Evaluation of the pore structure of tight sandstone reservoirs based on multifractal analysis: a case study from the Kepingtage Formation in the Shuntuoguole uplift, Tarim Basin, NW China

Peng, Jun; Han, Haodong; Xia, Qingsong; Li, Bin

doi:10.1088/1742-2140/aaab9d

Cited by 18 publications

(13 citation statements)

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“…Furthermore, multifractal analysis is a multiscale method based on power-law relationships and can describe local irregular fluctuations more effectively than the monofractal method [15][16][17]. In multifractal analysis, the self-similarity measure can be regarded as the singularity strength and parameters of the multifractal spectrum through scale decomposition of interrelated fractal series [17][18][19][20]. To date, multifractal analysis has been widely used to depict the statistical properties of scale variation for studies in soil science, geosciences, and materials due to its advantage in heterogeneity analysis [21][22][23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Multifractal Analysis of Pore Structure and Evaluation of Deep-Buried Cambrian Dolomite Reservoir with Image Processing: A Case from Tarim Basin, NW China

et al. 2020

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Reservoir pore space assessment is of great significance for petroleum exploration and production. However, it is difficult to describe the pore characteristics of deep-buried dolomite reservoirs with the traditional linear method because these rocks have undergone strong modification by tectonic activity and diagenesis and show significant pore space heterogeneity. In this study, 38 dolostone samples from 4 Cambrian formations of Tarim Basin in NW China were collected and 135 thin section images were analyzed. Multifractal theory was used for evaluation of pore space heterogeneity in deep-buried dolostone based on thin section image analysis. The physical parameters, pore structure parameters, and multifractal characteristic parameters were obtained from the digital images. Then, the relationships between lithology and these parameters were discussed. In addition, the pore structure was classified into four categories using K-means clustering analysis based on multifractal parameters. The results show that the multifractal phenomenon generally exists in the pore space of deep-buried dolomite and that multifractal analysis can be used to characterize the heterogeneity of pore space in deep-buried dolomite. For these samples, multifractal parameters, such as αmin, αmax, ΔαL, ΔαR, Δf, and AI, correlate strongly with porosity but only slightly with permeability. However, the parameter Δα, which is usually used to reveal heterogeneity, does not show an obvious link with petrophysical properties. Of dolomites with different fabrics, fine crystalline dolomite and medium crystalline dolomite show the best petrophysical properties and show significant differences in multifractal parameters compared to other dolomites. More accurate porosity estimations were obtained with the multifractal generalized fractal dimension, which provides a new method for porosity prediction. The various categories derived from the K-means clustering analysis of multifractal parameters show distinct differences in petrophysical properties. This proves that reservoir evaluation and pore structure classification can be accurately performed with the K-means clustering analysis method based on multifractal parameters of pore space in deep-buried dolomite reservoirs.

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Section: Introductionmentioning

confidence: 99%

Multifractal Analysis of Pore Structure and Evaluation of Deep-Buried Cambrian Dolomite Reservoir with Image Processing: A Case from Tarim Basin, NW China

et al. 2020

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“…A common approach to estimating the D ( q )s is the so‐called box‐counting method, described, for example, by Perrier, Tarquis, and Dathe (2006), and the use of which is illustrated on a slice of a binarized 3D image taken from Monga et al (2013). This method has been widely adopted in the soil science literature (e.g., Dathe et al, 2006; Peng et al, 2018; Grau, Mendez, Tarquis, Diaz, & Saa, 2006; Jiang et al, 2018; Zhu et al, 2019). The special case D (0) corresponds to the fractal dimension of the support on which the measure is distributed, and its value is d when the support is Euclidean of dimension d .…”

Section: Theorymentioning

confidence: 99%

“…Via the multiscale characterization of 1D curves, multifractal theory has been applied to model price evolution in finance (e.g., Mandelbrot, 1999), flux intensity in meteorology (Schertzer & Lovejoy, 1989) or pore and solid size distributions in soil science (e.g., Caniago, Martin, & San José, 2002; Liu, Ostadhassan, Gentzis, & Fowler, 2019). The multifractal formalism has also helped to characterize quantitatively the spatial variability of measurements in 2D or 3D, namely the spatial variability of rainfall (Lovejoy, Schertzer, & Allaire, 2008), geometric features of medical images (e.g., Lopes & Betrouni, 2009), the variable altitude of a given surface (van Pabst & Jense, 1995), the spatial distribution of species richness in a given area (Laurie & Perrier, 2010, 2011; Perrier & Laurie, 2008), and the spatial distribution of solid and pore mass in soils or other multiscale porous media (e.g., Dathe, Perrier, & Tarquis, 2006; Kravchenko, Martin, Smucker, & Rivers, 2009; Karimpouli & Tahmasebi, 2018; Lafond, Han, Allaire, & Dutilleul, 2012; Peng, Han, Xia, & Li, 2018; Saucier & Muller, 1999; Tarquis, Gimenez, Saa, Diaz, & Gasco, 2003; Torre, Losada, & Tarquis, 2018; Jiang et al, 2018; Zhu, Zhen, & Zhang, 2019). The main goal of most multifractal modelling efforts is to provide statistical descriptors of the variability in time or space of measured properties in order to classify datasets (e.g., Posadas, Gimenez, Quiroz, & Protz, 2003) and to correlate different properties, which can be helpful in quantitatively predicting those that are difficult to measure.…”

Section: Introductionmentioning

confidence: 99%

To what extent can multifractal measures provide an accurate model of the porosity of soils?

Perrier

Baveye

Garnier

2020

European J Soil Science

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Over the last decades, several authors have suggested that multifractal measures, that is, self‐similar measures defined on fractal or non‐fractal objects, could be useful to describe soil properties, to model soil processes, and to deal with their extreme microscale heterogeneity. In this context, a key question relates to the extent to which multifractal measures can indeed fulfill all the expectations they have generated. To address this question, we discuss the possibility of generating a synthetic soil image exhibiting multifractal porosity. To this end, a simple geometrical multifractal model in 2D is developed, which helps us to better understand the concept of multifractality and to generate images. We show that it is possible to generate synthetic binary images over a limited range of scales, but that a pure multifractal model for the distribution of the solid or pore mass cannot be developed due to physical constraints. Moreover, in the generated images mimicking multifractal solid space, a higher degree of multifractality corresponds to a larger porosity, rendering it difficult to tune model parameters to match actual soil properties. In addition, simple statistics relying on power‐law fits appear insufficient to characterize soil architecture even if they may capture some key multiscale indicators of observed spatial heterogeneity. We argue that the same conclusions would be reached in a three‐dimensional space, as well as for grey‐scales images.

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“…This leaves the prediction of the structural behavior and performance of sandstone as a significant challenge. Consequently, mechanical properties and failure patterns of sandstone have been the common research focus of various disciplines integrating mechanics, material science, and engineering [6,7].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Fracture Damage of Sandstone Using Digital Image Correlation

et al. 2020

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Featured Application: A non-contact digital image correlation technique is used to predict sandstone failure. Failure strain for the tested sandstone is estimated to be around 0.004. Finite element analysis verifies the accuracy of prediction results based on experiments.Abstract: Investigation on the deformation mechanism of sandstone is crucial to understanding the life cycle patterns of pertinent infrastructure systems considering the extensive adoption of sandstone in infrastructure construction of various engineering systems, e.g., agricultural engineering systems. In this study, the state-of-the-art digital image correlation (DIC) method, which uses classical digital photography, is employed to explore the detailed failure course of sandstone with physical uniaxial compression tests. Four typical points are specifically selected to characterize the global strain field by plotting their corresponding strain-time relationship curves. Thus, the targeted failure thresholds are identified. The Hill-Tsai failure criterion and finite element simulation are then used for the cross-check process of DIC predictions. The results show that, though errors exist between the experimental and the theoretical values, overall, they are sufficiently low to be ignored, indicating good agreement. From the results, near-linear relationships between strain and time are detected before failure at the four chosen points and the failure strain thresholds are almost the same; as low as 0.004. Failure thresholds of sandstone are reliably determined according to the strain variation curve, to forecast sandstone damage and failure. Consequently, the proposed technology and associated information generated from this study could be of assistance in the safety and health monitoring processes of relevant infrastructure system applications.

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Evaluation of the pore structure of tight sandstone reservoirs based on multifractal analysis: a case study from the Kepingtage Formation in the Shuntuoguole uplift, Tarim Basin, NW China

Cited by 18 publications

References 45 publications

Multifractal Analysis of Pore Structure and Evaluation of Deep-Buried Cambrian Dolomite Reservoir with Image Processing: A Case from Tarim Basin, NW China

Multifractal Analysis of Pore Structure and Evaluation of Deep-Buried Cambrian Dolomite Reservoir with Image Processing: A Case from Tarim Basin, NW China

To what extent can multifractal measures provide an accurate model of the porosity of soils?

Prediction of Fracture Damage of Sandstone Using Digital Image Correlation

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