Pore System Characterization and Petrophysical Rock Classification Using a Bimodal Gaussian Density Function

Xu, Chicheng; Torres‐Verdín, Carlos

doi:10.1007/s11004-013-9473-2

Cited by 67 publications

(30 citation statements)

References 8 publications

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“…Xu and Torres-Verdín (2013) found good correlations between some of these attributes and their associated petrophysical properties for a specific field case. Such a bi-Gaussian model generates five independent attributes, providing a quantitative basis for petrophysical modeling and rock typing.…”

Section: Introductionmentioning

confidence: 82%

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Truncated Multi-Gaussian Pore-Throat-Size Decomposition and a New Universal J-Function for Rock Characterization of Complex Carbonate Reservoirs

Ferreira

Booth

Oliveira

et al. 2015

Day 3 Thu, October 29, 2015

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This paper was selected for presentation by an OTC program committee following review of information contained in an abstract submitted by the author (s). Contents of the paper have not been reviewed by the Offshore Technology Conference and are subject to correction by the author (s). The material does not necessarily reflect any position of the Offshore Technology Conference, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Offshore Technology Conference is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of OTC copyright. topology may result in significant deviations in the quality of reservoir characterization and simulation results. In this context, proper multi-Gaussian decomposition and the introduction of a new multi-Gaussian universal J** -function are therefore crucial for carbonate reservoir simulations.The proposed truncated multi-Gaussian pore-throat-size decomposition presents significant additional benefits when compared to Thomeer's method. It also improves dynamic-reservoir-rock-typing and reservoir simulation processes. The new universal J** -function can be used to reconstruct capillary pressure curves from the information provided by multi-Gaussian pore-throat-size decomposition. Therefore, the new concepts presented in this paper have a clear potential to enhance the simulation of IOR and EOR in complex carbonate and clastic reservoirs.

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Section: Introductionmentioning

confidence: 82%

“…In this paper, we further develop Xu and Torres-Verdín (2013) work to propose a precise mathematical multi-Gaussian decomposition of the pore-throat-size distribution considering the truncation of the capillary pressure data. We match its parameters to experimental data through both direct and inverse approaches.…”

Section: Introductionmentioning

confidence: 99%

Truncated Multi-Gaussian Pore-Throat-Size Decomposition and a New Universal J-Function for Rock Characterization of Complex Carbonate Reservoirs

Ferreira

Booth

Oliveira

et al. 2015

Day 3 Thu, October 29, 2015

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show abstract

“…where σ is the Gaussian spread, and gamma ( γ ) is the Gaussian parameter (Oyang et al, 2001;Xu et al, 2013). The Gaussian kernel function is also called the radial basis (RBF).…”

Section: Kernel Functionsmentioning

confidence: 99%

Support-vector-regression machine technology for total organic carbon content prediction from wireline logs in organic shale: A comparative study

Tan

Song

Yang

et al. 2015

Journal of Natural Gas Science and Engineering

120

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“…By adapting and generalizing the c.d.f equation of bimodal Gaussian estimation [9], then the value of c.d.f from using KDE can be searched using the equation…”

Section: B Numeric Attribute Handlingmentioning

confidence: 99%

Predicting smart home lighting behavior from sensors and user input using very fast decision tree with Kernel Density Estimation and improved Laplace correction

Dinata

Hardian

2014

2014 International Conference on Advanced Computer Science and Information System

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One way to predict the behavior of smart home lighting is by using machine learning. Currently many methods of supervised learning that used for this problem, one of them is decision tree method. Very Fast Decision Tree (VFDT) as one of the decision tree method that has advantages in online machine learning that may useful in smart home, but there are still some room of improvisation that can improve accuracy of VFDT. The experiment result is obtained that VFDT is better than Naïve Bayes (NB) and Artificial Neural Network (ANN) in offline and online experiment. In addition, Kernel Density Estimation (KDE) and improved Laplace correction that is used as improvisation of VFDT is able to increase the accuracy and Matthews Correlation Coefficient (MCC) of VFDT in predicting smart home lighting switch usage.

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Pore System Characterization and Petrophysical Rock Classification Using a Bimodal Gaussian Density Function

Cited by 67 publications

References 8 publications

Truncated Multi-Gaussian Pore-Throat-Size Decomposition and a New Universal J-Function for Rock Characterization of Complex Carbonate Reservoirs

Truncated Multi-Gaussian Pore-Throat-Size Decomposition and a New Universal J-Function for Rock Characterization of Complex Carbonate Reservoirs

Support-vector-regression machine technology for total organic carbon content prediction from wireline logs in organic shale: A comparative study

Predicting smart home lighting behavior from sensors and user input using very fast decision tree with Kernel Density Estimation and improved Laplace correction

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