Abstract: Adaptive Stratigraphic Forward Modeling
: Making Forward
Modeling
Adapt to Conditional Data
DUAN, TAIZHONG, Department of Geology and Geological Engineering, Colorado School of Mines; CEDRIC GRIFFITHS, National Center for Petroleum Geology and Geophysics, University of Adelaide; TIMOTHY CROSS, and MARGARET LESSENGER, Department of Geology and Geological Engineering, Colorado School of Mines
Quantitative stratigraphic models are in more common use for predicting stratigraphic attributes in locations where observations and control points are lacking. The general philosophy behind these predictions is that if the models match stratigraphic attributes in places where there are observations for comparison with model output, then model predictions in other places also are probably good.
These quantitative models can be categorized into three groups:
(1) geostatistical models; (2) forward models; and (3) syntactic or
rule-based models. Although geostatistical models may be
conditioned to honor observations, they cannot overcome the
fundamental limitation that the simulations are based purely on
mathematical descriptions, rather than some understanding and
description of the stratigraphic process-response system. With a
given observed dataset, a geostatistical model may produce a
simulation which is not reasonable geologically, even if
mathematically correct. Forward modeling
simulates stratigraphic
process-response relations directly. However, current versions of
forward
modeling
match observations qualitatively through
trial-and-error simulations. Rule-based approaches can provide a
more general and flexible framework for stratigraphic simulation.
In rule-based models, empirical rules can substitute for
mathematical equations when quantitative expressions are unknown,
and non-numerical stratigraphic attributes, such as lithology, can
be treated symbolically rather than numerically.
By combining the strengths of these model types, we can build an
adaptive stratigraphic forward modeling
system, which uses a
process-response oriented stratigraphic forward model, a syntactic
model for comparison and an optimization genetic algorithm that
modifies values of forward model parameters to achieve better
matches between simulations and observed stratigraphy. The
geological process-based forward model produces synthetic
stratigraphy as output; the comparison technique measures the
difference between the forward model output and the observed
dataset; and the optimization genetic algorithm as adaptive
simulation technique automatically adjusts the suitable parameters
of the forward model, so that the system can produce a output
stratigraphy that honors or closely matches the observed
dataset.
Experimentation of the method on synthetic stratigraphy as the dataset shows that the system tries to approximate the original parameters, and when converged, it almost recovers them. Application of the method to a real stratigraphic example as the dataset is in progress.
AAPG Search and Discovery Article #90937©1998 AAPG Annual Convention and Exhibition, Salt Lake City, Utah