Gonzalez_edit.qxp 28/10/08 2:35 pm Page 91
An Emerging Trend – Reservoir Characterisation Using Seismic Inversion and Multipoint Geostatistics
conditioning data, we generated a set of MPG simulations using
Figure 9: Probability Map for Sand (top) and Shale (bottom) Lithofacies
MSIMPAT, conditioned only to well data. Figure 8 presents the
Computed with 30 Solutions of the Presented Inversion (Inverting
Seismic Data Shown in Figure 6)
probability maps for sand and shale, obtained from 30 MSIMPAT
realisations (without seismic data). High probability values in the
CDPs near the wells indicate that most of the realisations provided
the same lithofacies at these locations. However, without any
constraint between the wells, such as that provided by seismic data,
it is not possible to fix the locations of the channels.
Figure 9 shows the probability maps for sand and shale lithofacies
obtained from 30 solutions of the proposed inversion technique.
Observe the clear localisation of the main channels. The main
geological bodies (sand channels), which coincide with those in the
true model (see Figure 5), can be clearly identified in the probability
maps. This result validates the idea of the inversion technique itself,
as well as its implementation.
Real Data Application
The proposed technique was applied on a real data set from a clastic
marine environment on the continental slope. Well-logs from two
wells, a near-offset 2D seismic section (extracted from a 3D volume)
intersecting the well locations) and the training image were used as
input data. Based on the two available wells and the geological
information, three lithofacies were defined: sand, overbank and
shale. Figure 10 shows the gamma ray (GR), Vp and ρ logs of the
The values at common depth points (CDPs) 40 and 120 were used as well-log data.
two wells, indicating the lithofacies assigned to each depth level.
The bi-variate distributions representing the elastic properties of
Figure 10: Well-logs of the Two Wells Used for the Real Data
each lithofacies were computed from the well-log data. Application, Indicating the Lithofacies Assigned to Each Depth Level
Figure 11 shows the 3D geological model from which the training
image was constructed. Extracting all cross-sections parallel to the
direction of the seismic data generated the complete 2D training
image needed for the inversion. Figure 13 presents the probability
maps for the sand, overbank and shale lithofacies computed with 10
solutions, demonstrating the potential applicability of the proposed
technique to real field data.
Comments on the Initial Model and Multiphysics
The proposed inversion technique can start the search for solutions
with an initial guess or initial model. This initial model is not a
requirement, nor must all the cells be pre-filled. For example, the initial
state of the solutions can be derived from the interpretation of a
seismic attribute or from the result of a conventional impedance
inversion after converting the impedances to lithofacies. The initial
model is fully specified by the initial lithofacies grid; initial elastic
properties grids are not required. Use of an initial model (although
incomplete) can speed up the convergence. An initial model is also a
way to integrate geological interpretations with quantitative inversion.
An important characteristic of the presented inversion technique is
that it can be extended to account for different types of geophysical
data simultaneously (multiphysics, e.g. seismic and electromagnetic
data). For the proposed implementation, the main practical
constraint for inverting different types of geophysical data
simultaneously is that the inversion needs to use unidimensional
forward operators or full forward-modelling operators that can be
approximately factored into a series of unidimensional forward
operators. In order to obtain solutions simultaneously constrained by
EXPLORATION & PRODUCTION – OIL & GAS REVIEW 2008 – VOLUME 6 ISSUE II
91
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