Neural network and rock physics for predicting and modeling attenuation logs

Parra, Jorge O.; Iturrarán-Viveros, Ursula; Parra, Jonathan S.; Xu, Peicheng

doi:10.1190/segam2014-0095.1

Cited by 5 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A schematic representation of this NN architecture is shown in Figure 1. For a review of neural networks and their geophysical applications see van der Baan and Jutten (2000), Poulton (2002) or Saggaf et al (2003), whereas for more specific applications of the NN approach to derive a relation between petrophysical rockproperties and other attributes (such as seismic velocity, density, quality factors) see for example de Roos et al (2001), Valenti (2009, Leite and Vidal (2011) or Parra et al (2014).…”

Section: Neural Network Optimizationmentioning

confidence: 99%

Assessment of different approaches to rock-physics modeling: A case study from offshore Nile Delta

Aleardi

Ciabarri

2017

GEOPHYSICS

View full text Add to dashboard Cite

The estimation of a reliable rock-physics model (RPM) plays a crucial role in reservoir characterization studies. We assess different methods in deriving a reliable RPM that will be used in conjunction with amplitude-versus-angle inversion for the characterization of a clastic reservoir located in offshore Nile Delta. The reservoir zone is located in gas-saturated sand channels surrounded by shale sequences within a depth interval ranging between 2.3 and 2.7 km. One theoretical and three empirical approaches to derive a RPM are analyzed: The theoretical RPM is established using the well-known rock-physics equations valid for granular materials, whereas the empirical RPMs are derived using one multilinear stepwise regression and two nonlinear regression procedures based on neural networks (NNs) and genetic algorithms (GAs). A proper calibration and validation of the derived RPMs is conducted by using the extensive log suite of four existing wells drilled over an area of 100 km2. For the investigated reservoir interval and for the encasing shales, all the analyzed methods give a final RPM that is able to reliably predict the elastic attributes (P-wave velocity, S-wave velocity, and density) from the petrophysical properties of interest (porosity, water saturation, and shaliness). Among the empirical approaches, the RPM predicted by the multilinear regression is characterized by a prediction capability very similar to the RPMs predicted by the nonlinear GA method, thus demonstrating that in the investigated zone, the relation linking the petrophysical properties to the elastic attributes can be conveniently described by a multilinear model. Differently, the NN method seems to be affected by the overfitting problem that produces a RPM with a lower prediction capability than the RPMs estimated by the other methods. The theoretical method yields predictions of elastic properties very similar to those produced by multilinear regression

show abstract

Section: Neural Network Optimizationmentioning

confidence: 99%

Assessment of different approaches to rock-physics modeling: A case study from offshore Nile Delta

Aleardi

Ciabarri

2017

GEOPHYSICS

View full text Add to dashboard Cite

show abstract

“…One of the most common problems of reservoir geological-petrophysical modeling workflow (Cosentino, 2001) consists in predicting petrophysical properties considering the dependency relationships with the seismic attributes. For this purpose, different methods and approaches have been used, ranging from empirical relationships (Diaz-Viera et al, 2006), regression methods, neural networks (Parra et al, 2014), to spatial stochastic models. The latter being more flexible since they better reproduce the statistical and spatial behavior of petrophysical properties (Pyrcz & Deutsch, 2014).…”

Section: Introductionmentioning

confidence: 99%

Joint stochastic simulation of petrophysical properties with elastic attributes based on parametric copula models

Vázquez-Ramírez

Díaz-Viera

et al. 2023

Geofis Int

View full text Add to dashboard Cite

El método de co-simulación estocástica espacial, basado en cópulas, es un método general que permite simular variables con cualquier tipo de dependencia y funciones de distribución de probabilidad. Esta flexibilidad proviene del uso de un modelo de cópula para la representación de la función de distribución de probabilidad conjunta. El método se ha implementado principalmente a través de un enfoque no paramétrico utilizando cópulas de Bernstein y se ha aplicado con éxito para la simulación de propiedades petrofísicas usando atributos sísmicos elásticos como variables secundarias. En el presente trabajo este método se implementa mediante otros dos enfoques: paramétrico y semi-paramétrico. Específicamente, para el enfoque paramétrico se usa la familia de cópulas Arquimedianas. Primero, el enfoque paramétrico se valida con un caso publicado y luego se realiza una comparación de los tres enfoques en términos de precisión y rendimiento. Los resultados mostraron que el enfoque paramétrico es el que peor reproduce las estadísticas de los datos y presenta mayor incertidumbre con un menor costo computacional, mientras que el enfoque no-paramétrico resultó el que mejor reproduce la dependencia de los datos a un alto costo computacional. El enfoque semi-paramétrico reduce un 10% el costo computacional respecto al no-paramétrico, pero se degrada significativamente su precisión.

show abstract

Features denoising-based learning for porosity classification

Bedi

Toshniwal

2019

Neural Comput & Applic

View full text Add to dashboard Cite

Neural network and rock physics for predicting and modeling attenuation logs

Cited by 5 publications

References 10 publications

Assessment of different approaches to rock-physics modeling: A case study from offshore Nile Delta

Assessment of different approaches to rock-physics modeling: A case study from offshore Nile Delta

Joint stochastic simulation of petrophysical properties with elastic attributes based on parametric copula models

Features denoising-based learning for porosity classification

Contact Info

Product

Resources

About