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GEOLOGICAL WELL TESTINGHAMIDREZA HAMDI1, PHILIPPE RUELLAND2, PIERRE BERGEY2, PATRICK CORBETT3, MARIO COSTA SOUSA1 1UNIVERSITY OF CALGARY, 2TOTAL, 3HERIOT-WATT UNIVERSITY 1E-Mail: [email protected] Web: http://ires.cpsc.ucalgary.ca/
The term “geological well-testing”, in a broader sense, can
be used instead of “numerical well-testing”. This is
referred to the numerical simulations of transient tests by
setting up the detailed geological models within which
different heterogeneity scales are spatially distributed inthe model. The complex fluid implications can also be
deliberated, which gives the unique opportunity to
investigative the competing effects of the geology and
fluid in altering the dynamic behaviour of the well. This
process requires a “geoengineering” workflow
(Corbett,2009) in order to integrate the multi-domain
information (e.g. Geology, Geophysics and Engineering)
and to constrain the well-test modeling and interpretation
within a unified framework (i.e. a geological model).
Meanwhile, the analytical methods are the pre-steps to
numerical well-tests and are still relevant for most of the
realistic petroleum reservoirs
The well-test interpretation is an inverse problem with
non-unique solutions. This is partly related to sparse data
over large 4-D domain. However, the external information
(e.g. well-log, core, production log, spatial pressure
measurements and seismic data) can be employed to
reduce the non-uniqueness nature of the solution. This is
possible by applying geological well-testing and a
geoengineering workflow rather than the classical
analytical well-testing. A novel geoengineering approach
is implemented to integrate the multi-domain information
(e.g. outcrop, core and log data) to describe the well-test
response of certain geological deposits. Comprehensive
modeling and numerical simulations are then employed to
study the dynamic behaviour of such systems.
The geoengineering workflow adopted for geological well-
testing assists in dynamic illumination of geological and
fluid heterogeneities. This is a forward/inverse modeling
approach to analyse the independent or combined effect
of reservoir and fluid properties and/or validate the static
model based on the well-test dynamic data. The outcrop
data, experimental laboratory fluid data, seismic data,
core and log date along with considerable uncertainties
are integrated within a geological model to build a spatial
static model.
BACKGROUND
PURPOSE
An example of a geoengineering workflow is to interpret
a real well-test data using sophisticated multi-point
facies statistics (MPFS) approach (Hamdi et al. 2012).
The MPFS approach was successfully implemented toread the key patterns from a 3-D training image and to
generate the geologically realistic features in stochastic
geological model.
(A) Anal yti cal Wel l-Test
METHODOLOGY
A geoengineering approach aims at incorporation of the
production data (e.g. well-test data and 4-D seismic
data) into static model to validate the static model which
leads towards a better reservoir model for future
prediction. This process requires the ranking and the
updating of heterogeneities based on their ability to
revamp the output of geological model. A visual steering
for geological well-testing provide a tool to visualize
reservoir model and different simulation and data, and
to visually update simulation model.
RESULTSThe final quality match to the real test is obtained by generating multiple
facies and petrophysical realisations and applying hybridization
algorithm to combine different models.
(B) Training Image and MPFS Modeling
Satellite Image Training Image Stochast ic Model
(C) Multiple Facies Realizations
(D) Facies Hybridization Matching
Corbett, P.W.M., 2009, Petroleum Geoengineering: Integration of Static and Dynamic Models, EAGE/SEG, 90 p.
Hamdi, H. Ruelland, P.J., Bergey, P., 2012. Dynamic Validation of a Multi-Point Statistics Model u sing Extended Well Test Data, EAGE Integrated Reservoir Modelling
1E-5 1E-4 1E-3 0.01 0.1 1 10 100 1000
Time[hr]
10
100
1000
P r e s s u r e
[ p s i ]
mps.ks3-real1
mps.ks3-real2
mps.ks3-real3
mps.ks3-real4
mps.ks3-real5
kessog_shifted_total_new.ks3-Analysis1(ref)
Real well testdata
Faciesrealization 1
Faciesrealization 2
Faciesrealization 3
Faciesrealization 4
Faciesrealization 5
1E-5 1E-4 1E-3 0.01 0.1 1 10 100 1000
Time [hr]
10
100
1000
P r e s s u r e
[ p s i ]
mps6a.ks3-mps6_real1_n
kessog_shifted_total_new.ks3-Analysis1(ref)
Real well test
Final hybrid model
1E-5 1E-4 1E-3 0.01 0.1 1 10 100 1000Time [hr]
10
100
1000
kessog_shifted_total_new.ks3-Analysis1(ref)
K~30 md
K~4 md
K~0.03 md
Half slope:w~74 m
Unit slope:compartmentalizedor Composite
Real well testdata
1E-4 1E-3 0.01 0.1 1 10 100 1000
Time[hr]
10
100
1000
P r e s s u r e [ p s i ]
mps.ks3-real4
mps.ks3-real5
kessog_shifted_total_new.ks3-Analysis1(ref)
Facies realization 5 Facies realization 4
Real well test
FaciesRealization5
FaciesRealization4
