Borehole Acoustics and Logging
and
Reservoir Delineation
Consortia
Annual Report
2000
Earth Resources Laboratory
Department of Earth, Atmospheric, and Planetary Sciences
Massachusetts Institute of Technology
Cambridge, MA 02139
Copyright © 2000 Massachusetts Institute of Technology
Earth Resources Laboratory
Copying is permitted only for internal purposes of the sponsors of the
M.LT. Borehole Acoustics and Logging/Reservoir Delineation Consortia
This report was typeset at ERL in Computer Modern Roman using 'lEX.
Borehole Acoustics and Logging
and Reservoir Delineation
Consortia
Annual Report
2000
Principal Investigator
M. N. Toks6z
Contributors
A. Al-Dajani
D. R. Burns
R. Coates
M. S. Edie
R. Greaves
J. Haldorsen
F. J. Herrmann
H.Hu
X. Huang
J. Kane
M. L. Krasovec
R. L. Mackie
M. 1. Marhoon
H. M. Mustafa
E. L. Nebrija
J. F. Olson
B. Paulsson
W. L. Rodi
M. M. Saggaf
M. N. Toks6z
R. M. Turpening
J. Wang
K. Wang
Z. Zhu
Report Editors
D. R. Burns
E. A. Henderson
Table of Contents
1. EXECUTIVE SUMMARY
by Daniel R. Burns
Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ..
Facies Analysis and Reservoir Property Estimation From Seismic and Well Log
Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Fluid Flow Characterization and Property Estimation . . . . . .
Anisotropy Estimation From Borehole and Surface Seismic Data
I-I
1-1
1-2
1-3
2. ESTIMATION OF RESERVOIR PROPERTIES FROM SEISMIC
DATA BY SMOOTH NEURAL NETWORKS
by Muhammad M. Saggaf, M. Nafi Toksoz, and Husam M. Mustafa
Abstract . . . . . . . . . . .
2-1
Introduction. . . . . . . . . . . . . . . .
2-2
Neural Networks Structure
2-4
Regularized Back-Propagation Networks
2-6
Radial Basis Networks . . . . . . . . . .
2-12
Input/ Output Architecture . . . . . . .
2-14
Relation to Model-Based Inversion and Global Methods
2-16
Summary and Conclusions.
2-18
Acknowledgments.
2-20
References .
2-21
Figures
2-23
3. APPLICATION OF SMOOTH NEURAL NETWORKS FOR INTERWELL ESTIMATION OF POROSITY FROM SEISMIC DATA
by Muhammad M. Saggaf, M. Nafi Toksoz, and Husam M. Mustafa
3-1
Abstract
.
3-2
Introduction. . . . . . . . . . . . . . .
Data Description and Pre-Processing.
3-4
Cross-Validation Results ..
3-5
Final Porosity Distribution
3-10
3-10
Multi-Attribute Analysis ..
Summary and Conclusions .
3-13
3-15
Acknowledgments. . . . . .
v
References .
Figures ..
3-16
3-18
4. SEISMIC FACIES CLASSIFICATION AND IDENTIFICATION BY
COMPETITIVE NEURAL NETWORKS
by Muhammad M. Saggaf, M. Nafi Toksiiz, and Maher 1. Marhoon
Abstract . . . . . . .
4-1
Introduction. . . . .
4-2
Method Description
4-3
4-7
Confidence Measures
Synthetic Data Example.
4-8
4-11
Application to Field Data
Conclusions . . . .
4-15
Acknowledgments.
4-16
References .
4-17
4-18
Appendix
Figures ..
4-21
5. 3-D GEOSTATISTICAL SEISMIC INVERSION WITH WELL LOG
CONSTRAINTS
by Jonathan Kane, William Rodi, and M. Nafi Toksiiz
Abstract . . . . . . . . . . . . . . .
Introduction. . . . . . . . . . . . .
Stochastic Description of Geology.
Bayesian Inversion . . . . . . . . .
Kriging and Seismic Inversion on Synthetic Data
Application to Texaco Data Set .
Conclusions . . . .
Acknowledgments.
References .
Figures
5-1
5-2
5-3
5-5
5-10
5-12
5-13
5-14
5-14
5-15
6. SCALING AND SEISMIC REFLECTIVITY: IMPLICATIONS OF
SCALING ON AVO
by Felix J. Herrmann
Abstract . . . . . . . . . . . . . . . .
6-1
Introduction. . . . . . . . . . . . . .
6-1
Seismic Reflectivity Imaging Method
6-3
6-7
Imaged Seismic Reflectivity Versus the Wavelet Transform.
AVO Analysis. . . . . . . . . . . . . . . . . . .
6-10
6-13
Scale Renormalization by Monoscale Analysis .
6-17
Reconstruction
Examples . . . . . . . . . . . . . . .
6-17
VI
Conclusions . . . .
Acknowledgments.
References .
Figures
6-18
6-19
6-19
6-22
7. SEISMIC FACIES CHARACTERIZATION BY SCALE
ANALYSIS
by Felix J. Herrmann
Abstract. . . . . . . . . . . . . . .
Introduction. . . . . . . . . . . . .
Basic Concepts and Methodology .
Application to Well Data ..
Application to Seismic Data. . . .
Well Tie. . . . . . . . . . . . . . .
Facies Categorization by Sharpness Characterization
Conclusions . . . .
Acknowledgments.
References .
Figures
8. IMAGING WITH REVERSE VERTICAL SEISMIC PROFILES
USING A DOWNHOLE, HYDRAULIC, AXIAL VIBRATOR
by Mary Krasovec, Roger Tnrpening, Bjorn Paulsson, Jakob Haldorsen, Richard Coates, and Robert Greaves
Abstract. . . . .
Introduction. . . . . . . . . . . .
Data Processing
Imaging Results and Discussion .
Conclusions . . . .
Acknowledgements
References .
Figures
7-1
7-2
7-3
7-6
7-6
7-7
7-7
7-8
7-8
7-9
7-11
8-2
8-2
8-3
8-4
8-4
8-5
8-6
8-7
9. SHEAR-WAVE REFLECTION MOVEOUT FOR AZIMUTHALLY
ANISOTROPIC MEDIA
by AbdulFattah Al-Dajani and M. Nafi Toksoz
Abstract . . . . . . . . . . . . . . . . . . . . . . .
9-1
Introduction. . . . . . . . . . . . . . . . . . . . .
9-2
Analytic Approximations of Reflection Moveout .
9-3
Conclusions . . . .
9-10
Acknowledgments.
9-11
References .
9-12
Figures
9-13
vii
lO.EFFECTS OF FORMATION STRESS ON LOGGING
MEASUREMENTS
by Xiaojun Huang, Zhenya Zhu, M. Nafi Toksoz, and
Daniel R. Burns
Abstract
. 10-1
Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-1
Effects of Formation Stresses on Velocity Field Around A Borehole: Theory 10-2
10-6
Borehole Model and Measurements .
10-8
Conclusions . . . .
10-9
Acknowledgments.
10-10
References .
10-11
Figures
.
11.THE EFFECT OF IMAGE RESOLUTION ON FLUID FLOW
SIMULATIONS IN POROUS MEDIA
by Margaret S. Edie, John F. Olson, Daniel R. Burns, and M. Nafi
Toksoz
Abstract . . .
Introduction.
Methods ..
Results ..
Discussion .
Conclusions
Acknowledgments.
References .
Figures
.
11-1
11-2
11-2
11-3
11-4
11-5
11-5
11-6
11-7
12.AEOLIAN AND FLUVIAL DEPOSITIONAL SYSTEMS DISCRIMINATION IN WIRELINE LOGS: UNAYZAH FORMATION,
CENTRAL SAUDI ARABIA
by AbdulFattah Al-Dajani, Daniel Burns, and M. Nafi Toksoz
Abstract
.
12-1
12-2
Introduction. . . . . .
12-3
Study Area and Data
12-4
Approach
.
12-4
Why Neural Networks?
12-6
Supervised Neural Network via Competitive Learning
12-6
Data Analysis . . . . . . . .
12-7
Power-Law Analysis . . . .
Discussion and Conclusions
12-8
12-9
Acknowledgments.
12-10
References .
Figures
.
12-12
viii
l3.SIMULATION OF AN ACOUSTICALLY INDUCED ELECTROMAGNETIC FIELD IN A BOREHOLE EMBEDDED IN A POROUS
FORMATION
by Hengshan Hu, Kexie Wang, and Jingnong Wang
Abstract
.
13-1
Introduction
.
13-2
13-2
Formulation . . . . . . . . . . . . . . . . . . . . . . . . .
Converted Electromagnetic Field for a Given Formation
13-3
Influence of Formation Parameters on Acoustoelectric Conversion.
13-7
Discussion and Conclusions
13-10
Acknowledgments
.
13-11
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
13-12
Appendix A. Pride Theory for a Fluid-Filled Porous Formation
13-13
Appendix B. The Acoustic and Electromagnetic Field Expressions
13-15
Appendix C. The Boundary Conditions .
13-17
Appendix D. Expressions for 1>, 0', and L
13-19
Figures
.
13-21
l4.NONLINEAR CONJUGATE GRADIENTS ALGORITHM FOR
2-D MAGNETOTELLURIC INVERSION
by William L. Rodi and Randall L. Mackie
Abstract
.
14-1
Introduction
.
14-2
Problem Formulation. . .
14-4
Minimization Algorithms
14-6
Numerical Experiments .
14-17
Discussion and Conclusions
14-21
Acknowledgments
.
14-23
References . . . . . . . . . .
14-24
Appendix: Jacobian Computations
14-27
Figures
.
14-31
ix
x
EXECUTIVE SUMMARY
Daniel R. Burns
Earth Resources Laboratory
Department of Earth, Atmospheric, and Planetary Sciences
Massachusetts Institute of Technology
Cambridge, MA 02139
INTRODUCTION
During the past year we have continued to make progress on the difficult problems associated with extracting geological information from geophysical data sets. Our
research results focus on three major areas of characterization of reservoir heterogeneity and its effects on fluid flow: (1) facies analysis and reservoir property estimation
from seismic and well log data, (2) fluid flow characterization and property estimation,
and (3) anisotropy estimation from borehole and surface seismic data. Our theoretical
work continues to be supported by laboratory experimentation (in the large sediment
dynamics tank, as well as the borehole scale model facilities) and field data analysis.
Finally, because of our interest in data inversion for reservoir properties, we include
a paper from Rodi and Mackie on a nonlinear conjugate gradient inversion method.
They show results applied to magnetotelluric data, although the method can also be
applied to other types of data.
The following sections provide a brief summary of the papers included in this report.
FACIES ANALYSIS AND RESERVOIR PROPERTY ESTIMATION
FROM SEISMIC AND WELL LOG DATA
In a series of papers, Saggaf et al. apply smooth neural network methods to the estimation of reservoir properties (such as porosity) and the identification of facies variations
from seismic data. They develop a methodology based on regularized back propagation
and radial basis networks that are then applied to a 3-D data set from the Ghawar
Field in Saudi Arabia. The training data set consisted of porosity logs from 30 wells
in the area and the seismic traces nearest to those wells. Resnlting porosity estimates
using this method are in agreement with the geological models for the field and provide
significantly enhanced resolution of porosity estimates in the interwell regions. Sys1-1
Burns
tematic tests of the method indicate that the accuracy remained consistent as network
parameters were varied, while traditional back propagation method results were sensitive to parameter selection. In another paper, Saggaf et al. apply a method based on
competitive neural networks to the problem of classifying different facies from seismic
data. Their results indicate that such methods, used either in the supervised (using
well log data to identify facies) or unsupervised mode, can provide stable, consistent
estimates of the spatial distribution of geologic facies based on seismic characteristics.
Quantitative confidence ranges are also output as a measure of accuracy.
Dajani et al. look at facies identification using competitive neural networks on well
log data from a field in Saudi Arabia. Results indicate that the method can differentiate
between aeolian and fluvial sand deposits in these wells.
Kane et al. apply kriging to well log data to obtain an estimated velocity field and its
covariances for use as a priori constraints on the inversion of seismic traces for interval
velocity values. The method is applied to synthetic and field 3-D data sets with some
success. Cross validation tests (comparison of inversion results to a 'blind' well log in
the field) indicate that the method is quite robust, although some questions remain
concerning the extraction of the seismic wavelet and the estimation of the velocity
covariance function from the field data set.
Herrmann presents two papers which look at the nature of interfaces that generate
seismic reflections. He develops a 'monoscale' analysis method that defines the sharpness
of an interface by its fractional degree of differentiability. This approach allows us to
estimate the order of the singularity related to the interface at the scale of the seismic
wavelet. This estimate provides information, based on the reflected waveform, about
the type of geologic interface which can then be related to environments of deposition
and facies distribution. Initial tests of the method on 2-D seismic sections indicate that
this attribute is robust and allows for more accurate ties of the seismic data to well
log information. In a second paper, Herrmann shows that if the nature of an interface
changes, due to a facies change for example, the AVO signature can be strongly affected.
He proposes a renormalization method to account for these effects.
Krasovec et al. present the results of a reverse VSP experiment over a pinnacle reef
reservoir in Michigan using a downhole vibrator source and a random deployment of
surface receivers. Depth migrated images indicate that the method greatly improves
resolution over conventional VSP and surface seismic data in this area of highly attenuating near surface glacial till. The use of random receiver locations may be beneficial
in eliminating acquisition footprints in 3-D surveys.
FLDID FLOW CHARACTERIZATION AND PROPERTY
ESTIMATION
Edie et al. investigated the effect of image resolution on fluid flow simulation at the pore
scale. Previous studies that used NMR images of a core sample as input to a lattice gas
fluid flow simulator suggested that the image resolution was not adequate to represent
1-2
Executive Summary
the pore structure. By decimating a fine scale image, however, they show that for many
samples, the fine scale pore structure has some impact on permeability estimates based
on simulation, but the effect is not too large.
In an invited paper, Hu et ai. present a theoretical study of electroseismic effects in
borehole acoustic logging. When the interdependence between porosity, tortuosity, and
permeability is ignored, they find that the ratio of the generated electric field to the
acoustic pressure field is most sensitive to porosity. These results are consistent with
previous studies which suggest that permeability variations may be estimated through
frequency dependent changes in the ratio of the electric field to the pressure field.
ANISOTROPY ESTIMATION FROM BOREHOLE AND SURFACE
SEISMIC DATA
Dajani and Toksoz present a study of the effects of orthorhombic symmetry on shear
wave reflection moveout. They find that in a homogeneous medium the moveout is
purely hyperbolic in the direction normal to the shear wave polarization, while the nonhyperbolic portion of the moveout reaches a maximum along the polarization direction.
This may have a significant effect on shear wave reflection imaging.
Huang et ai. investigate the effect of stress concentrations in the vicinity of a borehole
on acoustic logging measurements. Theoretical and laboratory models suggest that a
combination of monopole and dipole logs will provide the most information about the
intrinsic and stress induced anisotropy in the formation.
1-3
Burns
1-4