Method and apparatus for identifying features in carbonate reservoirs having high resistivity from high resolution oil-based mud images

Abstract

Presented embodiments provide a method for identifying defects (referred to as holes) in a geological formation. Hole identification is performed on data from high resolution oil-based mud images obtained from cables and / or drilling activities.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to and the benefit of U.S. Provisional Patent Application No. 63 / 509,840, filed on June 23, 2023, entitled “Method and Apparatus to Identification of Features in a Carbonate Reservoir with High Resistivity from High Resolution Oil-Based Mud Images,” the entire disclosure of which is incorporated herein by reference. Technical Field

[0003] Aspects of the present disclosure relate to identification of features in carbonate reservoirs. More specifically, aspects of the present disclosure relate to identification of vug features in carbonate reservoirs having high resistivity based on high resolution oil-based mud images. Background Art

[0004] Void features are important for carbonate reservoir characterization. Void features are defects in geological formations. These defects can be, for example, cracks, voids, or other types of spaces. These voids can contain fluids or can be empty. Voids are commonly found in many types of geological formations. In order to determine the presence of voids, operators can obtain borehole images. Borehole image data is one of the basic data sources for void feature analysis in addition to expensive core data. In water-based mud environments, there are conventional algorithms for void feature identification. Surface density assessment can be performed from resistivity image data. To date; however, there is no workflow or method available for void analysis from resistivity images in oil-based mud environment data. Although ultrasonic images can be used for void analysis in oil-based mud environments, the mud solids have a great impact on image quality. The lower logging speed used to obtain such images is also a major factor in the application in offshore drilling rigs with oil-based mud.

[0005] It is difficult to measure true formation resistivity in an oil-based mud environment. Typically, attempts are made to obtain resistivity values ​​using multi-array induction tools. High-resolution induction logging tools can provide detailed geological textural features, but the resistivity measurements provide relative resistivity contrasts rather than true formation resistivity. This relative resistivity value is achieved in the so-called "wash zone" of the wellbore. Such high-resolution oil-based mud (OBM) tools are pad-based micro-electric imagers that operate at high frequencies to establish capacitive contact with the formation in the wellbore filled with non-conductive mud. From multiple operating modes, formation resistivity-like images are generated using an effective composite data processing scheme that approximates the formation resistivity by filtering or applying corrections to minimize the contribution of the OBM to the measured signal. Data from different modes are blended based on estimated formation parameters to generate optimized images.

[0006] In addition to the above-mentioned composite processing schemes, a model-based parameter inversion for quantitative interpretation has been developed. The Gauss-Newton algorithm matches the measurements with an accurate, computationally efficient approximate forward model built by multidimensional fitting of data generated using finite element simulations. The conventional workflow overcomes the underdetermined inversion problem and the calibration limitations of the measurements. The inversion allows flexible model definition and parameterization, including refinement of the calibration, and can process logging data and measured intervals from multiple sources simultaneously. The conventional workflow stabilizes the inversion and improves the consistency of the processing results.

[0007] Compared with the constant conductive features of resistivity images in water-based mud environments, the response of hole features on resistivity images in oil-based mud environments is very complex. Depending on the resistivity contrast between the formation resistivity in the flushing zone and the mud or mud filter, the hole features can be based on resistivity or conductivity. Hayman factor images are applied to hole feature classification; however, the absolute value of the Hayman factor image can present mud or mud filter in the hole. Local contrast is very useful for hole feature identification, but hole features can be high or low contrast values ​​of the Hayman factor image, depending on the hole size and the surrounding rock matrix. The inverted standoff image represents the roughness of the borehole, and relatively low or high standoffs can indicate hole features. When the size of the hole feature is large enough, the hole is usually filled with mud filters and is represented as a low standoff feature. There is no effective way to identify hole features based only on image response. In addition, the research depth of image tools is very shallow. Image measurements may also be affected by mud cake thickness.

[0008] There is a need to provide an apparatus and method that is easier to operate than conventional apparatus and methods for identifying hole features based on oil-based mud images.

[0009] There also remains a need to provide apparatus and methods that do not suffer from the above-mentioned disadvantages of not being able to determine the presence of voids in certain wells.

[0010] There remains a further need to reduce the economic costs associated with the above operations and equipment using conventional tools and allow operators to cost effectively determine the presence of vugs in carbonate reservoirs. Summary of the invention

[0011] In order to be able to understand in detail the manner in which the above-mentioned features of the present disclosure are achieved, a more specific description of the present disclosure briefly summarized below can be obtained by reference to the embodiments, some of which are shown in the accompanying drawings. It should be noted that the accompanying drawings only show typical embodiments of the present disclosure and should not be considered as limiting the scope thereof, as the present disclosure may allow other equally effective embodiments without specific description. Therefore, the following invention content only provides a few aspects of the description and should not be used to limit the described embodiments to a single concept.

[0012] In one example embodiment, a method for identifying features in a carbonate reservoir is disclosed. The method may include obtaining an induction resistivity log from a borehole within the carbonate reservoir. The method may also include obtaining inverted resistivity image data. The method may also include obtaining inverted gap image data. The method may also include obtaining inverted Hayman factor image data. The method may also include calculating an image average resistivity calculation based on the inverted resistivity image data. The method may also include performing resistivity zoning of the borehole based on the induction resistivity log and the average resistivity calculation. The method may also include performing patch extraction based on the inverted resistivity image data. The method may also include performing image local histogram equalization from the inverted gap and Hayman factor images. The method may also include performing high and low patches from the local dynamic gap and Hayman factor images. The method may also include classifying patches of vug features based on the extraction and the resistivity zoning. The method may also include performing a surface density calculation.

[0013] In another example embodiment, a method for identifying features in a carbonate reservoir is described. The method may include obtaining an induction resistivity log from a borehole within the carbonate reservoir. The method may also include obtaining inverted resistivity image data. The method may also include obtaining inverted gap image data. The method may also include obtaining inverted Hayman factor image data. The method may also include calculating an image average resistivity calculation based on the inverted resistivity image data. The method may also include performing resistivity zoning of the borehole based on the induction resistivity log and the average resistivity calculation, wherein the resistivity zoning includes four different regions. The method may also include performing extraction of patches based on the inverted resistivity image data. The method may also include performing local histogram equalization of the image based on the inverted gap and Hayman factor images. The method may also include performing high and low patches based on the local dynamic gap and Hayman factor images. The method may also include classifying patches of void features based on the extraction and the resistivity zoning. The method may also include filtering the void features and performing a surface density calculation.

[0014] In another example embodiment, an article of manufacture is disclosed that includes a set of instructions stored to run on a computer, the article of manufacture being configured to store the set of instructions in a non-volatile manner. The instructions contained on the article of manufacture may include obtaining an induction resistivity log from a wellbore within a carbonate reservoir. The instructions contained on the article of manufacture may also include obtaining inverted resistivity image data. The instructions contained on the article of manufacture may also include obtaining inverted gap image data. The instructions contained on the article of manufacture may also include obtaining inverted Hayman factor image data. The instructions contained on the article of manufacture may also include calculating an image average resistivity calculation based on the inverted resistivity image data. The instructions contained on the article of manufacture may also include performing resistivity zoning on the wellbore based on the induction resistivity log and the average resistivity calculation. The instructions contained on the article of manufacture may also include performing a patch extraction based on the inverted resistivity image data. The instructions contained on the article of manufacture may also include performing an image local histogram equalization from the inverted gap and Hayman factor images. The instructions contained on the article of manufacture may also include performing high and low patching from the local dynamic gap and Hayman factor images. The instructions contained on the article may also include classifying patches of void features based on extraction and resistivity zoning. The instructions contained on the article may also include filtering the void features. The instructions contained on the article may also include performing a surface density calculation based on void filtering. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order that the manner in which the above-mentioned features of the present disclosure can be understood in detail, a more particular description of the present disclosure briefly summarized above may be given by reference to embodiments, some of which are shown in the accompanying drawings. It should be noted, however, that the drawings illustrate only typical embodiments of the present disclosure and are therefore not to be considered limiting of its scope, as the present disclosure may admit to other equally effective embodiments.

[0016] Figure 1 is a method for analyzing oil-based mud resolution images in an example embodiment of the present disclosure.

[0017] Figure 2 is an illustration of high and low value patches of local histogram equalization processed on the inverted gap and Hayman factor images.

[0018] Figure 3 Illustration of inhomogeneity features on a dynamic image and patches extracted in high resistivity areas.

[0019] Figure 4 is an illustration of inhomogeneity features on an image in a low resistivity region with a larger resistivity difference.

[0020] Figure 5 is an illustration of inhomogeneity features on an image in a low resistivity region with a smaller resistivity difference.

[0021] Figure 6 is an illustration of inhomogeneity features on the image in the intermediate resistivity region.

[0022] Figure 7 is a graphic representation of the classified and filtered images and the comparison of hole density.

[0023] Figure 8 is an illustration of the stacked hole images used for resistivity threshold estimation.

[0024] To facilitate understanding, identical reference numerals have been used, where possible, to designate identical elements that are common to the figures. It is contemplated that elements disclosed in one embodiment may be beneficially utilized on other embodiments without specific recitation. DETAILED DESCRIPTION

[0025] In the following, reference is made to embodiments of the present disclosure. However, it should be understood that the present disclosure is not limited to the specifically described embodiments. On the contrary, any combination of the following features and elements, whether or not related to different embodiments, is contemplated to implement and practice the present disclosure. In addition, although the embodiments of the present disclosure can achieve advantages over other possible solutions and / or advantages over the prior art, whether a specific advantage is achieved by a given embodiment is not a limitation of the present disclosure. Therefore, the following aspects, features, embodiments and advantages are merely illustrative and are not considered to be elements or limitations of the claims unless expressly stated in the claims. Similarly, reference to the "present disclosure" should not be interpreted as a summary of the inventive subject matter disclosed herein, and should not be considered to be elements or limitations of the claims unless expressly stated in the claims.

[0026] Although the terms first, second, third, etc. can be used here to describe various elements, components, regions, layers and / or parts, these elements, components, regions, layers and / or parts should not be limited by these terms. These terms can only be used to distinguish an element, component, region, layer or part from another region, layer or part. When used in this article, terms such as "first", "second" and other numerical terms do not imply order or sequence unless the context clearly indicates. Therefore, without departing from the teaching of the exemplary embodiment, the first element, component, region, layer or part discussed herein can be referred to as the second element, component, region, layer or part.

[0027] When an element or layer is referred to as being "on another element or layer," "engaged to," "connected to," or "coupled to" another element or layer, it may be directly on, engaged to, connected to, coupled to, or there may be intermediate elements or layers. Conversely, when an element is referred to as being "directly on another element or layer," "directly engaged to," "directly connected to," or "directly coupled to" another element or layer, there may be no intermediate elements or layers. Other words used to describe the relationship between elements should be interpreted in a similar manner. As used herein, the term "and / or" includes any and all combinations of one or more associated listed terms.

[0028] Some embodiments will now be described with reference to the accompanying drawings. For consistency, the same elements in each of the accompanying drawings will be represented by the same reference numerals. In the following description, many details are set forth to provide an understanding of various embodiments and / or features. However, it will be appreciated by those skilled in the art that some embodiments may be put into practice without many of these details, and that many variations or modifications from the described embodiments are possible. As used herein, the terms "above" and "below", "upwards" and "downwards", "top" and "bottom", "upwardly" and "downwardly" and other similar terms indicating relative positions above or below a given point are used in this specification to more clearly describe certain embodiments.

[0029] refer to Figure 1 , illustrating one aspect of the present invention, wherein an integrated method 100 for identifying vugs from OBM high resolution images is presented. In an embodiment, although the inverted resistivity images provide a more accurate resistivity image of the wellbore, the absolute image values ​​are not the true formation resistivity in the image measurement depth range. Since these images cannot provide the true formation resistivity, it is necessary to provide such a value. In an embodiment; furthermore, under downhole conditions, the mud or mud filter is not calculated from two megahertz measurements. In conjunction with external induction resistivity logging, the relationship between the formation resistivity and the resistivity of the mud filter layer is identified. The inverted gap and Hayman factor images have a complex response to vug features and are embedded in a workflow for vug feature identification and filtering. The conductive vug texture features or low amplitude vug features extracted from the water-based mud image are reused and applied to extract conductive and resistive sheet blocks from the oil-based mud image.

[0030] refer to Figure 1, a method 100 for identifying features in a carbonate reservoir with high resistivity from a high resolution oil-based mud image is shown. In the method, data related to induction resistivity logging is obtained at 102. In addition to the data obtained at 102, inverted resistivity image data is obtained at 106. The induction resistivity logging 102 can be obtained from a resistivity tool placed in the wellbore during a wireline operation, or it can be obtained during the drilling process. In a similar manner, the inverted resistivity image data can be obtained during a wireline operation, or the inverted resistivity image data can be obtained during the drilling process. Starting with the induction resistivity logging data at 102, the process of resistivity partitioning 104 occurs. In such a process, the resistivity of the wellbore is decomposed into discrete zones. The size and shape of the discrete zones can vary. Starting with the inverted resistivity image data obtained at 106, the data can be used to calculate the image average resistivity at 108, and then resistivity partitioning is performed at 104, as previously described. The inverted resistivity image data at 106 may also be used to calculate patches that may be extracted at 110. Starting with the inverted gap and Hayman factor image data obtained at 112, this data may be used to generate a dynamic image using a local histogram equalization process at 114. From the dynamic image, patches with high or low values ​​may be extracted at 116. After the extraction at 110, the method proceeds to identifying void features from both 104, 110, and 116 at 118. After the voids identified at 118, a void density calculation may be performed at 120.

[0031] In the method 100, pre-processing may be completed. This pre-processing may create full-hole image configurations for both oil-based mud and water-based mud types.

[0032] In method 100, at 108, an average resistivity log is calculated from the inverted resistivity image at each single depth and smoothed at a sampling rate consistent with the induction resistivity log. At 118, the process of analyzing and identifying patches may include evaluating which values ​​will be high value patches and which values ​​will be low value patches.

[0033] In this section, classification and filtering of vug features from identified patches extracted from high-resolution OBM images is disclosed. In an embodiment, method 100 is applicable to carbonate reservoirs. As will be appreciated, method 100 can be used in reservoirs with different formation resistivities.

[0034] At 114, local histogram equalization of the image is performed on the inverted gap and Hayman factor images. The local contrast of the image is used to classify the hole features in the Hayman factor image and the gap image. In the local histogram equalization of the Hayman factor image and the gap image, adaptive histogram equalization (AHE) is selected. Adaptive histogram equalization (AHE) improves the dispersed distribution of pixel values ​​by transforming each pixel with a transformation function derived from a neighborhood area. Therefore, the transformation function of each area can be different. Therefore, it is suitable for improving local contrast and enhancing the definition of edges in each area of ​​the image. However, when there is a small amount of noise in a largely uniform area, the transformation function will be greatly affected by the noise. Therefore, Gaussian smoothing is applied to remove noise before applying AHE.

[0035] Reference Figure 2 , patches with high and low values ​​are extracted from the processed dynamic image and Hayman factor image for gaps, respectively. The hole feature has a complex response to the gap and Hayman factor images.

[0036] At 104, resistivity zoning is performed. Resistivity zoning is achieved because the induction resistivity tool cannot directly calculate or measure the true formation resistivity; and the induction resistivity tool and high-resolution resistivity imaging tool cannot measure and calculate the resistivity of the flushed zone. There are four (4) different resistivity zones (as shown below) classified based on the difference between the induction resistivity log and the image-based average resistivity:

[0037]

[0038] Where R I is the inductive resistivity. R m is the resistivity based on the image average. R th_h R is the high resistivity threshold, usually this value is set to 2000Ohm.m, limited by the measurement limit of the tool. th_l is the low resistivity threshold, usually the same as the mud resistivity estimated from the high frequency image. th_dif is the resistivity difference threshold between the induction resistivity log and the image-based average resistivity at the same depth.

[0039] At 112, patch classification for void features is performed. For void classification in different resistivity regions, both resistive patches and conductive patches are extracted. It should be noted; however, not all resistive and conductive patches are void features. In regions with very high resistivity, conductive / resistive patches are extracted based on contrast. These patches are only visible on dynamic images and represent more texture non-uniformity than void features, although conductive / resistive patches are extracted from the image.

[0040] about Figures 2 to 7 Example embodiments are described. As will be appreciated, such embodiments should not be considered limiting. Figures 2 to 4 The different partitions described above, labeled conditions 1, 2, and 3, are shown.

[0041] refer to Figure 3 , describing the inhomogeneity features on the dynamic image and the patches extracted in the high resistivity areas. In the very high resistivity areas, conductive / resistive patches are still extracted based on contrast. However, these patches are only visible on the dynamic image and represent more textural inhomogeneities than void features, although conductive / resistive patches are extracted from the image.

[0042] refer to Figure 4 , an inhomogeneity feature on the image in a low resistivity zone with a large resistivity difference. In the Mark 1 zone, the formation resistivity from the induction resistivity log is relatively low, but the image-based resistivity is higher than the induction resistivity log. Although both conductive / resistive patches can be extracted from the resistivity image, only the larger resistive patches represent vug features, and the smaller conductive patches can also be vug features. The classification rules are as follows: (1) The resistivity patches extracted from the inverted resistivity image are vug features when the patches of the gap image have relatively low values ​​and the patches of the Hayman factor image have relatively low values. (2) The conductive patches extracted from the inverted resistivity image are vug features when the patches of the gap image have relatively low values ​​and the patches of the Hayman factor image have relatively high values. The reason for the higher image-based resistivity is that the mud resistivity is higher than the formation resistivity, and the vugs are filled with oil-based mud or mud cake, the gap value is relatively low, and the Hayman factor value of the mud is lower than the surrounding rock matrix. The small conductive patches also represent slurry filling, but the higher Hayman factor is caused by the lower resistivity affected by the water component in the slurry.

[0043] refer to Figure 5 , showing the inhomogeneity features on the image in low-resistivity zones with smaller resistivity differences. In the Marker 2 zone, the formation resistivity is relatively low according to the induction resistivity log, and the image-based resistivity is also higher than the induction resistivity log; however, the resistivity difference is smaller compared to the Marker 1 zone. Both conductive / resistive patches can be extracted from the resistivity image, and only the small conductive patches represent the vug features. In such intervals, the mud resistivity is less than the formation resistivity; the vugs are filled with oil-based mud or mud cake. The higher image-based resistivity is caused by the different tool measurement principles.

[0044] refer to Figure 6, showing inhomogeneity features on an image with an intermediate resistivity range. For example, in the Mark 3 zone, the formation resistivity is between the low and high thresholds, and the image-based resistivity can be higher or lower than the induction resistivity log. In most cases, the resistivity difference is small. Both conductive / resistive patches can be extracted from the resistivity image, with only small conductive patches indicating vug features.

[0045] refer to Figure 7 , showing the classified and filtered vug image. The vug feature verification was performed using the water-based mud image from the same well. The vug feature was verified using the heterogeneity analysis results of the water-based mud image from the same well. The vug surface density log was calculated using the same window size. The vug density log is almost at the same scale in most intervals. There may be greater differences in the breakthrough or formation damage intervals of the water-based mud image because the two images were recorded at different times.

[0046] refer to Figure 8 , showing overlapping hole images for resistivity threshold estimation. Threshold estimation based on patches extracted from resistivity images in water-based mud or ultrasonic images in oil-based mud in the same well is shown. Confirmed hole features are extracted from water-based mud resistivity images or oil-based mud ultrasonic images. Patches extracted from oil-based mud are marked as hole features if the patch area overlaps with patch areas from water-based mud images or ultrasonic images. Selected input data (hole size, induced resistivity, and image-based average resistivity) are extracted at the same depth. Resistivity thresholds are calculated from statistical results accordingly.

[0047] Example embodiments of the claims are given. The embodiments should not be considered limiting. In an example embodiment, a method for identifying features in a carbonate reservoir is disclosed. The method may include obtaining an induction resistivity log from a borehole within the carbonate reservoir. The method may also include obtaining inverted resistivity image data. The method may also include obtaining inverted gap image data. The method may also include obtaining inverted Hayman factor image data. The method may also include calculating an image average resistivity calculation based on the inverted resistivity image data. The method may also include performing resistivity zoning of the borehole based on the induction resistivity log and the average resistivity calculation. The method may also include performing extraction of patches based on the inverted resistivity image data. The method may also include performing image local histogram equalization from the inverted gap and Hayman factor images. The method may also include performing high and low patches from the local dynamic gap and Hayman factor images. The method may also include classifying patches of vug features based on the extraction and the resistivity zoning. The method may also include performing a surface density calculation.

[0048] In another example embodiment, the method may be performed wherein the carbonate reservoir has a varying resistivity range.

[0049] In another example embodiment, the method may be performed wherein filtering the hole features is performed based on relatively higher or lower values ​​of the gap and the value of the Hayman factor image.

[0050] In another example embodiment, the method may be performed wherein the patch is based on at least one of conductivity and resistivity.

[0051] In another example embodiment, a method for identifying features in a carbonate reservoir is described. The method may include obtaining an induction resistivity log from a borehole within the carbonate reservoir. The method may also include obtaining inverted resistivity image data. The method may also include obtaining inverted gap image data. The method may also include obtaining inverted Hayman factor image data. The method may also include calculating an image average resistivity calculation based on the inverted resistivity image data. The method may also include performing resistivity zoning of the borehole based on the induction resistivity log and the average resistivity calculation, wherein the resistivity zoning includes four different zones. The method may also include performing extraction of patches based on the inverted resistivity image data. The method may also include performing local histogram equalization of the image based on the inverted gap and Hayman factor images. The method may also include performing high and low patches based on the local dynamic gap and Hayman factor images. The method may also include classifying patches of void features based on the extraction and the resistivity zoning. The method may also include filtering the void features and performing a surface density calculation.

[0052] In another example embodiment, the method may be performed where each of the four different zones is classified as 0, where R I >R th_h , classified as 1, where R I <R th_h ,R I <R th_l and Classification is 2, among which R I <R th_l and R m -R I <R th_sif , and classification as 3, where R I <R th_h , Where R I is the inductive resistivity, R m is the average resistivity based on the image, R th_h is the high resistivity threshold, Rth_l is the low resistivity threshold, R th_dif is the resistivity difference threshold.

[0053] In another example embodiment, the method may be performed wherein the carbonate reservoir has a varying resistivity range.

[0054] In another example embodiment, the method may be performed wherein filtering of hole features is performed based on relatively higher or lower values ​​of gap and Hayman factor images.

[0055] In another example embodiment, the method may be performed wherein the patch is based on at least one of conductivity and resistivity.

[0056] In another example embodiment, an article of manufacture is disclosed that includes a set of instructions stored to run on a computer, the article of manufacture being configured to store the set of instructions in a non-volatile manner. The instructions contained on the article of manufacture may include obtaining an induction resistivity log from a wellbore within a carbonate reservoir. The instructions contained on the article of manufacture may also include obtaining inverted resistivity image data. The instructions contained on the article of manufacture may also include obtaining inverted gap image data. The instructions contained on the article of manufacture may also include obtaining inverted Hayman factor image data. The instructions contained on the article of manufacture may also include calculating an image average resistivity calculation based on the inverted resistivity image data. The instructions contained on the article of manufacture may also include performing resistivity zoning on the wellbore based on the induction resistivity log and the average resistivity calculation. The instructions contained on the article of manufacture may also include performing a patch extraction based on the inverted resistivity image data. The instructions contained on the article of manufacture may also include performing an image local histogram equalization from the inverted gap and Hayman factor images. The instructions contained on the article of manufacture may also include performing high and low patching from the local dynamic gap and Hayman factor images. The instructions contained on the article may also include classifying patches of void features based on extraction and resistivity zoning. The instructions contained on the article may also include filtering the void features. The instructions contained on the article may also include performing a surface density calculation based on void filtering.

[0057] In another example, an article of manufacture may be configured such that a method included in the article of manufacture is performed on a carbonate reservoir having a range of different resistivities.

[0058] In another example, an article of manufacture may be configured to cause performance of a method embodied in the article of manufacture, wherein the patch is based on at least one of conductivity and resistivity.

[0059] For the purpose of illustration and description, the foregoing description of the embodiment has been provided. It is not intended to be exhaustive or limit the present disclosure. The individual elements or features of a particular embodiment are generally not limited to that particular embodiment, but are interchangeable and can be used in selected embodiments where applicable, even if not specifically shown or described. It can be changed in many ways. Such changes should not be considered as departing from the present disclosure, and all such modifications are intended to be included within the scope of the present disclosure.

[0060] Although embodiments have been described herein, those skilled in the art having benefit of this disclosure will appreciate that other embodiments may be conceived without departing from the scope of the invention.Thus, the scope of this claim or any subsequent claim should not be unduly limited by the description of the embodiments described herein.

Claims

1. A method for identifying features in a carbonate reservoir, comprising: Obtaining induction resistivity logs from boreholes within carbonate reservoirs; Obtain inverted resistivity image data; obtaining inverted gap image data; Obtain inverted Hayman factor image data; Calculate image average resistivity from inverted resistivity image data; Perform resistivity zoning of the wellbore based on induction resistivity logs and average resistivity calculations; Performing patch extraction based on the inverted resistivity image data; Performing local histogram equalization of the image based on the inverted gap and Hayman factor images; Perform high and low patching based on local dynamic gap and Hayman factor images; classifying patches of void features based on the extraction and the resistivity partitioning; and Perform a surface density calculation.

2. The method of claim 1, wherein the carbonate reservoir has a varying resistivity range.

3. The method of claim 1, wherein filtering of the hole features is performed based on higher or lower values ​​of gap and Hayman factor image data.

4. The method of claim 1, wherein the patch is based on at least one of conductivity and resistivity.

5. A method for identifying features in a carbonate reservoir, comprising: Obtaining induction resistivity logs from wellbores within carbonate reservoirs; Obtaining inverted resistivity image data; obtaining inverted gap image data; Obtain inverted Hayman factor image data; Calculate image average resistivity from inverted resistivity image data; performing resistivity zoning of the wellbore based on the induction resistivity log and the average resistivity calculation, wherein the resistivity zoning includes four different zones; Performing patch extraction based on the inverted resistivity image data; Performing local histogram equalization of the image based on the inverted gap and Hayman factor images; Perform high and low patching based on local dynamic gap and Hayman factor images; classifying patches of void features based on the extraction and the resistivity partitioning; filtering the hole features; as well as Perform a surface density calculation.

6. The method of claim 5, wherein each of the four different regions is classified as 0, wherein R I >R th_h , is classified as 1, where R I <R th_h ,R I <R th_l and is classified into 2, where R I <R th_l and R m -R I <R th_dif , and is classified as 3, where R I <R th_h , Where R I is the inductive resistivity, R m is the average resistivity based on the image, R th_h is the high resistivity threshold, R th_l is the low resistivity threshold, and R th_dif is the resistivity difference threshold.

7. The method of claim 5, wherein the carbonate reservoir has a varying range of resistivities.

8. The method of claim 5, wherein filtering of the hole features is performed based on high-value or low-value patches extracted from the inverted gap and Hayman factor images.

9. The method of claim 5, wherein the patch is based on at least one of conductivity and resistivity.

10. An article of manufacture comprised to store a set of instructions for execution on a computer, the article of manufacture being configured to store the set of instructions in a non-volatile manner, the stored instructions comprising: Obtaining induction resistivity logs from wellbores within carbonate reservoirs; Obtaining inverted resistivity image data; obtaining inverted gap image data; Obtain inverted Hayman factor image data; Calculate image average resistivity from inverted resistivity image data; performing resistivity zoning of the wellbore based on the induction resistivity log and the average resistivity calculation; Performing patch extraction based on the inverted resistivity image data; Performing local histogram equalization of the image based on the inverted gap and Hayman factor images; Perform high and low patching based on local dynamic gap and Hayman factor images; classifying patches of void features based on the extraction and the resistivity partitioning; filtering the hole features; as well as Perform surface density calculation based on hole filtering.

11. The article of claim 10, wherein the method contained in the article is performed on a carbonate reservoir having a varying resistivity range.

12. The article of claim 10, wherein the method embodied in the article of manufacture is performed such that filtering of hole features is based on high or low values ​​of gap and Hayman factor image data.

13. The article of claim 10, wherein a method contained in the article is performed, wherein the patch is based on at least one of conductivity and resistivity.

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