ANALYSIS OF RESERVOIR PORE COMPLEXES

Abstract: Analog signals corresponding to color images or pixels of a thin section of a rock reservoir are digitized and then filtered to isolate the pixels representative of pores in the thin section. The pores so isolated are then counted, measured for their total pore perimeter and labelled. The pixels representative of pores are then progressively eroded and dilated, pursuant to which one layer of pixels on the surface is eroded and, if a seed pixel remains, one layer of pixels is added. Thereafter, the original object undergoes two successive erosions followed by two dilations if a seed pixel remains. Successive iterations of the erosion and dilation cycle continue until the last erosion eliminates the seed pixel. The number of pixels lost with each degree of erosion constitutes a pore spectrum consisting of information relating to the total amount of pore image lost each erosion-dilation cycle, the pore size lost each erosion-dilation cycle and the pore roughness lost each cycle. The spectra developed from the erosion-dilation cycle and corresponding to each pore complex are then analyzed into end members and the end member proportions for each field of view are calculated.

Description: Description Analysis of Reservoir Pore Complexes Background of the Invention In sedimentary petrography, small-scale samples of reservoir rocks, such as the sections, peels, and slabs, are typically analyzed and studied. An early objective of the study of such samples was the deduc¬ tion of the characteristics of the sediment shortly after deposition. Carbonates recrystallize much more readily and pervasively than detrital sandstones. As a result, an awareness of an accessible record of post deposi- tional history came earlier to carbonate petrologists than to sandstone petrologists. Interest in diage- netic state and history increased as it became clear that much porosity in petroleum reservoirs, both car¬ bonate and detrital, is secondary and also that dia- genetic mineral phases growing on pore walls could adversely affect hydrocarbon recovery. Intensive research on reservoir quality using thin section and scanning electron microscope (SEM) imagery, together with complementary geochemical/isotropic data, has moved this part of the study of diaσenesis from an area of speculation to the presently applied science of reservoir assessment. Petrologists have come to treat pores as compo¬ sitional phases. This has more than pragmatic justi¬ fication. Pores are not mere voids, but signify the occurrence of a fluid or gaseous phase. A pore/pore wall interface possesses surface energy exactly in the same way as a quartz/feldspar interface ? growth or loss of pores can be thought to operate under the dynamic/kinetic parameters which affect the stability of the surrounding solid phases. Indeed, in order to OMPI define a general measure of the extent and direction of diagenesis of a rock unit, measurements of pore characteristics can serve as a first order diagenetic variable. Permeability in reservoir rocks occurs through a three-dimensional interconnected pore network. Con¬ ventionally, the wider parts of the network are termed "pores" and the narrower parts are termed "pore throats". The three-dimensionality of the pore complex has been directly observed by dissolving the rock matrix to leave an epoxy-impregnated framework. Most observations of pores are from thin sections or SEM imagery which provide limited direct three- dimensional information. In reservoir studies, it is important that three-dimensional information concern¬ ing the pore complex be developed for an understanding of fluid flow and its correlation with petrophysical data such as capillary pressure curves and wire line log response. What is desired is a quantitative vari- able or variables derived from the two-dimensional pore complex which can then be correlated with petro¬ physical and geophysical measurements as well as with the response of the reservoir to production. It is assumed that the pore system displayed on an essentially two-dimensional slice bears some rela¬ tionship to the three-dimensional network from which it was extracted. Direct extrapolation from two- dimensional observations to the third dimension has not been achieved and may never be achieved without simplifying assumptions, e.g., spherical, hexagonally packed, grains. There is little doubt, however, that there must be some relationship, termed a "transfer function", between the pore complex intersected by a plane and the three-dimensional network. It is thus an assumption of the present application that signifi¬ cant changes in the three-dimensional network are reflected in changes in the two-dimensional section. Sedimentary petrography represents a discipline which concerns analysis of micro-scaled imagery of sedimentary rocks. The data necessary to characterize pore-complex geometry in a single field of view gener- ally is most expeditiously developed through computer- assisted analysis of the images. The general field of image analysis is relatively mature so that general principles and strategies have already been defined. That such an approach is required for pore complex analysis has been realized for more than a decade. Image analysis requires an image acquisition sys¬ tem consisting of (1) a sensor such as a videoscanner, (2) an analog/digital converter to convert the analog television signal to digital form, and (3) a data pro- cessor. In the data processor, the digital represen¬ tation of the scene is electronically arranged into an array of grid points or pixels. Each pixel is defined by three values: two spatial coordinates (X, Y) and a "gray level" intensity value. The gray level, mea- sures of brightness, is restricted to integral values. Because the pixels form a grid, once the grid spacing is known, the coordinates of each pixel are known implicitly by knowing the location of one pixel in the array. In the system of the present invention, three scenes of the same field of view are digitized through red, green and blue filters respectively. These three "color planes" when combined will produce a complete color image. One of the main objectives in image analysis is image segmentation. Segmentation is the determination of which pixels in the array belong to the same cate¬ gory. For instance, an algorithm which subdivides a thin section image into the categories "quartz" and "others" necessarily accomplishes a segmentation with respect to quartz. In the segmentation of pores, advantage is taken of the fact that prior to sectioning, the rocks can be impregnated with pigmented, typically, blue, epoxy. Because few, if any, constituents in reservoir rocks are naturally colored blue, segmentation can be achieved through a digital filter. A filter may con¬ sist of the average ratio of gray-level intensities from each color plane of red to green to blue of pixels located in pores. An image processing algo- rithm then compares that ratio with that of every pixel, and assigns, for example, a value of "one" to those with the "correct" ratio for pores and a "zero" for all others. The result is a binary image which ensures that in the subsequent analysis of pore geome- try only the pores will be analyzed. With the pore-complex identified, analysis of porosity can commence. Porosity is the proportion of pore pixels to total pixels in the scene. The poros¬ ity value estimated in this way is not the same as porosity as measured by physical tests. Pores are measured by the presence of blue-dyed epoxy impreg¬ nation. Thus, the porosity defined by petrographic image analysis is more closely linked to effective or interconnected porosity than to total porosity. Most minerals in sedimentary rocks are transpar¬ ent to translucent and this characteristic can in some cases affect porosity estimates. As the illumi¬ nation level increases, more and more blue-dyed pores can be seen through mineral grains. Thus, increasing proportions of pores inclined or even parallel to the plane of section will be detected. The problem can be minimized by careful control of illumination and adjustment of the values of the digital filter. Total pore perimeter is another property that is evaluated. This is an especially useful variable in that it has been shown that total oore perimeter per unit area is directly proportional to pore surface area per unit volume. The ratio of total pore area to the total pore perimeter can provide information concerning pore roughness or tortuosity. Another variable tied to roughness/tortuosity is bending energy. It has been pointed out that perime¬ ter measured from pixel to pixel along a periphery may deviate significantly from perimeter measured continu¬ ously. Bending energy, representing the energy neces- sary to deform a circle into the shape of the pore, is defined as the normalized sum of squares of curvature of the vertices of the periphery. Bending energy can be calculated on a pore-by- pore basis. When summed or averaged, the pore mea- surements can be a global measure. Considering the fact that pores have quite complex geometries, bending energy is in fact a more generally useful variable than simpler measures of geometry such as measure¬ ments of long and intermediate axes. However, pores of many shapes can yield equivalent values of bending energy. What is needed in many cases is a way to measure subfeatures of a pore. It has been recog¬ nized that often pores can possess extended complex geometries and so conventional shape measurement variables would often be of little value. One prior art solution was to develop an algorithm that would subdivide the pore imagery into subdivisions of relatively simple geometry — each of which would then be efficiently evaluated by conventional shape and size variables. Summary of the Invention It is accordingly an object of the present inven¬ tion to provide a system for analyzing reservoir rock samples to produce data representative of the geometry of the pore complexes therein. It is another object of the present invention to provide a system for analyzing reservoir rock samples to produce data representative of the number, size and type pores which exist in the reservoir rock. These and other objects of the present invention are accomplished by developing digitized color images or pixels of a thin section of a rock reservoir and then filtering such pixels to isolate the pixels representative of pores in the thin section. The pores so isolated are then counted, measured for their total pore perimeter and labeled. The pixels representative of pores are then pro¬ gressively eroded and dilated, pursuant to which one layer of pixels on the surface is eroded and, if a seed pixel remains, one layer of pixels is added. Thereafter, the original object undergoes two succes¬ sive erosions followed by two dilations if a seed pixel remains. Successive iterations of the erosion and dilation cycle continue until the last erosion eliminates the seed pixel. The number of pixels lost with each degree of erosion constitutes a pore spec¬ trum consisting of information relating to the total amount of pore image lost each erosion-dilation cycle, the pore size lost each erosion-dilation cycle and the pore roughness lost each cycle. The spectra developed from the erosion-dilation cycle and corresponding to each pore complex are then analyzed into end members and the end member propor¬ tions for each field of view are calculated. Brief Description of the Drawings In the drawings: Figure 1 is a flow chart illus¬ trating the system for analyzing reservoir pore com¬ plexes arranged according to the present invention; Figure 2 illustrates the segmentation and label- ing of pores in a binary image; Figure 3 illustrates an erosion-dilation cycle; Figure 4 illustrates the product of an erosion- dilation cycle for an illustrative pore size distribution; Figure 5 illustrates the product of an erosion- dilation cycle for an illustrative pore roughness; Figure 6 illustrates an erosion-dilation cycle involving a scene that is a composite of size and roughness; Figure 7 illustrates a spectrum of information relating to size, roughness and total; Figure 8 illustrates a pore throat and a pore throat frequency distribution; and Figure ' 9 illustrates the mixing proportions of three end members. Detailed Description of the Preferred Embodiment Images of a thin section of reservoir rock are developed by digitizing an analog signal representing an electronic image of the thin section. As shown by the block 10 in Figure 1, a scanning electron micro¬ scope develops an analog signal representing a time varying voltage which is proportional to scene bright¬ ness. The analog signal is then supplied to an analog- to-digital converter 12 which converts the analog signal into a digital signal. As a part of the con¬ verter 12, the resulting digital signals are also sampled periodically to develop a grid of points or pixels. One pass is sufficient for a black and white image, as produced by the scanning electron microscope. For the color scenes of the optical microscope, each field of view is digitized three times, once each through red, green and blue filters for each scene. Generally, at least four fields of view are ana- lyzed per thin section to measure small-scale spatial variability. Magnifications are chosen with respect to reservoir character and the problem to be solved. Pores at least as small as 0.3 microns can be detected. Image segmentation is the process which identifies those pixels which belong to particular categories. Preferably, the pores in each thin section are filled with blue-dyed epoxy. Thus, a digital filter 14 con¬ sisting of the ratio and differences of red, green and blue intensities is sufficient to segment pores from non pores. Other more complex digital filters can distinguish clay from pore even if the clay is blue tinged. Carbonates are commonly stained and the spectral character of the stain is sufficient to dis¬ tinguish carbonate types. Finally, gray-level segmen- tation can be used to distinguish carbonate textural types or detrital minerals. Once pores are distinguished from the rock matrix, a binary image is developed wherein all pixels repre¬ senting pores are set to black and all other pixels are set to white. At this point the pore complex is in a form suitable for analysis. Referring to Figure 2, the first step in analysis is to assign a unique iden¬ tification number to each pore in the image. Subse¬ quent pore analysis operates on this catalogued set. Analysis of the pore complex is accomplished at 16 through a succession of operations each more pro¬ gressively complex. The first operations include estimates of total porosity which is the proportion of blue pixels and, most importantly, total pore perimeter. As explained in Geometrical Probability by Kendall and Moran, Hetner Publishing Co., 1963, for thin sections, total pore perimeter is directly proportional to surface * area per unit volume as long as the pixel array represents the same total image area. Total pore perimeter is one of the few opera¬ tions that can be directly related to measured permeability. Other operations exist, such as "unsu- pervised learning" routines which can erect classifi¬ cation schemes and special programs such as corner detectors which measure asperities. These and other operators have the advantage of being very fast, e.g., a few seconds or less of mini-computer time. However, more than one sort of pore network can generate similar results from simple operations. Com¬ plex operators actually measure the nuances of pore geometry and, as will be shown below, generate pore geometry spectra. Such spectra represent a diagnos¬ tic finger print such that it is very unlikely — but not impossible — that two significantly different complexes will yield identical spectra. The concept of image erosion is a well known one in image processing where it is used both as a smooth¬ ing technique and a shape classifier. The concept of erosion has been described, as early as 1968, as use of a "prairie fire" in order to shrink an object to a skeleton or a point. This technique tended to smooth and simplify the object as the "fire" burned in evenly from all sides toward the center. Dilation is described as an operation which will expand (as the "fire" burns out from the center) from the skeleton to be a simplified version of the original object after a number of expansions. For an object such as a pore, one may strip off the outermost layer of pixels in a manner analagous to peeling an onion. This strip¬ ping is termed erosion. Repeated erosion, layer by layer, progressively simplifies the object. In the case of pore complexes, progressive erosion, layer by layer, first eliminates micropores as well as small- scale roughness on the pore walls. As cycle after cycle of erosion proceeds, pore throats of greater widths are severed and the surviving elements of the pore complex appear as isolated regions of relatively simple geometry. Dilation is the reverse of erosion. A layer (or layers) of pixels is added to the object. Dilation after erosion only occurs if "seed" pixels remain. As explained in the article by Young, et al. entitled "A New Implementation for the Binary and Minkowski Operators," Comp. Graph and Image Proc. , pp. 189-210 (1981) and as shown in Figure 3, any objects completely destroyed by erosion cannot undergo dilation. There¬ fore, size information is carried by the difference between the number of pore-pixels in the original image and an image in which the pores have suffered a certain degree of erosion and dilation (Figure 4) . Dilation after erosion need not restore the object to its original shape because irregularities lost via erosion cannot be replaced by dilation (Figure 5) . The erosion-dilation cycle is a process carried out at block 18 of Figure 1 by which an erosion of one layer of pixels on the surface takes place and then, if a "seed" pixel(s) remains after the erosion is completed, one layer of pixels is added. The sec¬ ond erosion-dilation cycle will perform two successive erosions of the original object followed by two dila- tions if a "seed" pixel(s) remains. Successive itera¬ tions of the erosion-dilation cycle continue until the last erosion destroys the "seed" pixel(s). The pore analysis process classifies pixels lost after erosion into a category consisting of those removed from a still existing core (or seed) and those whose loss results in the total loss of a pore. The algorithm thus produces the amount of pixels lost due to roughness and size as erosion-dilation cycles of progressively greater magnitude operates on the original image (Figure 6) . The number of pixels lost with each degree of erosion (1 pixel, 2 pixels. etc.) constitutes a pore spectrum. Each field of view thus produces a spectrum for pore size and another for pore roughness (Fiqure 7) . Examination of the result of successive itera- tions of the erosion-dilation cycle (Figure 8) shows the number of iterations necessary to break (and therefore define) the thinnest pore throat(s), if any. Each iteration will break larger and larger pore throats until finally the basic pore (the areas of largest diameter) remains. This technique may be viewed as the reverse of a mercury injection technique where largest areas of the pore are filled. The applicants have discovered that, in order to obtain the best results in determining the amount of pore lost during each iteration of the erosion-dilation cycle, the original image is subjected to the N cyςles of erosion-dilation and then the difference between the amount of pore lost after the — cycle and the amount of pore lost after the previous N~^ cyσles is calculated. For the purposes of this invention, all pore boundaries are classified as interior (completely sur¬ rounded by pore) or exterior. At this time, erosion or dilation occurs only from exterior surfaces. Also, the assumption is made that any pore that crosses the scene boundary extends to infinity. Therefore, the boundary of the scene is not subject to erosion or dilation, only the true exterior pore boundaries are so processed. With the pore complex now delineated by the seg¬ mentation procedure, pore geometry is examined in a pattern recognition and classif cation block 20 (Fig¬ ure 1) . If pores are either simple two-dimensionally compact objects, e.g., circles, ellipses, triangles, or are even interconnected networks of uniform width, standard measures of size, shape or network properties ___UM?I would serve to characterize the pore complex. Such measurements then would by definition evaluate essen¬ tial features . needed for correct classification or evaluation of the pore system. Accordingly, in image analysis terminology, use of a measurement or set of measurements, which characterizes or classifies the segmented image, is termed feature extraction. Identification of the correct features is a most critical step in image analysis. If the features are not information-rich with respect to the specific prob¬ lem being addressed, subsequent analysis of the. data carried by the feature will be ineffectual. Conven¬ tionally, it is usually not self-evident which measure¬ ment of an almost infinite variety will be most useful. Successful feature definition is often a matter of trial-and-error. One way to minimize the risk of choosing the wrong set of features is to use a measure which in some man¬ ner completely describes the system. An example of such a measure in the study of particle shape is a finite Fourier series in polar form. A set for Fourier coefficients which, when graphed, converges to the empiric shape contains all the two-dimensional infor¬ mation present. This sort of variable allows postpone- ment of a choice of features that numerical/statistical methods can be used a posteriori to define the most information-rich portions of the series. However, a variable similar to a Fourier series is necessary to evaluate pore complexes quantitatively, because it is not known at this time which features correlate best with nuances of flow, log responses or seismic properties. Simple measurements such as diame¬ ter, width, length, are not necessarily sufficient in that the discrete parts of the pores viewed in thin section can form extended objects consisting of wider areas or pores connected in complex ways by narrower areas or pore throats. Pore throats in one part of the field of view may be larger than pores in another part. When intergranular porosity is present, the pores may largely follow the grain boundary network. In such cases, where tendrils of pores connect irregu¬ larly shaped "blobs" of pores, measures related to simple geometric concepts are difficult to define. In accordance with our invention, the erosion/ dilation concept is used for pore complex analysis. Pore complex analysis requires the use of a data/ analytical procedure to evaluate and classify large data sets consisting of pore complexes from many thin sections, each with many fields of view. The optimal situation is that after the erosion-dilation cycle the data be in a form which can be most efficiently and unambiguously analyzed by the analytical algorithm. In that light, the erosion-dilation process of this invention is one which will produce data in the exact form required for analysis by the QMODEL family of pattern recognition/classification algorithms. See, Full, et al., "FUZZY QMODEL — A New Approach for Linear Unmixing," Math. Geol. , Vol. 13, n. 4, pp. 331-344. The measure of pore-complex scale and geometry described below takes advantage of the fact that a given amount of erosion followed by the same amount of dilation need not restore the image to its origi¬ nal state. Small pores may be lost completely during erosion leaving no "seed" pixels for subsequent dila- tion (Figures 3, 4, 6). Similarly, roughness elements lost under the action of erosion will not be restored by subsequent dilation because the dilation process has no "memory" of their existence (Figures 5, 6, 7). The pore complex measure consists of monitoring the proportion of porosity "lost" under progressively more severe cvcles of erosion and dilation. Each cycle measures the loss after erosion and dilation involving a single layer of pixels, the second two layers, the third three layers, etc. At some point erosion overwhelms the entire pore complex and the subsequent dilation thus has nothing to restore. The result of the complete process is a frequency distri¬ bution of the proportions of total image porosity lost at each cycle — the total equal to 100% (Figure 7) . These distributions will hereafter be termed "pore- complex spectra." Since the loss due to any particular erosion- dilation cycle can constitute the loss of an entire pore or the loss of an angular corner or pore throat, the algorithm of the invention checks to see whether the loss of pixels is due to the loss of an entire pore or to the loss of a portion of a pore, e.g., pore roughness (Figure 5). Thus, for each field of view three spectra are produced: (1) the total amount of pore image lost per erosion-dilation cycle, (2) pore size lost per erosion-dilation cycle, and (3) pore roughness lost per erosion-dilation cycle. Each erosion-dilation cycle is therefore related to an absolute spatial scale defined by microscope mag¬ nification and the size of the pixel grid. The spec- tra can therefore be scaled in terms of a linear scale such as microns (Figure 7) . Any given pore is thus partitioned into two parts, roughness and size, in a manner slightly analogous to ascribing a single shape to sphericity and roundness. The relative proportions of the smooth and rough components of porosity (determined by erosion-dilation cycles) are variables important in assessing reservoir quality. The size distribution of each of these com¬ ponents (the erosion-dilation spectra) is also of use in this regard. These variables (smooth proportion, roughness proportion and class-interval proportions of the erosion-dilation spectra) can be used directly to estimate reservoir parameters such as permeability, initial water saturation and residual oil saturation. The erosion-dilation spectra can also be used in objective classification of reservoir pore complexes. That is, the number of kinds of pores (in terms of size and geometry) and their relative proportions can be determined if the erosion-dilation spectra are used as input data for classification algorithms. The pore complex within a given rock volume rep¬ resents the time-integrated interaction between the initial properties of a sedimentary deposit and post- depositional, chemical and physical processes. Pres¬ sure, temperature, chemistry of formation waters and intrinsic rock properties usually vary in time and space in such a way to augment or detract from a pre¬ existing pore complex. When sampled on a broad enough scale, the pore complex can be thought of and so clas¬ sified as a mixture of sub-complexes. For instance, if a rock volume contained only circular pores then everywhere in that volume could be characterized as mixtures of two end member complexes: one consisting of micropores, the other of large pores. In the case of the circular large and small pores, each end member would be represented by a narrow peak on the pore-size spectrum generated by the erosion-dilation algorithm described above (Figure 4). However, there is no rea¬ son to believe that an end member which represents a two-dimensional slice of the three-dimensional pore network need be unimodal, nor that only two end mem¬ bers suffice. End members can be considered to occupy the vertices of a geometric figure which will enclose all observed samples (Figure 9). This figure, termed a polytope, relates all samples as mixtures of end members. A triangle diagram familiar to all geologists is an example of a three end member polytope. If more than four end members are needed, polytopes have dimensions greater than three. Thin sections containing end member pore com¬ plexes may not be represented in a sample set. Pore complex end members represent extreme conditions in a rock body. They may not be encountered during sampl¬ ing because such conditions may have affected only a small portion of the rock volume and so be missed in sampling. Indeed, petrogenetic conditions may not have persisted long enough, or were not intense enough, to drive the pore complex into an end member condition. In that case an end member with an asso¬ ciated erosion-dilation spectrum necessary to classify the pore complex in terms of end member proportions which could not have been captured during sampling and would have to be deduced from the pattern of variability of the observed erosion-dilation spectra. Algorithms which analyze a collection of spectra into end members erect a polytope and calculate end member proportions for each field of view are termed unmixing algorithms. These algorithms have as their basis the vector analysis algorithm CABFAC, sometimes termed a Q-MODΞ factor analysis algorithm. The algo¬ rithm EXTENDED CABFAC developed by Klovan and Meisch ("EXTENDED CABFAC and QMODEL computer programs for Q-mode factor analysis of compositional data: Comput. Geosci. , VI, pp. 161-178) for determining the number of end members in a constant sum system where each pore complex spectrum is viewed as a multidimensional vector may also be used in the present invention. In classifying pore complex spectra, the unmixing algorithms perform three functions: (1) determine the number of end members, (2) identify the pore complex spectra of end members, and (3) determine mixing pro- portions of each end member for each observed pore complex spectrum. The first two functions can be thought of as a pore classification system objectively derived from the reservoir complex. It is linked to pre-existing classifications but carries implicitly within the concept of relative proportion. A pore complex end member might not be unimodal. For instance, if cir¬ cular macropores and micropores occurred in the same ratio from all thin sections, then that pore combina¬ tion would be defined analytically as a single end member. Thus, the unmixing algorithms can provide a means of determing the degree of independence of pore varieties observed in thin sections. The mixing proportions of end member spectra can be used as mappable variables. Changes in pore geome- try can be contoured and trends determined. Such maps may prove to be of value in modeling the integrated response of a rock body to flow. In addition, extrapo¬ lation of gradients might be useful in either a devel¬ opment or exploration context. Some elements of pore roughness impede fluid flow because the presence of sharp corners produces points of high surface energy. At these points the water film wetting the pore may thin and even break, allow¬ ing petroleum to adhere directly to a portion of the wall, thus producing the deleterious condition of mixed wettability. Pore throats are another component ascribed to pore roughness by our algorithm. Pore throats, as viewed in thin sections, pertain to any constriction in a pore. The concept is a loose one: a pore throat in one portion of a thin section may be larger than a pore in another. A thin section size frequency distri¬ bution of pore throats can be obtained by recording the number of erosion-dilation cycles necessary for a given pore to separate into two pores. A variable considered of value to many reservoir scientists is surface area of pore per unit volume. This can be obtained using geometric probabilistic results. For a first approximation, one may choose counts of intersections of pores along rows of pixels. As discussed previously, by comparing counts from dif¬ ferently oriented parallel arrays one may also develop an index of pore orientation. OMPI

Claims: Claims 1. A process for analyzing reservoir rock complexes comprising the steps of developing a digital representation of at least one scene of the same field of view of a rock sample, arranging the digital representation into an array of pixels, separating the pixels representative of the pores and pore throats formed in the rock sample from the pixels representative of the non-porous areas of the rock sample, progressively eroding and dilating the pore and pore throat represen¬ tative pixels until the last erosion eliminates the pixels in their entirety and the subseσuent dilation has nothing to restore, and producing spectra relating to the total amount of pore image, the number of pores and the pore rough¬ ness lost during each cycle of eroding and dilating. 2. A process according to claim 1 comprising the further step of generating a pore throat fre¬ quency distribution spectrum representative of the number of pore throats severed during each cycle of eroding and dilating. 3. A process according to claim 1 wherein the amount of pore lost during each iteration of the eroding and dilating cycle is determined by subjecting the original image to N cycles of eroding and dilating and then calculating the difference between the amount of pore lost after the N— cycle and the amount of pore lost after N~l cycles of eroding and dilating. ' J . -. CM?! 4. A process according to claim 2 comprising the further step of analyzing the spectra relating to the total amount of pore image, the number of pores and the pore roughness into one or more end members to thereby classify such spectra. 5. A process according to claim 4 further comprising the steps of producing spectra corresponding to multiple fields of view of the same scene of a rock sample and relating to the total amount of pore image, the number of pores and the pore roughness lost during each cycle of eroding and dilating, analyzing said spectra into end mem¬ bers, identifying the pore-complex spectra of said end members and determining the mixing pro- portions of each end member for each field of view. 6. A process according to claim 4 comprising the initial steps of scanning the rock sample to develop an analog signal representative of the brightness of the rock sample, and digitizing said analog signal by incrementally sampling the analog signal. 7. A process according to claim 6 wherein each field of view of the rock sample is viewed through red, blue and green filters to develop color analog signals representative of the intensity of the red, blue and green colors in the rock sample. 8. A process according to claim 7 comprising the further steps of impregnating the porous areas of the rock sample with blue-dyed epoxy and fil¬ tering the digital signals representative of the intensity of the red, blue and green colors in the rock sample to separate the signals repre¬ sentative of the epoxy impregnated pores and pore throats from the nonporous areas of the 5 rock sample. 9. A process according to claim 4 comprising the further step of defining and tabulating the pixels representative of the pores and pore throats formed in the rock sample. 10 10. A process according to claim 9 comprising the further steps of comparing the pixels represen¬ tative of the pores and pore throats against the pixels representative of the non-porous areas in the rock sample to develop an indication of the 5 total porosity of the rock sample, measuring the perimeters of the pore and pore throat represen¬ tative pixels, and adding the measured perimeters to provide an indication of total pore perimeter.

Inventors: CANNON ROBERT, CRABTREE STERLING JAMES JR, EHRLICH ROBERT, CANNON, ROBERT, CRABTREE, STERLING, JAMES, JR., EHRLICH, ROBERT
Applicants: CANNON ROBERT, CRABTREE STERLING JAMES JR, EHRLICH ROBERT, CANNON, ROBERT, CRABTREE, STERLING, JAMES, JR., EHRLICH, ROBERT
Application year: 1984
Main classification: G01N33
IPC codes: G01N15, G01N33, G06T1