10 | Halliburton Landmark
Petroleum System Index
Proven Frontier
Number of reservoirs
Petroleum System Index
© 2020 Halliburton
0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95
Figure 7 > Cross plot of holistic petroleum system index scores versus number of identified reservoir intervals . Note that the range of qualities identified in frontier systems approximates that of proven systems . This suggests that the collated dataset is appropriate for analogue identification .
The first group comprises petroleum systems where the processes that operated had a better score than the elements within the system ( shown in purple on Figure 6 ). These are by far the most numerous , making up 66 % of sampled petroleum systems .
The second group comprises those petroleum systems that have elements with a better score than the processes that operated ( shown in blue on Figure 6 ). This population is the smallest , accounting for 14 % of the sample .
The final group comprises petroleum systems that have both high-quality elements and highquality processes ( shown in yellow on Figure 6 ). This population makes up 20 % of the sample .
In hindsight , the observed abundance of processenhanced petroleum systems is not surprising , as processes control the retention of hydrocarbons over time .
Systematic Analogue Definition
One of the main objectives of the defined schema was to compare potential between proven and frontier petroleum systems ( Figure 7 ). When the qualities of these systems are compared , there is significant overlap between them ; although , proven petroleum systems do have a greater range of qualities at both ends of the spectrum ( Figure 7 ). This reflects the level of knowledge that can be attributed to the proven systems . For example , proven systems are often associated with many more reservoir intervals than frontier systems . This is likely because proven petroleum systems are better explored , so more reservoirs have been identified .
Due to the substantial overlap in the range of index scores , it is suggested that the subset of proven plays will provide a good analogue dataset for the range of frontier plays . One way of defining appropriate analogues is to identify those petroleum systems that occupy a similar domain on the cross plots shown in Figure 6 .
CONCLUSIONS
The holistic , semi-quantitative petroleum system index defined here not only has a significant impact on qualifying and understanding the character of a petroleum system , but can also provide a method for identifying similarities or differences between these complex systems . As shown by the use cases discussed , the index can reveal factors that limit the development of petroleum systems , and enable better analogues to be defined when considering frontier petroleum systems .
18 | Halliburton Landmark
Exploration Insights | 19
mantle reference frame. Although hotspots
are not truly stationary and move with respect
to one another, their motion is relatively slow.
By combining data from many hotspot tracks,
an approximately fixed reference frame can be
assumed, at least throughout the Cenozoic (e.g.
O’Neill et al., 2005).
Reconstructions are often made with respect to a
single fixed reference craton. An apparent polar
wander curve, with respect to the fixed spin axis
or mantle reference, for that fixed craton is used
to change the reference frame (e.g. Torsvik and
Cocks, 2005).
Absolute plate positioning (with respect
to the spin axis or mantle reference frame)
is particularly challenging when it comes to
paleo‑longitudinal constraints (Figure 2), and even
with respect to paleo-latitude in cases where
additional constraints to paleomagnetic data are
not available to resolve in which hemisphere a
GDU was located.
Periods of true polar wander, where the planet
as a whole rotates off its spin axis, also need
to be accounted for and applied to the models
(Torsvik et al., 2012; Mitchell et al., 2012). These
rely on paleomagnetic data, which provide
absolute paleo-latitudinal constraints, and relative
plate positioning by comparing apparent
polar wander paths for
different plates.
Hotspots are assumed
to define a static
Siberia
North China Block
Rheic Ocean
Paleo-Tethys Ocean
Plate model supporting information
Gondwana
Mineral deposits
Siberia
A
West
Africa
Paleo-climate / -environments
Geochronology
Wells (sequence stratigraphic interpretation)
East European
Craton
B
Figure 4> Using multi-disciplinary and
large datasets helps to better constrain
plate models, and push them significantly
further back into geological time. A) Neftex ®
Plate Model (Late Devonian), paleo-digital
elevation model, dynamic plate tectonic
boundaries and intra-plate linework (e.g.
synthetic isochrones for consumed ocean
crust) and various supporting datasets (see
legend); B) PalaeoPlates model at 1,800 Ma
including various hard rock supporting
datasets (see legend).
VMS deposits
(StratDB)
Metamorphic ages
(DateView)
Igneous ages
(DateView and Neftex)
Igneous activity with
spatio-temporal buffer
(DateView and Neftex)
Amazonia
proto-Laurentia
North
Pole
Back-arc
magmatism
Ridge
Accretionary wedge
Magmatic arc
S.L.
3
4
1
6
Foreland basin
1 Slab-pull
5
2
7
Cordillera
2 Slab roll-back
Accretionary wedge
Ridge
Magmatic arc
3 Trench suction
Hot spot
4 Ridge push
S.L.
5 Basal drag
4
6 Slab suction
7 Collisional resistance
5
1
Figure 5> Representation of the main plate tectonic driving forces (modified after Forsyth and Uyeda, 1975).
Other than where a number of coeval
paleomagnetic poles are available for two or
more GDUs, paleomagnetic data can only
provide paleo-latitudinal control, and suffer
from hemisphere uncertainty. The position of
a subducted oceanic lithosphere slab could be
used to improve the paleo-longitudinal positioning
of a plate (e.g. van der Meer et al., 2018). Slabs
typically sink about 1.1 cm per year, so this slab
approach can only offer information back to about
260 Ma (van der Meer et al., 2018).
The level of detail in the GDUs is another
important aspect to consider (Figure 4). The
greater the number of continental GDUs
constrained from observable geological or tectonic
features, the more detailed the plate tectonic
model can be. More detailed subdivisions also
facilitate the recognition of reactivation loci along
inherited structures, and help paleo-continent
assemblies to be constrained. It is also worth
emphasizing that any model, and its sequence of
GDU accretion or rifting, is dependent on the
shape, extent, and relative positions of the GDU
boundaries. If these are incorrect, the model will
be incorrect to some degree.
South China Block
Laurussia
Back-arc basin
models can provide excellent results, supported
by mathematical and physical rules and avoiding
errors introduced by geological interpretation,
their application remains limited to the most
recent (e.g. last 200 Myr) and less chaotic
geodynamic settings (e.g. oceanic basins).
The addition of large, integrated, multi‑domain
geoscience datasets combined with regional
knowledge supports plate reconstruction
accuracy, and confidence in tectonically active
settings (e.g. rift or convergent zones) further
back into geological time.
Structured, multi-disciplinary, big data
compilations have an important role to play in
improving the definitions of GDUs, constraining
the relative positions of plates, defining
geodynamic settings, evolutionary scenarios,
and the type and direction of plate boundaries
(Figure 4).
Magnetic isochrones record the changes in the
polarity of Earth’s magnetic field as oceanic crust
formed at mid-ocean ridges. Where present,
oceanic magnetic pick datasets (e.g. Seton et al.,
2014) can be used in combination with transform
fault traces to provide robust constraints on
reconstructions for the Cenozoic and Late
Mesozoic (Pérez-Díaz and Eagles, 2014).
Constraining geodynamic settings outside extant
oceanic basins and further back in time relies
on a variety of structured big data available in
commercial data products (e.g. Neftex ® ) or in the
public domain. These include geochronology and
isotope geochemistry (DateView and StratDB
databases), geochemistry (EarthChem portal,
several databases), paleontology (PaleoBioDB
and GeoBioDB), paleomagnetics (MAGIC and
GPMDB databases), ore deposits (MinDat and
StratDB), large igneous provinces and dyke
swarms (LIPs database), and many more that are
being developed by different groups (Figure 4).
THE IMPORTANCE OF PLATE
BOUNDARIES
Plate Tectonic Driving Forces
The Earth’s rigid and buoyant lithosphere
is divided into plates that “float” over a
comparatively low viscosity asthenosphere.
Gravity and the Earth’s internal heat dissipation
create tectonic forces that lead to the motion of
the lithospheric plates, their deformation, and to
magmatic activity (Figure 5).
Thus, in a rigid Earth approach to plate tectonic
modeling, the creation of plate boundaries
provides information about the geodynamic