30 | Halliburton Landmark
By: Duncan Macgregor
The objective of petroleum systems modeling (also known as basin modeling) is to predict source rock
maturity through geological time, and thus assess when, if, and how hydrocarbons were generated
and migrated. This aspect will be emphasized in this article, although it should be noted that a range of
other physical parameters can also be modeled, including pressure and reservoir quality.
Petroleum systems modeling may be performed in 1D, 2D, or 3D, plus the extra dimension of time,
but should begin with the calibration of a data-rich well in 1D. A program of 1D modeling of wells
and pseudo wells (predicted lithological columns) can give a quick, but necessarily rough, view of a
petroleum system. 3D modeling provides a fuller set of products, incorporating the effects of lateral
heat and fluid flow, and a pressure-driven prediction of hydrocarbon migration, but is far more time
consuming. 2D modeling is the intermediate approach, between 1D and 3D, in terms of resource
requirements and the range of products created. The illustrations of each type of modeling given in this
article were produced in Permedia ® petroleum systems modeling software.
TEMPERATURE AND HEAT FLOW
Temperature is a key control on oil generation, and calibration is crucial. This involves determining the
thermal history and rock properties that honor temperature and maturity data from calibration wells. If
calibration well data are unavailable, the geodynamic setting can inform the likely heat flow and history,
and pseudo wells can be created. If the thermal history or the nature of the crust is uncertain, several
different models can be run; however, this increases uncertainty.
Geothermal gradient, as measured over a section of a well, changes according to the ability of different
rock types to transfer heat. Heat flow can more readily be applied between sites of different geology,
so geothermal gradient is converted to heat flow, by multiplying by the thermal conductivity, estimated
over the well section (Hantschel and Kauerauf, 2009).
This relationship is expressed as:
q=K(dT/dZ) (Fouriers Law)
where, q=Heat Flow, K=thermal conductivity, and dT/dZ = geothermal gradient.
Heat flow, measured in this way in well columns, can vary substantially. For example, over the African
continent, it range between 40 and 200 mW/m 2 (Macgregor, 2019), resulting in the depth of the onset
of the oil window ranging between 1.5 and 8 km. Modeling requires the input of a ‘basal heat flow’,
which will be slightly lower than these calculations, due to the removal of the small contribution of
heat from the sedimentary column. The determination of the basal heat flow in any given setting is,
therefore, critical, not only at present day, but also at key stages in the geological past. The estimation
of basal heat flow history through time is probably the main uncertainty encountered in petroleum
systems modeling studies and is the cause of many mispredictions (e.g. Baudino et al, 2018).
As illustrated by Fourier’s Law, thermal conductivity is a critical factor. Unfortunately, the sparse
literature on this property, and the use of different thermal conductivity models across academia
and the industry, means that different workers may calculate very different heat flows for the same
temperature data. It is, thus, difficult to apply previous workers’ heat flow calculations directly into
models.
KEY INPUTS
Petroleum Systems and Basin
Modeling
Exploration Handbook | 31
The key inputs to the early (calibration) stages of any modeling
study are:
Calibration well column(s), with good lithological control
» Rock properties for each lithology input (such as
thermal conductivity, porosity, permeability, density,
and compressibility) — These properties control
how the rock compacts through time, and its ability
to transmit heat and fluids. Permedia petroleum
systems modeling software delivers lithology files that
provide default relationships of the properties with
pressure and depth. These can be edited and, where
critical, should at least be reviewed and calibrated to
measurements, such as pressure and porosity.
» Source rock properties (particularly kerogen type) —
Different kerogen types generate hydrocarbons in
different temperature ranges, so modeling results of
the proportions of oil and gas produced are strongly
dependent on source rock type.
The structural, thermal, and volcanic history of the basin
This forms the basis of the prediction of the thermal history.
Most extensional basins are assumed to have experienced
their highest heat flows at the time of rifting or continental
break-up, and to have cooled exponentially, thereafter; a
model loosely known as the ‘Mckenzie model’ (Mckenzie,
1978; Hantschel and Kauerauf, 2009). There are many cases,
however, of significant departures from this model, where
other controls, such as mantle heating, can be demonstrated
(e.g. Macgregor, 2019). Upper thermal boundary conditions,
such as seabed depth and temperature, are usually predicted
from plate and paleoclimate reconstructions.
» Geochemical maturity data — Vitrinite reflectance acts
as a paleothermometer, responding to the maximum
temperature that the rock has been subjected to
throughout its history. If these data suggest that
the thermal regime has been hotter in the past than
at present day, then additional data or analyses are
needed to assess when this period of higher maturity
occurred.
» Temperature data for these calibration wells — The
most accurate temperatures are derived from drill stem
test (DST) data, with less accurate data obtained by
statistical extrapolations of the temperatures measured
from downhole logs. The geothermal gradients
measured by the latter technique may differ from the
true gradients by 10% or more (Goutorbe et al, 2007).
“The objective of most
petroleum systems
modeling projects is to
provide a prediction of
source rock maturity
through geological time”