistivity imaging image of a depth section of a well 4621.9m-4623.6m, (b) is an 8-bit resistivity imaging gray scale image after filling of blank stripes, and (c) is a synthesized high-resolution image.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Taking a well A of a certain oil field in China as an embodiment, the invention relates to an oil-gas reservoir high-resolution shaft imaging characterization method based on a cyclic generation countermeasure network, which comprises the following steps as shown in figure 1:
s1, preprocessing resistivity imaging images
Collecting data collected by an A-well resistivity imaging logging instrument, generating an original dynamic resistivity imaging image by a speed correction method, an abnormal electrode correction method, a voltage correction method, a swing arm correction method and an image enhancement method, as shown in figure 2 (a), filling a blank band of the resistivity imaging dynamic image by combining a multipoint statistical algorithm, removing an abnormal value of the resistivity, replacing the abnormal value with an average value in a range of 3 x 3 around an abnormal point, calculating the content of formation mud by using gamma logging, converting the resistivity into the porosity by using a Siemens formula of a mud rock model, obtaining a resistivity imaging image representing the porosity, and converting the resistivity imaging image into an 8-bit resistivity imaging gray image distributed in a [0,255] interval, as shown in figure 2 (b);
s2, preprocessing the image of the outer surface of the full-diameter rock core
Collecting full-diameter cores of A wells collected by drilling, performing common light scanning on cylindrical cores on the outer surfaces of the A wells at 360 degrees by using a Charge Coupled Device (CCD) in a core scanning analyzer to obtain a common light scanning image with micron-sized resolution, distinguishing pores, clay minerals and different framework components according to the shape and geometric characteristics of the rock components, and converting the images into 8-bit full-diameter core outer surface gray images distributed in a [0,255] interval;
s3, mapping of resistivity imaging image to full-diameter core outer surface image
S3.1: segmenting a resistivity imaging image and an image of the outer surface of the full-diameter core;
dividing the resistivity imaging gray scale image obtained in the step S1 into 7400 images (the resolution is 2.0 mm) with 100 x 100 pixels, compressing the full-diameter core outer surface gray scale image (the resolution is 0.021 mm) obtained in the step S2 to be 16 times of the resolution (the resolution is 0.125 mm) of the resistivity imaging image, and dividing the full-diameter core outer surface gray scale image into 7400 images with 1600 x 1600 pixels;
s3.2: establishing a mapping relation from the resistivity imaging image to the full-diameter core outer surface image;
taking the 7400 resistivity imaging gray level images segmented by S3.1 as an X domain, taking the segmented 7400 Zhang Quan diameter core outer surface gray level images as a Y domain, learning a mapping function from the X domain to the Y domain by using a countermeasure loss function of a circularly generated countermeasure network algorithm, and establishing a mapping relation from the resistivity imaging images to the full-diameter core outer surface images;
s4, high-resolution shaft imaging characterization of oil and gas reservoir
Comparing image effects generated under different training times in the S3.2, determining that the number of the optimized loop iterations is 40, applying the optimal mapping model to 7400 resistivity imaging gray images obtained in the S3.1, and finally splicing the images according to sampling depth to realize the imaging representation of the high-resolution shaft of the oil and gas reservoir as shown in the figure 2 (c).
It is to be understood that the above description is not intended to limit the present invention, and the present invention is not limited to the above examples, and those skilled in the art may make modifications, alterations, additions or substitutions within the spirit and scope of the present invention.
Claims (5)
1. A high-resolution wellbore imaging characterization method for an oil and gas reservoir based on a cycle generation countermeasure network is characterized by comprising the following steps:
s1, preprocessing a resistivity imaging image;
s2, preprocessing an image of the outer surface of the full-diameter rock core;
s3, mapping the resistivity imaging image to a full-diameter core outer surface image;
and S4, carrying out high-resolution wellbore imaging characterization on the oil-gas reservoir.
2. The method for characterizing high-resolution borehole imaging of hydrocarbon reservoirs based on cycle-generated countermeasure networks according to claim 1, wherein in S1, the step of preprocessing the resistivity imaging image comprises:
collecting data collected by a resistivity imaging logging instrument, generating a resistivity imaging dynamic image through a correction and image enhancement method, filling a blank band of the resistivity imaging dynamic image by combining a statistical algorithm, removing an abnormal value of the resistivity, replacing the abnormal value with an average value in an A multiplied by A range surrounding the point, converting the resistivity into the porosity by utilizing a Siemens degree formula of a argillaceous rock model to obtain a resistivity imaging image representing the porosity, and converting the resistivity imaging image into an 8-bit resistivity imaging gray image distributed in a [0,255] interval.
3. The method for characterizing high-resolution wellbore imaging of a hydrocarbon reservoir based on a cycle-generated countermeasure network as claimed in claim 1, wherein in S2, the step of preprocessing the image of the outer surface of the full-diameter core comprises:
collecting full-diameter cores collected by drilling, performing cylindrical core plain scanning on the outer surface of 360 degrees by using a charge coupling device in a core scanning analyzer to obtain an image with micron-level resolution, distinguishing pores, clay minerals and different framework components according to the shape and geometric characteristics of rock components, and converting the images into 8-bit full-diameter core outer surface gray images distributed in a [0,255] interval.
4. The method for high-resolution imaging characterization of a hydrocarbon reservoir based on a cycle-generated countermeasure network as claimed in claim 1 wherein the step of mapping the resistivity imaging image to the full diameter core outer surface image in S3 comprises:
s3.1: dividing the resistivity imaging gray level image obtained in the step S1 into K resistivity imaging gray level images with M multiplied by M pixels, compressing the full-diameter core outer surface gray level image obtained in the step S2 to Q times of the resolution of the resistivity imaging image, and dividing the full-diameter core outer surface gray level image into K Zhang Quan diameter core outer surface gray level images with (M multiplied by Q) x (M multiplied by Q) pixels, wherein Q is an integer;
s3.2: and taking the K resistivity imaging gray level images obtained after the S3.1 segmentation as an X domain, taking the K Zhang Quan diameter core outer surface gray level images obtained after the S3.1 segmentation as a Y domain, and learning a mapping function from the X domain to the Y domain by using a confrontation loss function of a circularly generated confrontation network algorithm to realize the mapping from the resistivity imaging images to the full-diameter core outer surface images.
5. The method for characterizing high-resolution wellbore imaging of a hydrocarbon reservoir based on a cycle-generated countermeasure network of claim 1, wherein in S4, the step of characterizing high-resolution wellbore imaging of the hydrocarbon reservoir comprises:
and comparing image effects generated under different training times in the S3.2, optimizing the cycle iteration times, applying the optimal mapping model to the K resistivity imaging gray level images obtained in the S3.1, and finally splicing the images according to the sampling depth to realize the high-resolution shaft imaging representation of the oil and gas reservoir.
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