Facies Recognition Using Multifractal Hurst Analysis: Applications to Well-Log Data

Hernández‐Martínez, Eliseo; Perez-Muñoz, Teresa; Hernández, Juan González; Altamira-Areyan, Armando; Velasquillo-Martínez, Luis Germán

doi:10.1007/s11004-013-9445-6

Cited by 25 publications

(6 citation statements)

References 27 publications

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“…The methods are also readily applicable to subsurface datasets (e.g. Hernandez‐Martinez et al ., 2013). Meanwhile, statistical analyses in stratigraphic studies are rapidly evolving, and further testing is recommended in other areas and depositional systems.…”

Section: Discussionmentioning

confidence: 99%

“…This study utilizes integrated statistical methods to discuss potential sub‐environments, the degree of structural confinement, stacking patterns and intrinsic/extrinsic controls on sedimentation. The results of this study have implications for the interpretation and prediction of deepwater systems with limited exposures, subsurface datasets (Hernandez‐Martinez et al ., 2013) and possibly stratigraphy on other planets (Limaye et al ., 2012; Stack et al ., 2013).…”

Section: Introductionmentioning

confidence: 99%

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Statistical characterization of a confined submarine fan system: The Pennsylvanian Lower Atoka Formation, Ouachita Mountains, USA

2021

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This manuscript is a preprint and has been submitted to Sedimentology for consideration for publication. As of September 28, 2020, this manuscript is under review and has not been accepted for publication. The content of subsequent versions may be different from this one. Once the manuscript is formally accepted, we will add the peer-reviewed publication DOI, through which the final version will be available. Should you have any feedback, please contact the corresponding author. We appreciate it.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Statistical characterization of a confined submarine fan system: The Pennsylvanian Lower Atoka Formation, Ouachita Mountains, USA

2021

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“…To classify electrofacies, numerous other approaches are shown in the literature [22][23][24][25][26]. The majority of the available commercial software packages and approaches for subsurface modeling include electrofacies functions.…”

Section: Problem and The Backgroundmentioning

confidence: 99%

Machine Learning in Electrofacies Classification and Subsurface Lithology Interpretation: A Rough Set Theory Approach

et al. 2020

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Initially, electrofacies were introduced to define a set of recorded well log responses in order to characterize and distinguish a bed from the other rock units, as an advancement to the conventional application of well logs. Well logs are continuous records of several physical properties of drilled rocks that can be related to different lithologies by experienced log analysts. This work is time consuming and likely to be imperfect because human analysis is subjective. Thus, any automated classification approach with high promptness and accuracy is very welcome by log analysts. One of the crucial requirements in petroleum engineering is to interpret a bed’s lithology, which can be done by grouping a formation into electrofacies. In the past, geophysical modelling, petro-physical analysis, artificial intelligence and several statistical method approaches have been implemented to interpret lithology. In this research, important well log features are selected by using the Extra Tree Classifier (ETC), and then five individual electrofacies are constructed by using the selected well log features. Finally, a rough set theory (RST)-based whitebox classification approach is proposed to classify the electrofacies by generating decision rules. These rules are later on used to determine the lithology classes and we found that RST is beneficial for performing data mining tasks such as data classification and rule extraction from uncertain and vague well log datasets. A comparison study is also provided, where we use support vector machine (SVM), deep learning based on feedforward multilayer perceptron (MLP) and random forest classifier (RFC) to compare the electrofacies classification accuracy.

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“…To achieve these aims, an approach that combines multifractal techniques that allow for the reproduction of scale invariance and the intermittency of rainfall events, and non-parametric statistical techniques that allow for spatial-temporal comparisons of the distributions in the study area, was proposed in this paper. Multifractal analysis was developed to study turbulent intermittency and was adapted to provide a good description of the statistics at all orders and for a wide range of fields [18][19][20][21][22][23]. One of these fields consists of rainfall changes that result from climate change [24,25].…”

mentioning

confidence: 99%

“…One of these fields consists of rainfall changes that result from climate change [24,25]. The statistical multifractal nature of rainfall has been intensively studied over the last two decades [18][19][20][21][22][23][24][25][26][27][28][29][30][31]. Among the different multifractal analyses, the multifractal analysis used by Lavallée et al [32,33] and Royer et al [24] was applied.…”

mentioning

confidence: 99%

Spatial Modeling of Rainfall Patterns over the Ebro River Basin Using Multifractality and Non-Parametric Statistical Techniques

Valencia

Tarquis

Saa

et al. 2015

Water

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Rainfall, one of the most important climate variables, is commonly studied due to its great heterogeneity, which occasionally causes negative economic, social, and environmental consequences. Modeling the spatial distributions of rainfall patterns over watersheds has become a major challenge for water resources management. Multifractal analysis can be used to reproduce the scale invariance and intermittency of rainfall processes. To identify which factors are the most influential on the variability of multifractal parameters and, consequently, on the spatial distribution of rainfall patterns for different time scales in this study, universal multifractal (UM) analysis-C1, α, and γs UM parameters-was combined with non-parametric statistical techniques that allow spatial-temporal comparisons of distributions by gradients. The proposed combined approach was applied to a daily rainfall dataset of 132 time-series from 1931 to 2009, homogeneously spatially-distributed across a 25 km × 25 km grid covering the Ebro River Basin. A homogeneous increase in C1 over the watershed and a decrease in α mainly in the western regions, were detected, OPEN ACCESSWater 2015, 7 6205 suggesting an increase in the frequency of dry periods at different scales and an increase in the occurrence of rainfall process variability over the last decades.

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Facies Recognition Using Multifractal Hurst Analysis: Applications to Well-Log Data

Cited by 25 publications

References 27 publications

Statistical characterization of a confined submarine fan system: The Pennsylvanian Lower Atoka Formation, Ouachita Mountains, USA

Statistical characterization of a confined submarine fan system: The Pennsylvanian Lower Atoka Formation, Ouachita Mountains, USA

Machine Learning in Electrofacies Classification and Subsurface Lithology Interpretation: A Rough Set Theory Approach

Spatial Modeling of Rainfall Patterns over the Ebro River Basin Using Multifractality and Non-Parametric Statistical Techniques

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