Отправлено: 02.06.11 02:17. Заголовок: Fast probabilistic nonlinear petrophysical inversion

Fast probabilistic nonlinear petrophysical inversion
Mohammad S. Shahraeeni1 and Andrew Curtis1
GEOPHYSICS, VOL. 76, NO. 2 (MARCH-APRIL 2011); P. E45–E58, 12 FIGS., 3 TABLES.


ABSTRACT
We have developed an extension of the mixture-density
neural network as a computationally efficient probabilistic
method to solve nonlinear inverse problems. In this method,
any postinversion (a posteriori) joint probability density
function (PDF) over the model parameters is represented by
a weighted sum of multivariate Gaussian PDFs. A mixturedensity
neural network estimates the weights, mean vector,
and covariance matrix of the Gaussians given any measured
data set. In one study, we have jointly inverted compressional-
and shear-wave velocity for the joint PDF of
porosity, clay content, and water saturation in a synthetic,
fluid-saturated, dispersed sand-shale system. Results show
that if the method is applied appropriately, the joint PDF estimated
by the neural network is comparable to the Monte
Carlo sampled a posteriori solution of the inverse problem.
However, the computational cost of training and using the
neural network is much lower than inversion by sampling
(more than a factor of 104 in this case and potentially a much
larger factor for 3D seismic inversion). To analyze the performance
of the method on real exploration geophysical data,
we have jointly inverted P-wave impedance and Poisson’s ratio
logs for the joint PDF of porosity and clay content. Results
show that the posterior model PDF of porosity and clay content
is a good estimate of actual porosity and clay-content log
values. Although the results may vary from one field to
another, this fast, probabilistic method of solving nonlinear
inverse problems can be applied to invert well logs and large
seismic data sets for petrophysical parameters in any field.


http://www.geos.ed.ac.uk/homes/acurtis/Shahraeeni_Curtis_Geop_2011.pdf

Исключительно по Google вышел ;-)