Quantification and Prediction of the 3D Pore Network Evolution in Carbonate Reservoir Rocks

Boever, Eva De; Varloteaux, Clément; Nader, Fadi H.; Foubert, Anneleen; Békri, S.; Youssef, S.; Rosenberg, Elisabeth

doi:10.2516/ogst/2011170

Cited by 46 publications

(44 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the invasion percolation simulations, all external boundary pores were initially filled with mercury [22]. The pressure was incrementally increased until it overcame the entry pressure of an interface throat.…”

Section: Invasion Percolationmentioning

confidence: 99%

“…MicroCT facilitates subsequent analyses, including pore space extraction used to determine the characteristics of the pore microstructure. With this technique the porosity can be estimated directly from a binary volumetric image of the sample [22]. The volumetric images can be used directly in pore-scale flow simulations and compared to experimental studies.…”

Section: Introductionmentioning

confidence: 99%

“…One modeling technique that has gained popularity in recent years is Pore Network Modeling (PNM), which involves a coarse-graining of a sample's void space so that it can be efficiently analyzed numerically [22,[32][33][34][35][36][37]. Techniques for extracting pore network models from digital microCT images were pioneered by Oren et al [38].…”

Section: Introductionmentioning

confidence: 99%

“…A number of experimental techniques exist for microstructural characterization of carbonates [12,[21][22][23][24][25][26][27][28][29][30][31]. These include thin-section analysis [23], Scanning Electron Microscopy (SEM) [23,24], focused ion beam-SEM [25], and mercury intrusion porosimetry [12,21,26,27].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Pore Structure Characterization of Indiana Limestone and Pink Dolomite from Pore Network Reconstructions

Freire-Gormaly

Ellis

MacLean

et al. 2015

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

View full text Add to dashboard Cite

-Carbon sequestration in deep underground saline aquifers holds significant promise for reducing atmospheric carbon dioxide emissions (CO 2 ). However, challenges remain in predicting the long term migration of injected CO 2 . Addressing these challenges requires an understanding of pore-scale transport of CO 2 within existing brine-filled geological reservoirs. Studies on the transport of fluids through geological porous media have predominantly focused on oil-bearing formations such as sandstone. However, few studies have considered pore-scale transport within limestone and other carbonate formations, which are found in potential storage sites. In this work, high-resolution micro-Computed Tomography (microCT) was used to obtain pore-scale structural information of two model carbonates: Indiana Limestone and Pink Dolomite. A modified watershed algorithm was applied to extract pore network from the reconstructed microCT volumetric images of rock samples and compile a list of pore-scale characteristics from the extracted networks. These include statistical distributions of pore size and radius, pore-pore separation, throat radius, and network coordination. Finally, invasion percolation algorithms were applied to determine saturation-pressure curves for the rock samples. The statistical distributions were comparable to literature values for the Indiana Limestone. This served as validation for the network extraction approach for Pink Dolomite, which has not been considered previously. Based on the connectivity and the pore-pore separation, formations such as Pink Dolomite may present suitable storage sites for carbon storage. The pore structural distributions and saturation curves obtained in this study can be used to inform core-and reservoir-scale modeling and experimental studies of sequestration feasibility.Résumé -Caractérisation de la structure de pore de l'Indiana limestone de calcaire et de Pink dolomite provenant des reconstitutions de réseaux de pores -La séquestration du carbone dans les aquifères salins profonds souterrains est très prometteuse pour la réduction des émissions de dioxyde de carbone (CO 2 ) dans l'atmosphère. Cependant, des problèmes demeurent dans la prédiction de la migration à long terme du CO 2 injecté. Relever ces défis nécessite une compréhension du transport de CO 2 à l'échelle du pore dans des réservoirs géologiques existants remplis de saumure. Les études sur le transport des fluides en milieu poreux géologique ont principalement porté sur les formations oléagineuses telles que le grès. Cependant, peu d'études ont

show abstract

Section: Invasion Percolationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Pore Structure Characterization of Indiana Limestone and Pink Dolomite from Pore Network Reconstructions

Freire-Gormaly

Ellis

MacLean

et al. 2015

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

View full text Add to dashboard Cite

show abstract

“…While developing this pore-network model discrimination between porous space and throat has to be resolved to the best possible extent. Currently this discrimination is still subjective (Piller, et al, 2009 andDe Boever et al, 2012). A well-segmented 2-D or 3-D image of porous geometry provides a good foundation to obtain effective permeability and porosity trends.…”

Section: Introductionmentioning

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

Multiscale approach to (micro)porosity quantification in continental spring carbonate facies: Case study from the Cakmak quarry (Denizli, Turkey)

Boever

Foubert

Oligschlaeger

et al. 2016

Geochem Geophys Geosyst

View full text Add to dashboard Cite

Carbonate spring deposits gained renewed interest as potential contributors to subsurface reservoirs and as continental archives of environmental changes. In contrast to their fabrics, petrophysical characteristics -and especially the importance of microporosity (< 1mm) -are less understood. This study presents the combination of advanced petrophysical and imaging techniques to investigate the pore network characteristics of three, common and widespread spring carbonate facies, as exposed in the Pleistocene Cakmak quarry (Denizli, Turkey): the extended Pond, the dipping crystalline Proximal Slope Facies and the draping Apron and Channel Facies deposits formed by encrustation of biological substrate. Integrating mercury injection capillary pressure, bulk and diffusion Nuclear Magnetic Resonance (NMR), NMR profiling and Brunauer-Emmett-Teller (BET) measurements with microscopy and micro-computer tomography (m-CT), shows that NMR T 2 distributions systematically display a single group of micro-sized pore bodies, making up between 6 and 33% of the pore space (average NMR T 2 cut-off value: 62 ms). Micropore bodies are systematically located within cloudy crystal cores of granular and dendritic crystal textures in all facies. The investigated properties therefore do not reveal differences in micropore size or shape with respect to more or less biology-associated facies. The pore network of the travertine facies is distinctive in terms of (i) the percentage of microporosity, (ii) the connectivity of micropores with meso-to macropores, and (ii) the degree of heterogeneity at micro-and macroscale. Results show that an approach involving different NMR experiments provided the most complete view on the 3-D pore network especially when microporosity and connectivity are of interest.

show abstract

Quantification and Prediction of the 3D Pore Network Evolution in Carbonate Reservoir Rocks

Cited by 46 publications

References 27 publications

Pore Structure Characterization of Indiana Limestone and Pink Dolomite from Pore Network Reconstructions

Pore Structure Characterization of Indiana Limestone and Pink Dolomite from Pore Network Reconstructions

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

Multiscale approach to (micro)porosity quantification in continental spring carbonate facies: Case study from the Cakmak quarry (Denizli, Turkey)

Contact Info

Product

Resources

About