Exploration Insights September 2020 - Page 18

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 .
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