SEISMIC INVERSION TO FLOW
SIMULATION
TESTING OF TWO DIFFERENT METHODS OF
STOCHASTIC MODELLING OF FACIES
DISTRIBUTION, CONDITIONED TO SEISMIC DATA.
(Dalai FIELD, ANGOLA BLOCK 17)
Eirik Vik, Alfhild Lien Eide, Christophe Basire
SEISMIC INVERSION TO FLOW
SIMULATION - ANGOLA CASE
Purpose of the modelling
project:
Test fast workflows from interpretation of
inverted seismic data to stochastic reservoir
modelling and flow simulation. One workflow
using a Multipoint statistical method developed
at Stanford, and one workflow using Marked
Point methodology implemented in Irap RMS.
Why do stochastic reservoir modelling?
Uncertainty evaluation
Integration of data -> Better models ->
Increased hydrocarbon recovery
Why use seismic data in reservoir modelling?
Spatial coverage of reservoir
Composite figure of the Dalai Field,
showing seismic, channel envelope and
“bodies” extracted from the seismic.
SEISMIC INVERSION TO FLOW
SIMULATION - ANGOLA CASE
The Early to Middle Miocene Dalia field is located offshore Angola in water depths between 1200 and 1450m, 700 to
900 m below the sea bottom. The field can be divided into a number of different reservoir systems that are
characterised by deposition within confined and unconfined turbidite systems. API gravity of the oil is in the order of 22
to 23° API.
The accumulations are composed of stacked channelised turbidites deposited within confined fairways, enclosed within
hemipelagic shales. Located laterally to each of the main channels are a series of unconfined “flanking” turbidite
deposits, believed to represent sediments that pre date the main channels that are now in apparent depositional
juxtaposition due to the incision of the main channels into older sediments.
SEISMIC INVERSION TO FLOW
SIMULATION - ANGOLA CASE
•
TASKS
–
MODEL BUILDING AND SIMULATION
• Simple sand/shale model derived from the more complex model based on the
Dalia-1 well.
• Make correlation function for seismic to lithology based on Dalia-1 well
petrophysics.
• Simulate sand bodies conditioned to lithology cube (seismic) bounded by
channel ‘envelope’
– Stochastic object model conditioned to seismic data using Irap RMS
» Facies composite: Rectangular objects
– Stochastic object model conditioned to seismic data using Irap RMS
» Facies Composite: Channel form objects
– Alternative stochastic model: Using a multipoint geo-statistical model
from Stanford Centre for Reservoir Forecasting (SCRF)
–
FLOW SIMULATIONS
• Flow simulation using streamline simulator in Irap RMS.
–
DATA ANALYSIS
–
CONCLUSIONS
SEISMIC INVERSION TO FLOW
SIMULATION - ANGOLA CASE
Seismic to lithology:
Correlation based on Well: Dalia-1.
Figure show relation between Poisson
ratio and Acoustic impedance of log
data, discriminated between interpreted
lithology.
NB: Plot separates shale (black) and oil
sand (green) fairly well, while shale and
brine sand (blue) are not separated !
Lithology cube constructed by taking
AI* PR^2 and rescaling (black curves of
above plot). Colour scale gives shale
as red and more sandy facies as green
(and blue), yellow is intermediate.
SEISMIC INVERSION TO FLOW
SIMULATION - ANGOLA CASE
Depth converted lithology cube,
bounded by the upper and lower
bounds of the Dalia channel
complex. Resolution 50*50*2 m
(522 711 cells, the full resolution
25*25 cube would have been 2092
051 cells, which is too large for the
modelling).
Red/pink colours designate shaly
facies while the blue/green indicate
sand. Note the layered “sand” objects
in the middle of the section. Channel
complex is approximately 500 m
wide. The display has a 5x vertical
exaggeration.
Frequency plot of values of the lithology cube.
SEISMIC INVERSION TO FLOW
SIMULATION - ANGOLA CASE
Shale
Sand
Shale
DALIA-1 well with facies breakdown, compared
to the simplified 2-facies model used .
Note correspondence between facies and LFP
values in well position
SEISMIC INVERSION TO FLOW
SIMULATION - ANGOLA CASE
WORKFLOW
Seismic data mapped into the channel ’envelope’
Running 10 realisations of each of the 3 model versions.
The only differences are in the facies modelling.
•Same workflow the two GMPP jobs
•Facies composite in RMS, rectangles and channels,
conditioned to seismic data
•Multipoint statistical method:
•10 facies realisations conditioned to the seismic
cube done outside RMS, and then imported into
RMS.
•Stochastic modelling of flow parameters taken from the
Dalia-1 well
•IPL script to make vertical permeability 1/10 of
horizontal perm.
•Streamline simulations.
SEISMIC INVERSION TO FLOW
SIMULATION - ANGOLA CASE
Input parameters for GMPP
simulation:
Facies: Composite
RECTANGLES
Input parameters for GMPP
simulation:
Facies: Composite
CHANNELS
Geometry of objects (rectangles):
Length: 250 m (100 m)
With: 100 m (25 m)
Thickness:
5 m (3 m) Geometry of objects (channels):
Seismic:
Length: 20000m (6000m)
Lithology cube
With: 1000m (100m)
Resolution 50 ‘ 50 ‘ 2 m
Thickness: 10 m (3m)
Seismic:
Simulation parameters:
Lithology cube
Iterations: 3*10^6
Resolution 50 ‘ 50 ‘ 2 m
Seismic factor : 2e+05
Annealing function:
Simulation parameters:
T0 = 1
Iterations: 3*10^6
t1 = 1*e^-7
Seismic factor : 2 e+05
a=0.999893
Annealing function:
Volume fraction of sand: 0.3
T0 = 1
t1 = 1*e^-7
a=0.999893
Volume fraction of sand: 0.3
Relationship between lithology cube
value and probability for sand used
in the simulation.
Simulation cells with values below
~150-200 have a probability of 0.8
to 0.9 of being filled with some sort
of oil-filled sand facies. Values
higher than that are most likely
shale., except for very high values.
SEISMIC INVERSION TO FLOW
SIMULATION - ANGOLA CASE
Results from conditioning:
Input parameters for the conditioning to the
left (sand probability as function of
lithology cube value) and results to the
right:
Upper right figure show the probability
graph calculated based on the results from
the conditioning.
Lover right figure show the fraction of sand
and shale in the corresponding lfp value
bins.
Observations:
The graphs show that the input distribution
is fairly well reproduced in the resulting
facies cube. The misclassification of cells
may be due to the differing size between
modelled objects and the seismic resolution.
Of greater concern is the lower graph
showing that the larger number of cell have
seismic values in the area where the sand
and the shale distributions overlap !!
SEISMIC INVERSION TO FLOW
SIMULATION - ANGOLA CASE
MULTIPOINT STATISTIC
•Developed at Stanford Centre for Reservoir
Forecasting (SCRF)
•Probability of facies in each grid cell is defined from
the configuration of facies in a neighbourhood template
•Probabilities are inferred from a training image.
• Multipoint geostatistics is better at reproducing
curvilinear geometry than two-point geostatistics
•More flexible than object-based methods in situations
with large amounts of conditioning data?
•Is called multipoint because it uses multiple points at a
time (the template).
APPLICATION TO DALIA STUDY
•Model for two facies: Sand and shale
•The LFP cube was transformed to an input
probability cube for sand/shale.
•Training images were generated by
unconditional simulation from a stochastic
object-based model to obtain stationarity
(must have many repetitions of a pattern in
the training image)
•A channel training image was chosen as
training image for the simulation on the full
grid.
Pixel based methods (works on grids)
Object based (drops ’objects’
randomly in space)
(Indicator) Kriging / Cokriging
•Uses variograms (two-point
statistic)
Multipoint method
•Uses templates
Marked point models (GMPP)
Example: Facies: Geoplex in RMS
Does good reproduce
curvilinear geometry
Can reproduce
curvilinear geometry
Reproduces geometry (shapes) explicitly
SEISMIC INVERSION TO FLOW
SIMULATION - ANGOLA CASE
SCRF model training image, constructed using
unconditional modelling of ”channel objects”and example
of resulting facies model (red is sand)
SEISMIC INVERSION TO FLOW
SIMULATION - ANGOLA CASE
LFP cube
Facies realisation
Typical results from stochastic
facies and petrophysical
modelling, using
rectangular
objects in Irap RMS.
Porosity
realisation
Permeability
realisation
SEISMIC INVERSION TO FLOW
SIMULATION - ANGOLA CASE
LFP cube
Facies realisation
Typical results from stochastic
facies and petrophysical
modelling, using
channel- formed
objects in Irap RMS.
Porosity
Permeability
SEISMIC INVERSION TO FLOW
SIMULATION - ANGOLA CASE
LFP cube
Facies realisation
Typical results from stochastic
facies- and petrophysical
modelling, using the
SCRF methodology.
Porosity
Permeability
SEISMIC INVERSION TO FLOW
SIMULATION - ANGOLA CASE
RMS-streamline simulation:
A simple flow simulation useful for rapid assessment of flow
properties of a model and for the ranking of models and
realisations. Speed is accomplished by decoupling pressure
modelling and flow modelling. The simple version
implemented in RMS assumes single phase, thus avoiding
the complexities of gravity flow and capillary effects.
Parameters:
Simplified model, the same as used by Statoil in
early RMS modelling of the Dalia field.
10 realisations.
11 producer/ injector pairs (fixed positions):
Production rate: 1835 m^3/day
Injection rate: 1835 m^3/day
Producer bhp: 50 bar
Injector pressure: 10000 bar
Completed in all sand intervals for each realisation.
Model run for 1e+6 days.
Fluid compressibility 0,0001 /bar;
Rock compressibility 1e-5 /bar;
Viscosity 0,25 cp.
Visual results from streamline-simulation of model with
rectangular objects. Black pillars designate pseudo-wells,
coloured lines are streamlines connection produces and
injectors while the yellow lines represent streamlines linked
to ”black” and ”white” holes.
SEISMIC INVERSION TO FLOW
SIMULATION - ANGOLA CASE
Results from facies modelling:
Visual impression: The three models have
different looks: the “rectangles model” seem to
be more heterogeneous, with smaller sand
bodies than the two other models. This was to
be expected as the two other models are
constructed using larger elementary bodies.
Rectangles
Channels
The rectangles methodology is thus better able
to “mimic” the seismic, and would look more
heterogeneous.
220000000
channels
Sand Volume M^3
200000000
SCRF model
scrf
180000000
rectangles
160000000
Results from volumetric modelling:
140000000
120000000
100000000
1
2
3
4
5
6
Realisations
7
8
9
10
Little variation between realisations within each series (standard
deviation is 1 to 3 % of average!) Figure show sand volume for each
of the 10 realisations of the three series, with standard deviation within
the series. Note the difference between the series, this is difficult to
explain as the target sand volume in all three series were set to 40 %
Results from streamline simulations (1):
Breakthrough:
Days to ”breakthrough” for 10 producer wells (and
average) for 10 realisations of the rectangles RMS
model. Note the relatively large spread in values.
Further analyses show that there is no correlation
between the breakthrough times and the facies
volumes show in the diagram on the last slide.
Similar values and spread are observed for the
channels and the SCRF model.
6000
rectangles
channels
scrf
Time to breakthrough
5000
4000
3000
2000
1000
0
P01 P02 P03 P04 P05 P06 P07 P08 P09 P10 P11
Producers
Breakthrough (days)
SEISMIC INVERSION TO FLOW
SIMULATION - ANGOLA CASE
10000
Realisation 1
9000
8000
Realisation 2
Realisation 3
7000
Realisation 4
6000
5000
4000
Realisation 5
3000
Realisation 7
2000
1000
Realisation 8
Realisation 6
Realisation 9
0
P01 P02 P03 P04 P05 P06 P07 P08 P09 P10
Producers
Realisation 10
AVERAGE
Average time to breakthrough for the 11 producers of the three
models. Note the lack of correlation between the channels model
and the two others. This is interesting as all three models were
conditioned to the same seismic data, and one could expect a larger
similarity
Note also that the time to breakthrough for the producers of the
channel model is on average larger than for the two other models.
This may be due to the channels being less continuous than the two
other models: the rectangle model with a better fit because of the
small size of the elementary building blocks , and the SCRF model
with branching channel elements and therefore better connections!
(No 1 is not effective in the SCRF model and no 11 is shut in the two others)
SEISMIC INVERSION TO FLOW
SIMULATION - ANGOLA CASE
Yellow lines represent total volumes
for each realisation, the other lines
represent volumes for each
producer.
Note that the total volume drained
from the “SCRF” model is larger
than for the other two models. At
the same time the production as part
of total fluid in place for the SCRF
model is also greater than for the
two others (51 % vs. 42% and
37%), all indicating that the SCRF
model has the better internal
connection.
250
P11
P10
P09
200
Drained volume [million Sm3]
Results from streamline
simulations (2):
Drained volumes as function of
model and realisation at end of
simulation.
P08
SCRF model
P07
150
Rectangles
Channels
P06
P03
P05
100
P04
P02
50
P01
SUM
0
Realisations
SEISMIC INVERSION TO FLOW
SIMULATION - ANGOLA CASE
Results and conclusions (1)
– An efficient work flow for using seismic data to condition stochastic
modelling of facies distribution is explored on data from the Dalai
field of offshore Angola block 17
– The workflow is used for 2 different Irap RMS composite
models(GMPP) and one model derived by a Stanford University
methodology (SCRF).
– The workflow is found to be feasible and easy to use. The SCRF
model takes some more effort to implement but no big difficulties
were encountered.
SEISMIC INVERSION TO FLOW
SIMULATION - ANGOLA CASE
Results and conclusions (2)
– 10 realisation of each model were made and RMS streamline simulations
performed to compare and to rank the results.
– Comparison of visual and numerical results from the simulations show that
• the sand / shale fraction of the different model/realisations are very similar,
This is most likely a function of the modelling set-up and as such inevitable.
• the RMS model using small rectangular objects in the stochastic modelling do
look more heterogenic than the two other models.
This is most likely caused by the small size of the elementary rectangles
compared to the grid size of the conditioning fabric, which makes it easier to
“follow” the minor variations” in the seismic pattern.
• time to breakthrough (as defined in RMS stream) is slightly larger for the
channels model than for the two others (but “inter realisation” variation is very
large),
indicating that the channels models may be more heterogeneous (?) than the
others.
• the SCRF model has slightly larger total drained volume and is producing a
larger part of the total fluid in place that the two other model.
This is most likely because of a greater connectivity.
SEISMIC INVERSION TO FLOW
SIMULATION - ANGOLA CASE
Results and conclusions (3)
Overall:
The models are quite similar but have some differences.
The most conspicuous feature is the very large variation between
different realisations.
This may partly be due to the modelling set-up either wells in fixed
positions and no conditioning to well results. The potential for such
large variations is however something to bear in mind assessing
more complex models with fewer realisations.
SEISMIC INVERSION TO FLOW
SIMULATION - ANGOLA CASE
Further work
– The presented case is very simple (having only two facies!), the
workflows should be implemented on more complex cases, either by
developing the Dalai case or by continuing the work on another field.
– The SCRF methodology has been show to be feasible, this work
should be continued on other cases with other deposits types and
more complex facies relationship.
– Work on different ways of comparing and ranking models and
realisations should be continuing.