Machine Learning as a Seismic Prior Velocity Model Building Method for Full-Waveform Inversion: A Case Study from Colombia
Por:
Iturraran-Viveros, Ursula, Munoz-Garcia, Andres M., Castillo-Reyes, Octavio, Shukla, Khemraj
Publicada:
1 feb 2021
Resumen:
We use machine learning algorithms (artificial neural networks, ANNs) to
estimate petrophysical models at seismic scale combining well-log
information, seismic data and seismic attributes. The resulting
petrophysical images are the prior inputs in the process of
full-waveform inversion (FWI). We calculate seismic attributes from a
stacked reflected 2-D seismic section and then train ANNs to approximate
the following petrophysical parameters: P-wave velocity (V-p), density
(rho) and volume of clay (V-clay). We extend the use of the V-clay by
constraining it with the well lithology and we establish two classes:
sands and shales. Consequently, machine learning allows us to build an
initial estimate of the earth property model (V-p), which is iteratively
refined to produce a synthetic seismogram that matches the observed
seismic data. We apply the 1-D Kennett method as a forward modeling tool
to create synthetic data with the images of V-p, rho and the thickness
of layers (sands or shales) obtained with the ANNs. A nonlinear
least-squares inversion algorithm minimizes the residual (or misfit)
between observed and synthetic full-waveform data, which improves the
V-p resolution. In order to show the advantage of using the ANN velocity
model as the initial velocity model for the inversion, we compare the
results obtained with the ANNs and two other initial velocity models.
One of these alternative initial velocity models is computed via P-wave
impedance, and the other is achieved by velocity semblance analysis:
rootmean-square velocity (RMS). The results are in good agreement when
we use rho and V-p obtained by ANNs. However, the results are poor and
the synthetic data do not match the real acquired data when using the
semblance velocity model and the rho from the well log (constant for the
entire 2-D section). Nevertheless, the results improve when including
rho, the layered structure driven by the V-clay (both obtained with
ANNs) and the semblance velocity model. When doing inversion starting
with the initial V-p model estimated using the P-wave impedance, there
is some gain of the final V-p with respect to the RMS initial V-p. To
assess the quality of the inversion of V-p, we use the information for
two available wells and compare the final V-p obtained with ANNs and the
final V-p computed with the P-wave impedance. This shows the benefit of
employing ANNs estimations as prior models during the inversion process
to obtain a final V-p that is in agreement with the geology and with the
seismic and well-log data. To illustrate the computation of the final
velocity model via FWI, we provide an algorithm with the detailed steps
and its corresponding GitHub code.
Filiaciones:
Iturraran-Viveros, Ursula:
Univ Nacl Autonoma Mexico, Fac Ciencias, Circuito Escolar S-N, Mexico City 04510, DF, Mexico
Munoz-Garcia, Andres M.:
Inst Tecnol Metropolitano Medellin ITM, Antioquia, Colombia
Castillo-Reyes, Octavio:
Barcelona Supercomp Ctr BSC, C Jordi Girona,29,Edif Nexus II, Barcelona 08034, Spain
Shukla, Khemraj:
Brown Univ, Div Appl Math, Providence, RI 02906 USA
|