Quantifying Uncertainty in Geological Structures*
By
A.D. GIBBS1
Search and Discovery Article # 40033 (2001)
*Adapted for online presentation from poster session by the author at the AAPG Convention, Denver, CO, June, 2001.
1Midland Valley Exploration Ltd (www.mve.com), 14 Park Circus, Glasgow, UK. ([email protected])
Editorial Note: This article, which is highly graphic (or visual) in design, is presented as: (1) three posters, with (a) each represented in JPEG by a small, low-resolution image map of the original; each illustration or section of text on each poster is accessible for viewing at screen scale (higher resolution) by locating the cursor over the part of interest before clicking; and (b) each represented by a PDF image, which contains the usual enlargement capabilities; and (2) searchable HTML text with figure captions linked to corresponding illustrations with descriptions.
Users without high-speed internet access to this article may experience significant delay in downloading some of these illustrations due to their sizes.
First Poster
Second Poster
Third Poster
The
poster illustrates a standardised procedure to be applied to faulted and folded
structures. This enables statistics of structural misfit to be calculated in
terms of gap and overlap along restored faults. The statistics provide a direct
measure of uncertainty in characterising the structure, both at field scale and
on a
fault
compartmental basis. The uncertainty or "error bar" arises
from a combination of seismic resolution in
fault
zones, picking strategy and
geological factors such as missing reservoir volume through mass wastage,
erosion or the
effect
of unresolved
fault
damage zones and sub-seismic faulting.
Fault
linkages to depth are also analysed to quantify consistency throughout the
mapped reservoir horizons. Knowledge of the uncertainty in
fault
mapping, as
well as the position and linkages, allows risk to be managed through both
further targeted technical work and through commercial risk management
techniques.
Locating
wells in structurally complex regions to optimise recovery is difficult, even in
well understood provinces with modern 3D seismic. This can result in expensive
side tracking or in some cases re-drilling to achieve the desired objectives. An
understanding of the uncertainty or inherent error in imaging structurally
complex zones provides a key tool in reducing risk of technical failure and
managing commercial risk. Direct benefits arise from quantifying the
effect
of
continued work in the technical cycle relative to an improved understanding of
the impact on commercial risk.
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Click here for sequence of interpretations.
Every
interpreter recognises that a range of data-consistent interpretations
can be developed from seismic (Figure 1.1). With good quality 3D data,
this may be less evident than with older 3D or 2D data, but imaging and
picking strategies will lead to alternative interpretations which may
critically Multiple
realisations of structure reflect the uncertainty or "error
bar" in the process of structural Attempts to directly compare and quantify uncertainty prior to drilling are rarely attempted. Therefore, subsequent post-mortem reinterpretation may be ad hoc and result in increased overall uncertainty. Modern
techniques of model building improve understanding and communication but
may disguise hidden uncertainty in the model. As well as uncertainty in
the 3D structure (Figure 1.3), a geological
The
geological model implies a process from the deposition of undeformed
sediments through to final development of the observed structure. We can
use this basic assumption of geological evolution to test our
The simplest key steps are:
Once we have unfolded and unfaulted our model, we can then identify and quantify misfits in our starting model.
For a particular region a standard workflow (Figure 2.4) can be set up to allow multiple realisations of the structure or structures to be validated and direct comparisons carried out. More complex geologies lead to additional workflow steps - not to an alternative approach. Alternate geohistories or validation strategies can also be compared by applying different workflows to the same model.
By applying the chosen workflow (Figure 2.5) to the model, the uncertainty in the model can be presented in different ways (Figures 2.6, 2.7, 2.8). Graphical or visual output can be used to communicate uncertainty as well as statistics to use in additional analysis. In
3D visualisation of uncertainty in the model in Figure
2.9, gap/overlap
is displayed as an attribute in the same x-y space as the model. Note in
this case the model carries maximum uncertainty at a single
Identification
of areas of highest uncertainty allows technical work to focus on
resolution of problem leading to a revised
Monitoring the uncertainty at different stages in a project provides a new tool to identify cost effectiveness of the technical work program (Figures 3.3, 3.4). Repeated reinterpretation may change the model but not change the underlying uncertainty. At this stage in a project the model can be changed retrospectively to account for new data but the predictive capacity of the model does not improve. Technical uncertainty should be addressed through other risk management strategies. Technical performance between different projects can be compared to identify resource and training needs as well as technical complexity or data issues.
For
a particular For exploration wells in a range of plays, plotting proven or expected value as a function of uncertainty (Figure 3.6) provides a significant additional tool in managing assets.
The author wishes to thank the numerous colleagues and clients who have contributed to the development of this approach. The validation software used in these studies is QuickMove developed and supplied by Midland Valley. The Hubei Field model used to illustrate this poster was provided by Dynamic Graphics. Stephen Calvert assisted in preparing the poster. |
