Download Future Directions in Reservoir Modelling

Draft abstract for 8th Petroleum Geology of Northwest Europe Conference, 28-30 Sept 2015.
Future Directions in Reservoir Modelling
Mark Bentley (AGR TRACS Training) and Philip Ringrose (Statoil ASA & NTNU)
Reservoir models in themselves do not generate prospects or
development scenarios, increase productivity or reduce risk
and uncertainty. They are simply tools which reflect our
ability to achieve these goals. Advances in reservoir
modelling cannot therefore be compared to technology
breakthroughs such as 4D seismic imaging, horizontal drilling
or EOR methods. Reservoir modeling tools are, however,
often essential for integrating knowledge and for making strategic decisions.
The pertinent issue is the capability of the modelling toolbox to achieve the goals mentioned above;
whether it increases or decreases our efficiency and whether it assists us in our attempt to generate
insights in the understanding of the subsurface.
In this respect, five areas are highlighted as current areas of development, and can be judged against
these ideals of capability, efficiency and insight.
1. Algorithms. Commonly used algorithms are either objectbased, beloved of sedimentologists, or pixel-based techniques
such as indicator simulation, focused on geostatistical
estimation. The use of two genuinely new algorithms is
emerging: the texture-based approaches of multi-point
geostatistics and process-based approaches. Textural
approaches avoid many of the limitations of the simpler
approaches and require sensible training images. Libraries of textures will therefore become
more important than traditional dimension (e.g. width:thickness) databases. Process-based
approaches attempt to re-create geological history and some tools are available now: e.g.
SBED for small-scale clastic depositional architectures; 3DMove for creating or recreating
structural architectures. The next step would be nimble integrated sequence-based
packages which recreate the complete reservoir and overburden picture. ‘Sim-Reservoir’.
The 'GeoChron' approach developed by Mallet (2014) represents an important step in this
direction.
2. Determinism and probability. A more subtle subtext to the
above is our expectation of geostatistical methods. This has
varied considerably over the last 20 years and the general
conclusion today is that many of our expectations have been ill
founded. The fundamental limitation is data density and
statistical insufficiency. A ‘sufficient’ data set, rich and regular,
such as 3D seismic data, lends itself well to statistical
techniques. Sparse well data do not. The consequence is a
need to distinguish between circumstances where geostatistics and probabilistic
components of algorithms can deliver insight and where they can’t. In the latter eventuality,
modelling must be conceptually led and not restricted to the underlying data set. Currently,
many models are overly data-led, where the data support is clearly insufficient. Useful
models tend to be those which are more firmly founded on deterministic guidance in the
form of reservoir concepts (even just simple sketches) with weaker constraints to the
available data.
3. Grid independence. Current workflows are grid-centric. A major structural change is to
consider the grid as a disposable item. The fixed element is then the underlying database
and the conceptual understanding of the reservoir both of which evolve through the field
life cycle. Two technical developments are emerging
which develop this theme: surface–based modelling
and adaptive gridding. Surface-based modelling has
been around for many years in various forms but has
tended to be only a step in the construction of a fixed
grid. Leaving the grid as a variable, to be built and
discarded quickly once a decision has passed, introduces a fundamentally different workflow
in place of building a detailed full-field grid. Adaptive gridding goes one step further – the
grid is literally never fixed, even during a single modelling exercise. Such approaches seem
radical in the E&P world, but are common in other industries.
4. Non-linear workflows. Acceptance of the disposable grid and model, with or without
surface-based or adaptive components, opens the door to multi-size and multi-scale
modelling workflows which have been explored over recent years but not become
established as common tools. This is most notably the case in mature fields where there is a
need for handling production data, and current multi-model tools are premised on assisting
the user in repeating the single full-field model approach multiple times. These tend to
neglect multi-scale options (although this need not necessarily be the case). The
development of these workflows is more about user attitude and awareness than software
capability. Current modelling tools do not guide us down the path of multi-scale models,
although this where efficiency and insight often lie.
5. Uncertainty and behavioral bias. Also founded on attitude, this concerns our awareness of
cognitive bias and its impact on quantification uncertainty and forecasting. This issue is
presently emerging in the social and cognitive sciences, but is only just beginning to find its
way into practical reservoir modeling workflows.
Of the five items above we would argue that an awareness of
the last one is the key (Ringrose & Bentley, 2015). A firm
emphasis on the decision making process places the other issues
in context and naturally causes us to depart from current
workflow norms. At that point we question value and efficiency
and begin to gain insight; insights which will be accelerated by
the developments in modelling algorithms, especially the
process-based and multi-scale approaches.
References
Mallet, J.-L., 2014. Elements of Mathematical Sedimentary Geology: the GeoChron Model, EAGE
Publications, 388 p.
Ringrose, P., & Bentley, M., 2015. Reservoir Model Design: A Practitioner's Guide. Springer, 249 p.