Intelligent Seismic
Inversion; From Surface
Seismic
to Well Logs via VSP
Artun, Emre 1, Mohaghegh, Shahab D. 1, Toro, Jaime 1, Wilson, Tom 1, and Sanchez, Alejandro 2
1West Virginia University
2Anadarko Petroleum Corporation
In the petroleum exploration work flow, geologists and
geophysicists use seismic
data
to forecast the possible existence of hydrocarbon
resources by structural mapping of the subsurface, and making interpretations of
the reservoir’s facies distribution. Engineers use this information to make
decisions on possible locations for new exploration or development wells. The
relatively low resolution of
seismic
data
usually limits its further use. Yet,
its areal coverage and availability suggest that it has the potential of
providing valuable
data
for more detailed reservoir characterization studies
through the process of
seismic
inversion.
In this study, a novel intelligent seismic
inversion methodology
is presented to achieve a desirable correlation between relatively low-frequency
seismic
signals, and the much higher frequency wireline-log
data
. Vertical
seismic
profile (VSP) is used as an intermediate step between the well logs and
the surface
seismic
. A synthetic
seismic
model is developed by using real
data
and
seismic
interpretation
. In the example presented here, the model represents
the Atoka and Morrow formations, and the overlying Pennsylvanian sequence of the
Buffalo Valley Field in New Mexico. Artificial neural networks are used to build
two independent correlation models between; 1) Surface
seismic
and VSP, 2) VSP
and well logs. After generating virtual VSP’s from the surface
seismic
, well
logs are predicted by using the correlation between VSP and well logs. Density
logs were predicted with 87% accuracy through the
seismic
line. The same
procedure can be applied to a complete 3D
seismic
block to obtain a detailed
view of reservoir quality distribution.