Patent Application
20140052420
Microscopy offers scientists and engineers a way to gain a better understanding of the materials with which they work. Under high magnification, it becomes evident that many materials (including rock and bone) have a porous microstructure that permits fluid flows. Such fluid flows are often of great interest, e.g., in subterranean hydrocarbon reservoirs. Accordingly, significant efforts have been expended to characterize materials in terms of their flow-related properties including porosity, permeability, and the relation between the two. Scientists typically characterize materials in the laboratory by applying selected fluids with a range of pressure differentials across the sample. Such tests often require weeks and are fraught with difficulties, including requirements for high temperatures, pressures, and fluid volumes, risks of leakage and equipment failure, and imprecise initial conditions. (Flow-related measurements are generally dependent not only on the applied fluids and pressures, but also on the history of the sample. The experiment should begin with the sample in a native state, but this state is difficult to achieve once the sample has been removed from its original environment.)
Accordingly, industry has turned to digital rock analysis to characterize the flow-related properties of materials in a fast, safe, and repeatable fashion. A digital representation of the material's pore structure is obtained and can be used to characterize the properties of the material. Efforts to increase the amount of information that can be derived from digital rock analysis are ongoing.
Accordingly, there are disclosed herein digital rock analysis systems and methods that estimate a maturity level of a rock sample. In the drawings:
It should be understood, however, that the specific embodiments given in the drawings and detailed description below do not limit the disclosure. On the contrary, they provide the foundation for one of ordinary skill to discern the alternative forms, equivalents, and other modifications that are encompassed in the scope of the appended claims.
For context,
For high resolution imaging, the observation chamber 102 is typically evacuated of air and other gases. A beam of electrons or ions can be rastered across the sample's surface to obtain a high resolution image. Moreover, the ion beam energy can be increased to mill away thin layers of the sample, thereby enabling sample images to be taken at multiple depths. When stacked, these images offer a three-dimensional image of the sample to be acquired. As an illustrative example of the possibilities, some systems enable such imaging of a 40×40×40 micrometer cube at a 10 nanometer resolution.
In an example process, the sample area identified for 3D imaging is mounted and inserted into a Zeiss Auriga™ FIB-SEM which uses a GEMIN™ electron column. The design of this column is what permits imaging at low energy with no surface coating. During the creation of the 3D dataset the FIB-SEM removes about 10 nm of material from a prepared area, SE2 and ESB images are taken, and then the FIB removes another 10 nm creating a new plane parallel to the one previously imaged. This process of milling and imaging is repeated around 600 to 1,000 times and vertical orientation of all images is preserved. After all individual FIB-SEM images are captured, they are aligned and merged into separate SE2 and BSE 3D objects with each image voxel having dimensions of from 10 to 15 nanometers. An example FIB-SEM volume used for analysis represents about 1×10-10 g of rock.
The system of
The source of the sample, such as in the instance of a rock formation sample, is not particularly limited. For rock formation samples, for example, the sample can be sidewall cores, whole cores, drill cuttings, outcrop quarrying samples, or other sample sources which can provide suitable samples for analysis using methods according to the present disclosure.
Typically, a user would employ a personal workstation 202 (such as a desktop or laptop computer) to interact with the larger system 200. Software in the memory of the personal workstation 202 causes its one or more processors to interact with the user via a user interface, enabling the user to, e.g., craft and execute software for processing the images acquired by the scanning microscope. For tasks having small computational demands, the software may be executed on the personal workstation 202, whereas computationally demanding tasks may be preferentially run on the high performance computing platform 206.
One way to characterize the porosity structure of a sample is to determine an overall parameter value, e.g., porosity. For example, the image 302 may be processed to categorize each voxel as representing a pore or a portion of the matrix, thereby obtaining a pore/matrix model in which each voxel is represented by a single bit indicating whether the model at that point is matrix material or pore space. Further, non-pore voxels may be categorized as organic matter or non-organic matter. The process of classifying voxels as pores, organic matter, or non-organic matter is sometimes called segmentation. Through the voxel classification process, porosity volumes, organic matter volumes, and non-organic matter volumes for a sample can be estimated with a straightforward counting procedure. Further, 3D volumes may be segmented using 3D algorithms that separate pore space, porosity associated with organic material (PAOM), solid OM, and solid matrix framework into separate 3D volumes. Without limitation to other examples, the local porosity theory set forth by Hilfer, (“Transport and relaxation phenomena in porous media” Advances in Chemical Physics, XCII, pp 299-424, 1996, and Biswal, Manwarth and Hilfer “Three-dimensional local porosity analysis of porous media” Physica A, 255, pp 221-241, 1998), when given a subvolume size, may be used to determine the porosity of each possible subvolume in the sample or its 3D model.
In
In accordance with examples of the disclosure, the amount of porosity within organic matter bodies is estimated for a rock sample (e.g., from a shale of interest). Further, the amount of porosity may be correlated to a thermal maturity level for the rock sample based on the assumption that porosity associated with organic matter, PAOM, is created by the conversion of solid organic matter to hydrocarbons (gas or oil or both).
As an example, the amount of porosity within organic matter (OM) may be estimated by using high resolution SEM images of ion-polished shale samples.
In accordance with examples of the disclosure, PAOM results may be normalized to the bounds of the organic matter bodies using the following calculation: Conversion Ratio (CR)=PAOM/(PAOM+OM). For example, if PAOM corresponds to 2.7% of an image and solid OM corresponds to 7.4% of the image, then the CR for the image is 2.7/(2.7+7.4)=0.27 or 27%. The CR for a plurality of images or slices corresponding to a rock sample may similarly be calculated and used to estimate the CR for the rock sample. Further, the CR may be correlated to a maturity level of the rock sample. For example, a CR of 27% may be interpreted to mean that 27% of available OM for a rock sample (or region from which the rock sample was taken) has been converted to hydrocarbons.
As previously noted, it should be understood that various digital rock analysis techniques for determining porosity within organic matter are possible, and that the CR or maturity level calculation may he determined based on these different techniques. For example, U.S. Provisional Application 61/618,265 titled “An efficient method for selecting representative elementary volume in digital representations of porous media” and filed Mar. 30, 2012 by inventors Giuseppe De Prisco and Jonas Toelke (and continuing applications thereof) be used to determine porosity within organic matter of a sample, and may determine whether reduced-size portions of the original data volume adequately represent the whole for porosity- and permeability-related analyses. Further, various methods for determining permeability from a pore/matrix model are set forth in the literature including that of Papatzacos “Cellular Automation Model for Fluid Flow in Porous Media”, Complex Systems 3 (1989) 383-405. Any of these permeability measurement methods can be employed in the current process to determine a permeability value (or a correlated porosity value) for a given subvolume.
The disclosed CR calculation and maturity level calculation may be based on digital rock models of various sizes. The size of the model may be constrained by various factors including physical sample size, the microscope's field of view, or simply by what has been made available by another party.
For explanatory purposes, the operations of the foregoing method have been described as occurring in an ordered, sequential manner, but it should be understood that at least some of the operations can occur in a different order, in parallel, and/or in an asynchronous manner.
Numerous variations and modifications will become apparent to those skilled in the art once the above disclosure is fully appreciated. It is intended that the following claims be interpreted to embrace all such variations and modifications.
This application claims priority to Provisional U.S. Application Ser. No. 61/849,978 titled “Digital Rock Analysis Systems and Methods that Estimate a Maturity Level” and filed Aug. 20, 2012 by Timothy Cavanaugh, which is hereby incorporated herein by reference.
| Number | Date | Country | |
|---|---|---|---|
| 61684978 | Aug 2012 | US |
