Glutenite reservoir classification evaluation method based on electric imaging image segmentation and porosity spectrum

Abstract

The invention relates to a glutenite reservoir classification evaluation method based on electric imaging image segmentation and a porosity spectrum, and belongs to the technical field of reservoir classification evaluation. The invention discloses a glutenite reservoir classification evaluation method based on electric imaging image segmentation and a porosity spectrum. The glutenite reservoir classification evaluation method comprises the following steps: converting an imaging logging diagram into a grey-scale map; segmenting the grey-scale map by using an OTSU and watershed combined image segmentation algorithm, and extracting particle size parameters; processing the grey-scale map into a porosity spectrum and extracting porosity spectrum parameters; calculating a reservoir evaluation factor S according to the particle size parameter and the porosity spectrum parameter; and performing classification and evaluation according to the reservoir evaluation factor S. By means of the method, the capacity of logging information in the aspect of describing the anisotropism of the reservoir is greatly improved. Particularly, for glutenite reservoirs without obvious argillaceous interlayers, key information such as rock particle sizes, sorting conditions, directional arrangement and sedimentary interfaces can be captured, and quantitative evaluation on the heterogeneity of the reservoirs is further realized.

Description

Technical Field

[0001] The invention relates to a sandstone reservoir classification and evaluation method based on electrical imaging image segmentation and porosity spectrum, belonging to the technical field of reservoir classification and evaluation. Background Art

[0002] Reservoir classification and evaluation is a key link in petroleum geology, which runs through all stages of oil and gas resource exploration, development and management. Its goal is to analyze the physical properties and heterogeneous distribution of reservoirs in detail, and provide basic data support for the optimization of development plans. Reservoir classification and evaluation integrates information on rocks, pore structures, fluid types and distribution, etc., to help accurately determine the fluid storage capacity of reservoirs and the migration capacity of fluids in reservoirs, which is the core content of oil and gas development. Through the detailed classification and analysis of rock types and pore structures, the fluid storage characteristics of different types of reservoirs can be effectively identified, and the mining strategy can be optimized accordingly. Reservoirs are usually divided into different types such as sandstone, carbonate rock, and tight rock according to the differences in rock genesis, composition and structure. Sandstone reservoirs have high porosity and permeability and usually have good oil and gas conductivity; while carbonate rocks have various pore types due to the complexity of sedimentation and diagenesis, including intergranular pores, caves, and fractures, resulting in large heterogeneity in their physical parameters. The pore structure types corresponding to different types of rocks, such as pore type, fracture type, cave type, etc., directly affect the storage capacity of the reservoir and the flow mode of fluid. Furthermore, reservoir physical parameters such as porosity, permeability, and saturation reflect the basic characteristics of the reservoir and affect the occurrence state of oil and gas and its flow resistance. The changes in these physical parameters make different types of reservoirs respond differently to treatment methods such as pressure and water injection during the development process.

[0003] Traditional methods for classifying conglomerate reservoirs, such as the capillary pressure curve characteristic method, predict changes in reservoir properties based on the characteristics of the capillary pressure curve combined with nuclear magnetic resonance (NMR) logging data; the multidimensional parameter method, which uses core observation, microscopic lithofacies identification and physical property determination techniques to construct a sophisticated reservoir classification system based on multidimensional parameters such as porosity, spatial morphology of reservoir bodies, and geometric distribution of pore throat structures; the sedimentary phase, diagenetic phase and fracture characteristic method, which scientifically and meticulously classifies and evaluates reservoirs through detailed analysis of sedimentary phases, diagenetic phases and fracture development characteristics; and statistical analysis based on conventional logging curves, etc. These methods comprehensively evaluate the characteristics of conglomerate reservoirs from different angles and provide a scientific basis for the exploration and development of oil and gas resources.

[0004] Although traditional sandstone reservoir classification methods have played an important role in oil and gas exploration and development, they also have certain limitations. Petrological classification is limited by the representativeness of core sampling and the difficulty of observing microscopic features; sedimentological classification faces the challenges of complex sedimentary environment and large lateral changes; diagenetic classification is difficult due to the complexity of diagenetic history and the problems of time and space scale. Summary of the invention

[0005] In order to overcome the defects existing in the prior art, the present invention aims to provide a classification and evaluation method for sandstone reservoirs based on electrical imaging image segmentation and porosity spectrum, which greatly improves the ability of logging data in describing reservoir heterogeneity. Especially for those sandstone reservoirs without obvious mud interlayers, FMI logging has high resolution and wide coverage, and can capture key information such as rock particle size, sorting, directional arrangement and sedimentary interface, thereby realizing quantitative evaluation of reservoir heterogeneity. In addition, FMI logging images can also be compared with core data in detail. This method shows great potential in evaluating reservoir effectiveness and solving the problem of evaluating the production capacity characteristics of strongly heterogeneous reservoirs.

[0006] The present invention provides a technical solution to solve the above technical problems: a classification and evaluation method for sandstone reservoirs based on electrical imaging image segmentation and porosity spectrum, comprising the following steps:

[0007] A. Convert the imaging logging image into a grayscale image;

[0008] B. Use OTSU combined with watershed image segmentation algorithm to segment the grayscale image and extract particle size parameters;

[0009] C. Processing the grayscale image into a porosity spectrum and extracting porosity spectrum parameters;

[0010] D. Calculate the reservoir evaluation factor S based on the particle size parameters and porosity spectrum parameters;

[0011] E. Classify and evaluate according to reservoir evaluation factor S.

[0012] A further technical solution is that the specific process of step B is as follows:

[0013] B1, image preprocessing;

[0014] B2. Based on the preprocessed binary image, calculate the distance transform to obtain the distance from each pixel to the nearest background point to form a distance map;

[0015] B3, perform watershed segmentation based on the local maximum points in the distance map;

[0016] B4, extracting the contours formed by all continuous non-zero pixels in the image, and calculating the area and perimeter of each contour respectively;

[0017] B5. Save the image after OTSU binarization;

[0018] B6. First read the image and obtain the size information of the original image, then input the wellbore parameters to reset a new image width, calculate the new height based on the new width, adjust the image size while maintaining the aspect ratio, and finally use the new image parameters to extract the contour and obtain the actual conglomerate particle size d.

[0019] A further technical solution is that the porosity spectrum parameters include porosity spectrum variance and porosity spectrum average porosity.

[0020] A further technical solution is that the specific process of step C is as follows: normalizing the grayscale distribution in the grayscale image, that is, calculating the grayscale average value of each logging depth point, and taking the value as the representative grayscale index at the depth; converting the imaging grayscale distribution histogram into a porosity distribution histogram through the conversion of the Archie formula, and then extracting the porosity spectrum parameters reflecting the reservoir heterogeneity from the porosity distribution histogram.

[0021] A further technical solution is that the Alzi formula is:

[0022]

[0023] Where: φ is the porosity; F gray is the grayscale normalization number of the FMI image.

[0024] A further technical solution is that the calculation formula of the porosity spectrum parameter includes:

[0025]

[0026] Where: φ is porosity; σ is porosity spectrum variance; φ i is the porosity spectrum porosity; F i is the porosity spectrum amplitude; n is the number of statistical intervals of the porosity spectrum; is the average porosity of the porosity spectrum.

[0027] A further technical solution is that the calculation formula of the reservoir evaluation factor S is:

[0028]

[0029] Where: f1(), f2(), f3(), f4(), f5() are normalized functions; K is permeability; F is fracture factor; σ is porosity spectrum variance; S is reservoir evaluation factor.

[0030] A further technical solution is that the specific evaluation process in step E is:

[0031] When the reservoir evaluation factor S>0.25, the reservoir is a Class I reservoir. Class I reservoirs have good porosity, permeability and fluid properties, and the oil and gas in the reservoir can flow smoothly to the wellbore;

[0032] When 0<reservoir evaluation factor S≤0.25, the reservoir is a Class II reservoir, which has better physical properties, better pore structure and permeability, and has certain heterogeneity;

[0033] When -0.25≤reservoir evaluation factor S≤0, the reservoir is a Class III reservoir, which has relatively poor physical properties and large heterogeneity;

[0034] When the reservoir evaluation factor S is less than -0.25, the reservoir is a Class IV reservoir, which has the characteristics of low porosity and low permeability.

[0035] The present invention has the following beneficial effects:

[0036] (1) This method effectively removes noise in complex backgrounds, improves the clarity and connectivity of particle contours, and thus improves the quality of image segmentation.

[0037] (2) It can not only provide information on reservoir pore structure, but also help identify key factors affecting reservoir performance.

[0038] (3) The reservoir evaluation factor S can comprehensively reflect the intrinsic quality of the reservoir and quantitatively analyze its correlation with the reservoir production capacity. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 This is the flow chart of the watershed algorithm;

[0040] Figure 2 This is the formation microresistivity imaging map processing map;

[0041] Figure 3 Comparison of the original well logging image, OTSU segmentation image and watershed segmentation image;

[0042] Figure 4 It is the intersection diagram of grayscale and resistivity. DETAILED DESCRIPTION

[0043] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] A classification and evaluation method for sandstone reservoirs based on electrical imaging image segmentation and porosity spectrum of the present invention comprises the following steps:

[0045] A. Convert the imaging logging image into a grayscale image;

[0046] In a grayscale image, the value of each pixel represents the brightness level of that location, usually represented by an integer between 0 and 255, where 0 represents black, 255 represents white, and intermediate values ​​represent different shades of gray;

[0047] B. Use OTSU combined with watershed image segmentation algorithm to segment the grayscale image and extract particle size parameters;

[0048] B1, image preprocessing;

[0049] First, use the `cv2.imread` function to load the image to be processed and convert it to a grayscale image, laying the foundation for subsequent processing steps.

[0050] Next, the `cv2.threshold` function in the OpenCV library was used to perform the OTSU binarization operation, and the optimal threshold was automatically selected to segment the image into foreground and background. At the same time, in order to ensure that the target area is displayed in white in the image and the background area is displayed in black, the `cv2.THRESH_BINARY_INV` mode was used. In order to further optimize the binarization results and reduce the influence of noise, the `morphology.binary_opening` function in the Scikit-image library was used to perform morphological opening operations on the binary image, and a disk with a radius of 3 was selected as the structural element, which effectively removed small noise points and made the target outline clearer;

[0051] B2. Based on the preprocessed binary image, calculate the distance transform to get the distance from each pixel to the nearest background point to form a distance map. Then, use the `peak_local_max` function in the Scikit-image library to find the local maximum points in the distance map. These points are the seed points or marker points in the watershed algorithm.

[0052] B3, perform watershed segmentation based on the local maximum points in the distance map;

[0053] The `segmentation.watershed` function in the Scikit-image library is used to perform a watershed transformation based on the marker points determined in the previous step. Based on the terrain characteristics of the distance map, the area is "flooded" starting from the marker points until different areas meet to form a boundary, thus completing the image segmentation task.

[0054] In order to facilitate the observation and analysis of the segmentation effect, the `color.label2rgb` function is used to convert the segmentation label map into a color image, so that different segmentation areas are displayed in different colors, which enhances the visual effect;

[0055] B4. Call the `cv2.findContours` function in the OpenCV library to detect and extract the contours formed by all continuous non-zero pixels in the image. In this process, select the `cv2.RETR_EXTERNAL` mode to retrieve the outermost contour, and the `cv2.CHAIN_APPROX_SIMPLE` parameter is used to simplify the contour points and retain only the key points, thereby reducing the amount of data and speeding up the processing speed. By traversing all the detected contours, the area and perimeter of each contour are calculated separately. In order to filter out possible small noise interference, only when the contour area is greater than 0, it is included in the total area and perimeter statistics;

[0056] B5. Use the `cv2.imwrite` function to save the OTSU binarized image, which facilitates the subsequent possible data analysis or image processing;

[0057] B6. According to the data of the study area, the well diameter (cal) curve value corresponding to this depth is given as 8 inches, and this well diameter value is added to the code; first read the image and obtain the size information of the original image, then input the well diameter parameter to reset a new image width, and then calculate the new height according to the new width, adjust the image size while maintaining the aspect ratio, and finally use the new image parameters to extract the contour and obtain the actual conglomerate particle size d;

[0058] C. Processing the grayscale image into a porosity spectrum and extracting porosity spectrum parameters;

[0059] Normalize the grayscale distribution in the grayscale image, that is, calculate the grayscale average value of each logging depth point, and use this value as the representative grayscale index at this depth; convert the imaging grayscale distribution histogram into a porosity distribution histogram through the conversion of the Alch formula, and then extract the porosity spectrum parameters reflecting the reservoir heterogeneity from the porosity distribution histogram;

[0060] Archie established the first formation factor formula describing the relationship between formation resistivity and porosity through a series of experiments. In resistivity imaging, white usually represents the rock matrix particles with the highest resistivity, and the change of color in the image indicates the change of formation resistivity;

[0061] Archie's formula is:

[0062]

[0063] Where: φ is the porosity; F gray is the grayscale normalization number of the FMI image;

[0064] The following two key characteristic parameters are adopted:

[0065] (1) Variance: This parameter measures the degree of dispersion of the porosity spectrum relative to the average value. The smaller the variance, the more concentrated the porosity distribution and the weaker the reservoir heterogeneity. The calculation formula of variance is as follows:

[0066]

[0067] (2) Mean: This index reflects the overall level of the reservoir porosity spectrum. The larger the mean, the higher the overall reservoir porosity. The calculation formula for the mean is as follows:

[0068]

[0069] Where: φ is porosity; σ is porosity spectrum variance; φ i is the porosity spectrum porosity; F i is the porosity spectrum amplitude; n is the number of statistical intervals of the porosity spectrum; is the average porosity of the porosity spectrum;

[0070] D. Calculate the reservoir evaluation factor S based on the particle size parameters and porosity spectrum parameters;

[0071] The storage capacity of the reservoir is characterized by the particle size d of the sandstone; the variance σ and mean of the porosity spectrum are used To describe the microscopic pore structure heterogeneity of the reservoir; use permeability K and fracture factor F to reflect the flowability of the reservoir. The reservoir evaluation factor S is constructed through the five influencing parameters of particle size, variance, mean, permeability and fracture factor. The better the storage capacity, the weaker the heterogeneity, the better the reservoir properties, the larger the corresponding S value, and vice versa.

[0072] Once the classification parameters are determined, the weight coefficient of the parameters determines the quality of the classification effect. Generally, the objective weighting method or the subjective weighting method is used to calculate the weight coefficient. The objective weighting method determines the weight coefficient completely based on the classification parameters. This method is likely to cause the weight coefficient to deviate from the actual situation and cannot truly reflect the importance of the classification parameters. The subjective weighting method, as the name suggests, reflects the subjective intention of the weight granter in the weighting process, but it is also easy to bring about the problem of excessive subjective randomness. Therefore, try to use the coefficient of variation method to determine the weight coefficient of each classification parameter.

[0073] The coefficient of variation is a statistic used to measure relative variability. It indicates the dispersion of a data set by normalizing the standard deviation to the mean. The coefficient of variation is often used to compare the variability of data sets of different scales or units because it eliminates the effect of the measurement unit and provides an idea of ​​the relative standard deviation.

[0074] The formula for calculating the coefficient of variation is:

[0075] CV=σ / μ×100%

[0076] Among them, σ is the standard deviation of the sample; μ is the mean of the sample; CV is the coefficient of variation, usually expressed as a percentage.

[0077] After obtaining the parameter weight coefficient according to the calculation formula, the formula of reservoir evaluation factor S is as follows:

[0078]

[0079] Where: f1(), f2(), f3(), f4(), f5() are normalized functions; K is permeability; F is fracture factor; σ is porosity spectrum variance; S is reservoir evaluation factor;

[0080] E. Classify and evaluate according to reservoir evaluation factor S;

[0081] Table 1 Classification standards for reservoir types

[0082]

[0083] When the reservoir evaluation factor S>0.25, the reservoir belongs to Class I reservoir. This type of reservoir is characterized by extremely high production capacity, with daily oil production being able to stably exceed 5 tons. A high S value means that the reservoir has good porosity, permeability and fluid properties, and the oil and gas in the reservoir can flow smoothly to the wellbore, ensuring a high daily production.

[0084] When 0<reservoir evaluation factor S≤0.25, the reservoir is a Class II reservoir. The daily production of this type of reservoir ranges from 3 to 5 tons, and its production capacity is not as good as that of Class I reservoirs. Class II reservoirs have better physical properties, better pore structure and permeability, and have certain heterogeneity. By optimizing mining technology and management measures, Class II reservoirs can still be effectively developed.

[0085] When -0.25≤reservoir evaluation factor S≤0, the reservoir belongs to Class III reservoir. Its daily oil production is generally between 1 and 3 tons. The reservoir physical properties are relatively poor, and there is a large heterogeneity, which leads to a large oil flow resistance and affects the output efficiency of the oil well. The application of enhanced recovery technology, such as water flooding and chemical flooding, can improve the flow conditions of the reservoir to a certain extent and increase the output of the oil well.

[0086] When the reservoir evaluation factor S is less than -0.25, the reservoir is a Class IV reservoir. The daily oil production of this type of reservoir is less than 1 ton, and the production capacity is poor. Class IV reservoirs usually have characteristics such as low porosity and low permeability, which leads to great resistance to oil flow and difficulty in forming effective oil flow channels. Since the development of this type of reservoir is difficult and the economic benefits are limited, they are often not the priority for development in the traditional sense. By adopting advanced exploration and development technologies, such as horizontal well drilling, staged fracturing and other means, combined with efficient oil production processes, it is possible to improve the recovery rate of Class IV reservoirs.

[0087] The above description is not intended to limit the present invention in any form. Although the present invention has been disclosed through the above embodiments, it is not intended to limit the present invention. Any technician familiar with the profession can make some changes or modifications to equivalent embodiments of equivalent changes using the technical contents disclosed above without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are within the scope of the technical solution of the present invention.

Claims

1. A classification and evaluation method for sandstone reservoirs based on electrical imaging image segmentation and porosity spectrum, characterized in that: The following steps are involved: A. Convert the imaging logging image into a grayscale image; B. Use OTSU combined with watershed image segmentation algorithm to segment the grayscale image and extract particle size parameters; C. Processing the grayscale image into a porosity spectrum and extracting porosity spectrum parameters; D. Calculate the reservoir evaluation factor S based on the particle size parameters and porosity spectrum parameters; E. Classify and evaluate according to reservoir evaluation factor S.

2. The method for classifying and evaluating sandstone reservoirs based on electrical imaging image segmentation and porosity spectrum according to claim 1, characterized in that: The specific process of step B is as follows: B1, image preprocessing; B2. Based on the preprocessed binary image, calculate the distance transform to obtain the distance from each pixel to the nearest background point to form a distance map; B3, perform watershed segmentation based on the local maximum points in the distance map; B4, extracting the contours formed by all continuous non-zero pixels in the image, and calculating the area and perimeter of each contour respectively; B5. Save the image after OTSU binarization; B6. First read the image and obtain the size information of the original image, then input the wellbore parameters to reset a new image width, calculate the new height based on the new width, adjust the image size while maintaining the aspect ratio, and finally use the new image parameters to extract the contour and obtain the actual conglomerate particle size d.

3. The method for classifying and evaluating sandstone reservoirs based on electrical imaging image segmentation and porosity spectrum according to claim 1, characterized in that: The porosity spectrum parameters include porosity spectrum variance and porosity spectrum average porosity.

4. The method for classifying and evaluating sandstone reservoirs based on electrical imaging image segmentation and porosity spectrum according to claim 3 is characterized in that: The specific process of step C is as follows: normalizing the grayscale distribution in the grayscale image, that is, calculating the grayscale average value of each logging depth point, and taking the value as the representative grayscale index at the depth; converting the imaging grayscale distribution histogram into a porosity distribution histogram through the conversion of the Alch formula, and then extracting the porosity spectrum parameters reflecting the reservoir heterogeneity from the porosity distribution histogram.

5. The method for classifying and evaluating sandstone reservoirs based on electrical imaging image segmentation and porosity spectrum according to claim 4, characterized in that: The Alzi formula is: Where: φ is the porosity; F gray is the grayscale normalization number of the FMI image.

6. The method for classifying and evaluating sandstone reservoirs based on electrical imaging image segmentation and porosity spectrum according to claim 4, characterized in that: The calculation formula of the porosity spectrum parameter includes: Where: φ is porosity; σ is porosity spectrum variance; φ i is the porosity spectrum porosity; F i is the porosity spectrum amplitude; n is the number of statistical intervals of the porosity spectrum; is the average porosity of the porosity spectrum.

7. The method for classifying and evaluating sandstone reservoirs based on electrical imaging image segmentation and porosity spectrum according to claim 4, characterized in that: The calculation formula of the reservoir evaluation factor S is: Where: f1(), f2(), f3(), f4(), f5() are normalized functions; K is permeability; F is fracture factor; σ is porosity spectrum variance; S is reservoir evaluation factor.

8. The method for classifying and evaluating sandstone reservoirs based on electrical imaging image segmentation and porosity spectrum according to claim 1, characterized in that: The specific evaluation process in step E is as follows: When the reservoir evaluation factor S>0.25, the reservoir is a Class I reservoir. Class I reservoirs have good porosity, permeability and fluid properties, and the oil and gas in the reservoir can flow smoothly to the wellbore; When 0<reservoir evaluation factor S≤0.25, the reservoir is a Class II reservoir, which has better physical properties, better pore structure and permeability, and has certain heterogeneity; When -0.25≤reservoir evaluation factor S≤0, the reservoir is a Class III reservoir, which has relatively poor physical properties and large heterogeneity; When the reservoir evaluation factor S is less than -0.25, the reservoir is a Class IV reservoir, which has the characteristics of low porosity and low permeability.

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