System and method of predicting reservoir properties

A system and method for predicting oil reservoir properties throughout the reservoir using well data and seismic data. While the reservoir properties in the vicinity of a well borehole are usually known (i.e. by well logging), the reservoir properties between wells are unknown and difficult to estimate. Because the relationships between reservoir properties and seismic data is seldom obvious, the method uses Artificial Neural Networks (ANN's) to estimate these relationships. First, the method calculates the intersections between the seismic data and wellbore date, i.e. seismic reflectors are correlated to geological markers in the wellbores. Second, the method estimates the significance between seismic attributes and borehole properties. Next, the method models or calibrates the relationship between reservoir properties and seismic attributes by training an ANN using seismic attributes and wellbore data close to the intersections. Finally, the trained ANN uses the seismic attributes to predict the reservoir properties between wells. Advantageously, statistic methods are used to estimate the confidence of the predictions.

TECHNICAL FIELD 
This invention relates to a method of predicting oil reservoir properties 
using seismic data and wellbore data. In particular, the invention 
predicts reservoir properties, such as porosity or water saturation, 
between oil wells using an Artificial Neural Network trained by 
statistically significant, matched seismic attributes and wellbore data. 
BACKGROUND OF THE INVENTION 
1. Reservoir Modeling 
Seismic data are routinely and effectively used to estimate the structure 
of reservoir bodies, but often play no role in the essential task of 
estimating the spatial distribution of reservoir properties. Reservoir 
property mapping is usually based solely on wellbore data, even when high 
resolution 3D seismic data are available. "Wellbore data" includes 
information typically obtained from a wireline log or core sample from an 
oil well and is typically the desired fine grain information for 
characterizing a "reservoir property." 
Porosity, permeability, fluid and gas saturation, and other reservoir 
properties are measured at high accuracy near oil wells (e.g. by wireline 
logs), but these data do not assure reliable estimates of reservoir 
properties away from the wells. Seismic waves are not limited to wells, 
and seismic data may contain useful information about reservoir properties 
between the wells. 
Processing of seismic data produces "seismic attributes" which may be 
effectively used to delineate the structure. See e.g., M. T. Taner, F. 
Koehler, and R. E. Sheriff, 1979, Complex Trace Analysis, Geophysics 
44,1041-1063 and L. Sonneland, O. Barkved, and O. Hagness, 1990, 
Construction and Interpretation of Seismic Classifier Maps, EAEG meeting 
in Copenhagen. Seismic attributes are mathematical transformations on the 
data, computed either poststack (e.g., reflection intensity, instantaneous 
frequency, acoustic impedance, dip, azimuth) or prestack (e.g., AVO or 
moveout parameters). Well data are not used in their computation: 
attributes are purely properties of the seismic data alone. Otherwise, any 
analysis of the significance of seismic attributes to reservoir properties 
will be frustrated. Other seismic attributes include: acoustic impedance 
and velocity; reflection heterogeneity and instantaneous frequency; depth; 
dip and azimuth. 
Specific seismic attributes may be related to specific reservoir 
properties. For example, acoustic impedance estimated from reflectivity by 
inversion of seismic data is an important seismic attribute. FIG. 13, 
shows cross sections of seismic data--reflectivity and acoustic impedance, 
together with wellbore data--porosity and water saturation. From FIG. 13, 
porosity does not appear directly related to the reflectivity, but it 
seems related to acoustic impedance--high impedance seems to imply low 
porosity. However, it is unclear how to actually use acoustic impedance to 
estimate porosity. 
One problem in using seismic attributes is that their relation to rock 
properties is not obvious. For example, it is unclear how to use AVO to 
estimate gas saturation. Even if estimates are made, the confidence level 
in such estimates are unknown. There are unknown local factors that may 
affect the data in unexpected ways, and it is risky to predict functional 
relationships among seismic attributes and reservoir properties based on a 
simplified theoretical analysis with no familiarity of what "works" in a 
certain region. Region familiarity is built by comparing seismic and 
wellbore data. There is a need for interpretation methods and tools to 
build region familiarity, quantify its reliability, and subsequently use 
it to estimate properties. There is a need for a method to identify 
statistically-significant associations of seismic attributes and reservoir 
properties in any area, to determine the functional relationships implicit 
in these associations, to use them to predict the distribution of 
reservoir properties, and to quantify the reliability of the estimates. 
2. Artificial Neural Networks 
A great deal of recent research has been published relating to the 
application of artificial neural networks in a variety of contexts. See, 
for example, U.S. Pat. Nos. 5,134,685, 5,129,040, 5,113,483, and 5,107,442 
(incorporated by reference). Artificial neural networks are computational 
models inspired by the architecture of the human brain. As a result three 
constraints are usually imposed on these models. The computations must be 
performed in parallel, the representation must be distributed, and the 
adjustment of network parameters (i.e., learning) must be adaptive. From 
an engineering perspective ANNs are adaptive, model-free estimators that 
estimate numerical functions using example data. While many different 
types of artificial neural networks exist, two common types are radial 
basis function (RBF) and back propagation artificial neural networks. 
SUMMARY OF THE INVENTION 
The problems outlined above associated with predicting petroleum reservoir 
properties between wells is largely solved by the system and method of the 
present invention. That is, the present invention uses the simultaneous 
analysis of seismic data with wellbore data to better estimate the 
reservoir properties. The method identifies statistical correlations 
between seismic data and wellbore data, estimates the nonlinear functional 
relationships between seismic attributes and wellbore data using 
Artificial Neural Networks, and uses the functional relationships or 
"matches" to map reservoir properties between oil wells guided by the 
seismic data. An important additional result is the confidence level 
estimation of the reservoir property prediction. 
In more detail, the method of the present invention for predicting oil 
reservoir properties uses multiple types of seismic data and multiple 
types of wellbore data for one or more oil wells in the reservoir. As used 
herein, "seismic data" may include any type of seismic information, but 
specifically includes processed information called "seismic attributes." 
First, the method relates the seismic data to the wellbore data for at 
least one of the oil wells to determine the approximate intersections of 
the seismic and wellbore data. That is, the seismic data has to be 
calibrated or correctly correlated by depth to the wellbore data. Next, 
the method estimates the significance of a match between one or more types 
of seismic data to a type of wellbore data using the intersections. That 
is, some of the types of seismic data and wellbore data show statistical 
correlation or matches while others are only randomly related. 
Next, the method models or calibrates the relationship between seismic data 
and wellbore data by training an Artificial Neural Network (ANN) using one 
or more of the nonrandom matches. Where the match between seismic data and 
wellbore data is linearly related, resort to ANN analysis is not 
necessary. However, the important matches of interest are often 
nonlinearly related by a complex and usually unknown function and use of 
ANN's makes calibration possible or at least simplifies calibration. 
Finally, the reservoir property is calculated using the trained ANN based 
on the seismic data. As an additional step, the confidence level of the 
calculated reservoir property can be estimated. 
Preferably, the seismic data is a seismic attribute, such as acoustic 
velocity, acoustic computing, or amplitude versus offset (AVO). 
Preferably, the wellbore data is obtained by a wireline log or core sample 
such as porosity, water saturation, or permeability. In a preferred form a 
plurality of seismic attributes are input to the trained ANN and a 
reservoir property output.

DESCRIPTION OF THE PREFERRED EMBODIMENTS 
1. General Description 
The simultaneous analysis of seismic attributes with wellbore data from 
multiple wells leads to better predictions of reservoir properties. Such 
seismic guided predictions have higher resolution and predict reservoir 
properties with higher accuracy, compared to estimates based on well data 
in which the seismic data were used only for the geometry. For example, 
when acoustic impedance inverted from seismic data is used to guide the 
estimate of porosity using the method of the present invention, the error 
is cut in half as compared to conventional methods. 
The method identifies statistical correlations between seismic attributes 
and log properties, estimates linear or nonlinear functional relationships 
between attributes and properties, uses artificial neural networks for 
analyzing complex nonlinear relationships, and using these functional 
relationships and geostatistical technics to predict reservoir properties. 
Based on rock physics, certain seismic attributes are indicative of certain 
reservoir properties. However, exact functional relationships from theory 
is unreliable, and a learning phase is therefore valuable in every new 
reservoir context. Examples of known correlations of reservoir properties 
related to seismic attributes include porosity guided by acoustic 
impedance, velocity, and volume of clay; volume of clay and dolomite 
guided by heterogeneity and instantaneous frequency; water saturation 
guided by depth; thickness guided by amplitude. Also, borehole measured 
dip and azimuth are compared to dip and azimuth inferred from tracking 
seismic reflections. 
2. Detailed Description 
The 5 principle steps in the method of the present invention are 
illustrated in FIG. 1 and are as follows. 
1 ) Calculating and editing intersections 
Seismic data are measured in time, well data in depth and it is important 
that the seismic data be related or calibrated in physical space to the 
wellbore data. Relating seismic reflectors to geological markers seen in 
boreholes is a critical step; inaccuracy of a few meters may completely 
change subsequent results. The analysis is based on synthetic seismograms 
and time to depth conversion based on a velocity model. Eventually, the 
intersections may be refined interactively. FIG. 2 displays a single well 
view of well logs and acoustic impedance extracted from the seismic data 
along the well. It is necessary to tie control points in depth (called 
geological markers) to control points in time (called seismic markers). As 
can be seen from FIG. 2 adjustment of the acoustic impedance to match up 
with well logs is possible. 
In more detail, FIG. 2 shows a single well display with various logs and a 
seismic well trace plotted as a function of time and depth. The acoustic 
impedance log (first on the left) is very similar to the porosity log 
(second on the left) and quite similar to the acoustic impedance inverted 
from surface seismic data with no usage of well data in the inversion. The 
well trace is displayed in variable area on the right. It contains values 
from a 3-D acoustic impedance cube extracted on the trajectory of a 
deviated well. The acoustic impedance trace resembles the acoustic 
impedance log, except that it has a lower frequency content. One can 
identify the high porosity--low acoustic impedance component on both logs 
and well trace. This is further indication that acoustic impedance from 
inversion might be useful to estimate porosity. The volume of clay log 
(fourth log from left) does not resemble the well trace. Hence, inverted 
impedance would not be directly useful for estimation of shaliness. 
The resolution of well data is higher than that of surface seismic data. To 
use them together effectively, appropriate averaging is done at the 
intersections. The well data are averaged between the top and bottom of a 
geological component (layer). The seismic attributes are averaged 
temporally and spatially to isolate the influence of the geological 
component. 
2) Significance estimation (quality matrix) 
Wellbore data from multiple wells are extracted at horizon intersections to 
give a multidimensional scatter of independent values. In the case of 
mapping averaged properties within layers, each well contributes a point 
to this scatter. The purpose of significance estimation is to quantify 
which attributes are significant to which properties--i.e., a match 
between seismic attributes and wellbore data. For each match, the method 
calculates the probability of the values not being randomly related. 
Preferably, the Kendall tau indicator, .tau..sub.k, which is a measure of 
the monotonicity of a scatter, is used. See, M. Kendall, and A. Stuart, 
1977, The Advanced Theory of Statistics, 4th ed., Griffin & Co., London. 
It detects nonlinear relations, as opposed to other indicators which 
measure how close a relation is to being linear. Another advantage is that 
it is a robust indicator which is not easily affected by a few erroneous 
measurements, much like the median is more robust than the mean. The 
significance is the probability of the scatter not being random. It is 
calculated from the actual .tau..sub.k and the number of points in the 
scatter, N, as the probability of having a .tau..sub.k smaller than the 
actual one with N random points. 
Each pair of points in a scatter has a slope. If there are N points in the 
scatter, there are a total of N.sub.T =N(N-1)/2 slopes. Kendall's .tau. is 
defined as, 
##EQU1## 
where N.sub.P, N.sub.N, N.sub.Z, and N.sub..infin. are the numbers of 
positive, negative, zero, and infinite slopes. If all the slopes have the 
same sign, the scatter is monotonous and the absolute value of .tau..sub.k 
is 1. If there are as many negative as there are positive slopes, 
.tau..sub.k will be 0. If all the slopes are either zero or infinite, 
.tau..sub.k will be undetermined. .tau..sub.k can never exceed 1.0 in 
absolute value. 
By itself, .tau..sub.k is not a significance estimator. For example, with 
two points the absolute value of .tau..sub.k is (almost always) the 
highest possible 1, but the significance should be zero. The significance 
is estimated from a given .tau..sub.k and the number of points, N, 
according to the probability of getting the particular .tau..sub.k from N 
random points. Based on Kendall and Stuart analysis, 
##EQU2## 
(for N&gt;4). This gives, for example, 44% for .tau..sub.k of 0.5 with 5 
points, but 84% for .tau..sub.k of just 0.2 with 100 points. 
The significance values are summarized in a quality matrix (FIG. 3). The 
columns correspond to reservoir properties and the rows to seismic 
attributes. Previously estimated properties can be loaded as if they were 
seismic attributes. It is essential to examine the scatters of 
attribute-property pairs directly, and to disable outlier points that we 
wish to exclude from the analysis. FIG. 3 depicts the "Quality Matrix" or 
significance values useful for determining matches between seismic 
attributes and reservoir properties. The significance values of each 
seismic attribute and reservoir property are displayed on buttons, from 
which a cross plot editor can be popped. For example, Acoustic impedance 
is significant for porosity, depth for water saturation, and Instantaneous 
Frequency for Volume of Clay. 
An interactive cross plot editor for a workstation is shown in FIG. 4. FIG. 
4 illustrates specifically a technique for calibration editing. The names 
are well names. Volume of clay is expected to affect the Reflection 
Heterogeneity seismic attribute. (a) Before and (b) after exclusion of 
what might be interpreted as out-liars. The significance indicator 
increases from 13% to 72% in the process. 
3 ) Modeling or Calibrating the relationship (linear and nonlinear) 
To estimate a certain reservoir property, for example porosity, f, guided 
by velocity V.sub.p, and shaliness, VCL, a calibration function 
f(V.sub.p,VCL) is sought. A linear calibration function is for example, 
EQU .phi.(V.sub.p, VCL)=c.sub.0 +c.sub.1 .multidot.V.sub.p +c.sub.2 
.multidot.VCL 
where the coefficients, c.sub.0, c.sub.1 and c.sub.2 are found by solving a 
system of equations. Linear calibration is adequate when the relations are 
linear. Nonlinear calibration is needed when the relations are more 
complex. For example, water saturation increases with depth nonlinearly; 
faster at the water cut and slower above and below it. 
Since nonlinear relationships are unknown and varied, instead of 
prescribing a particular nonlinear model to perform the calibration (e.g., 
a polynomial typically used in regression), the method uses an ,artificial 
neural network (ANN) to learn a nonlinear model using example data (FIG. 
5). The method perform the nonlinear calibration after the linear 
calibration using the difference (residuals) between the well-measured 
properties to the estimated properties from linear calibration. 
The method uses a particular type of ANN, called a Radial Basis Function 
Network (RBFN). RBFN is a single hidden layer, feed-forward network that 
uses radially symmetric basis functions in the hidden layer, see, J. 
Moody, and C. Darken, 1989, Fast Learning in Networks of Locally-Tuned 
Processing Units, Neural Computation 1, 281-294. The RBFN is "trained" to 
approximate the unknown multivariate nonlinear target function using a 
weighted sum of basis functions. The method of the present invention uses 
the values of seismic attributes and wellbore properties, averaged about 
the intersections to "train" the network. 
A hybrid learning procedure is used to train the ANN. First, the mean 
vectors of the RBF nodes are found using the K-means clustering algorithm 
(Spath, 1980). The cluster analysis yields cluster-averaged values and 
widths. The cluster width is the distance between the mean vector and the 
mean vector of the nearest cluster. Each cluster corresponds to a basis 
function that occupies a node in the hidden layer of the ANN. The width of 
each basis function is by default the width of the corresponding cluster. 
However, the nearest neighbor distance is multiplied by a user controlled 
overlap factor, in order to form a smooth contiguous interpolation over 
the input space. The approximation learned by the RBFN is given by: 
##EQU3## 
where r is the residual property to be estimated. A is the attribute 
vector, n is the number of hidden nodes, w.sub.j is the weight associated 
with the j-th hidden node, Aj is the mean-cluster averaged attribute 
vector, and .sigma..sub.j is the extent of the response of the basis 
function. n and .sigma..sub.j are found in the cluster analysis but may be 
reset by the user. The weights w.sub.j are found using singular value 
decomposition to optimally fit the linear combination of basis functions 
to the residuals. Eventually, the basis function widths are determined 
iteratively by minimizing the approximation error. 
This local nature of the fit makes the solution generated by the network 
clearly understood, unlike some other network models which follow a 
"black-box" approach with little user control. In addition, a fast 
training procedure is available to determine the network parameters. 
As can be seen from FIG. 5, seismic attributes are the input layer. The 
hidden layer of a lesser number of nodes represents a set of Radial Basis 
Functions whose parameters are determined according to the well data. The 
output layer has a single node for the reservoir property which is 
predicted. The network parameters are found using the well data for 
training. 
4.) Calculation of reservoir properties 
After training, the network is used between wells for predicting reservoir 
properties where only seismic data exists. That is, the reservoir property 
at a location between existing oil wells is calculated using the trained 
ANN. The seismic attributes at the location(s) of interest is input to the 
trained ANN, with the reservoir property output (c.f. FIG. 5). 
5. Residual correction and Confidence estimation 
Reasonable calibration functions do not fit the intersection data exactly, 
implying that the estimates do not agree exactly with the borehole 
measurements. Often, an exact agreement will be a requirement, and may be 
handled by a residual correction. Geostatistical methods such as cokriging 
or gridding the differences between the calibrated attributes and the 
measured property are used to produce a seismic guided estimate that 
complies with the well data. See, G. Matheron, 1970, La theorie des 
variables regionalisees et ses applications, Tech. Rep. Fascicule 5, Les 
cahiers du centre de Morphologie Mathematique de Fontenebleau, Ecole 
Superieure des Mines de Paris and A. G. Journel and C. J. Huijbregts, 
1978, Mining Geostatistics, Academic Press. These same geostatistical 
methods are used to estimate the confidence in the prediction of the 
reservoir property. 
EXAMPLES 
The following examples were performed on a reservoir modeling workstation, 
where functionality corresponding to the present method is implemented. 
1. Porosity, impedance, velocity, and instantaneous frequency. 
Rock physics predicts that porosity is related to velocity, acoustic 
impedance, and petrophysical components such as clay content, see, D. 
Marion, A. Nur, H. Yin, and D. Han, 1992, Compressional Velocity and 
Porosity in Sand-clay Mixtures: Geophysics 57, 554-563. A velocity model 
is required for imaging seismic data, and can also be used for estimating 
porosity. Acoustic impedance is calculated by inversion programs, and can 
be used in addition to or instead of velocity. Both velocity and acoustic 
impedance exhibit high significance to porosity (FIG. 6). The porosity 
values in FIG. 6 are purely from log data. The velocity (AVGV) and the 
acoustic impedance (Avg.sub.13 Acoustic.sub.13 Impedance) are calculated 
purely from seismic data. Hence, the significance numbers in FIG. 6 are 
indicative of the quality of the data acquisition, processing, and 
interpretation, in addition to the rock physics aspect of whether porosity 
is related to velocity and acoustic impedance. It is reassuring to see 
that acoustic impedance based on surface seismic data (called Avg.sub.13 
Acoustic.sub.13 Impedance in FIG. 6) is indicative for the acoustic 
impedance based on borehole data (AIMP in FIG. 6). 
The petrophysical composition is an important parameter in the 
porosity-impedance relations. One difficulty is in estimating the 
petrophysical composition. For example, volume of clay (shaliness, or VCL 
in FIGS. 6 and 4) and volume of dolomite (VDOL). Sometimes, petrophysical 
heterogeneity affects the Instantaneous Frequency and the Reflection 
Heterogeneity seismic attributes, because clay lenses or dolomite layers 
cause a high frequency reflectivity series. In FIG. 2, the volume of clay 
log (VCL) shows what looks like shaley lenses or layers. Indeed, the cross 
plots in FIG. 6 show a trend of increased instantaneous frequency and 
heterogeneity with increased volume of clay and dolomite. 
FIGS. 6(a) and (b) show cross-plots leading to porosity estimation. In FIG. 
6(a) porosity and acoustic impedance are inverted from seismic data; the 
trend of higher porosity with lower acoustic impedance is predicted by 
rock physics theory and laboratory measurements. FIG. 6(b) illustrates 
porosity and average velocity based on moveout velocity analysis. FIG. 
6(c) shows a test for the quality of the seismic derived acoustic 
impedance; comparing the borehole based values to the surface based values 
of the same property. FIG. 6(d) is a test for the quality of the velocity 
estimation; comparing the average interval velocity from moveout analysis 
to the average sonic travel time-the higher the velocity, the shorter the 
travel time. FIG. 6(e) plots volume of clay and instantaneous frequency; 
high clay content values come from shale lenses that increase the 
instantaneous frequency. FIG. 6(f) compares volume of dolomite and 
heterogeneity; high dolomite content value indicate more dolomite layers 
which increase the Reflection Heterogeneity attribute. 
For comparison, estimates of porosity with and without seismic guidance are 
presented in FIGS. 7(a) and (b). FIG. 7(a) shows porosity estimation 
guided by acoustic impedance and volume of clay, using linear calibration 
while FIG. 7(b) shows porosity mapped from well logs without seismic 
guidance. Estimates in FIG. 7(a) and (b) agree exactly on the wells, but 
are quite different away from the wells. As can be seen, the FIG. 7(a) 
depiction of porosity with seismic guidance computed with the present 
method is qualitatively superior than FIG. 7(b). 
2. Saturation and depth. 
The depth map of a particular geological layer can be treated as an 
attribute. Such depth shows a correlation to water saturation, which is 
driven by gravity mechanisms (FIG. 8). FIG. 8 plots water saturation (SW) 
and depth. Within a hydro-carbon bearing reservoir component, the depth is 
significant for water saturation, as more water are in the deep and 
hydrocarbons on top. Arguably, one can see a water cut at about 3100 
meters. One rarely sees a water cut on a single well water saturation log. 
However, the multi-well cross plot displays water saturation in an 
interval that spans nearly 200 meters, while a single well crosses a 
reservoir component in 5-40 meters as can be seen in FIG. 10. 
Water saturation mapping can be guided by depth as shown in FIG. 9. FIG. 9 
illustrates water saturation estimation where FIG. 9(a) is guided by depth 
using an Artificial Neural network for nonlinear calibration and 9(b) is 
without seismic guidance. Estimations shown in FIG. 9(a) and (b) agree 
exactly on the wells but are quite different away from the wells. AVO 
indicators may be effective to estimate Gas saturation in a manner similar 
to FIG. 9. 
3. Thickness and amplitude. 
In thick reservoirs, the thickness can be obtained from tracking 
reflections. In thin reservoirs, reflection amplitude is affected by 
thickness tuning. See, A. R. Brown, R. M. Wright, K. D. Burkarl, and W. L. 
Abriel, 1984, Interactive seismic mapping of net producible gas sand in 
the gulf of Mexico, Geophysics 49, 686-714. To use thickness tuning, the 
reflection amplitude is used as a seismic attribute, and the well measured 
thickness is used as the reservoir property to be mapped. A cross plot of 
thickness versus amplitude is shown in FIG. 10. In FIG. 10, the amplitude 
is affected by the thickness because of interfering reflections from the 
top and the bottom of a component. 
4. Permeability and viscosity. 
Fluid and gas transmissibility are important reservoir properties, but 
seismic data are not directly affected by permeability and viscosity. 
Permeability is often proportional to porosity and this is, perhaps, why 
it seems to be related to seismic velocity. Seismic attenuation will be a 
useful attribute for the estimate of permeability distributions, see, N. 
Akbar, J. Dvorkin, and A. Nur, 1993, Relating P-wave Attenuation to 
Permeability, Geophysics 58, 20-29. When well log and core sample 
estimates of permeability are available, an effective match with seismic 
alternation is believed possible. 
5. Dip and Azimuth. 
Attribute calibration offers an opportunity to compare estimations of dip 
and azimuth based on wellbore data to those based on surface seismic data. 
Dip and azimuth estimated from pure seismic data (by tracking a 
reflection) versus those estimated purely from wellbore data (by tracking 
resistivity with a formation microscanner) are shown in FIG. 11. In FIG. 
10, the amplitude is affected by the thickness because of interfering 
reflections from the top and the bottom of a component. Unfortunately, the 
borehole dips and azimuths were acquired in only 4 wells which do not 
offer a sufficient sample, except to show that the results have the right 
order of magnitude for the azimuth, and that the dips from surface seismic 
data are underestimated, which might be due to underestimation of seismic 
velocity at the wells AGATE and SAPPHIRE. 
6. Porosity Comparison. 
The improvement in the reservoir property estimation by using seismic data 
is illustrated in FIG. 12. FIG. 12 illustrates the results of a controlled 
study where the porosity estimations with and without seismic guidance are 
illustrated. The control wells (not used in the estimations) are in 
yellow. Some wells are assigned as a validation group, then estimated 
porosity and Water Saturation without the validation wells, with and 
without seismic guidance, and compared seismic guided and unguided 
estimates to the measured values in the validation wells. Five examples 
were made that differ in the wells assigned as control wells. 
The improvement in the accuracy of the estimates varied from -3% (the 
estimates without seismic guided were slightly more accurate) to a factor 
of 2.5 reduction in average error. The results in FIG. 12 indicate a 
significant improvement in the quality of the property estimates, when 
seismic data are used in accordance with the present invention. In this 
case the average absolute value of the error in estimating porosity was 
twice smaller when acoustic impedance was used to guide the estimate. The 
average error in estimating water saturation was 2.5 times smaller when 
the depth was used. 
CONCLUSION 
Better estimates of reservoir properties using seismic data are obtained 
using the method of the present invention. Statistical correlations 
between seismic attributes and log properties are identified, estimates of 
linear or nonlinear functional relationships between attributes and 
properties are made, an ANN is used for the nonlinear relationships, and 
these functional relationships are used to predict reservoir properties. 
Seismic guided estimates have higher resolution and predict reservoir 
properties more accurately than estimates based on interpolation of well 
measurements on structures estimated from seismic data. This has been 
verified in a controlled study, in which some borehole data were withheld 
in the process and were later compared to the estimations with and without 
seismic guidance. For example, when acoustic impedance inverted from 
seismic data was used to guide estimation of porosity, the error was cut 
in half. 
All patent references cited herein are incorporated by reference. All 
nonpatent references cited herein are incorporated by reference for 
background.