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The most widespread
source for subsurface  data is 2-D and, increasingly, 3-D seismic. Data
related to boreholes - such as well logs and rock samples - provide
crucial complementary and calibration parameters. In the past, and even
today, the prevailing approach in the interpretation of these subsurface
data is static. This means that great efforts are made to describe
subsurface structures and property distributions in their present state.
However, understanding and modeling past geological processes that were
responsible for the present status of the subsurface has so far not been
sufficiently emphasized.
In petroleum
exploration and production it is an essential requirement to understand
these past geological processes - especially petroleum generation and
migration - which determine whether or not a trap contains hydrocarbons.
Hence, it is crucial to understand the dynamics of relevant processes
responsible for the present day geological conditions.
As
modeling of geological processes relies entirely on a subsurface
database and related, intelligently structured data archives (often
called data models), it is essential that the numerical simulation is
linked as closely as possible to these data sources. This is easily
achieved by direct binary access to seismic data and interpretation
tools like OpenWorks, GeoFrame, SeisWorks, IESX, etc. It is common
practice to organize and store subsurface data in more or less
sophisticated data archives that can be screened and manipulated
electronically. An electronic data archive enables information to be
exchanged, reviewed, and thereby enriched and updated. Even the most
refined interpretation utilizing advanced interpretation software and
databases, however, produces static information for stratal
terminations, seismic facies, lithofacies and property distributions,
etc. Such static data archives can be brought to life - and at the same
time generate a great deal of added value - by dynamically modeling the
geological processes behind it.
Figure Captions
 Figure
1. Regional scale 3-D basin model through geologic time, with evolving
oil and gas accumulations (green and red). (Courtesy of Norsk Hydro.)
 Figure
2. Close-up of one of the reservoirs. Red vectors indicate free-phase
gas transport (e.g., loss from the reservoir ) in the low-permeability
parts of the model. (Courtesy of Norsk Hydro.)
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Static to
Dynamic Process (Figures 1
and 2)
The conversion of
static data to a dynamic process interpretation starts with a rigorous
analysis of the stratigraphic time record of the sedimentary column and
by assigning absolute ages. In this way an absolute time sequence of
critical geological events is derived and a conceptual geological
process model is created, forming the backbone of the dynamic process
interpretation and the chain of logics for a computer model.
A petroleum system
includes the entire hydrocarbon source, carrier, and accumulation
system, and the goal must be to reconstruct the entire geological
history of a petroleum system, from its origin to the present. The main
focus must be on the location and 3-D configuration of drainage areas
for mature source rocks through time, and on possible migration pathways
to collect the corresponding hydrocarbon charge.
The modeling of the
petroleum system; i.e., the numerical simulation of the relevant
processes, rigorously follows the geological time axis. The principal
concepts and methods of this kind of modeling are well established in
existing basin modeling techniques. It commences with the deposition and
compaction of the oldest stratigraphic units at the bottom of the system
and works its way upward through younger and younger events to the
present day.
The
resulting dynamic modeling requirements mean that our models must be
able to take most important changing factors through geologic time into
account. These include:
-
Changing geometries.
- Multi-dimensional,
non-steady-state thermal histories.
- Overpressures due
to compaction disequilibrium and hydrocarbon generation.
- Changing
hydrocarbon phase relationships as a function of temperature and
pressure.
- Many other
processes.
Software programs
today can provide all of this functionality. Petroleum migration
processes can be modeled in two dimensions (2-D) along geological cross
sections, but any attempt to quantify hydrocarbons in a simulated system
must be based on three dimensional (3-D) data archives and modeling
techniques. The geometric resolution depends on one hand on the quality
and resolution of the data, and on the other hand on such crucial
parameters as the grid density, number of
cells, the computational power, and allowable computing time. Due to the
need to reduce cycle times (i.e., the time between acreage evaluation
and drilling of a successful well) in exploration and the need to run
multiple models to test sensitivities, computing time is an important
issue in petroleum systems modeling. Fast computing times are needed to
model changing configurations of source and migration pathways over
geologic times (i.e., 4-D).
Simulation runs that reconstruct the
geological history of a petroleum system inclusive of multi-phase
migration modeling should typically be performed in several hours on a
normal workstation or workstation cluster. Such “short” processing times
can only be achieved at present with hybrid migration simulators that
enable fully integrated 3-D Darcy flow/flowpath (also called ray
tracing) modeling to be performed. Simulation runs with this technology
not only reconstruct the most likely generation, migration,
accumulation, and spilling history in a petroleum system, but at the
same time show possible weaknesses of the 3-D data base and/or
inconsistencies in the conceptual geological model.
Overpressure zones can be fairly well
predicted by geological process modeling, so the technology can even
help to improve seismic interpretations, for instance, with respect to
selecting the right seismic interval velocities in overpressure prone
regions. The new simulation technology enables regional scale 3-D models
with as many as a million-plus cells - and consequently, very reasonable
resolutions - to be processed within acceptable time spans. It also
reduces the risks associated with upscaling geological models to a point
where oversimplifications can limit their value.
This kind of 3-D modeling can therefore now
be used as a guidance tool and a framework for play and prospect
evaluation throughout an entire exploration campaign. With new data or
insights it can be updated continuously. The great advantage of this
technology is its potential to provide directly and immediately the best
possible understanding of all crucial processes responsible for
petroleum accumulation in a reproducible and quantitative manner.
Geological process modeling, thus, is the logical continuation and
refinement of static subsurface data interpretation. It is the crucial
step from static to dynamic interpretation of subsurface data.
Today a complete array of
technological facilities is already available to extend “classical” but
static subsurface data interpretations into dynamic process modeling in
a sequential manner - firstly seismic interpretation, and secondly
process modeling.
The next step is to
extend the integration of the various technologies and data types to
create even more value by adding synergies. It is the provision and
availability of proper interfaces between the relevant software packages
and intelligent tools interactively to manipulate original
data and results on both sides. This step, without any doubt, will
dramatically accelerate the application of more intelligent (dynamic)
data interpretation tools.
The
cost of this type of dynamic interpretation compares favorably with, for
instance, the cost of sophisticated seismic processing including
attribute analysis, or obviously of drilling dry wells in deep water
environments. All in all, a dynamic interpretation of subsurface data
greatly improves our understanding of crucial geological processes - and
it narrows down the band width of uncertainties. Furthermore, it is the
ideal vehicle to integrate different geoscientific disciplines, to
create real links between exploration and exploitation data archives and
processing tools. The result? Logically organized work flows in
interdisciplinary teams.
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