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    • 专利摘要:
    upward scaling method with a pore scale digital rock modeling data processing system representing reservoir rock, system for generating upward scaled digital well scale rock modeling data, upward scaling method with an upward scaling system processing well-scale digital rock modeling data, system for generating upward-scaling inter-well scale digital rock modeling data, upward-scaling method with a system for processing inter-well scale digital rock modeling data , and system for generating up-scaling full-field scale digital rock modeling data. Methods for up-scaling digital rock modeling data are described. Core plug samples for pore scale modeling are strategically chosen using full core minipermeability grids and conventional CT (computed tomography) scans. pore models or pore network models are used for flow modeling. Computed numerical scal properties (special core analysis) are validated using laboratory-derived data, then used to populate well-scale models. Well scale models utilize mps (multipoint statistics) to combine minipermeability grids and conventional whole core ct scans with electrical borehole images to create 3d numerical pseudo-witnesses for each rrt (reservoir rock type). scal properties determined from pore scale models are distributed to each petrophysical facies in numerical pseudo-witnesses. Effective scal properties computed from various mps well scale realizations or models are used to populate inter-well scale models for each rrt. at the inter-well scale, seismic attributes and variogram statistics from lwd (profiling during drilling) data are used to populate digital rock models. Effective properties computed from flow simulations for inter-well volumes are used to populate full-field scale models. at full field scale, outcrop analogs, sequence stratigraphy, advanced stratigraphic models, diagenetic models, and basin scale models are combined using mps to improve flow simulations. at each stage, revs (representative element volumes) are computed to make sure rock heterogeneities have been captured.
    公开号:BR112013015288B1
    申请号:R112013015288-5
    申请日:2012-02-28
    公开日:2021-09-14
    发明作者:Neil F. Hurley；Weishu Zhao；Tuanfeng Zhang
    申请人:Schlumberger Holdings Limited；
    IPC主号:
    专利说明:

    FIELD OF INVENTION
    [001] The descriptive report of this patent refers generically to methods to characterize three-dimensional (3D) samples of reservoir rock. More particularly, this patent specification refers to up-scaling digital rock modeling data. FUNDAMENTALS OF THE INVENTION
    [002] Reservoir modeling is the process of constructing a digital representation of a reservoir that incorporates all the characteristics relevant to its ability to store and produce hydrocarbons. Reservoir models are subdivided into static and dynamic components. Static models are fine-scale simulations of rock properties such as porosity, permeability, capillary pressure, fracture, faults, seismic attributes, and parameters that do not change significantly with time. Dynamic models are coarser simulations that incorporate fluid properties that change with time. weather, such as oil, gas, and water flow rates and pressure.
    [003] Reservoir modeling covers at least twelve orders of magnitude, ranging from pore (nm to micron) to borehole (nm to am) to inter-well (tens to hundreds of m) to full field scale (tens of km) Reservoir rocks are complex and heterogeneous at all scales. Multiscale simulation is a major objective of the oil industry, and many scale-up approaches have been proposed. See, for example, Christie, M.A., 1996, Upscaling for reservoir simulation: JPT, v. 48, no. 11, p. 1004-1010 (hereinafter "Christie 1996"); and Durlofsky, L.J., 2003, Upscaling of geocellular models for reservoir flow simulation: a review of recent progress: 7th International Forum on Reservoir simulation, Buhl/Baden-Baden, Germany, June 23-27, p. 58 (hereinafter "Durlofsky 2003"). Most of these conventional upward scaling approaches start with fine-scale reservoir models that are thickened to a model that typical fluid flow simulators can handle. The biggest challenge in this type of upward scaling occurs because it is often difficult to preserve essential geological heterogeneities in the resulting coarse models.
    [004] Heterogeneity can be defined as the variation in rock properties as a function of location in a reservoir or formation. Many reservoirs are heterogeneous because mineralogy, grain size and type, deposit environment, porosity, permeability, natural fractures, faults, channels and other attributes vary from place to place. Heterogeneity causes problems in formation assessment and reservoir simulation because reservoirs occupy huge volumes, but there is limited core and profile control. For example, a typical grid block used in a reservoir simulator is 250m x 250m x 1 m, well scale numerical pseudo witnesses represent rock volumes at the cubic meter scale, and core buffers and micro CT scans or confocal scans represent even smaller volumes.
    [005] A geocellular model is a layered 3D grid model. Layers can have zero thickness, as in the case of bed crimps or truncations. Layers can be as thin as the spacing of profile measurements, or they can be thicker to reflect the known thickness of rock layers. Geocellular models capture geological scale heterogeneities, and commonly have millions of cells.
    [006] Upward scaling is the process of converting rock properties from fine scales to coarser scales. Upscaling algorithms assign appropriate values of porosity, permeability, and other flow functions to each thicker grid block. See Lasseter, T.J., Wagoner, J.R., and Lake, L.W., 1986, Reservoir heterogeneities and their influence on ultimate recovery, in Lake, L.W., andCarroll, H.B., Jr., eds. Reservd!l.r characterization: Academic Press, Orlando, Florida, p. 545-559 (hereinafter "Lasseter 1986"); Christie 1996, and Durlofsky 2003. Upscaling is necessary because reservoir simulators cannot handle large cell numbers in typical geocellular models.
    [007] There have been many attempts at ascending scaling in reservoir simulation. Common approaches are summarized in: Lasseter 1986, Christie 1996, and Durlofsky 2003. A number of authors have used multipoint statistics (MPS) and representative element volume concepts (REVI in digital rock modelling. Okabe and Blunt (2004, 2005, 2005) 2007) used MPS to generate 2D thin-section 3D pore systems.See Okabe, H., and Blunt, MJ, 2004, Prediction of permeability for reconstructed porous media using multiple-point statistics; Physical review, E., v. 70, 10 p; Okabe, H., and Blunt, MJ, 2005, Pore space reconstruction using multiple-point statistics: Journal of Petroleum science and Engineering, v. 46, p. 121-137; and Okabe, H., and Blunt, MJ, 2007, Pore space reconstruction of vuggy carbonates microtomography and multiple-point statistics: using Water resources research, v. 43. These authors assumed that the 2D horizontal view was the same as the 2D vertical view, and proceeded to generate their model. Due to this assumption, your model does not capture heterogeneous rock age, and does not represent true 3D pore geometry. MPS was used to model carbonate facies tracts. See Levy, M., PM Harris, S. Strebelle and EC Rankey, 2007a, Geomorphology of carbonate systems and reservoir modeling: carbonate training images, FDM cubes and MPS simulations (abs.): AAPG Annual Convention, Long Beach, Califomia, http ://searchanddiscovery.com/documents/2008/08054levy/inde x.htm (accessed 15 July 2008); Levy, M., W. Milliken, PM Harris, S. Strebelle and EC Rankey, 2007b, Importance of facies-based earth models for understaning flow behavior in carbonate reservoirs (abs): AAPG Annual convention, Long Beach, Califomia, http: //searchanddiscovery.com/documents/2008/08097harris25a /index.htm (accessed September 5, 2008); and Harris, P.M. 2009, Delineating and quantifying depositional facies pattems in carbonate reservoirs: Insight from modem analogs: AAPG Bulletin, v. 94, p. 61-86. MPS was used to generate well-scale numerical rock models. See Zhang, T., Hurley, N.F., and Zhao, W;. 2009, Numerical modeling of heterogeneous carbonates and multiscale dynamics: presented at SPWLA 50th Annual logging symposium, The Woodlands, Texas, June 21-24 (hereinafter "Zhang 2009") Representative Element Volume Concepts (for representative element volume, REV) and area (representative element area, REA) were applied to a photo of the heterolithic sediments outcrop. See Norris, R.J. and Lewis, J.J.M, 1991. The geological modeling of effective permeability in complex heterolithic facies: SPE Preprint 22692, presented at the 66th Annual Technical conference and exhibition, Dallas, TX, Oct. 6-9, p. 359-374. REV was discussed in relation to work with permeabilities in outcrop blocks of heterolithic sediments. See Jackson, M.D., Muggeridge, A.H., Yoshida, s., and Johnson, H.D., 2003, Upscaling permeability measurements within complex heterolithictidal sandstones: Mathematical Geology, v. 35, p. 499-520; and Jackson, M.D., Yoshida, S., Muggeridge, A.H., and Johnson, H.D., 2005, Three-dimensional reservoir characterization and flow simulation of heterolithic tidal sandstones: AAPG Bulletin, v. 89, p. 507-528. the concept of REV has been used in pore scale digital rock models. However, as they used overlapping subvolumes, they obtained questionable results. See Zhang, D., Zhang, R., Chen, S., Soll, W.E., 2000, Pore scale study of flow in porous media: Scale dependency, REV, and statistical REV: Geophysical research letters, vol. 27, no. 8, p. 1195-1198; and Okabe, H. and Oseto, K., 2006, Pore-scale heterogeneity assessed by the lattice-Boltzmann method: Intemational Symposium of the Soe. Of Core analysts, Trondheim, Norway, September 12-16, article SCA2006-44, 7 p. The REV concept of minimized variance was used to thicken (upwardly scale) reservoir simulations. See Qi, D.' 2009, Upscaling theory and application Techniques for reservoir simulation; Lambert Academic Publishing, Saarbrucken, Germany 230 p. (hereinafter "Qi 2009".
    [008] 3D pore scale models were built using 2D thin sections, using an approach known as Markov Chain Monte Carlo simulation. See Wu, K., Van Dijke, MIJ, Couples, GD, Jiang, Z., Ma, J., Sorbie, KS, Crawford, J., Young, I., and Zhang, X., 2006, 20 3D stochastic modeling of heterogeneous porous media applications to reservoir rocks: Transport in Porous Media, v. 65, p. 443-467. Upscaling issues were addressed by building composite pore models using thin section scans of different resolution. See Wu, K., Ryanazov, A., van Dijke, MIJ, Jiang, Z., Ma, J., Couples, GD and Sorbie, KS, 2008, Validation of Methods for multi-scale by reconstruction and their use in prediction of space flow properties of carbonate: article SCA2008-34, Intemational symposium of the society of core analysts, Abu Dhabi, October 29 - November 2, 12 p. , which states: "One possible approach is to refine the coarser scale 3D image to equivalent resolution as the finer scale and then combine these two structures with the same volume into a single model." The finer scale image is "overlaid" on the coarser scale image to form an integrated structure. See, id.
    [009] The US patent US 6,826,520 describes a method for upward permeability scaling using a Voronoi computational grid. US patent US 7,224,162 describes a method for estimating properties of a geological formation using well log data such as nuclear magnetic resonance, resistivity and other profiles. The method acquires directional formation property values and generates a directional property profile. US patent US 7,783,462 describes a method for populating a three-dimensional reservoir structure having a plurality of cells with one or more constant reservoir property values. US patent US 25 7,765,091 describes a multiscale method for reservoir simulation using a finite volume method.
    [0010] US patent application publication US 2011-0004448 describes a method for constructing 3D digital models of porous media using reflected white light and laser scanning confocal profilometry and multi-point statistics. US patent application publication US 2011-0004447 describes a method for constructing 3D digital models of porous media using transmitted laser scanning confocal microscopy and multipoint statistics. The VER concept of pore escét.J.a is also discussed. US patent application publication US 2009-0262603 describes a method for generating full bore images from bore images. U.S. patent application publication US 2009-0259446 describes a method for generating pseudonumerical witnesses from conventional CTscans and full bore images using multipoint statistics. SUMMARY
    [0011] According to some embodiments, a method is provided for ascending scaling with a processing system, well-scale digital rock modeling data representing reservoir rock. The method includes combining the well-scale digital rock modeling data with inter-well scale source data to generate inter-well scale digital rock modeling data that captures heterogeneity at an inter-well scale. A plurality of reservoir rock types are preferably identified in the well-scale digital rock modeling data. Inter-well scale source data is preferably collected using logging while drilling non-vertical wells, cross-well geophysical measurements, and seismic measurements; and may include computed variogram statistics. The ascending scaling process preferably includes using values computed on a well scale, of porosity, permeability, capillary pressure and/or relative permeability. According to some modalities, other values can be used for ascending scaling such as: resistivity indices, water saturations, irreducible water saturations, residual oil saturations, recovery factors, and Archie cementation exponents (m) and saturation (n) . Digital rock modeling data preferably includes multipoint statistics results and representative element volumes.
    [0012] The well-scale digital rock modeling data were preferably upward scaling of pore-scale digital rock modeling data using computed values at a scale of pore, porosity, permeability, capillary pressure and/or permeability relative. Pore-scale digital rock modeling data is preferably generated using conventional computed tomography (CT) scan data and mini-permeability of one or more core plates having a data grid spacing of miniperm. Between approximately 0.5 in and approximately 1 cm, and a CTscan slice spacing of approximately 1 mm to 2 mm. CT scan and mini-permeability data are preferably used to identify rock subvolumes known as petrophysical facies. Petrophysical facies are preferably characterized using 2D and 3D transmitted laser scanning fluorescence microscopy at a resolution of approximately 250 nm per pixel or voxel, micro CT scans at a resolution of approximately 1 to 5 microns per voxel, nano CT scans at a resolution of approximately 50 nm to 60 nm per voxel, focused ion beam scanning electron microscopy (FIB-SEM) at a resolution of approximately 5 nm to 10 nm per pixel using closely spaced serial sections, mercury injection capillary pressure (at acronym for Mercury injection capillary pressure, MICP) and/or nuclear magnetic resonance (NMR).
    [0013] The method may also include upscaling the generated inter-well scale digital rock modeling data to generate full-field scale digital rock modeling data based on computed values at an inter-well scale, of porosity, permeability, capillary pressure and/or relative permeability. BRIEF DESCRIPTION OF THE FIGURES
    [0014] The present disclosure is further described in the following detailed description, with reference to the aforementioned plurality of drawings by way of non-limiting examples of exemplary embodiments, in which like reference numerals represent similar parts throughout the various views of the drawings and where: Figure 1 illustrates an oil field scenario in which a multiscale digital rock modeling for reservoir simulation is performed, according to some modalities; Figures 2A-B are block diagrams that show recommended procedures for simulating a multiscale reservoir according to some modalities; Figure 3 is a block diagram showing a workflow with pore scale digital rock models according to some modality; Figure 4 is a block diagram showing a workflow with well-scale digital rock models according to some modalities; Figure 5 is a diagram showing a workflow with inter-well scale digital rock models according to some modalities; Figure 6 is a block diagram showing a workflow for full-field simulation, according to some modalities; Figure 7 is a flowchart showing preliminary steps to be performed before a multiscale reservoir simulation, according to some modalities; Figures 8A-B are a flowchart showing steps in performing an advanced digital token analysis, in the form of numerical pseudo-witnesses and flow simulation, according to some modalities; Figure 9 is a flowchart showing steps used to distribute facies and build the inter-well scale model, according to some modalities; and Figures 10A-B illustrate a flowchart showing recommended steps for ascending scaling of inter-well simulations for full field, according to some modalities. DETAILED DESCRIPTION
    [0015] The following description provides exemplary embodiments only, and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the following description of the exemplary embodiments will provide those skilled in the art with a description for implementing one or more exemplary embodiments. It being understood that various changes can be made to the function and arrangement of elements without departing from the spirit and scope of the invention as set out in the appended claims.
    [0016] Specific details are given in the following description to provide a full understanding of the modalities. However, it will be understood by a person of ordinary skill in the art that the modalities can be put into practice without these specific details. For example, systems, processes, and other elements in the invention can be shown as components in block diagram form so as not to obscure the embodiments in unnecessary detail. In other instances, well-known processes, structures, and techniques can be shown without unnecessary detail to avoid obscuring the modalities. Furthermore, like reference numerals and designations in the various drawings indicate like elements.
    [0017] Furthermore, it is noted that individual modalities can be described as a process that is represented as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart can describe operations as a sequential process, many of the operations can be performed in parallel or simultaneously. Also, the order of operations can be rearranged. A process can be determined when its operations are complete, but it can have additional steps not discussed or included in a figure. Furthermore, not all operations in any process particularly described can take place in all modalities. A process can correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination corresponds to a return from the calling function or main function.
    [0018] Furthermore, embodiments of the invention may be implemented, at least in part, manually or automatically. Manual or automatic implementations can be performed, or at least auxiliary, through the use of machines, hardware, software, firmware, middleware, microcode, hardware description languages, or any combination thereof. When implemented in software, firmware, middleware or microcode, the program code or code segments to perform the necessary tasks can be stored on machine-readable media. A processor(s) can perform the necessary tasks.
    [0019] Figure 1 illustrates an oil field scenario in which a multiscale digital rock modeling for reservoir simulation is performed, according to some modalities. In Figure 1, wells 110 and 120 are nearly horizontal in reservoir 102. On surface 100, the cable logging bogie 112 deploys a logging tool 140 in the well 110. Similarly, the cable logging bogie 122 is shown deploying a tool of logging 142 in well 120. Although Figure 1 illustrates nearly horizontal wells, those skilled in the art would appreciate that the techniques described could also be used in wells that are bypassed or are vertical. Profiling tools 140 and 142 can be, for example, hole imaging tools, witness tools, fluid sampling tools, resistivity tools, nuclear tools, hole seismic tools, sonic profiling tools, and electromagnetic tools ( including cross-well MS) . Data from profiling tricks 112 and 122 is transmitted to a processing center 150 which includes one or more central processing units 144 to perform the data processing procedures as described herein, as well as other processing. Processing center 150 also includes a storage system 142, communications and input/output modules 140, a user display 146, and a user input system 148. Under some embodiments, the processing center 150 may be located. in a location away from the well sites. Although not shown in Figure 1, processing center 150 also receives many other types of data used in multiscale digital rock modeling, such as core analysis data and surface seismic data.
    [0020] Digital rock models are used to develop conceptual workflows to perform multiscale simulations on heterogeneous rocks. Models range in size from pore (nm to micron) to bore (mm to m) to inter-well (tens to hundreds of m) to full-top scale (tens to hundreds of km) . According to some modalities, at each scale, petrophysical properties are calculated from digital rock models in the adjacent minor scale. The petrophysical properties are then used to populate simulations at the next major scale. This process, a form of ascending scaling, converts fine-scale models to coarse simulation grids.
    [0021] According to some modalities, at all scales, digital rock models employ multipoint statistics (the acronym in English for multi-point statistics, MPS) and representative element volumes (REV's). MPS is a geostatistical modeling approach that creates simulations of spatial geological and reservoir property fields conditioned to accept known results, such as those measured in wellbore or rock samples. REV's are the smallest volumes that can be modeled to capture rock heterogeneity, within specified limits of measured property variance, such as porosity or permeability.
    [0022] Core samples for pore scale modeling are strategically chosen using whole core mini-permeability grids and conventional CT scans. Pore scale models employ tools such as transmitted laser scanning fluorescence microscopy, micro CT scans, nano CT scans, and focused ion beam scanning electron microscopy to image pores in 3D. mercury injection capillary pressure data and/or nuclear magnetic resonance, if available, provide independent ways to quantify or modify pore size distributions. Analyzes preferably show that a REV was sampled for each petrophysical facies before segmentation into binary images should take place. MPS is used to create realistic models of arbitrary size and shape. Pore models are directly used for flow modeling, or converted to pore network models, which are then used for flow modeling. Computed numerical SCAL (Special Testimony Analysis) properties are validated with laboratory-derived data, and used for popular well scale models.
    [0023] Under some embodiments, well scale models combine mini-permeability grids and conventional whole core CT scans with electrical hole images to create 3D numerical pseudo-witnesses. SCAL properties determined from pore scale models are distributed for each petrophysical facies in pseudonumerical witnesses. Again, analyzes should show that a REV was sampled before flow modeling takes place. Computed effective SCAL properties from various MPS realizations are used to populate inter-well scale models.
    According to some modalities, at inter-well scale, seismic attributes and logging variogram statistics during drilling (LWD) are used for popular digital rock models. The computed effective properties of inter-well scale flow simulations are used to popular full-field scale models. At full-field scale, outcrop analogues, sequence stratigraphy, isopaque maps, facies proportion curves, advanced stratigraphic models, diagenetic models, basin scale models, and MPS models are combined to improve flow simulations.
    [0025] The approach described here provides better understanding of fluid flow path, fluid saturations, and recovery factors in hydrocarbon reservoirs. The workflow is suitable for any lithology such as carbonates, sandstones, shales, coals, evaporites and igneous or metamorphic rocks. At each scale, petrophysical properties are calculated, transferred to the next scale, and used to populate thicker digital rock models.
    [0026] Digital modeling of rock. According to some modalities, a workflow is described for multiscale reservoir simulation, based on digital rock models. Such models are built from cores, well logs and seismic data. One goal is to create 3D models to digitally represent heterogeneous rock tissue and pore space at all scales. This approach is valid for carbonates, sandstones, shales and other lithologics such as coals, evaporites and igneous or metamorphic rocks. fractures and flaws can be included on various scales. Across all revealed modalities, recurring digital rock modeling themes include multipoint statistics and representative element volumes.
    [0027] Multipoint statistics (MPS) create simulations of reservoir property and spatial geological fields. These conditional simulations use known results, such as those measured in wellboreholes or rock samples, as fixed or permanent data that are absolutely accepted during modelling. MPS uses 1D, 2D or 3D "training images" as quantitative templates to model subsurface properties.
    [0028] Representative element volumes (REV) provide a new application in reservoir modeling, based on techniques used in groundwater hydrology. In summary, REV is the smallest volume that can be modeled to provide compatible results, within acceptable limits of variance for the modeled property, such as porosity or permeability. Using this approach, we can determine the smallest volume that needs to be modeled, operate that flow model, and use the results for scale-up for larger scale simulations.
    [0029] Pore scale models. A primary objective of pore scale digital rock modeling is to build 3D models that use multipoint statistics (MPS) to combine laser scanning fluorescence microscopy and other high resolution techniques with micro CT scans, with relatively large volumes imaged. According to some embodiments, one or more of the following tools and technologies are used: 1. Transmitted laser fluorescence microscopy provides high resolution 3D pore models (approximately 250 nm) to quantitatively capture microporosity. From this we compute pore size distributions and simulated capillary pressure curves. 2. CT microscans utilize X-ray computed tomography (CT) on small samples (commonly 5mm diameter core plugs) to detect pore bodies, with resolutions that are typically 1 to 5 microns in size. See Knackstedt, MA, Ams, CH, Sakellariou, A., Senden, TJ, Sheppard, AP, Sok, RM, Pinczewski, WV, and Bunn, GF, 2004, Digital core Laboratory: Properties of reservoir core derived from 3D images: SPE Preprint 87009, presented at the Asia-Pacific conference on integrated modeling for asset management, March 29-30. Software converts pore images into pore network models, with resulting pore body and pore throat size distributions. 3. Nano CT scans utilize X-ray computed tomography (CT) on very small samples (core buffers commonly 60 micron in size) to detect pore bodies with resolutions that are typically approximately 50 nm to 60 nm in sizes. Software converts pore images into pore network models, with resulting pore body size and pore throat size distributions. 4. Focused ion beam scanning electron microscopy (FIB-SEM) uses ion beam thinning to create multiple, closely spaced 2D serial sections that are used to build 3D sub-micron scale pore models. Resolution is typically in the 5 nm to 10 nm scale. 5. Capillary pressure mercury injection (MICP) involves progressively injecting mercury into a clean sample, commonly a buffer, at constantly increasing pressures. See Jennings, J.B., 1987, Capillary pressure techniques: application to exploration and development geology: AAPG Bulletin, v. 71, p. 1196-1209; and Pittman, E.D., 1992: Relationship of porosity and permeability to various parameters derived from mercury injection-capillary pressure curves for sandstone: AAPG Bulletin, v. 76, p. 191-198. With each increased pressure, pore throats of a specific size are invaded by mercury. Throat pore size distributions are generally shown as histograms, computed from MICP results. Note that MICP is not generally useful for pore throats greater than 100 microns because these throats are inflated at very low injection pressures. The ideal pore throat size for MICP is 0.1 to 100 microns. 6. Nuclear magnetic resonance (NMR) is based on the interaction of hydrogen cores (protons) with a magnetic field and pulses of radio frequency signals. See Coates, G.R., Xiao, L and Prammer, M.G., 1999, NMR logging: Principles and applications; Halliburton Energy services, USA, 234 p. the transverse NMR relaxation time distribution (T_2 distribution) refers to the pore size distribution in the rock. NMR results can be used to divide porosity into micro, meso and macroporosity. See Ramamoorthy, R., Boyd, A., Neville, TJ, Seleznev, N., Sun, H., Flaum, C. and Ma, J., 2008, A new workflow for petrophysical and textural evaluation of carbonate reservoirs: SPWLA Preprint, 49th Annual Logging symposium, May 25-28, 15 p. such results can be used to limit pore scale digital rock models. 7. Petrophysical facies are areas enclosed by mini-permeability contours on core plate faces. See Bourke, L.T., 1993, Core permeability imaging: it's relevance to conventional core characterization and potential application to wireline measurement; Marine and Petroleum Geology, v. 10, p. 318-324; • and Dehghani, K., Harris, P.M., Edwards, K.A., and Dees, W.T., 1999, Modeling a vuggy carbonate reservoir: AAPG Bulletin, v. 83, p. 1942. Such regions also commonly have characteristic signatures on hole imaging profiles, such as druses, resistive adhesives, and conductive adhesives. See Zhang 2009. Conductive adhesives corresponding to regions of increased porosity and permeability provide continuity of flow between drusen. Such conductive or resistive adhesives have complex 3D shapes.
    [0030] Workflows can be used to generate the following products. Pore models or pore network models are used to compute, for representative element volumes (REV's) of individual petrophysical facies, the permeabilities, following pressures results: capillaries, resistivity, relative permeabilities, porosities, water saturation indices , irreducible water saturations, residual oil saturations, recovery factors, and Archie cementation exponents (m) and saturation (n). these numerical SCAL values, especially when validated with laboratory measurements, are used for popular well scale models.
    [0031] Well scale models. A primary objective of well-scale digital rock modeling is to build heterogeneous carbonate flow models using core cores and hole images. According to some embodiments, one or more of the following tools and technologies are used: 1. Conventional CT scans (approximately 1 mm to 2 mm spacing) are made and processed into 3D core images. these are used as MPS training images for pseudonumeric witnesses. 2. Full hole images are 360 degree views of the hole wall generated by "fill-in gaps" between the hole image profile pads using MPS. See Hurley, N.F. andZhang, T., 2009, Method to generate fullbore images using borehole images and multi-point statistics: SPE preprint 120671-PP, presented at Middle East Oil & Gas show and Conference, Bahrain, March 15-18. Rock heterogeneity is imaged in the nearby borehole volume, and used as permanent data to limit MPS models from numerical cores. 3. Minipermeometers capture permeability variation in sheet rock surfaces, and are used to: (a) segment digital rock models in appropriate petrophysical facies, (b) validate those facies in absolute permeability values, and (c) identify subvolumes for more detailed sampling.
    [0032] Workflows can be used to generate the following products: (1) pseudonumeric witnesses use conventional CT scans of cores, hole image profiles, and MPS to generate 3D models in which each cell in its own porosity, permeability, capillary pressure, and relative permeability attributes. Such models quantitatively capture rock heterogeneity at the well scale. See Zhang 2009; and (2) eclipse flux models of pseudonumeric witnesses are used to compute porosities, permeabilities, capillary pressures, resistivity indices, relative permeabilities, water saturations, irreducible water saturations, residual oil saturations, and Archie cementation exponents ( m) and saturation (n), effective, for REV's of main rock types.
    [0033] Inter-well scale models. A primary objective of inter-well scale digital rock modeling is to use geostatistical tools such as LWD data variogram statistics, seismic attributes, and cross-well geophysics to capture inter-well heterogeneity. Stream properties are provided by pseudonumeric witnesses. Under some modalities, one or more of the following tools and technologies are used: 1. Logging data during drilling (LWD), especially density and neutron profiles, is acquired for geosteering in horizontal wells. Outcrop studies show that cyclic variations (known as the borehole effect) occur in permeability and porosity cross sections along specific stratigraphic horizons. see Pyrcz, MJ, and Deutsch, CV, 2003. The whole story on the hole effect, in Searston, S. (ed.) Geostatistical Association of Australasia, Newsletter 18, May, 16 p, (hereinafter "Pyrcz 2003") ; and Pranter, M.J., Hirstius, C.B., and Budd, D.A., 2005, Scales of lateral petrophysical heterogeneity in dolomite lithofacies as determined from outcrop analogs: Implications for 3-D reservoir modeling: AAPG Bulletin, v. 89, p. 645-662 (hereinafter "Pranter 2005"). If these cycles occur in reservoirs, LWD data variogram statistics can be used to provide heterogeneous 'framework' to help popular models of the inter-well region. 2. Pseudonumerical testimonies are made for specific reservoir rock types as multiple realizations, or as models with selected porosity ranges. Predicted numerical SCALs of REV's from these models can be used to popular inter-well scale digital rock models. 3. Variograms are geostatistical tools used to represent spatial variance in data sets, plotted as a function of distance between data points. When variograms are constructed from profile data in horizontal wells, they can be used to map spatial variability at the inter-well scale. 4. Seismic attributes, such as amplitude and acoustic impedance, commonly refer to porosity. Such attributes can be used as temporary data to limit MPS models of the inter-well volume. 5. Cross-well geophysics, such as EM (electromagnetic) or seismic tomography, can be used to limit property variations in inter-well volume.
    [0034] Workflows can be used to generate the following products: numerical pseudowitness eclipse flow models are used to populate inter-well scale flow models with porosities, permeabilities, capillary pressures, resistivity indices, relative permeabilities, water saturations, irreducible water saturations, residual oil saturations, recovery factors and Archie cementation exponents (m) and saturation (n), effective for REV's of major rock types.
    [0035] Full-field scale models. A primary objective of full-field-scale digital rock modeling is to build static models of the reservoir using cores, well logs, outcrop analogues, and sequence stratigraphy. Geostatistical models based on variogram, MPS, or advanced stratigraphic models can be used to help populate inter-well regions. According to some modalities, one or more of the following tools and technologies are used: 1. Sequence stratigraphy is an interdisciplinary field of study that combines seismic, profile, fossil and outcrop data at local, regional and global scales. Fill basin sedimentary deposits are interpreted in sedimentation structure and relative changes in sea level, caused by tectonic or eustatic effects or both. The .. approach is used to correlate layers and predict stratigraphy in relatively unknown areas. Sequence stratigraphy promotes understanding of basin evolution, while allowing the interpretation of potential source rocks and reservoir rocks in both boundary areas and more mature hydrocarbon provinces. Neal, J., Risch, D, and Vail, P., 1993, Sequence stratigraphy - a global theory for local success: Oilfield Review, January Issue, p. 51-62. two. . Outcrops are exposed rock bodies on the earth's surface. Outcrops with analogous lithologies and deposit environments for subsurface reservoirs can be used to help build static models. 3. Advanced stratigraphic models are used to build realistic 3D layer patterns based on hydraulic principles that apply to sediment transport. Such models can deal with subaqueous and eolian transport of silicyclastic material, and organic growth of vegetation-related carbonates and sediments such as coals. See Sedsim, 2010, https://wiki.csiro.au/confluence/display/seabedchange/home, accessed October 10, 2010. 4. Isopaque maps represent specific rock layer thicknesses, or rock thicknesses with properties given petrophysics, such as porosity. 5. Facial proportion curves are generated from core descriptions and profiles. They are used to estimate and limit the relative quantities of each facies in a layer of a geomodel. See iReservoir, 2010, http: I /www. ireservoir. com/ case_jonah. html., accessed October 10, 2010. 6. Seismic attributes, such as acoustic impedance and amplitude, commonly refer to porosity. Such attributes can be used as temporary data to limit inter-well volume MPS models. 7. Diagenetic models are used to simulate cementation, compaction, and other diagenetic processes that accompany sediment burial. Approaches range from transport reaction models, based on thermodynamics and kinetics, to models based on texture, composition, and burial history of the original sediment. See Geocosm, 2010, http://www.geocosm.net/, accessed October 10th. 8. Basin scale models are used to model oil systems, commonly at scales much larger than oil fields. An oil system is defined as the combination of geological elements and processes necessary to generate and store hydrocarbons. Elements and processes include loading, collection, and timing of hydrocarbon generation, migration, and loss. See Petromod., 2010, http://www.ies.de/, accessed October 10th.
    [0036] Multipoint statistics. Multipoint (or multiple point) statistical methods (MPS) are a family of spatial statistical interpolation algorithms proposed in the 1990s used to generate conditional simulations of discrete variable fields, such as geological facies, through training images. See Guardiano, F., and Srivastava, R.M., 1993, Multivariate geostatistics: Beyond bivariate moments: Geostatistics-Troia, A. Soares, Dordrecht, Netherlands, Kluwer Academic Publications, v. 1, p. 133-144 (hereinafter "Guardian 1993"). MPS generates realistic models that can be constrained by different types of data. Unlike conventional variogram or 2-point geostatistical approaches, MPS uses a training image to quantify complex deposit patterns that exist in studied reservoirs. These training patterns are then reproduced in the final MPS models with conditioning for local data collected from the reservoirs. Therefore, MPS allows modelers to use their previous geological interpretations as conceptual models (training images) in the reservoir modeling process and assess the uncertainty associated with previous interpretations by using different training images.
    [0037] In addition to category variables, MPS can handle continuously variable training images, such as spatial distribution of porosity. Two families of MPS algorithms are available to handle these different types of training images: Snesim for category variables, and Filtersim for continuous variables. An efficient Snesim algorithm introduced the concept of a search tree to store all pattern replicas found in a template over the training image. See Strebelle, S. 2002, Conditional simulation of complex geological structures using multiple point statistics: Mathematical geology, vol. 34, p. 1-22. This makes Snesim code orders of magnitude faster than the original algorithm proposed by Guardiano 1993. Filtersim applies a set of local filters on the training image, which can be categorical or continuous, to group local patterns into pattern classes. See Zhang, T. 2006, Filter-based training image pattem classification for spatial pattem simulation: Unpublished Ph.D. dissertation, Stanford University, Palo Alto, CA. pattern simulation then proceeds based on that classification.
    [0038] Algorithms Snesim and Filtersim accept absolute or "permanent" limitations of data acquired in wells or outcrops, and other interpreted trend maps of the reservoir under study. Training images are the main driver of any MPS approach. One issue implicitly raised by current MPS algorithms is how to generate training images. Training images seek to model or reproduce real geological features and should be derived as much as possible from existing geologically significant images.
    [0039] Representative element volumes. Representative element volumes (REV) provide a new way to deal with heterogeneity and upward scaling issues in reservoir modeling. See Qi 2009. In summary, REV is the smallest volume that can be modeled to provide compatible results, within acceptable limits of variance for a modeled property, such as porosity or permeability. Using this approach, we can scale up rock properties from fine to coarse scales. This is accomplished by determining the smallest volume that needs to be modelled, modeling the flow, and then using the results in subsequent larger scale simulations. After the REV has been modeled, larger volumes do not need to be modeled because heterogeneity for the specific rock type at that scale has been captured.
    [0040] The concept of REV was discussed in 1972. See Bear, J. 1972, Dynamics of fluids in porous media: Elsevier, New York, 746 p (hereinafter "Bear 1972"). Δui is defined as a volume in a porous medium, with a centroid of P. Δui is considered to be much larger than a single pore or grain. Δui is the volume of empty space, and ni is the ratio of empty space to volume, that is, the fractional porosity. At large values of Δui, there are minimal fluctuations in porosity as a function of volume. However, as the volume decreases, fluctuations in porosity increase, especially as Δu approaches the size of a single pore, which has a fractional porosity of 1, if the centroid P happens to lie in a grain, porosity is O when Δui=0. The Δu0 value is defined as the REV, below which porosity fluctuations are significant, and above which porosity fluctuations are minimal. In summary, the dimensions of Δu0 are sufficient so that "the effect of adding or subtracting one or more pores has no significant influence on the value of n." see Bear 1972.
    [0041] Using the REV approach, the porous medium is replaced by "a fictitious continuum: a structureless substance, to any point at which we can assign kinematic and dynamic variables and parameters that are continuous functions of the spatial coordinates of point and time. " See Bear 1972. Note that REV for porosity may differ from REV for permeability or other parameters. Also the REV for static properties VS. Dynamics may vary. In practice, the best method is to use the highest REV determined for various parameters.
    [0042] According to some embodiments, REV is determined for a rock property such as porosity. According to some modalities, a large volume can be modeled, undersampled, and the variance in porosity can be computed as a function of the subsample volume. This can be done on any scale, ranging from pores to holes to inter-well volumes.
    [0043] Within the limitations of available computing power, multipoint statistics (MPS) can generate models of any size and shape. Because of this, MPS models can be used to help compute REV's. for example, a pore-scale modeled volume might be 600 x 600 microns in area, and 150 microns in thickness. Smaller subvolumes, eg 10, 50 or 150 micron cubes, could be extracted from the modeled volume, and their porosities determined. At the inter-well scale, smaller subvolumes, eg 1, 10, and 20 m3, could be extracted from the modeled volume, and its porosities could be determined. All subvolumes, regardless of scale, must be independent, non-overlapping volumes. If the porosity variance is less than a chosen cutoff, for example +/- 5%, then that volume can be used as the REV. For flow modeling purposes, REV is sufficient to provide representative results.
    [0044] The generalized approach for determining the VER for any rock property is: (1) model a large block with rock properties of interest; (2) randomly select a subsample of a given small size in the block; (3) randomly select another non-overlapping subsample of the same size; (4) repeat this process many times; (5) increase subsample size, and sample many similar objects; (6) plot rock VS property. Subsample size, to see how the variance from the sample mean decreases as a function of subsample size; and (7) when the variance is within acceptable limits (eg ± 5%), this is the REV for the rock property under study.
    [0045] A step-by-step procedure will now be provided for creating digital rock models and scaling them up from pore to hole to inter-well to full field scale, according to some modalities. Variations in this procedure can be made, according to other modalities, for example, depending on the available datasets.
    [0046] Figures 2A-B are block diagrams showing recommended procedures for multiscale reservoir simulation according to some modalities. The block diagram shows a time sequence of steps (left to right) used to build field and laboratory validated digital rock models, and to scale upward results from pore to hole to inter-well to field scales total according to some modalities. Pore 210 scale steps, 220 well scale steps, 230 inter-well scale steps, and 240 full-field scale steps are performed as shown. Note as indicated by arrow 212, preferably conventional CT scan and mini-permeability data are obtained to help define the petrophysical facies on the core plug scale, and used to help determine where to drill core plugs. The computed numerical SCAL (Pc, krel, porosity and permeability) are used to scale up from pore scale to well scale, as indicated by arrow 214. As indicated by arrow 216, the computed numerical SCAL (P0 , kre1 , porosity and permeability) is again used to scale up from well scale to inter-well scale. Finally, as indicated by arrow 218, the computed numerical SCAL (krel, porosity, and permeability) from the inter-well scale is used to scale up to full-field scale 10 in the full-field MPS modeling.
    [0047] Figure 3 is a block diagram that shows a workflow with pore scale digital rock models, according to some modalities. In practice, entire cores are imaged with conventional CT scans using closely spaced slices (approximately 1 mm to 2 mm). Mini-permeability grids (approximately 0.5 em to 1.0 em) on plate faces allow to segment cores into sub-regions, validated with laboratory-measured permeabilities. Strategically chosen core plugs, sampled in the context of CT 312 and 314 scan results and 310 minipermeability results, represent petrophysical facies. Submicron-sized pores of each petrophysical facies are imaged for their corresponding REVs using laser scanning fluorescence microscopy (confocal) and microCT scans, and other high resolution techniques, which can be combined using MPS. Numeric SCAL 320, computed from pore models or pore network models, takes us to the next larger scale, ie, the well.
    [0048] Figure 4 is a block diagram that shows a workflow with well-scale digital rock models, according to some modalities. Pseudonumerical statements 414 are created using discrete variable MPS algorithms. Integer values are assigned to each petrophysical facies, eg dense rock matrix (0), drusen (1), resistive adhesives (2), and conductive adhesives (3). Conventional CT 410 scans of entire core rock samples are used as MPS training images, that is, they are the quantitative templates used to guide the modeling of 3D textures at the well scale. Full 412 hole images, derived from FMI or other hole image profiles, surround pseudo numeric witnesses with cylindrical envelopes that condition the models. Segmentation of conventional CT scans and full bore images into distinct petrophysical facies is done using grid mini-permeability data, such as contour, according to some modalities. Each pseudonumeric witness 414 preferably accepts absolutely the heterogeneity of the digital rock samples and the whole hole image data. Subvolumes can be sampled to confirm that REV's are being modeled for a given numerical pseudo-witness.
    [0049] Pseudonumerical testimonies are gridded in models used for fluid flow simulation. For each petrophysical facies, porosity, permeability, capillary pressure, and relative permeability curves are provided by numerical pore scale SCAL. Volume, or system scale, or effective properties are computed from flow model results for pseudonumeric witnesses: porosities, permeabilities, capillary pressures, resistivity indices, relative permeabilities, water saturations, irreducible water saturations, residual oil saturations , recovery factors, and Archie cementation exponents (m) and saturation (n) 416. These properties are used to populate digital rock models at the next scale, ie, inter-well grid blocks.
    [0050] Figure 5 is a diagram that shows a workflow with inter-well scale digital rock models, according to some modalities. Variogram 512 and 514 statistics from horizontal wells, such as well 510, drilled into specific rock layers can be combined with 516 seismic attributes and cross-well geophysics to capture heterogeneity at the scale of inter-wells. Multiple runs of digital rock models, or multiple samples at various porosity values 520, 522, 524, and 526, are used to populate inter-well volumes with well-scale numerical SCAL properties.
    [0051] Figure 6 is a block diagram that shows a workflow for full field simulation, according to some modalities. Full-field simulation uses advanced 614 stratigraphic modeling, 616 isopaque maps, 620 facies ratio curves, and 624 multipoint statistics to accept well data. The advanced stratigraphic model 614 is used as a reign image or facies probability cube for 624 multipoint statistical modeling. Sequence stratigraphy 610, outcrop analogues 612, and seismic attributes 618 are used to develop the static model. Diagenetic models are used to modify early 622 and late 626 petrophysical properties during deposition and burial.
    [0052] Figure 7 is a flowchart that shows preliminary steps to be performed before a multiscale reservoir simulation, according to some modalities. A definition of Reservoir Rock Types (RRT's) is preferably carried out together with conventional profile analyses. At step 710, cores are described to identify facies, rock tissue, and RRT's. A given field will generally have 5-10 RRT's. this exercise works best if RRT's are based on lithofacies combined with petrophysical properties (eg porosity, permeability, MICP, NMR). At step 712, conventional permeability and porosity analyzes are acquired for whole core samples and/or core buffers. At step 714' existing core analyses, core descriptions, and RRT's are evaluated and integrated. In step 716, conventional profile analyzes in the wells are performed. In step 718, interpret well images and other open hole profiles, and compare them with geological facies observed in the core. If the correlation is good, neural networks can be used to distribute facies along the length of the well(s).
    [0053] Pore and well scale modeling. Figures 8A-B are a flowchart showing steps in performing advanced digital token analysis, in the form of numerical pseudo-witnesses and flow simulation, according to some modalities. Reference is also made to the workflows shown in and described with respect to figures 3 and 4.
    [0054] In step 810, plate core or whole core ranges of 1- to 3-ft (0.3 to 1.0 m) are chosen from each main RRT. Preferably these ranges will be from well(s) with electrical hole imaging profiles (eg FMI) Added benefits will come from well(s) with elemental spectroscopy profiles (eg ECS) and nuclear magnetic resonance profiles (eg CMR) ) . These records will be useful for mineralogy and porosity division (macro, meso, microporosity), respectively. In step 812, conventional CT scans (1 or 2 mm step distance) are acquired for 1 to 3 ft (0.3 to 1.0 m) intervals. In step 814, mini grid permeability data (0.5 to 1.0 em grid spacing) is acquired for rocks that were CT scanned. The mini-permeability device is calibrated to witness buffers 10 which have a wide range of permeabilities (eg 0.1 md to 3,000 md). Calibrated absolute permeabilities are computed for each grid point. Minipermeability data is counted. This step 814 will lead to: (a) proper segmentation of the full bore and whole core CT scan images, (b) validation for laboratory measured permeabilities, and (c) identification of subvolumes for more detailed sampling.
    [0055] In step 816, mini-permeability data is recorded for plate face from conventional CT scans. Use this combination to choose appropriate subsample areas for core lamination or plugging. Drill strategically chosen core buffers from distinct petrophysical facies, and submit them to (a) thin (30 microns) or thick (5,000 microns) sections for transmitted laser scanning fluorescence microscopy and CT microscanning, and (b) porosity of laboratory, permeability, and SCAL (MICP preferably these data will be acquired under reservoir conditions. Such results will be used as endpoint rock properties for petrophysical facies. Under some preferred modalities this is an important part of the step of laboratory validation.
    [0056] Thin or thick sections should be vacuum pressure impregnated with epoxy with fluorescent dye (Rhodamine B) In step 818, laser transmitted scanning fluorescence microscopy is used to verify that one REV has been sampled for each petrophysical facies. In step 820, REV's imaged with transmitted laser scanning fluorescence microscopy are used to segment the micro CT scans. This step is especially preferred if significant amounts of porosity lie between the resolution threshold of the CT microscan device.
    [0057] In step 822, MPS is used to build 3D pore network models. such models are an approach that can be used to compute numerical SCAL (porosities, permeabilities, capillary pressures, resistivity indices, relative permeabilities, water saturations, irreducible water saturations, residual oil saturations, recovery factors, and cementation exponents Archie (m) and saturation (n) which will be used to populate larger scale numerical pseudowitnesses. If thick sections are made, micro CT scan at 5 micron resolution or better. MPS uses confocal scans like training images and micro CT scans as standing data to build composite "total porosity" models for each petrophysical facies. Numerical SCAL can be directly computed from such pore models, using Boltzmann lattice or other flow simulators, or from pore lattice models derived from the pore models .
    [0058] In step 824, if hole image profiles are available, generate total hole images for each 3 ft (1 m) interval spanning the CT swept core intervals. Create 3D pseudo numeric witnesses from hole images and CT scans using MPS. In step 826, numerical SCAL results from pore scale models are used to popular digital well scale models of rock, ie, pseudonumerical witnesses. In step 828, a confirmation is made that pseudo numeric tokens are REV's for the specific RRT.
    [0059] In step 830, determine porosities, permeabilities, capillary pressures, resistivity indices, relative permeabilities, water saturations, irreducible water saturations, residual oil saturations, recovery factors, and Archie cementation exponents (m) and saturation (n) in close hole volumes using conventional flow simulators such as Eclipse. If possible, evaluate computed SCAL determined from numerical pseudo-witnesses with full-witness SCAL done in the lab. Make multiple achievements, or make achievements for different porosity ranges. Integrate reservoir pressure, fluid properties, and other field data to make flow simulations.
    [0060] Inter-well scale modelling. Figure 9 is a flowchart showing steps used to distribute facies and build the inter-well scale model, according to some modalities. Reference is also made to the workflow shown in and described with reference to figure 5.
    [0061] In step 910, the stratigraphic profile intersected by horizontal wells that have LWD density data is determined. Variogram statistics are computed for intervals where the well remains within the same stratigraphic layer. Look for the bore effect, an indication of spatial cyclicity (see Pyrcz 2003; and Pranter 2005), construct variograms and construct geostatistical porosity maps for the inter-well area. In step 912, porosity voxels are populated in inter-well areas using LWD and open hole logs, combined with digital SCAL determined from numerical pseudo-witnesses at the appropriate RRT's and with the appropriate porosity ranges. In step 914 a confirmation is made that inter-well volumes are REV's for the specific RRT. In step 916, multiple realizations of pseudonumeric witnesses are made, or pseudonumerical witnesses are created for specific porosity ranges. Geostatistical maps are populated with numerical SCAL data from pseudonumeric witnesses.
    [0062] In step 918, porosity heterogeneity is mapped into inter-well volumes using seismic attributes if available. Seismic data is used to provide input for 3D facies distribution models. if seismic attributes correlate with porosity, use this as temporary data to limit geostatistical maps of inter-well regions. In step 920, rock properties are limited in the inter-well area using cross-well geophysical data such as EM (electromagnetic) and seismic tomography if available. In step 922, based on porosity models, permeability heterogeneity can be predicted using co-simulation placed with porosity being used as secondary variables. In step 924, effective porosities, permeabilities, capillary pressures, resistivity indices, relative permeabilities, water saturations, irreducible water saturations, residual oil saturations, recovery factors, and Archie (m) and saturation (n) cement exponents are computed for each inter-well volume using conventional flow simulators such as Eclipse.
    [0063] Full-field scale modelling. Figures 10AB illustrate a flowchart showing recommended steps for ascending scaling of inter-well volumes for full-field simulations, according to some modalities. Reference is made to the workflow shown in and described with reference to Figure 6.
    [0064] In step 1010, results for effective numerical SCAL are used for inter-well volumes to populate full-field simulations, built from static models with RRT's independently distributed in a sequence profile and/or stratigraphic correlation structure. In step 1012, outcrop analogues, if available, are used to limit facies types, facies associations, and lateral correlations in the static subsurface model. These outcrop analogues can be used to assist MPS modeling in generating training images. In step 1014, basin scale models, if available, are used to place the field in its regional context in terms of source rock, reservoir, collector and seal. Burial history and its effect on diagenesis are important considerations.
    [0065] In step 1016, sheets of profile tops, facies, and sequence boundaries are created for each described core and/or well profiled in a given field. Facial proportion curves are computed for each core and/or profiled well. In step 1018, facies associations in vertical succession are determined using Markov chain analysis, or a similar approach. Isopaque maps of main facies and/or parameter thicknesses are constructed. In step 1020 seismic surfaces or conceptual models are used to provide paleotopography for the advanced stratigraphic model (FSM). FSM's are created to visualize scenarios for hydraulic transport or organic sediment growth. In step 1022, string thickness and approximate range of interest are matched. FSM time step adjustments are used to approximate layer thicknesses, facies proportions, and facies associations observed in core wells.
    [0066] In step 1024, pseudowells are created at arbitrary locations in the field. "Lithology" FSM or "grain size" are used as Proxy for witness description facies. In step 1026, the FSMs are adjusted using core descriptions and profile interpretations for: (a) layer thicknesses, (b) facies proportions, and (c) facies associations. In step 1028 the adjusted FSM are used as a training image or facies probability cube (temporary data) for MPS simulation. If the FSM is used as a facies probability cube, MPS training images could be layered models, with comparable thicknesses, facies proportions, and facies associations for cores and profiles described in the wells. If necessary, due to the variable layer architecture, different training images can be used in different regions of the field. In step 1030, diagenetic models are used to represent changes in porosity and permeability as a function of cementation and compaction due to burial.
    [0067] Although the invention is described through the above exemplary embodiments, it will be understood by those of ordinary skill in the art that modification in and variation of the illustrated embodiments can be made without departing from the inventive concepts disclosed herein. Furthermore, although preferred embodiments are described with respect to various illustrative structures, one of skill in the art will recognize that the system can be incorporated using a variety of specific structures. Therefore, the invention is not to be viewed as limited except by the scope and spirit of the appended claims.
    权利要求:
    Claims (29)
    [0001]
    1. METHOD FOR UPGRADING WITH A DIGITAL ROCK MODELING DATA PROCESSING SYSTEM, well-scale representing reservoir rock, the method characterized by the fact that it comprises: scaling up digital rock modeling data to scale inter-wells to inter-well scale digital rock modeling data based, at least in part, on combining the well-scale digital rock modeling data with inter-well scale source data to generate modeling data inter-well scale digital rocks that at least partially capture heterogeneity at an inter-well scale; scaling up of full-field digital rock modeling data, where the inter-well scale digital rock modeling data has been at least partially scaled up from full-scale digital rock modeling data. pore; and where scaling up to generate full-field scale digital rock modeling data is based, at least in part, on computed values at a scale.
    [0002]
    2. The method of claim 1, further comprising identifying a plurality of reservoir rock types at least in the well-scale digital rock modeling data.
    [0003]
    3. Method according to claim 1, characterized in that the inter-well scale source data is collected using one or more techniques selected from a group consisting of: logging data during drilling of a non-vertical well , cross-well geophysical measurements and seismic measurements.
    [0004]
    4. Method according to claim 3, characterized in that the inter-well scale source data is collected using logging data during the drilling of a non-vertical well.
    [0005]
    5. Method according to claim 3, characterized in that the inter-well scale source data includes computed variogram statistics.
    [0006]
    6. Method according to claim 1, characterized in that the well scale digital rock modeling data used to generate the inter-well scale digital rock modeling data includes computed values at a well scale , from one or more properties selected from a group consisting of: porosities, permeabilities, capillary pressures, resistivity indices, relative permeabilities, water saturations, irreducible water saturations, residual oil saturations, recovery factors, and cementation exponents Archie (m) and saturation exponents (n).
    [0007]
    7. Method according to claim 1, characterized in that the digital rock modeling data are three-dimensional data.
    [0008]
    8. Method according to claim 1, characterized in that the digital rock modeling data includes multi-point statistics results.
    [0009]
    9. Method according to claim 1, characterized in that the digital rock modeling data includes a plurality of representative element volumes.
    [0010]
    10. Method according to claim 1, characterized in that the well scale digital rock modeling data has been at least partially scaled up from pore scale digital rock modeling data and in that that ascending scaling of pore scale digital rock modeling data is based at least in part on computer values on a pore scale of one or more properties selected from a group consisting of: porosities, permeabilities, capillary pressures, indices of resistivity, relative permeabilities, water saturations, irreducible water saturations, residual oil saturations, recovery factors, and Archie cementation exponents (m) and saturation exponents (n).
    [0011]
    11. Method according to claim 6, characterized in that the pore scale digital rock modeling data includes pore geometry that is quantified using one or more techniques selected from a group consisting of: fluorescence microscopy of transmitted laser scanning, micro CT scans, nano CT scans, focused ion beam scanning electron microscopy, mercury injection capillary pressure, and/or nuclear magnetic resonance.
    [0012]
    12. Method according to claim 6, characterized in that the pore scale digital rock modeling data is generated at least in part using strategically selected core buffers chosen using mini-grid permeability and CT scan data conventional plates of one or more witness plates.
    [0013]
    13. Method according to claim 12, characterized in that the mini-permeability data are divided into a grid at a spacing between about 0.5 cm and 1 cm.
    [0014]
    14. Method according to claim 12, characterized in that the conventional CT scan data have a slice spacing between about 1 mm and 2 mm.
    [0015]
    15. Method according to claim 1, characterized in that it further comprises constructing a heterogeneous rock flow model at least in part on the generated inter-well scale digital rock modeling data.
    [0016]
    16. The method of claim 1 further comprising up-scaling the inter-well scale digital rock modeling data to generate full field scale digital rock modeling data and in that the upscaling to generate full-field scale digital rock modeling data is based at least in part on computed values at an inter-well scale of the inter-well scale digital rock modeling data, the computed values being of a or more properties selected from a group consisting of: porosities, permeabilities, capillary pressures, resistivity indices, relative permeabilities, water saturations, irreducible water saturations, residual oil saturations, recovery factors, and cementation exponents Archie (m) and saturation exponents (n).
    [0017]
    17. Method according to claim 1, characterized in that the full-field scale digital rock modeling data is generated using one or more techniques selected from a group consisting of: stratigraphic sequence modeling, analogs of outcrop, isopaque maps, facies proportion curves, seismic attributes, advanced stratigraphic modeling, diagenetic modeling, basin scale modeling and/or multipoint statistics modelling.
    [0018]
    18. Method according to claim 1, characterized in that the reservoir rock includes one or more types of lithologies selected from a group consisting of: carbonates, sandstone shales, coals, evaporites and igneous or metamorphic rocks.
    [0019]
    19. Method according to claim 1, characterized in that the generated inter-well scale digital rock modeling data includes one or more faults or fractures.
    [0020]
    20. Method according to claim 1, characterized in that the digital rock modeling data at full field scale is generated using direct stratigraphic modeling.
    [0021]
    21. METHOD FOR UPGRADING WITH A PORe-scale DIGITAL ROCK MODELING DATA PROCESSING SYSTEM representing reservoir rock, the method characterized by the fact that it comprises: generating pore-scale digital rock modeling data, at least in part using mini-permeability and CT scan data from one or more core sheets of reservoir rock, combined with pore geometry data; scaling pore scale digital rock modeling data to well scale digital rock modeling data based, at least in part, on combining pore scale digital rock modeling data with source data well scale; and scaling up the inter-well scale digital rock modeling data to generate full-field scale digital rock modeling data, in which well scale digital rock modeling data has been at least partially scaled up from pore scale digital rock modeling data, where scaling up to generate full field scale digital rock modeling data is based, at least in part, on computed values at an inter-well scale from inter-well scale digital rock modeling data.
    [0022]
    22. Method according to claim 21, characterized in that the pore geometry data are obtained using one or more techniques selected from a group consisting of: transmitted laser scanning fluorescence microscopy, micro CT scans, scans nanoCT, focused ion beam scanning electron microscopy, mercury injection capillary pressure, and nuclear magnetic resonance.
    [0023]
    23. The method of claim 21, characterized in that the upscaling of pore scale digital rock modeling data is based at least in part on computed pore scale values of one or more properties selected from a group consisting of: porosities, permeabilities, capillary pressures, resistivity indices, relative permeabilities, water saturations, irreducible water saturations, residual oil saturations, recovery factors and Archie cement exponents (m) and exponents of saturation (n).
    [0024]
    24. METHOD FOR UPGRADING WITH A DIGITAL ROCK MODELING DATA PROCESSING SYSTEM, well scale, the method characterized by the fact that it comprises: generating inter-well scale digital rock modeling data at least in part using well data from at least one non-vertical well; scaling up the well-scale digital rock modeling data to inter-well scale digital rock modeling data based, at least in part, on combining well-scale digital rock modeling data with data from inter-well scale sources, where the well scale digital rock modeling data has been at least partially augmented from pore scale digital rock modeling data; and scaling the inter-well scale digital rock modeling data to generate full-field scale digital rock modeling data, in which well scale digital rock modeling data has been at least partially scaled up from pore scale digital rock modeling data, where scaling up to generate full field scale digital rock modeling data is based, at least in part, on computed values at an inter-well scale the inter-well scale digital rock modeling data.
    [0025]
    25. Method according to claim 24, characterized in that the well data includes one or more types of data selected from a group consisting of: logging data, variogram statistics, cross well seismic data, data electromagnetic and seismic attribute data.
    [0026]
    26. The method of claim 24, characterized in that up-scaling of well scale digital rock modeling data is based at least in part on computed well scale values of one or more selected properties from a group consisting of: porosities, permeabilities, capillary pressures, resistivity indices, relative permeabilities, water saturations, irreducible water saturations, residual oil saturations, recovery factors and Archie cementation exponents (m) and saturation exponents (n).
    [0027]
    27. METHOD FOR UPGRADING SCALE WITH A DIGITAL ROCK MODELING DATA PROCESSING SYSTEM, inter-well scale, the method characterized by the fact that it comprises: generating the full-scale digital rock modeling data; and up-scaling the inter-well scale digital rock modeling data to generate full-scale digital rock modeling data based at least in part on combining the inter-well scale digital rock modeling data with data from full-field-scale source, where the scale-up to generate full-field-scale digital rock modeling data is based at least in part on values computed at an inter-well scale from the modeling data of digital rock in inter-well scale.
    [0028]
    28. Method according to claim 27, characterized in that the full-scale digital rock modeling data is generated at least in part using one or more techniques selected from a group consisting of: stratigraphic modeling of sequence, outcrop analogues, isopaque maps, facies proportion curves, seismic attributes, advanced stratigraphic modeling, diagenetic modeling, basin scale modeling, and multipoint statistical modeling.
    [0029]
    29. The method of claim 27, wherein the ascending scaling of inter-well scale digital rock modeling data is based at least in part on computed values at an inter-well scale of a or more properties selected from a group consisting of: porosities, permeabilities, capillary pressures, resistivity indices, relative permeabilities, water saturations, irreducible water saturations, residual oil saturations, recovery factors, and cementation exponents Archie (m) and saturation exponents (n).
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    同族专利:
    公开号 | 公开日
    RU2573739C2|2016-01-27|
    US20120221306A1|2012-08-30|
    BR112013015288A2|2020-08-11|
    WO2012118864A3|2012-12-13|
    RU2013143803A|2015-04-10|
    US9134457B2|2015-09-15|
    WO2012118864A2|2012-09-07|
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    法律状态:
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    2020-09-01| B06U| Preliminary requirement: requests with searches performed by other patent offices: procedure suspended [chapter 6.21 patent gazette]|
    2021-04-06| B25A| Requested transfer of rights approved|Owner name: SCHLUMBERGER HOLDINGS LIMITED (VG) |
    2021-07-06| B09A| Decision: intention to grant [chapter 9.1 patent gazette]|
    2021-09-14| B16A| Patent or certificate of addition of invention granted [chapter 16.1 patent gazette]|Free format text: PRAZO DE VALIDADE: 20 (VINTE) ANOS CONTADOS A PARTIR DE 28/02/2012, OBSERVADAS AS CONDICOES LEGAIS. |
    优先权:
    申请号 | 申请日 | 专利标题
    US13/036,770|US9134457B2|2009-04-08|2011-02-28|Multiscale digital rock modeling for reservoir simulation|
    US13/036,770|2011-02-28|
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