Seismic, well, and outcrop data provide an abundance
of information that explorationists use to determine
the quality and viability of these two constituents.
Assessing the hydrocarbon charge risk, however, is not
a routine in all oil companies. This important function
is usually limited to a few geologists or a special
group.The reason for the oversight of such an important
part of the puzzle is two-fold: terminology and an understanding
of the science. As geologists, we have all taken courses
in structure, stratigraphy, and petrology. These courses
qualify us to make an informed judgment about the trapping
configurations and the reservoirs that may be found
in a basin. However, not many of us have taken courses
in organic geochemistry, rock and fluid dynamics, and
thermodynamics. These things are at the core of a hydrocarbon
charge risk evaluation. The terms are unfamiliar and
we do not have a good handle on these aspects of geoscience.
Basin Hydrocarbon Charge Risk
are three risks that must be addressed prior to
drilling a well: trap and seal, reservoir, and
hydrocarbon charge. These three components must
be in-place for an exploration, or production
well to be successful. Assessing the trap and
reservoir risks are routine tasks of earth scientists
in all hydrocarbon exploration companies.
Is Basin Modeling?
basin modelling has been used by the oil and gas industry
to study the physical and thermal histories of basins.
The models lead to an estimation of the timing of
hydrocarbon generation and expulsion.
recent years, however, basin modelling has expanded
to include evaluation of secondary migration and trapping
of hydrocarbons within basins.
Is Hydrocarbon Charge?
and most usefully, hydrocarbon charge is the filling,
with hydrocarbon fluids, of traps (prospects) within
trapping configurations and reservoir characteristics
of a prospect may be very local in scale, the hydrocarbon
charge of a prospect may involve a significant portion
of the basin. All hydrocarbons that migrate to a prospect,
in physical and temporal space, make up the hydrocarbon
charge of that prospect. A prospect may be gas charged,
oil charged, under-charged, or the area may have no
Should Basin Modelling Be Used?
is a very simple scenario. A geologist and geophysicist
look at a seismic line in a frontier basin and see
an undrilled structure. They are able to map its areal
extent and establish an area of closure. No faulting
is evident. The risk of trap failure is low. The seismic
data gives them some information about the lithology
and thickness of the potential reservoir sections.
The risk of reservoir failure is moderate. Through
a simple volume calculation they determine that the
structure can hold a commercial quantity of hydrocarbons.
Should a well be drilled? A comprehensive evaluation
of the hydrocarbon charge risk using basin modelling
could possibly give the answer.
is how. The only data available to the basin modeller
in the above scenario are the seismic data. From these
data we are able to construct a 'guess' about the
geologic development of the basin (evidence of erosions,
age of sediments, tectonic history, etc.). We call
this a conceptual model. We may also determine, using
seismic stratigraphy, which seismic intervals represent
periods of geologic time favorable for the development
of a source rock. We add these 'potential source rocks'
to our conceptual model. We make use of modern analogs
to add a thermal history to the conceptual model.
We submit the conceptual model to a computer program
(BasinMod 1-D) that mathematically reconstructs the
geologic history of the basin applying common physical
laws to the basin as it develops. The results (a mathematical
model) tell us about the thermal maturity of the basin
1: BurHist.gif), if our 'potential source
rock' generated and expelled hydrocarbons, when it
generated and expelled hydrocarbons, and the type
of hydrocarbons generated and expelled. So, returning
to our scenario, the basin modelling results may tell
us that the basin is thermally immature and any 'potential
source rock' has not generated hydrocarbons. They
may tell us that the basin is thermally mature and
a potential source rock generated and expelled hydrocarbons
. . . millions of years before the trap was formed.
They may tell us that the early charge, generally
oil, occurred prior to the structuring event, however,
the structure was in place to receive a late gas charge.
Regardless of the results, we have one more piece
of the puzzle to use in the overall risk assessment.
if well data are present?
the previous scenario, no well data were available.
The conceptual geologic model we built for our basin
is just one possible model out of many. For example,
ages may be incorrect, the amount of eroded section
may be wrong, and so on. Our model results, in the
previous case, come from creating best and worst case
scenarios. Basin modelling is at its peak when well
data are present. Why? Because now we can compare
our mathematical results to real data taken from the
well. For example, we can compare our calculated temperature
vs. depth to measured bottom-hole temperatures
2: TempDep.gif). If the two do not compare
favorably, then we know our conceptual geologic model
does not adequately represent the real world. In this
case, we rethink, and rebuild, our conceptual model
and compare the new mathematical results with the
measured well data. Depending on the amount of measured
data available from the well, the process of comparison-rethink-rebuild
may occur several times during the course of an evaluation.
This optimization of the mathematical model is called
calibration and it is the most important aspect of
basin modelling. When we have constructed a conceptual
geologic model yielding mathematical results that
compare favorably with many independent groups of
measured well data, then our conceptual model is an
accurate representation of the real basin. We can
then use this model as a template for looking at other
areas of the basin where wells are not present and
maintain a relatively high degree of accuracy. As
more wells are drilled in the basin, new data become
available, more calibration can occur, and the conceptual
model is further refined.
type of well data can we use for calibration?
data used for calibration in basin modelling can be
classified into three categories: Physical, Thermal,
and Geochemical. Physical data include porosity, permeability,
pore pressure, mud weights, etc. Thermal data include
temperature data from BHT's, DST's, RFT's and the
like. Geochemical data include pyrolysis (Rock-Eval)
data, maturity data (Ro, TAI, SCI, etc.), and biomarker
There Any Other Geochemical Data we Can Use in Basin
recent years, new techniques have emerged that tell
us something about how and when various types of kerogens
convert to hydrocarbons. The branch of science that
deals with this is called kinetics. Each kerogen has
a unique combination of activation energy levels at
which it 'transforms' into hydrocarbons. It serves
our purpose to think of the varying energy levels
as changes in temperature. As the temperature increases,
the energy level increases. Generally, Type I kerogens
have a very narrow energy range in which they transform,
whereas, Type III kerogens have a much broader range.
The importance of kinetics to basin modelling is two-fold.
We can better model timing of hydrocarbon generation
and we can begin to investigate types and volumes
of hydrocarbons generated.
of Hydrocarbon Generation
basin modellers used thermal maturity based on vitrinite
reflectance to estimate the timing of oil and gas
generation. We have all heard the statement that a
rock is in the 'oil window' or 'gas window'. However,
two problems exist here. First, although vitrinite
reflectance is a measurable quantity, the relationship
between vitrinite reflectance data and oil and gas
generation zones is basically empirical and may not
be the same in every basin. In fact, 'oil window'
and 'gas window' can be somewhat misleading because
a source rock can have a vitrinite reflectance value
that puts it in the 'oil window' , however it will
not be generating oil. Vitrinite reflectance data
are a one way street. The vitrinite reflectance of
a rock can never decrease. An analogy would be our
own chronological age. We begin as children ('immature'),
continue through adulthood ('mature'), and wind up
in old age ('overmature'). The problem is we can take
something that is overmature and put it in an immature
surrounding and it is still overmature. To continue
with the analogy; we walk into a kindergarten class
room and realize the setting is for children. If a
90 year old man is sitting in the class, do the surroundings
make him chronologically younger? No, he is still
90. In a like fashion, a source rock with a reflectance
of .9 Ro (in the oil window), but at the surface,
still has a reflectance of .9 Ro. However, it is not
actively generating hydrocarbons.
second problem in using vitrinite reflectance data
to estimate timing of hydrocarbon generation is that
it is independent of rock or kerogen type. It is strictly
a time-temperature index. A prodelta shale with a
Type III kerogen can have the same maturity (oil window/gas
window) as a basin floor shale with a Type II kerogen.
Does this mean they are both producing hydrocarbons?
Kinetics allows us to deal with these two problems
to better model the timing of generation of hydrocarbons.
First, kinetic properties of kerogens can be measured
in a laboratory and, unlike vitrinite reflectance
data, tell us the distribution of activation energies
at which the kerogens transform to hydrocarbons. Second,
the distribution of activation energies are unique
to each kerogen. Therefore, two kerogens may behave
differently at the same energy level. We return to
our earlier analogy of the 90 year old man to demonstrate.
The 90 year old man is now joined by a second 90 year
old man. The first 90 year old man is weak and stares
through feeble eyes as the second 90 year old man
jogs into the room. The second man runs around the
room several times, does 50 one-armed push-ups, then
jogs out. Both men are the same maturity (90 years),
but the second man can do more at that maturity than
can the first man. The Type III kerogen in the prodelta
shale and the Type II kerogen in the basinal shale
are at the same maturity, however the Type II kerogen
may have completed its transformation to hydrocarbons
while the Type III kerogen is only partially complete.
modellers build a specific kerogen, or kerogens, into
the conceptual model. We then use the measured kinetic
parameters for the kerogens to model the transformation
history of each individual source rock. This is a
more accurate methodology for the estimation of the
timing of hydrocarbon generation than using maturity
(Figure 3: CumHc.gif 600x600 pixels).
and Volume of Hydrocarbons
coupled with other geochemical measurements, provides
the ability to predict the type and volume of hydrocarbons
generated. Geochemists have known for some time that
different types of kerogens produce different types
of hydrocarbons. Generally, a Type I kerogen is a
prolific oil producer while a Type III kerogen produces
less oil and more gas. Also, generally, the API gravity
of a hydrocarbon fluid increases from early generation
to late generation. This is because the earliest fluids
generated tend to be the heaviest compounds (C15+)
and subsequent generative products become lighter
until we end with the lightest, methane. Whereas kinetics
data can give us the information about the energy
level needed to transform the kerogen into oil and
gas, or, into the different components (C15+, C6-C15,
C2-C5, and C1), Rock-Eval data tells us about the
generative potential of this kerogen. The final piece
of geochemical data needed to complete the evaluation
is total organic carbon (TOC). This tells us how rich,
in organic material, is the rock. Simply put, Rock-Eval
and TOC data tell us how much we are going to get;
kinetics tells us how we are going to get it. These
data, along with density information of hydrocarbon
fluids, allow the basin modeller to predict the type
and volume of hydrocarbons available in different
parts of a basin at any time of its development.
Dimensional Basin Modelling
basin modelling is most useful in providing information
about the timing of hydrocarbon generation and the
type of products that are generated. Two-dimensional
basin modelling is most useful in providing information
about the petroleum system. The ability to model a
series of cross-sections in a basin allows, for the
first time, some insight into how hydrocarbon fluids
migrate and are trapped.
data are similar between the two model types. However,
two-dimensional modelling requires incorporation of
lateral changes in rock, source rock, and thermal
properties. Lateral changes in these properties mean
that each point along the cross-section undergoes
a different burial and thermal history. Porosity of
a formation in one area might be reduced due to normal
compaction. The same formation a few kilometers away
might have a lower permeability which does not allow
the pore fluids to escape rapidly. This maintains
porosity. Changing depths and thermal properties mean
the source rock will generate and expel hydrocarbons
at different times. All these factors control when
and where hydrocarbons move.
dimensional basin modelling also introduces a new
calibrant; the present distribution of hydrocarbons
in the basin. Each of the critical components, source
rock, migration paths, reservoirs, seals, and traps
have undergone a geologic history that yields the
petroleum distribution we see today. When we explore,
we see a snapshot of a dynamic petroleum system. Some
of the hydrocarbons we identify from well data as
"shows" may be in the process of migrating
to another part of the basin. We caught them in transit.
Even trapped hydrocarbons are only temporarily stalled.
Structural trap failure, seal failure, and reservoir
destruction have been well documented. We are lucky
when we find a situation where all the components
in this dynamic system are, for the present, in place.
The results of a two dimensional modelling exercise
should show hydrocarbons in traps and reservoirs where
they are known to exist. If not, then some aspect
of the geologic model is incorrect. The solution is,
like one-dimensional modelling, to rethink and rebuild
our geologic model. In this way we will develop risk
parameters for the basin; not only for hydrocarbon
charge, but also for reservoirs, seals, and traps.
more information on Basin Modelling,
check out the Platte
River Associates, Inc. home page.