Method for estimating the hydrocarbon saturation of rocks

Abstract

The present invention provides a method for estimating hydrocarbon saturation of hydrocarbon-bearing rock based on resistivity logs and rock images. The image is segmented to represent pore space or solid material in the rock. Image porosity is estimated based on the segmented image, and a corrected porosity is determined to account for sub-resolution porosity missing in the image of the rock. A corrected cementation index of the rock is determined based on the image porosity and the corrected porosity and used to estimate the hydrocarbon saturation. A trained model that supports backpropagation can be used to segment the image. A method that supports backpropagation can be used to estimate the hydrocarbon saturation using an image selected from a series of 2D projection images, 3D reconstructed images, and combinations thereof.

Description

Technical Field

[0001] The present invention relates to a method for estimating hydrocarbon saturation of a rock, and in particular, to a method for estimating hydrocarbon saturation of a rock based on a resistivity log map and a 3D image of the rock. Background Art

[0002] Accurately determining hydrocarbon saturation within a hydrocarbon containing reservoir is an important factor in determining whether to select a hydrocarbon containing reservoir to be developed and in developing and managing the hydrocarbon containing reservoir.

[0003] Saturation models such as Archie, Waxman-Smits and other conductivity models are used to calculate hydrocarbon saturation from resistivity logs. Because these equations contain empirical parameters, they must be calibrated in the laboratory on core samples.

[0004] One such empirical parameter is Archie's cementation index (often referred to as Archie's m). The Archie equation (Archie "Classification of Carbonate Reservoir Rocks and Petrophysical Considerations: AAPG Bulletin 1952; and Archie "The Electrical Resistivity Log as an Aid in Determining Some Reservoir Characteristics" Transactions of the AIME 146:54-62; 1942) states that the effective electrical properties of a reservoir rock depend entirely on the brine conductivity, the total porosity of the sample, and the connectivity of the pore spaces. The cementation index is typically determined by physical testing of rock samples using laboratory measurements of the rock's effective conductivity, the rock's total porosity, and the conductivity of the rock's pores fully saturated with brine.

[0005] However, this type of testing requires a significant amount of time and is very expensive. In addition, the number of samples that can be processed is relatively limited due to the time and expense required to perform each test. Information needs to be provided more quickly so that more timely decisions can be made.

[0006] Digital rock physics is a technology that has been developed to provide faster, more extensive and cheaper analysis of hydrocarbon-bearing formation rocks to determine key petrophysical characteristics of the rocks. Digital rock physics uses digital images of formation rocks to simulate rock multiphysics at the pore scale and predict the properties of complex rocks.

[0007] For example, US Pat. No. 8,311,788 B2 (Hurley et al.) describes a method for quantifying pore shape, volume, and surface area using confocal profilometry.

[0008] US10,466,386B2 (Hurley) discloses a method for determining a saturation-height function in a hydrocarbon reservoir. The saturation-height function is determined by analyzing core samples to quantify the absolute value of microporosity and calculating based on lithofacies and the distribution of microporosity, which are determined as a function of absolute height. A correlation is established between microporosity and lithofacies. The absolute value of microporosity is determined using laser scanning confocal microscopy and / or mercury intrusion capillary pressure (MICP). Microporosity-weighted water saturation is determined using Archie (n) values, which are determined by calculation from a digital model based on 3D confocal scanning to combine water saturation values ​​with pore network values ​​and borehole image lithofacies.

[0009] US9,134,457B2 (Hurley et al.) relates to multi-scale rock modeling for reservoir simulation. According to Hurley'457, pore-scale (nm to μm) digital rock modeling data is upscaled to borehole-scale (mm to m) digital rock modeling data, which is then upscaled to interwell scale (10 to 100 meters), and then upscaled to full-field scale (10 km) digital rock modeling data. The pore-scale digital rock modeling data includes pore geometry, which is quantified by transmission laser scanning fluorescence microscopy, micro-CT scanning, nano-CT scanning, focused ion beam-scanning electron microscopy, MICP and / or nuclear magnetic resonance, using a core plug selected from the grid permeability of the core plate and conventional CT scan data. Full-field digital rock modeling data are based on interwell-scale calculated values ​​such as porosity, permeability, capillary pressure, resistivity index, relative permeability, water saturation, irreducible water saturation, residual oil saturation, recovery factor, and Archie cementation (m) and saturation (n) indices.

[0010] Digital rock physics can bring fast results to upstream exploration and development projects and offers the possibility to simulate rock properties using the properties of the field crude oil and brine, as well as providing digital changes to any mineral conductivity. The effective conductivity of the calculated digital rock depends directly on the proportion of conductive pore space accessible to the simulator.

[0011] Digital rock physics modeling conventionally relies on the assumption that the pore volume of hydrocarbon-bearing rocks can be accurately determined from micron-scale images. However, the resolution of micron-scale X-ray computed tomography images generated by current imaging techniques is limited, so a large portion of the rock pore volume may remain unresolved. The direct numerical solvers used to calculate effective conductivity to effectively estimate the Archie cementation index cannot reach the missing pore volume. This limits the applicability of digital rock techniques.

[0012] For example, as discussed in Saxena et al., 2019 (“Estimating Pore Volume of Rocks from Pore-Scale Imaging,” Transport in Porous Media 129:403-412; 2019), due to the limitations of the resolution of micron-scale images currently generated by micro-CT detectors, up to half of the total pore volume may be missing in micro-CT images of reservoir rocks.

[0013] Saturation models need to be properly parameterized to include the effect of sub-resolution pore volume on effective conductivity so that associated empirical constants, such as cementation index, can be accurately estimated. Summary of the invention

[0014] According to one aspect of the present invention, a method for estimating the hydrocarbon saturation of hydrocarbon-bearing rocks based on resistivity logs and rock images is provided, which comprises: obtaining a resistivity log of an oil field having hydrocarbon-bearing formations; obtaining a 3D image of rocks from hydrocarbon-bearing formations in the oil field, wherein the 3D image is composed of a plurality of voxels and the 3D image has a resolution; processing the 3D image by selecting each voxel of the 3D image to segment the 3D image to represent pore spaces in the rock or solid matter in the rock; estimating the image porosity of the rock based on the segmented 3D image, the image porosity lacking the sub-resolution porosity of the rock; determining a corrected porosity suitable for taking into account the sub-resolution porosity of the rock; estimating a corrected cementation index of the rock based on the image porosity and the corrected porosity of the rock; and estimating the hydrocarbon saturation of the hydrocarbon-bearing rock using the corrected cementation index.

[0015] According to another aspect of the present invention, a back-propagation-enabled method for estimating hydrocarbon saturation of hydrocarbon-bearing rocks is provided, which comprises the following steps: obtaining a resistivity logging map of an oil field having hydrocarbon-bearing formations; obtaining a 3D image of rocks from hydrocarbon-bearing formations in the oil field, the 3D image having a resolution; applying a trained model that supports back-propagation to segment the 3D image; estimating image porosity of the rock based on the segmented 3D image, the image porosity lacking sub-resolution porosity of the rock; determining a corrected porosity suitable for interpreting the sub-resolution porosity of the rock; estimating a corrected cementation index of the rock based on the image porosity and the corrected porosity of the rock; and estimating the hydrocarbon saturation of the hydrocarbon-bearing rock using the corrected cementation index.

[0016] According to another aspect of the present invention, a back-propagation-enabled method for estimating the hydrocarbon saturation of a rock based on an image of the rock is provided, comprising the following steps: obtaining a resistivity logging map of an oil field having a hydrocarbon-bearing formation; obtaining an image of a rock from the hydrocarbon-bearing formation in the oil field, the image being selected from a group consisting of a series of 2D projection images, 3D reconstructed images, and a combination thereof; and applying a back-propagation-enabled trained model to obtain the hydrocarbon saturation of the rock. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The present invention will be better understood by reference to the following detailed description of the preferred embodiments and the accompanying drawings referred to therein, in which:

[0018] Figure 1 is a graphical illustration comparing laboratory measurements of Archie m with corresponding values ​​of Archie m determined from 3D imaged rocks; and

[0019] Figure 2 is a graphical illustration comparing laboratory measurements of Archie m with the corresponding corrected values ​​of Archie m. DETAILED DESCRIPTION

[0020] According to the method of the present invention, the hydrocarbon saturation of a rock can be estimated from a 3D image faster than performing laboratory measurements. Specifically, the method of the present invention estimates hydrocarbon saturation based on a cementation index derived from direct simulation, specifically Archie m. More specifically, a meaningful estimate of the cementation index is determined by correcting the image-derived cementation index determined from the micron-scale image. In a preferred embodiment, the image-derived cementation index is corrected for sub-resolution pore volumes missing in the lower resolution image.

[0021] The inventors have unexpectedly discovered that capillary physics in rocks can be used to quantify the effect of image resolution on porosity. This discovery enables digital rock or digital rock physics to provide the ability to determine cementation index from 3D images of rocks generated by micro-CT technology. The inventors have discovered a method for estimating a corrected cementation index. Advantageously, the corrected cementation index and therefore hydrocarbon saturation compensates for limited image resolution without the need for higher resolution imaging that is only possible at the expense of image field of view or physical laboratory measurements.

[0022] The present invention provides a method for more accurately estimating the hydrocarbon saturation of a rock based on a raw 3D pore-scale image of the rock that has a limited resolution relative to the actual pore structure of the rock. Contrary to current assumptions in digital rock physics modeling, the present inventors recognize that a significant portion of the pore volume of hydrocarbon-bearing rocks is contained in pores of a size below the image resolution provided by the 3D pore-scale imaging techniques typically used to provide images of such rocks. As a result, conventional digital rock physics modeling significantly underpredicts the effective electrical conductivity of the rock due to its failure to account for pores smaller than the image resolution of the pore-scale imaging techniques in terms of cementation index, and therefore fails to calculate hydrocarbon saturation.

[0023] The present invention also provides a back-propagation enabled method for estimating hydrocarbon saturation of a rock from a 3D image of the rock. A back-propagation enabled trained model is applied to the 3D image to segment the 3D image of the rock.

[0024] In a preferred embodiment, a trained model is generated by providing a training set of images of rock, segmenting the images into a plurality of labeled voxels representing pore space and solid material in the rock, and training the model using the labeled voxels via back propagation.

[0025] The image training set of the rock may include, for example, 2D projection images obtained from pore-scale imaging techniques, 3D images reconstructed from 2D projection images, synthetic 2D images, synthetic 3D images, and combinations thereof. In a preferred embodiment, the training set of images is obtained from a cloud-based tool adapted to store 2D projection images from pore space imaging techniques, in particular from micro-CT and thin sections. The tool is adapted to process the 2D projection images to produce reconstructed 3D images. The tool is also adapted to store the resulting 3D images.

[0026] Examples of processes supporting back propagation include, but are not limited to, artificial intelligence, machine learning, and deep learning. Those skilled in the art will appreciate that advances in processes supporting back propagation continue rapidly. Even under different names, the methods of the present invention are expected to be applicable to those advances. Therefore, even if not explicitly cited herein, the methods of the present invention are also applicable to other advances in processes supporting back propagation.

[0027] A preferred embodiment of a process supporting back-propagation is a deep learning process, including but not limited to convolutional neural networks.

[0028] The process supporting back-propagation can be supervised, semi-supervised, unsupervised, or a combination thereof. In one embodiment, a supervised method is made semi-supervised by adding unsupervised techniques.

[0029] In a supervised back-propagation process, a training set of images is labeled to provide examples of pore space and solid matter of interest. In an unsupervised back-propagation process, pore space and / or solid matter of interest can be identified by, for example, drawing a polygon around the image of interest in the image. The trained process will then identify regions of interest with similar latent space characteristics. When the training set is labeled as images, the dimensions of the labels can be 1D-3D.

[0030] In one embodiment, the supervised back-propagation-enabled process is a classification process. The classification process can be performed voxel-wise, slice-wise, and / or volume-wise.

[0031] In another embodiment, the unsupervised back-propagation-enabled process is a clustering process. The clustering process can be performed voxel-wise, slice-wise, and / or volume-wise.

[0032] In another embodiment, the unsupervised back-propagation-enabled process is a generative process. The generative process can be performed voxel-wise, slice-wise, and / or volume-wise.

[0033] Preferably, the process supporting back-propagation is a segmentation process.

[0034] In a preferred embodiment, the training step includes validation and testing.

[0035] In the method of the present invention, the petrophysical characteristics of the rock, specifically the hydrocarbon saturation of the rock, can be estimated from the well resistivity log and the image of the rock. For an oil field having a hydrocarbon-bearing formation, a resistivity log is obtained. The resistivity log is obtained in a conventional manner and form known to those skilled in the art. The rock image is obtained from a rock from a hydrocarbon-bearing formation, and the petrophysical characteristics of the formation or part thereof are of interest. Preferably, the rock can be a sandstone, a carbonate rock, a shale and a combination thereof from a hydrocarbon-bearing formation. The rock can be obtained by conventional means for obtaining a rock sample from a hydrocarbon formation. In a preferred embodiment, a core sample of the rock is obtained by coring a portion of the formation from a well in the formation. Alternatively, a rock sample can be obtained from drill cuttings produced in a well being drilled in the formation. The rock can be obtained from the same borehole as the resistivity log. Alternatively, the rock can be obtained from another borehole in the same oil field as the borehole in which the resistivity log was produced.

[0036] The rock sample should be of sufficient size to obtain a 3D image with sufficient volume at the scale at which the image is to be generated. Specifically, the rock sample should be of sufficient size so that features of the bulk of the sample dominate over features of the edges of the sample at the scale or field of view of the image to be generated.

[0037] A 3D image consisting of a plurality of voxels is obtained from a rock sample. A 3D image of a rock may be obtained using pore-scale imaging techniques. A 3D image of a rock may be obtained by X-ray computed tomography, including but not limited to X-ray micro-computed tomography (micro-CT) and X-ray nano-computed tomography (nano-CT), acoustic microscopy, or magnetic resonance imaging. Most preferably, a 3D image of a rock is obtained by micro-CT to provide a sufficient field of view of the rock to avoid edge pores distorting the overall porosity of the resulting image, and to reduce the scanning time and computational requirements required for higher resolution tomography (e.g., nano-CT).

[0038] In a preferred embodiment, the 3D image is obtained from a cloud-based tool adapted to store 2D projection images from pore space imaging techniques, in particular from micro-CT and thin sections. The tool is adapted to process the 2D projection images to produce a reconstructed 3D image. The tool is also adapted to store the resulting 3D image.

[0039] A 3D image of a rock obtained by pore-scale imaging techniques has a resolution. The voxels of a 3D image define the resolution of the image. An image is composed of a plurality of voxels, wherein the volume defined by each voxel represents the maximum resolution of the image. The resolution of the image should be selected to provide a voxel size at which the major pore throats for fluid flow in the rock are adequately resolved and to provide a sufficient field of view to represent the entire rock to be analyzed for a given rock physical property, such as cementation index and hydrocarbon saturation. For the purposes of this paper, the major pore throat size (D d ) is the size of the pore throat of the pore into which a non-wetting liquid enters at the pore entry pressure (Pd), where the pore entry pressure is the minimum pressure required before a non-wetting liquid can begin to invade the pore structure of the rock.

[0040] The resolution of the micro-CT images may be selected based on the size of the rock sample, the relative average pore size of the rock type, the time required for imaging, and the computational power required to store the image data and perform additional computational activities on the image data. The image resolution should be selected to be detailed enough so that a non-wetting liquid capillary injection curve can be plotted based on a segmented image generated from the image, while maintaining a sufficient field of view to avoid edge pores distorting the overall porosity of the resulting image. In a preferred embodiment, the image resolution is selected to require little computational power to store the image and perform additional computational activities on the image, while providing sufficient detail to construct a capillary injection curve based on the segmented image. The image resolution may be selected based on the type of rock, with sandstones typically having a larger pore structure than carbonates and requiring a lower image resolution than carbonates, and carbonates having a larger pore structure than shale and requiring a lower image resolution than shale. Micro-CT image resolution may be as low as 0.1 μm per voxel. 3 Up to 30μm 3 For sandstone, it is preferred to use 1 μm per voxel. 3 Up to 25μm 3 or 2.5 μm per voxel 3 Up to 15μm 3 Micro-CT images can be produced at a resolution of 0.5 μm for carbonate rocks. 3 Up to 20μm 3 or 1μm 3 Up to 10μm 3 and for shale, the resolution of micro-CT (or nano-CT) images can be within 0.1 μm 3 Up to 10μm 3 , or 0.5μm 3 Up to 5μm 3 within the range.

[0041] In a preferred embodiment, the acquired image may be processed to reduce noise and image artifacts. Noise may be filtered out of the acquired image by filtering using a local mean filter to reduce noise. Imaging artifacts primarily at the outer edges of the acquired image may be reduced by processing the image while excluding the outer edges of the image.

[0042] A 3D image obtained for a rock is processed to segment voxels of the image into voxels representing pore space in the rock or solid matter in the rock, thereby producing a binary image in which the pore voxels have a value of 0 and the solid matter voxels have a value of 1 (or vice versa). The image can be a grayscale image, and the processing of the voxels of the image to segment the image into voxels representing pore space or solid matter can be achieved by assigning the voxels a name of pore space or solid matter based on a threshold, wherein voxels with image intensity above the threshold can be assigned a value representing pores (or solid matter), and voxels with image intensity below the threshold can be assigned a value representing solid matter (or pores). The threshold can be calculated using the Otsu method described in Otsu ("A Threshold Selection Method from Gray-level Histogram" IEEE Trans. SMC 9:62-66; 1979), or other threshold calculation algorithms known in the art.

[0043] The 3D image of the rock may be processed using a segmentation algorithm known in the art to segment the voxels into pore space voxels and solid matter voxels.Preferably, the segmentation method is selected to distinguish between conductive pores and non-conductive minerals. Examples of segmentation methods are described in Otsu (1979), Andra et al. (“Digital Rock Physics Benchmarks-Part II: Computing Effective Properties” Computers and Geosciences 50:33-43; 2013), Saxena et al. (“Effect of Image Segmentation & Voxel Size on Micro-CT Computed Effective Transport & Elastic Properties” Marine and Petroleum Geology 86:972-990; 2019), and Chuang et al. (“Fuzzy C-Means Clustering with Spatial Information for Image Segmentation” Segmentation) "Comput. Med. Imaging Graph. " 30:9-15; 2006). A person skilled in the art will understand the desired segmentation selection. Preferably, segmentation using a segmentation algorithm is performed automatically using a data processing system.

[0044] After the image has been segmented, the image porosity, Φ, is estimated from the segmented 3D image of the rock. I . Image porosity of a rock can be estimated by summing the number of voxels representing pore space in the segmented image, summing the total number of voxels in the segmented image (or obtaining the total number of voxels from imaging parameters), and then dividing the sum of the number of voxels representing pore space in the segmented image by the total number of voxels in the segmented image. The sum of the number of voxels representing pore space in the segmented image can be determined by adding the number of voxels assigned a binary value (e.g., 1 or 0) representing pore space. The sum of the total number of voxels in the segmented image can be determined by adding the total number of voxels assigned a binary value (pore space voxels and solid matter voxels). Image porosity lacks the sub-resolution porosity of the rock.

[0045] According to the invention, a corrected porosity is determined that is suitable for taking into account the sub-resolution porosity of the rock. In one embodiment of the invention, the corrected porosity is measured using conventional laboratory measurements. In another embodiment of the invention, a correction factor is applied to the image porosity, e.g., by the image porosity. True porosity The correlation between α and α is used to determine the corrected porosity, and the correction factor (α) can be expressed as follows:

[0046] Where 0<α<1. (1)

[0047] The correction factor can be estimated by a transformation. In a preferred embodiment, the correction factor is determined by determining a non-wetting liquid capillary pressure curve from a segmented 3D image of the rock for pores identifiable in the segmented image at pressures up to the image limit pressure. Preferably, mercury or Wood's metal is selected as the non-wetting liquid. The non-wetting liquid capillary pressure curve can be determined from the segmented image by plotting the porosity of the rock occupied by the non-wetting liquid at selected pressures up to the image limit pressure based on a simulation of the non-wetting liquid filling the pore space of the image. In order to simulate the non-wetting liquid filling the pore space of the image, the pore throat size that penetrates at a given pressure can be determined according to the Young-Laplace equation:

[0048] D=4σcosθ / P (2)

[0049] where P is a given pressure, σ is the non-wetting liquid-air surface tension (480 dynes / cm for mercury-air), θ is the contact angle (140° for mercury), and D is the size of the pore throats penetrated at a given pressure. To assess the porosity of a rock occupied by a non-wetting liquid at a given pressure, a simulation can be performed in which the voxels of pore space with pore throat size D or larger are assumed to be filled with the non-wetting liquid at a given pressure, the voxels "filled" with liquid are summed, and the porosity of the rock occupied by the non-wetting liquid is calculated by dividing the sum of the number of pore space voxels filled with liquid by the total number of voxels in the image. Then, for values ​​above the entry pressure (P d ) until the image limit pressure (P max ), the porosity of the rock occupied by the non-wetting liquid can be plotted against the given pressure. d ) is the pressure at which the non-wetting liquid initially enters the pores of the rock, and the image limit pressure (P max ) is the minimum pressure required to overcome the capillary pressure of the narrowest pore throat identifiable in the segmented image.

[0050] The Thomeer model of the capillary pressure curve provides:

[0051]

[0052] in is the porosity of the rock occupied by the non-wetting liquid at pressure P, is the porosity of the rock occupied by the non-wetting liquid at infinite pressure (true porosity, because the pressure is sufficient to force the non-wetting liquid through the smallest pore throats in the rock), G is the pore geometry factor reflecting the pore throat distribution and its associated pore volume, and N is the pore throat resolution parameter. When P is equal to the image limit pressure, then

[0053]

[0054] And the porosity correction factor Therefore, the porosity correction factor α R It can be determined based on the pore geometry factor G and the pore resolution parameter N.

[0055] The pore resolution parameter N is determined based on the capillary pressure curve of the non-wetting liquid derived from the image and the image resolution determined based on the size of the voxel. The pore resolution parameter N is the capillary pressure curve of the non-wetting liquid at the entry pressure (P d ) The pore throat size (D d ) to the voxel size (Δx), N = (D d / Δx). The size of the voxel can be determined according to the parameters of 3D imaging (i.e., the resolution of the image). d ) The pore throat size (D d ) can be determined from the non-wetting liquid capillary pressure curve derived from a segmented 3D image of the rock according to the following equation:

[0056] D d =4σcosθ / P d (5).

[0057] The pore geometry factor G is determined from the non-wetting liquid capillary pressure curve derived from the image. The pore geometry factor G can be determined by plotting a best fit curve onto the non-wetting liquid capillary pressure curve simulated from the segmented image and determining the pore geometry factor based on the shape of the curve. The best fit curve can be plotted by the least squares method or by any conventional curve fitting method.

[0058] As mentioned above, the pore geometry factor G and the pore throat resolution parameter N are used to determine the porosity correction factor. The porosity correction factor α can be calculated from the pore geometry factor G and the pore throat resolution parameter N according to the following equation:

[0059]

[0060] Then the porosity correction factor α R Applied to the image porosity of the rock to obtain the corrected porosity. The following equation can be used to calculate the corrected porosity based on the image porosity of the rock: and the porosity correction factor (α) to estimate the corrected porosity

[0061] The corrected porosity is essentially the same as the true porosity determined from laboratory measurements.

[0062] As mentioned above, the Archie equation (Archie, 1942, 1952) states that the effective electrical properties of a reservoir rock depend entirely on the brine conductivity, the total porosity of the sample, and the connectivity of the pore space:

[0063]

[0064] Among them C w is the conductivity of the brine that completely saturates all pores, Φ ∞ is the total porosity of the rock, C 0∞ is the effective conductivity of the rock saturated with brine, FF ∞ is the so-called formation factor, and m ∞ is the cementation index. The symbol ∞ indicates properties calculated using a hypothetical image of very high resolution, so as to resolve the entire pore volume. The properties calculated for very high resolution (indicated by ∞) are comparable to laboratory measurements.

[0065] However, for rock images with finite voxel size, such as micro-CT, the inventors' Archie equation is rewritten as

[0066]

[0067] Where the subscript "I" refers to the properties calculated using a segmented image divided into conductive pores and non-conductive minerals.

[0068] Equation (9) represents the inventors' recognition that image-derived properties, especially for lower resolution images, do not match those measured in the laboratory. The inventors recognized that without correction for missing sub-resolution porosity, since Φ I <Φ ∞ Therefore, the rock effective conductivity calculated using segmented micro-CT images is underestimated (i.e., C 0I <C 0∞ and FF I >FF ∞ ).

[0069] Therefore, the method of the present invention can be solved by modifying the image-derived properties to estimate the cementation index m ∞ The Archie equation is used to compensate for the sub-resolution pore volume:

[0070]

[0071] or

[0072]

[0073] The term (Φ) in equation (10) ∞ -Φ I ) k is the normalized conductivity correction term that compensates for the underprediction of rock effective conductivity due to the missing sub-resolution porosity in the micro-CT images.

[0074] The inventors found that the index k and α R There is a nearly linear association with the following empirical relationship:

[0075] k=(k0-m ∞ )α R +m ∞ , where k0=0.9 (12)

[0076]

[0077] According to one embodiment of the present invention, the image Archie cementation index (m I ). As discussed in Saxena et al. 2019, the parameter α can be estimated directly from the image R , without using any laboratory measurements except a single micro-CT image of a homogeneous rock sample. Therefore, m is estimated using equation (13) ∞ All the parameters required can be estimated directly using micro-CT images. The remaining parameters that need to be estimated using micro-CT images are: porosity (Φ) of the segmented micro-CT images I ), brine conductivity (C w ), and the effective conductivity (C 0I ).

[0078] The effective conductivity of a rock sample depends on the conductivity of the components including the minerals and the fluid that fills the pores. In the static DC limit, the current density field is described by the diffusion equation for the electric potential. For example, it can be assumed that all solid particles, including any clay, are completely insulating, and therefore in the solid matrix, the conductivity can be assigned a value of 0, while in the pore fluid, a finite fluid conductivity can be assigned. The effective conductivity is then obtained using Ohm's law relating the total current and voltage between the inlet and outlet boundaries. One approach is described in Garboczi ("Finite element and finitedifference programs for computing the linear electric and linear elastic properties of digital images of random materials" NISTIR 6269; 1998).

[0079] The hydrocarbon saturation can then be estimated by calculating the water saturation. The Archie equation is expressed as follows:

[0080]

[0081] Where S w is the water saturation of the uninvaded zone, n is the saturation index (Archie n), R w is the formation water resistivity, Φ is the porosity, m is the cementation index (Archie m), and R t is the true resistivity of the formation, corrected for invasion, drilling, thin layers, and other effects. Hydrocarbon saturation is 1 minus the water saturation value.

[0082] R w It can be determined by well logs or physical measurements known to those skilled in the art. Archie n can be determined by conventional laboratory measurements or by estimation according to the guidance of Archie (e.g., as discussed at https: / / wiki.aapg.org / Archie_equation).

[0083] Preferably, the estimated hydrocarbon saturation is used to convert the resistivity log into a continuous saturation characteristic curve in a manner known to those skilled in the art, for example, using a density log of the oil field from which the resistivity log was obtained. The more accurate estimate of hydrocarbon saturation and the continuous saturation characteristic curve derived therefrom are particularly suitable for exploration and production decisions and oil field evaluation. The method of the present invention enables rapid decision making. For example, the process of estimating hydrocarbon saturation using physical measurements of rock samples may take about 7 to 8 months, while the method of the present invention can provide substantially the same or better accuracy in about a few days.

[0084] Examples

[0085] The following non-limiting examples of embodiments of the methods of the present invention claimed herein are provided for illustrative purposes only.

[0086] The True Cementation Index is determined in a physical laboratory using laboratory-measured rock conductivity, laboratory-measured porosity, and the conductivity of brine-saturated pores of different rock types from the Gulf of Mexico and Brunei. True Cementation Index, m 实验室 , calculated by equation (8). The cementation index was derived from 3D micro-CT images of the same rock sample. Image-derived cementation index, m I , calculated by equation (9). m 实验室 / m I Ratio comparison α R Shown in Figure 1 middle. Figure 1 It is shown that when α R When α is close to 1, there is good agreement between the laboratory-measured and image-derived cementation indices. However, for example, when α R When =0.5, there is a deviation between the cementation index measured in the laboratory and the cementation index derived from the image.

[0087] Figure 2 It is shown that before the conversion using equation (13) (m I ) and after (m ∞ ) Cementation indices estimated using micro-CT images compared to laboratory-measured cementation indices, m 实验室 . Figure 2 It is shown that without correction according to the method of the present invention, m I Overrated.

[0088] The present invention is well suited to obtain the objects and advantages mentioned and the objects and advantages inherent therein. The specific embodiments disclosed above are illustrative only, because it is obvious to those skilled in the art who benefit from the teachings herein that the present invention can be modified and practiced in different but equivalent ways. In addition, except as described in the appended claims, there is no intention to limit the details of the construction or design shown herein. Therefore, it is obvious that the specific illustrative embodiments disclosed above can be changed, combined or modified, and all such changes are considered to be within the scope of the present invention. The invention disclosed illustratively herein can be appropriately practiced in the absence of any element not specifically disclosed herein and / or any optional element disclosed herein. Although compositions and methods are described according to "comprising", "containing" or "including" various components or steps, the compositions and methods can also be "essentially composed of various components and steps" or "composed thereof". All numbers and ranges disclosed above can vary by a certain amount. Whenever a numerical range with a lower limit and an upper limit is disclosed, any number and any included range falling within the range are specifically disclosed. In particular, each range of values ​​disclosed herein (in the form of "about a to about b", or equivalently, "about a to b", or equivalently, "about ab") should be understood to list each number and range covered within the broader range of values. In addition, the terms in the claims have their ordinary general meaning unless otherwise explicitly and clearly defined by the patentee. In addition, the indefinite articles "a" or "an" used in the claims are defined herein to mean one or more of the elements they introduce. If there is any conflict in the use of words or terms in this specification and one or more patents or other documents that may be incorporated herein by reference, the definition consistent with this specification should be adopted.

Claims

1. A method for estimating hydrocarbon saturation of a hydrocarbon-bearing rock from a resistivity log and a rock image, comprising: - Obtaining a resistivity log of an oil field having hydrocarbon-bearing formations; - obtaining a 3D image of rock from the hydrocarbon-bearing formation in the oil field, wherein the 3D image is composed of a plurality of voxels and the 3D image has a resolution; - processing the 3D image to segment the 3D image by selecting each voxel of the 3D image to represent pore spaces in the rock or solid material in the rock; - estimating an image porosity of the rock from the segmented 3D image, the image porosity lacking a sub-resolution porosity of the rock; - determining a corrected porosity suitable for taking into account said sub-resolution porosity of said rock; - estimating a corrected cementation index of said rock based on said image porosity and said corrected porosity of said rock; and - Using said corrected cementation index, estimating said hydrocarbon saturation of said hydrocarbon containing rock.

2. The method according to claim 1, further comprising the following steps: - obtaining a density log of the oil field; and - Converting the resistivity log into a continuous saturation signature using the estimated hydrocarbon saturation and the density log.

3. The method of claim 1, wherein the 3D image of the rock is obtained by X-ray computed tomography.

4. The method of claim 1, wherein the step of estimating the hydrocarbon saturation comprises determining formation water resistivity and a saturation index.

5. The method of claim 1, wherein the rock is obtained from a hydrocarbon-bearing formation consisting of sandstone, carbonate rock, shale, and combinations thereof.

6. The method of claim 1, wherein the corrected porosity of the rock is determined by laboratory measurements.

7. A method according to claim 1, wherein the corrected porosity of the rock is determined by: deriving a non-wetting liquid capillary pressure curve from the segmented 3D image of the rock; and determining the corrected porosity based on the segmented 3D image and the non-wetting liquid capillary pressure curve.

8. A method according to claim 7, wherein the non-wetting liquid capillary pressure curve is derived from the segmented 3D image of the rock at pressures up to an image limit pressure, wherein the image limit pressure is the minimum pressure that can be applied to the non-wetting liquid to overcome the capillary pressure of the narrowest pore throat identifiable from the segmented 3D image of the rock.

9. The method of claim 1, wherein the 3D image is obtained from a cloud-based tool adapted to store and process 2D projection images from pore-scale imaging techniques.

10. A back-propagation-enabled method for estimating hydrocarbon saturation of hydrocarbon-bearing rock, comprising the following steps: - Obtaining a resistivity log of an oil field having hydrocarbon-bearing formations; - obtaining a 3D image of rocks from the hydrocarbon-bearing formation in the oil field, the 3D image having a resolution; - applying the trained model with back-propagation support to segment the 3D image; - estimating an image porosity of the rock from the segmented 3D image, the image porosity lacking a sub-resolution porosity of the rock; - determining a corrected porosity suitable for taking into account said sub-resolution porosity of said rock; - estimating a corrected cementation index of said rock based on said image porosity and said corrected porosity of said rock; and - Using said corrected cementation index, estimating said hydrocarbon saturation of said hydrocarbon containing rock.

11. The method of claim 10, wherein the trained model is generated by: - Provide a training set of rock images; - segmenting the rock image into a plurality of labeled voxels representing pore space and solid material in the rock; and - Train the model via back-propagation using the labeled voxels.

12. The method of claim 10, wherein the corrected porosity of the rock is determined by laboratory measurements.

13. The method according to claim 10, wherein the corrected porosity of the rock is determined by: deriving a non-wetting liquid capillary pressure curve from the segmented 3D image of the rock; and determining the corrected porosity based on the segmented 3D image and the non-wetting liquid capillary pressure curve.

14. The method of claim 10, wherein the 3D image is obtained from a cloud-based tool adapted to store and process 2D projection images from pore-scale imaging techniques.

15. The method of claim 11, wherein the image training set is obtained from a cloud-based tool adapted to store and process 2D projection images from pore-scale imaging techniques.

Images