A new methodology for estimating porosity and shaliness of a petroleum reservoir,
based on petrophysical and geostatistical techniques, has been developed
and is presented here. We describe a procedure for combining both techniques,
usually applied separately, in a way to capitalize on the advantages of both.
Petrophysics gives us a reliable average relationship between acoustic properties
of rocks and petrophysical parameters like porosity and shaliness,
and by means of geostatistics we try to assess the spatial variability of
these parameters as a function of the heterogeneity of the reservoir.
The importance of knowing the porosity in a reservoir description is obvious,
considering the effects it has in the improvement of the recovery of
hydrocarbons. Shaliness is qualitatively related to permeability, because
there is a tendency of reduction in permeability with increase in clay or
shaliness, due to the characteristics of the shale particles. So the information
about the shaliness can be useful to identify flow barriers inside the reservoir.
Studies have shown that porosity and clay, e.g. shaliness, are the most
influential factors affecting the velocity of seismic waves in reservoir
rocks, specially sandstones. The inclusion of shaliness improves the modeling
of compressional and shear velocities and makes the prediction of porosity
from velocities even better.
The information we use comes from tomographic data based on crosswell seismic
measurements and well logs: the definition of an empirical model for elastic
wave velocities, compressional and shear, along with reservoir and petrophysical
data on porosity and shaliness at the boreholes. The methodology may be applied
to surface seismic, crosswell data or VSP data as well.
The methodology is tested on synthetic data and shows better results in the
estimation of the petrophysical parameters along the reservoir than that
obtained with the separate use of either a petrophysical or a geostatistical
method alone. We also show predictions of porosity and shaliness for a real
dataset for a reservoir from a sedimentary basin of Texas, E.U.A..
Seismic data, when used properly with petrophysical models and integration
tools such as geostatistics, is very important for estimating porosity and
permeability away from the borehole.
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