3D printing sandstone porosity models

Abstract

The petroleum industry requires new technologies to improve the economics of exploration and production. Digital rock physics is a methodology that seeks to revolutionize reservoir characterization, an essential step in reservoir assessment, using computational methods. A combination of X-ray computed microtomography, digital pore network modeling, and 3D printing technology represents a novel workflow for transferring digital rock models into tangible samples that can be manufactured in a variety of materials and tested with standard laboratory equipment. Accurate replication of pore networks depends on the resolution of tomographic images, rock sample size, statistical algorithms for digital modeling, and the resolution of 3D printing. We performed this integrated approach on a sample of Idaho Gray Sandstone with an estimated porosity of 29% and permeability of 2200 mD. Tomographic images were collected at resolutions of 30 and 7 μm per voxel. This allowed the creation of digital porosity models segmented into grains and pores. Surfaces separating pores from grains were extracted from the digital rock volume and 3D printed in plastic as upscaled tangible models. Two model types, normal (with pores as voids) and inverse (with pores as solid), allowed visualization of the geometry of the grain matrix and topology of pores, while allowing characterization of pore connectivity. The current resolution of commodity 3D printers with a plastic filament (30 μm for pore space and 16 μm for grain matrix) is too low to precisely reproduce the Idaho Gray Sandstone at its original scale. However, the workflow described here also applies to advanced high-resolution 3D printers, which have been becoming more affordable with time. In summary, with its scale flexibility and fast manufacturing time, 3D printing has the potential to become a powerful tool for reservoir characterization. IntroductionThe petroleum industry has always been faced with a problem of correlation across multiple scales of investigation, for example, among seismic, well log, and core data. Although seismic profiles and wireline logs capture field-scale features, and while petrography and computed microtomography (CT) provide insight into pore-to bed-scale features of reservoir rocks, uncertainty in petrophysical properties due to differences in scale still persists. Moreover, calculations of petrophysical properties from microscopy images do not always match experimental data from cuttings and core plugs due to deficiencies in computational algorithms used for pore network modeling and fluid transport simulations. The physical pore network is an essential element of petroleum reservoir that is defined by the sizes, orientations, and connectivity of pores in a rock. Thus, accurate detection of pore space in reservoir rocks is crucial for a proper assessment of porosity-permeability relationships that ultimately affect prediction of hydrocarbon flow and ultimate recovery.

Fracture network created by 3‐D printer and its validation using CT images

“…Ishutov et al . [] and Head and Vanorio [] created porous samples to measure petrophysical properties. They created samples by using CT scan images of real rock samples, which can reproduce the same geometries as the specific rock sample at the core scale.…”

Validating 3D-printed porous proxies by tomography and porosimetry

“…Currently 3D printers can build proxies near the scale of the coarsest natural reservoir rocks (e.g., Ishutov et al, 2015;Head and Vanorio, 2016;Sukop et al, 2016;Ishutov et al, 2017b).…”

“…While previous studies have attempted to "photocopy" natural porous rocks using CT (Ishutov and Hasiuk, 2014;Ishutov et al, 2015), no validation methods were established for 3D printing geologically relevant pore networks. For example, no methods exist for describing the accuracy or resolution of a 3D printer with respect to complex porous models.…”

Using Resin‐Based 3D Printing to Build Geometrically Accurate Proxies of Porous Sedimentary Rocks

“…We present an improved system for generating physical models of porous rocks (referred herein as "proxies," Ishutov et al 2017a) that involves digital modeling, 3D printing, post-processing, and validation. Iterations of this workflow were developed in previous studies (Ishutov et al 2015(Ishutov et al , 2017a(Ishutov et al , 2017b, but post-processing and validation were not clearly defined in those studies as significant steps and were carried out by vendors with no clear control on procedures. Ishutov et al (2017b) involved 3D printing of Fontainebleau sandstone proxies from the same tomographic volume (Lindquist et al 2000) used in this study (13% porosity and 30-μm effective pore throat diameters).…”