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The authors would like to acknowledge Neftex staff members , past and present , who have contributed to building the underlying content that forms the basis of the assessment described herein .
Treloar , M ., D . Slidel and O . Sutcliffe 2018 . Illuminating the Anatomy of Super Basins . Exploration Insights Magazine . Exploration Insights Magazine no . May , p . 6-12 . ( XURBB _ 639037
Dr . Owen E . Sutcliffe , Head of Global Geology and Geophysical Practices , Halliburton Landmark
Owen started his career as a postdoctoral research assistant with the University of Wales , Aberystwyth and LASMO , researching the petroleum geology of the Late Ordovician glacial clastics of North Africa . In 2000 , he joined Badley Ashton & Associates as a sedimentologist , before his employment began at Neftex Petroleum Consultants in 2003 . Since the acquisition of Neftex by Halliburton in 2014 , Owen has held roles as Head of Stratigraphy and as Manager of the Neftex ® Insights portfolio . He is a member of the Geological Society , London and the Petroleum Exploration Society of Great Britain ( PESGB ).
Mike Treloar , Product Owner — Screening Applications , Halliburton Landmark
Mike is responsible for guiding the development of cloud-based technologies that drive efficiency gains and integration , in screening workflows . He has six years of industry experience , having performed different roles across several areas , including content management , technical marketing , and exploration-focused regional geoscience . Mike has a Master ’ s degree in Geology from Imperial College London , U . K .
Daniel Slidel , Team Lead — Petroleum Systems Analysis , Halliburton Landmark
Daniel started his career as a field assistant , working for CASP in the Canadian Arctic Islands . He joined Neftex Petroleum Consultants in 2011 , where he was involved in building regional exploration projects for the Arctic . In his current position at Neftex ® , Daniel is responsible for operational management of the Petroleum Systems Analysis team . He has eight years of industry experience , and holds an MSci Geology degree from Royal Holloway University , London , U . K .
This article is a synthesis based upon published data and information , and derived knowledge created within Halliburton . Unless explicitly stated otherwise , no proprietary client data has been used in its preparation . If client data has been used , permission will have been obtained and is acknowledged . Reproduction of any copyrighted image is with the permission of the copyright holder and is acknowledged . The opinions found in the articles may not necessarily reflect the views and / or opinions of Halliburton Energy Services , Inc . and its affiliates including but not limited to Landmark Graphics Corporation .
20 | Halliburton Landmark
context. When plate tectonic models are created
using a dynamic plate boundaries approach,
plate boundaries additionally provide geometrical
constraints to reduce the degrees of freedom
in the models, and enable true plate tectonic
reconstructions to be created.
Dynamic Plate Boundaries
The vast majority of plate tectonic models focus
on the reconstruction of present-day continental
blocks, while paying little or no attention to plate
limits. The results are closer to continental drift
than to plate tectonics (Hochard, 2008). While
the rotations and paleo-positions of GDUs are
important, we must also consider plates in their
entirety, rather than solely continents/terranes.
Plates can comprise a contiguous association of
continental and oceanic lithosphere separated by
a passive margin and sharing the same motion
for a given period of time.
Oceans open and close, plates separate
and amalgamate. For this reason, the plate
framework evolves through geological time —
tectonic ‘plates’ are, therefore, time-dependent.
The motions of plates change and with them the
plate boundaries evolve.
The concepts of ‘dynamic plate
boundaries’ (Stampfli and
Borel, 2002; Hochard,
2008) or ‘dual control
evolve from one
reconstruction to the
next one, following a
evolution. The concept
of dynamic plate boundaries
emphasizes the importance of plates
as entities defined by their limits (i.e. ridges,
rifts, subductions, collisions, obductions, and
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transforms), making the assertion that plates are
mostly rigid, and the only evolving elements are
the plate boundaries (Stampfli and Borel, 2002;
At any time within a plate tectonic model,
the plate boundary connections, or triple
junctions, must be stable. Thus, each boundary
configuration must fall into one of the following
categories: 1) it is unchanged through time; 2) it
moves geographically with its three boundary
components unchanged (Fowler, 2005); or 3)
it follows a consistent geodynamical evolution
(e.g. an active margin can become a collision)
(Mckenzie and Morgan, 1969).
Constructing Dynamic Plate Boundaries
Once plate positions through time have been
established on key assemblies, one can calculate
the relative stage rotation poles for each pair of
plates, and determine the plate boundary types
using the Eulerian pole of rotation and associated
circles. Small circles determine the orientation of
transform faults, while plate boundaries following
great circles are mid-ocean ridges or convergent
boundaries, depending on the relative
plate motion (Figure 6).
P L A T E
Only this rigorous approach
enables dynamic plate
boundaries (i.e. dual
control) to create
control on the
Relative Plate Motion
Figure 6> Construction of plate boundaries
between plates A and B, using a calculated
Euler relative stage pole (E). Mid-ocean ridge segments
follow great circles passing through E. Transform faults follow
small circles centered on E (modified after Hochard, 2008).
reconstructions that adhere to the core of the
plate tectonic theory. Unfortunately, the process
remains largely manual and, thus, calls for more
automation in order to increase efficiency and
Developments in the Craft of Plate
There has been consistent improvement over
the past few decades in the level of complexity
of plate tectonic models and their extension back
in time (Figure 2). Many of these improvements
were led by early developments in more recent
geological time, such as Cenozoic plate models,
where oceanic data provide additional constraints,
followed by extrapolation into deeper time.
Earlier models are invariably based on fewer,
less-well-defined GDUs. Later models are
increasingly complex and integrate additional
information, culminating in the integration of
plate boundaries, and possibly reaching the level
of dynamic plate boundaries (in the sense of
Stampfli and Borel, 2002). Plate tectonic models
with dynamic plate boundaries bring together
data intrinsic to individual GDUs, as well as
information related to their interactions along
plate boundaries, in the context of a roughly
spherical globe, which in itself provides additional
Paleozoic and Precambrian reconstructions bring
additional challenges, requiring the integration of
more data types to facilitate model development.
Structured, big data compilations are critical to
addressing these challenges. Their availability will
remain one of the limiting factors for continued
development of deep-time models, as will our
geoscience understanding of how processes in
early Earth history may have differed from more
recent processes. Many models are constrained
by geoscience concepts; for example, if there
was no concept of supercontinent cyclicity,
Precambrian models could look very different!
The availability of open source software, such
as GPlates, has facilitated the implementation
of rapidly developed and changeable models. It
is now possible to develop and visualize model
changes interactively within seconds, allowing
model developers to make rapid improvements.
Previously, models were, of necessity, developed
as a series of snapshots, often at 50 to 100 Ma
intervals in the Precambrian, and 10 to 50 Ma
intervals in the Phanerozoic. It is now easy to
check plate motions at 1 Ma intervals, ensuring
that all motions before and after key time frames
are internally consistent.
Future developments will enhance our ability to
integrate even more information with increasing
levels of complexity. Some of this information
will include data in spatial 3D, from varying
depths in the mantle, placing further demands on
technology and visualization.
All models are inherently uncertain. Available
technology and procedures for developing plate
tectonic models do not provide mechanisms
to illustrate spatial and temporal variations
in uncertainty, by themselves. A number of
researchers in the academic community and at
Halliburton are investigating ways to address this,
including machine learning techniques to better
integrate numerous sources of information and
Rigorous delineation of plate boundaries is often
a time-consuming, manual process at present.
Semi-automated to fully-automated definition of
boundaries and their integration into models is
already being developed, providing yet another
way to assess and constrain their geological
validity more rapidly.
Scientific assessment and Earth processes,
including those near-surface and deep into the
mantle, will be achievable with these future
models, thereby, driving improved resource
targeting for users.
Earlier personal communication with C. Scotese
formed the initial seeds for this article, which the
authors would gratefully like to acknowledge. We
also acknowledge the contribution of all the many
authors cited in this article and the many more we
were not able to cite due to the article scope, who
have contributed to the field of plate tectonics and
associated software development over the years.
We salute your continued contribution to pushing
the boundaries of geodynamic comprehension.
Authors finally acknowledge the editors for their
useful suggestions on this manuscript.