Exa DigitalROCK: A Virtual Lab for Fast, Reliable Permeability Data

Transcription

1 Exa DigitalROCK: A Virtual Lab for Fast, Reliable Permeability Data Exa Corporation 2017

2 CONTACT Exa Corporation 55 Network Drive Burlington, MA USA Copyright 2017 Exa Corporation

3 Executive Summary Exa DigitalROCK computational modeling software uses a high-resolution 3D image of a rock sample as input to determine important fluid flow related properties of reservoir rock, including absolute and relative permeability. For the oil and gas industry, these properties are of central importance to petroleum reservoir characterization and production optimization. Exa DigitalROCK technology can be accessed as a cloud-based service requiring only an internet connection and a web browser, or it can be delivered via traditional licensed software and/or services. A Breakthrough Alternative to Physical Laboratory Testing The lab tests used to measure multi-phase (e.g. oil/ water) relative permeability of a reservoir rock core sample are expensive, exhibit poor reproducibility, and take a long time to carry out. A typical special core analysis laboratory (SCAL) test program requires months or even up to a year to obtain the relative permeability data that are critical inputs to most reservoir modeling and simulation activities. Due to the long wait times and challenges associated with generating reliable SCAL test data, usually very few core samples from a field are tested, leading to high uncertainty in relative permeability even though the reservoir modeling results are critically dependent on them. Even for single-phase absolute permeability, for which lab testing is routine and quite fast, the lab results can sometimes be unexpected or inconsistent and a cross-check is needed. Furthermore, reliable lab testing for absolute permeability requires the availability of whole core, which is taken only for selected wells due to the time and expense required for whole core extraction. Exa DigitalROCK is effectively a virtual lab for providing permeability data on rock samples that is faster, more reproducible, and cost effective compared to physical lab test. Absolute permeability requires hours, and relative permeability curves are generated in a matter of days instead of months. Given the drastic reduction in time to results, sensitivity studies can be performed to assess how specific rock and fluid properties influence the permeability results. Understanding sensitivities to changes in field conditions is valuable information that is difficult, and sometimes impossible, to do with laboratory testing but becomes feasible with Exa DigitalROCK. In addition to speed and cost, benefits of Exa DigitalROCK include: An entire permeability versus porosity correlation is generated from each individual scan image, by using a volume sub-sampling technique Reliable lab testing requires a clean plug of minimum 1 diameter, whereas Exa DigitalROCK requires only a very small physical sample - 2mm^3 is sufficient Sample preparation for acquisition of images needed for Exa DigitalROCK is minimal extensive cleaning is not needed; furthermore, it is touchless and can be used on fragile samples that do not hold up in lab testing Every Exa DigitalROCK permeability analysis also includes a mercury injection capillary pressure (MICP) curve, pore size distribution analysis, and other detailed information about the pore space Exa Corporation 3

4 Exa DigitalROCK Technology Leveraging deep core competence in lattice- Boltzmann method (LBM), computational fluid dynamics, multi-phase flow physics, and high-performance computing, Exa has developed a flow simulation technology that accurately predicts absolute permeability and two-phase relative permeability from pore-scale images of reservoir rocks. Given an image of sufficient resolution and quality to capture the pore-scale geometry of the rock, the Exa DigitalROCK automated workflow analyzes the image and performs direct numerical simulation of the flow within the pore space. For two-phase flow this includes capturing detailed dynamics of phase interfaces, contact lines, wetting-phase films, nonwetting-phase clusters, and related physical mechanisms that arise when immiscible fluids are driven through a porous medium. The technology has undergone extensive validation on sandstones with absolute permeability in the range 1 to 10,000 md (the ability to handle heterogeneous carbonates and tight rocks is under investigation). More details and validation studies of the Exa DigitalROCK LBM-based flow solver can be found in the published papers. Application to EOR The effectiveness of enhanced oil recovery (EOR) techniques depends on their ability to influence pore scale fluid dynamics, as reflected by changes in the relative permeability of oil and water and by the resulting residual oil saturation. Exa DigitalROCK enables assessment of a given EOR method by comparing relative permeability and residual oil before and after modifications to the fluid parameters representing the physical alterations of the flow system induced by the EOR method. For such a two-phase flow simulation study, a particular EOR method can be characterized by how it modifies Capillary number, oil/water viscosity ratio, and wetting condition (via the surface contact angle distribution). Variations in these properties can cause important changes in the two-phase flow behavior and can interact in complicated ways which are strongly dependent on the detailed nature (true geometry) of the pore space. While such sensitivity studies would be impractical and often impossible to carry out with physical experiments, they can be performed controllably and relatively quickly using EXA DigitalROCK, i.e. in a matter of days. Exa DigitalROCK Cloud Solution By choosing the Exa DigitalROCK cloud service, critical permeability information required for reservoir modeling and simulation can be obtained from micro- CT scan images without the lead time, IT issues, and upfront expense involved in implementing on-premises software. With the Exa DigitalROCK self-service, pay as you go approach, users get the power of an elite high-performance computing facility from just a few mouse clicks in a web browser. There is nothing to install or maintain, and it takes just a few minutes to become familiarized with the intuitive, streamlined user interface.. Exa DigitalROCK Workflow Exa DigitalROCK functions as a reservoir rock analysis technique that can provide important petrophysical properties including porosity and permeability. As with RCAL (routine core analysis lab) and SCAL (special core analysis lab) physical testing, Exa DigitalROCK uses a rock sample retrieved from the subsurface. Once a sample of interest is obtained, typically a small plug is drilled out and 3D imaging is performed on this plug with a micro-ct scanner. The raw image data is enhanced with image processing algorithms to better distinguish between the pore space and the solid material of the rock. The resulting 3D image is used to perform geometrical studies, analysis of the pore space available to fluids, and finally fluid flow simulations to predict critical flow properties needed for reservoir modeling/engineering.

5 The usual workflow steps can be listed as follows: 1. Sample Plug 2. 3D Imaging 3. Image Processing/Segmentation 4. Pore Space Analysis 5. Mercury Injection Capillary Pressure (MICP) 6. Absolute Permeability (K0) 7. Two-Phase Relative Permeability (KR) 2. IMAGE ACQUISITION Pore-scale imaging of the prepared plug is performed using a micro-ct scanner that generates a stack of 2D slices, usually oriented along the central axis of the cylinder. The pore space which governs fluid flow in most sandstone reservoir rocks and some carbonates consists of pores in the 1-100um size range, allowing the use of micro-ct technology to adequately resolve the pore space from the solid rock grains. When necessary, other imaging techniques (e.g. FIB-SEM) can be used to resolve sub-micron features. Steps 1 and 2 are carried out by the user (Exa can assist/support) in order to generate a suitable image for input to Exa DigitalROCK. Step 3 can be done automatically within Exa DigitalROCK or by an expert user with another tool. Steps 4-7 are performed automatically by Exa DigitalROCK, users only have to select which analyses they want done, and review the results when ready. 1. SAMPLE PLUG The first step in the Exa DigitalROCK workflow is to obtain a small piece of rock that is well-sized for micro-ct imaging. This can be done by drilling out a small cylindrical plug, dimensions of about 10mm long and 6mm diameter typically works well (Figure 1). If the rock sample is not strong enough to withstand the drilling, a polymer stabilizing process can be used. >> Figure 2: 3D micro-ct stack representation from a rock sample plug Micro-CT images capture the density within the sample, resulting in grayscale images like the ones shown in Figure 2. Dense materials appear as lighter shades, while lower density regions appear darker. Given that pores are filled with air (or fluid) and the rock matrix is composed of heavier solid minerals, a visual and quantitative distinction between pore and rock can be achieved. 3. IMAGE PROCESSING/SEGMENTATTION >> Figure 1: Typical plug (mddle) scanned for DigitalROCK In order to use a pore-scale 3D image for computational analysis and simulation, it is necessary to determine for each location in the image whether it should be considered pore space or grain space. Ideally it would be straightforward to segment the grayscale image into a black (pore) and white (grain) binary representation of the scan using a simple color threshold. However, even when the difference between pore and rock seems visually apparent, real rock images always contain noise, scanning artifacts, and other 2017 Exa Corporation 5

6 Figure 3: Exa DigitalROCK image segmentation (right) of a high noise, low pore to grain contrast ct-scan. features that interfere with the accuracy of simple color thresholding. To avoid altering the pore space geometry, and thereby affecting the downstream analyses, it is necessary to include various image processing techniques as part of the image segmentation process to correct for non-physical scan noise and defects. Figure 3 shows a before and after example of an image segmentation process in which noise has been removed and the overall pore to grain contrast has been enhanced. Image segmentation is a critical and often challenging step. Exa DigitalROCK includes a segmentation procedure which has been tested on a variety of sandstone images; however, a user may prefer to provide images segmented by another tool, so Exa DigitalROCK accepts either segmented or unsegmented images as input. As soon as a segmented image is available (i.e. uploaded by the user or generated by Exa DigitalROCK), a geometric representation of the 3D pore space (as shown in Figure 4) is automatically generated by Exa DigitalROCK. If desired, a high quality surface mesh of the pore space can be output in a selected format (STL, NASTRAN). 4. PORE SPACE ANALYSIS After generating the 3D pore space geometry, a Pore Space Analysis (PSA) is carried out to examine several important attributes of the rock sample. These include overall porosity, connected porosity, and pore size distribution. An example whole field visualization of pore size distribution is shown for 3 different samples in Figure 5, the pores along the samples are colored by size with the biggest pores in red and the smaller ones in blue. Another important quantity generated by PSA is the critical pore throat radius, which indicates the minimum pore size necessary to have a connected flow path through the sample. Exa DigitalROCK requires the PSA of an image to be complete before any fluid flow simulations for permeability can be performed. Figure 4: 3D reconstruction of segmented sandstone micro-ct scan Exa Corporation

7 Figure 5: Pore space visual comparison for high (sample 1), medium (Sample 3), and low (Sample 5) porosity rocks. 5: MERCURY INJECTION CAPILLARY PRESSURE MICP curves are one of the most common and basic types of core analysis data. During a MICP test, pressure is increasingly applied at an inlet boundary of a sample while the volume of intruded pore space is measured. The resulting curve is used to characterize the rock s pore structure and identify features such as dual porosity systems. Exa DigitalROCK provides a predicted MICP curve for a rock sample image by running an intrusion-test algorithm on the digital pore space. The plot in Figure 6 shows an example MICP curve predicted by the Exa DigitalROCK MICP analysis. The images below show a visual representation of the intruded pore space at different values of applied increasing pressure, going from right to the left. 6. ABSOLUTE PERMEABILITY (K 0 ) Once the PSA for an image is completed, the user can submit one or more flow simulations for predicting absolute permeability values. For each flow simulation, the user specifies a flow direction, and optionally may select a specific sub-volume of the image. The Exa DigitalROCK flow simulator is based on an enhanced lattice Boltzmann method with a surface element (surfel) based boundary scheme, providing a high fidelity geometric representation of the boundary and precise control of surface fluxes. The use of surfels effectively adds sub-voxel resolution to the pore/grain boundary. To highlight the importance of the flow simulation boundary scheme for digital rocks, Figure 7 zooms in on a tiny portion of a 2D image slice of a Berea sandstone Figure 6: DigitalROCK generated mercury intrusion capillary pressure curve (bottom) displaying intruded pore space at five different pressures Exa Corporation 7

8 stair-step boundary formed by the underlying cubic grid (left) to the surfelbased boundary (right) for a typical computational grid resolution (critical pore throat radius of less than 5 grid cells). Figure 7: Stair step (a) and surface element based (b) representation of sandstone pore space To alleviate stair-step boundaries, other LBM implementations commonly use pointwise interpolation schemes; however, these methods face challenges in controlling the fluxes at the wall, such as ensuring exact mass conservation. In contrast, Exa s unique proprietary method provides precisely defined boundary locations, exact control of surface fluxes, and robust handling of the extremely complex pore space geometry. Extensive validation of Exa s single-phase technology has been performed on both academic test cases and real rocks. Figure 8 shows a comparison of experimental and simulated absolute permeability values for real rocks between 1-10,000 md. Exa DigitalROCK single phase flow simulations for absolute permeability are typically performed on image domains of 8003 voxels, and larger domain size is feasible. Results include a variety of automatically generated flow visualizations in addition to the numerical permeability values. Furthermore, each Exa DigitalROCK flow simulation for absolute permeability provides a permeability-porosity correlation that is automatically generated by evaluating octant (1/8th) sub-volumes of the simulated domain. An example permeability versus porosity correlation plot generated by Exa DigitalROCK is shown below. This type of correlation can be used, for example, to estimate the permeability as a function of depth based on the porosities measured from well log data. In general, absolute permeability results can be used to compare samples from the same well, reservoir or field. Before and after studies for a variety of well stimulation techniques and formation damage tests can also be performed using the Exa DigitalROCK approach. 7 MULTI-PHASE RELATIVE PERMEABILITY (K 0 ) As with the single phase flow simulations for absolute permeability, once the PSA for an image is complete, Exa DigitalROCK multiphase flow simulations can be submitted for prediction of oil and water imbibition relative permeability curves. Figure 8: Single-phase absolute permeability validation vs. experimental values for real rocks Exa Corporation

9 Figure 9: Porosity-permeability trend for Berea sandstone using subsamples of full simulated domain The relative permeability simulations require a few additional user-specified inputs: initial water saturation, surface wetting condition, and overall flow rate. With a defined initial water saturation, the MICP curve prediction process is used to distribute oil and water throughout the domain. Pores that require high capillary pressures to be accessed are filled with water while the rest are considered to be filled with oil, this approach emulates a primary drainage process in which water is displaced by oil. In Exa DigitalROCK technology, every surface element in the pore walls can be assigned a different wetting condition through a contact angle. Multiple techniques are available in order to obtain a wetting condition, for instance the fluid distribution escribed earlier can be used so that surface elements in contact with water are made a water-wet while those in contact with oil are considered oil-wet. Additionally, experimental data such as synchrotron generated images or minerology based wettability distributions can be used as input. Overall, both the initial fluid distribution and wetting conditions are flexible and can be obtained from user input data or be automatically generated within the DigitalROCK workflow. Examples are shown below for 3D representation of the initial fluid distribution, 3D representation of the contact angle distribution (Figure 10), and the Exa DigitalROCK predicted relative permeability results (Figure 11). Figure 10: Initial fluid distribution (left) and spatially varying wetting distribution Exa Corporation 9

10 Figure 11: Linear (left) and log-scale (right) relative permeability wettability trend for Berea Sandstone 8 REFERENCES B. Crouse, D. Freed, N. Koliha, G. Balasubramanian, R. Satti, D. Bale, and S. Zuklic. A Lattice-Boltzmann Based Method Applied to Digital Rock Characterization of Perforation Tunnel Damage 30th SCA Annual Symposium (August 2016). H. Otomo, H. Fan, Y. Li, Dressler, I. Staroselsky, R. Zhang, and H.Chen. Studies of accurate multi-component lattice Boltzmann models on benchmark cases required for engineering applications Journal of Computational Science (2016). 9 APPENDIX: EXAMPLE OF STANDARD RESULTS FROM EXA DIGITALROCK Figure A.1: Berea sandstone raw (left), prepared (middle), and binarized (right) ct-scan image Figure A.2: Pore Size Distribution slices for Berea sandstone in x, y, and z planes Exa Corporation

11 Figure A.3: Grain enclosed isosurface of 3D Berea sandstone (left) and 3D Pore Size Distribution (right) Figure A.4: Velocity distribution slices of single-phase simulation on a Berea sandstone in x, y, and z planes Figure A.5: Velocity distribution volume visualization for Berea sandstone Figure A.6: Water/oil distribution at different pore volumes during unsteady state relative permeability simulation (Available in future cloud releases) 2017 Exa Corporation 11

12 Figure A.7: Water/oil distribution during steady state relative permeability multiphase simulation (Available in future cloud releases) Exa Corporation

13 EXA CORPORATION 55 Network Drive Burlington, MA