Digital rock physics benchmarks—Part I: Imaging and segmentation

Andrä, Heiko; Combaret, Nicolas; Dvorkin, Jack; Glatt, Erik; Han, Junehee; Kabel, Matthias; Keehm, Youngseuk; Krzikalla, Fabian; Lee, Minhui; Madonna, Claudio; Marsh, Mike; Mukerji, Tapan; Saenger, Erik H.; Sain, Ratnanabha; Saxena, Nishank; Ricker, S.L.; Wiegmann, Andreas; Zhan, Xin

doi:10.1016/j.cageo.2012.09.005

Cited by 521 publications

(269 citation statements)

References 21 publications

Supporting

Mentioning

258

Contrasting

Unclassified

Order By: Relevance

“…The pre-study was done on a synchrotron scanned Berea Sandstone dataset, a well-known "standard" in CT studies and lab investigations (Andrä et al, 2013b, a). We chose a cylindrical specimen of 3 mm diameter, which was also scanned at the TOM-CAT beamline of the Swiss Light Source at the Paul Scherrer Institute in Villigen, Switzerland.…”

Section: Pre-analysis Of the Effect Of Image Enhancement On Numericalmentioning

confidence: 99%

“…With the continuous improvement and popularization of high-resolution visualization methods (Holzer and Cantoni, 2012;Desbois et al, 2013;Hemes et al, 2015;Cnudde and Boone, 2013;Liu et al, 2016;Deville et al, 2013;Berg et al, 2013;Pak et al, 2015) digital rock physics gained a firm foothold in the geophysical world as a potent toolset in studies of rock properties (e.g., porosity and permeability) and pore-scale processes like, e.g., electric and heat conductivity or propagation of acoustic waves (Andrä et al, 2013b, a;Madonna et al, 2013;Saenger et al, 2011). In this approach, numerical simulations of transport and petrophysical properties are conducted on realistic rock representations that ultimately can be upscaled and compared to remote sensing data.…”

Section: Introductionmentioning

confidence: 99%

“…In this approach, numerical simulations of transport and petrophysical properties are conducted on realistic rock representations that ultimately can be upscaled and compared to remote sensing data. The advantage of this method over traditional, often oversimplified models lies in a more faithful description of complex pore geometries and microstructures found in natural formations (Andrä et al, 2013b, a). The most common base for the numerical simulation is segmented 3-D reconstructions from various nondestructive X-ray computed tomography (X-ray CT) techniques.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

On the path to the digital rock physics of gas hydrate-bearing sediments – processing of in situ synchrotron-tomography data

Sell

Saenger

Falenty³

et al. 2016

Solid Earth

Self Cite

View full text Add to dashboard Cite

Abstract. To date, very little is known about the distribution of natural gas hydrates in sedimentary matrices and its influence on the seismic properties of the host rock, in particular at low hydrate concentration. Digital rock physics offers a unique approach to this issue yet requires good quality, highresolution 3-D representations for the accurate modeling of petrophysical and transport properties. Although such models are readily available via in situ synchrotron radiation Xray tomography, the analysis of such data asks for complex workflows and high computational power to maintain valuable results. Here, we present a best-practice procedure complementing data from Chaouachi et al. (2015) with data postprocessing, including image enhancement and segmentation as well as exemplary numerical simulations of an acoustic wave propagation in 3-D using the derived results. A combination of the tomography and 3-D modeling opens a path to a more reliable deduction of properties of gas hydrate-bearing sediments without a reliance on idealized and frequently imprecise models.

show abstract

Section: Pre-analysis Of the Effect Of Image Enhancement On Numericalmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

On the path to the digital rock physics of gas hydrate-bearing sediments – processing of in situ synchrotron-tomography data

Sell

Saenger

Falenty³

et al. 2016

Solid Earth

Self Cite

View full text Add to dashboard Cite

show abstract

“…For this study andesite (Tongariro National Park, New Zealand); Berea sandstone (Andrä et al, 2013); Rotliegend sandstone (Rotliegend, Germany); and the synthetic sample "Musli" (henceforth referred to simply as Musli; provided by APS Antriebs, Prüf und Steuertechnik GmbH, Rosdorf, Germany), were used. Figure 2 shows the rock samples and respective histogram plots obtained from the XCT raw files.…”

Section: Experimental Approachmentioning

confidence: 99%

Phase segmentation of X-ray computer tomography rock images using machine learning techniques: an accuracy and performance study

Chauhan

Rühaak

Anbergen³

et al. 2016

Solid Earth

View full text Add to dashboard Cite

Abstract. Performance and accuracy of machine learning techniques to segment rock grains, matrix and pore voxels from a 3-D volume of X-ray tomographic (XCT) grayscale rock images was evaluated. The segmentation and classification capability of unsupervised (k-means, fuzzy c-means, self-organized maps), supervised (artificial neural networks, least-squares support vector machines) and ensemble classifiers (bragging and boosting) were tested using XCT images of andesite volcanic rock, Berea sandstone, Rotliegend sandstone and a synthetic sample. The averaged porosity obtained for andesite (15.8 ± 2.5 %), Berea sandstone (16.3 ± 2.6 %), Rotliegend sandstone (13.4 ± 7.4 %) and the synthetic sample (48.3 ± 13.3 %) is in very good agreement with the respective laboratory measurement data and varies by a factor of 0.2. The k-means algorithm is the fastest of all machine learning algorithms, whereas a least-squares support vector machine is the most computationally expensive. Metrics entropy, purity, mean square root error, receiver operational characteristic curve and 10 K-fold cross-validation were used to determine the accuracy of unsupervised, supervised and ensemble classifier techniques. In general, the accuracy was found to be largely affected by the feature vector selection scheme. As it is always a trade-off between performance and accuracy, it is difficult to isolate one particular machine learning algorithm which is best suited for the complex phase segmentation problem. Therefore, our investigation provides parameters that can help in selecting the appropriate machine learning techniques for phase segmentation.

show abstract

“…However, benchmarking tests of commonly used image processing methods have revealed unacceptably large variations in the results, and further development and optimization is therefore clearly warranted (e.g. Andrä et al, 2013). Furthermore, Leu et al (2014) determined the importance of image analysis of µXCT-generated data to provide accurate parameterization of porosity-permeability relationships.…”

Section: Introductionmentioning

confidence: 99%

Multi-phase classification by a least-squares support vector machine approach in tomography images of geological samples

2016

View full text Add to dashboard Cite

Abstract. Image processing of X-ray-computed polychromatic cone-beam micro-tomography (µXCT) data of geological samples mainly involves artefact reduction and phase segmentation. For the former, the main beam-hardening (BH) artefact is removed by applying a best-fit quadratic surface algorithm to a given image data set (reconstructed slice), which minimizes the BH offsets of the attenuation data points from that surface. A Matlab code for this approach is provided in the Appendix. The final BH-corrected image is extracted from the residual data or from the difference between the surface elevation values and the original grey-scale values. For the segmentation, we propose a novel least-squares support vector machine (LS-SVM, an algorithm for pixelbased multi-phase classification) approach. A receiver operating characteristic (ROC) analysis was performed on BHcorrected and uncorrected samples to show that BH correction is in fact an important prerequisite for accurate multiphase classification. The combination of the two approaches was thus used to classify successfully three different more or less complex multi-phase rock core samples.

show abstract

Digital rock physics benchmarks—Part I: Imaging and segmentation

Cited by 521 publications

References 21 publications

On the path to the digital rock physics of gas hydrate-bearing sediments – processing of in situ synchrotron-tomography data

On the path to the digital rock physics of gas hydrate-bearing sediments – processing of in situ synchrotron-tomography data

Phase segmentation of X-ray computer tomography rock images using machine learning techniques: an accuracy and performance study

Multi-phase classification by a least-squares support vector machine approach in tomography images of geological samples

Contact Info

Product

Resources

About