advanced uncertainty analysis using cmost-petrel link
TRANSCRIPT
Advanced Uncertainty Analysis Using CMOST-Petrel Link
Outline
1) Intro: Uncertainty
2) Challenge: Turbidite Reservoirs
3) Solution: CMOST – Petrel Link
4) Example: A Field in West Africa
5) Conclusion
Intro:
Uncertainty
Uncertainty
1 problem, but many possible solutions!
• Reservoir model is always built on incomplete (G&G) data
• Usually end result is a combination of best guesses
Capture uncertainty by creating artificial high/low version?
• “Industry standard” P10/P50/P90… Who calculates probabilities?
Why not use the entire range of possibilities?
• Instead of deciding on single value, use the whole range of an input!
• Age of computing, so we have the tools
Uncertainty
Parameter A
Parameter B
Parameter C
Parameter D
Conventional
SolutionOne or a few more models created with selected input parameters
Parameter A
Parameter B
Parameter C
Parameter D
Improved Solution
Sample a range of values for each input parameter and run multiple models with all!
Challenge:
Turbidite Reservoirs
Turbidite Reservoirs
Where is my sand?
• Continuity of reservoir facies usually is an issue
• Especially difficult to model when there is scarce data from few wells
OK, found the sand. What about the properties?
• Quality of sand usually show variation within identified “sand” intervals
Let’s give it a go!
• History match difficulties
• Many iterations necessary within disciplines
• Even a solution is found, validity of the model is usually questionable
Solution:
CMOST-Petrel link
CMOST-Petrel Link
STEP 1: Create workflow that generates multiple geological realisations based on a set of parameters
• Simulation seed, porosity limits and variation, sand bodies extent and orientation, shale content
STEP 2: Run the workflow manually once to generate origin for all subsequent runs (Petrel + IMEX)
STEP 3: Prepare the workflow in CMOST
• Define static and dynamic parameters and their ranges that will control Petrel and CMOST workflows
STEP 4: Perform dynamic runs
• Initiate a loop that automatically creates static models in Petrel and exports them directly to IMEX through CMOST
• Run until satisfactory number of matches are achieved or go back to previous steps
STEP 5: Select static/dynamic models for forecasts and well proposals
CMOST-Petrel Link
CMOST creates experiment (s),
defines parameters,
initiates Petrel silent run
Petrel opens,
runs the workflow and closes.
If there is no licence
available, process stops!
CMOST converts exported
ECLIPSE props to .inc files,
Creates simulation data set and
sends it to CMG scheduler
Simulation model is run
If there is no licence available,
scheduler waits until one is free.
Process continues.
CMOST reads the results,
updates proxy functions, deletes
unnecessary the data sets
START
END
OBJECTIVE
ACHIEVED
ITERATE
Petrel workflow defined
and CMOST links
prepared
Example:
A Field in West Africa
NTG
NTG
NTO
BATANGA
Transmissibility Barriers
Compartment Oil Volumes:OCM-01 2.8 MMstbOCM-02 2.8 MMstbOCM-03 8.2 MMstb
W-03
W-01
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A Field In West Africa
WELL-01
WELL-02
WELL-03
WELL-04 P&A
WELL-05 P&A
WELL-01 sidetrack(Hole lost during completion)
Gas lift started Field shut in due tolift gas availability
Lift gas fromavailable
Field
P&A
Oil producer
W-04
W-03
W-01
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W-05
• Significant STOIIP (60 – 200 MMstb), but small Np (6 MMstb) & only 5
wells, 2 currently in production from 2 reservoirs stacked (RES1 &RES2)
• Variable reservoir quality observed in the wells
• Mat.Bal. suggests limited connected volume, 10 – 15 MMstb
• Conventional history match requires boxes around wells
• Practically no benefit from dynamic model forecasts
Manually History
Matched Model:
W-01 W-02
W-03
A Field In West Africa – Dynamic modeling evolution
Mat.Bal.
match achieved with each well
represented by a single tank!
RES1
RES1
RES1RES1
RES2
RES2
W-01
W-02
W-03
Initial dynamic model
field pressure overestimation
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RES1
RES2Transmissibility Barriers
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FIELD
Sand lobes modelled
artificial barriers needed to match history
RES1 Net to Gross
RES1
RES1 Net to Gross
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FIELD FIELD
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RES2
RES2 Net to Gross RES2 Net to Gross
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FIELDFIELD
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A Field In West Africa – Dynamic modeling evolution
Final dynamic model using conventional (manual) approach
good match still not achieved after numerous (!) iterations
Use
CMOST-Petrel
link to
generate more!
A Field In West Africa – Petrel Workflow Example
Defined properties
controlled by CMOST
Property creation section
(where the magic happens)
Input from all G&G disciplines
Export to IMEX
A Field In West Africa – CMOST Workflow Example
To
Petrel
Parameter Effect and Range
SEED_RES1 RES1 sand lobe creation seed. A total of 20 integers defined in CMOST.
SEED_RES2 RES2 sand lobe creation seed. A total of 20 integers defined in CMOST.
SAND_RES1 Overall sand content of RES1. Uniform distribution from 1% to 40%.
SAND_RES2 Overall sand content of RES2. Uniform distribution from 5% to 50%.
MEAN_WIDTH_RES1 Sand lobe mean width in RES1. Uniform distribution from 11 m to 950 m.
MEAN_WIDTH_RES2 Sand lobe mean width in RES1. Uniform distribution from 16 m to 1500 m.
PHIE1_MAX Maximum porosity value of sand facies 1 in RES2. Uniform distribution from 8% to 30%.
PHIE2_MAX Maximum porosity value of sand facies 2 in RES2. Uniform distribution from 8% to 25%.
PHIE3_MAX Maximum porosity value of sand facies 3 in RES2. Uniform distribution from 8% to 20%.
PHIE1_RES1 Maximum porosity value of sand facies 1 in RES1. Uniform distribution from 8% to 30%.
PHIE2_RES1 Maximum porosity value of sand facies 2 in RES1. Uniform distribution from 8% to 25%.
PHIE3_RES1 Maximum porosity value of sand facies 3 in RES1. Uniform distribution from 8% to 20%.
Parameter Effect
outputNTG Net to gross, defined as 0 or 1.
outputPERM Permeability i, calculated using poro-perm relationship. Perm i = Perm j = 0.1 x Perm k
putputPHIE Porosity, distributed based on upscaled well logs and sand lobe creation algorithm.
outputSW Initial water saturation, calculated using SHF.
outputSWIR Irreducible water saturation, calculated using SWIR function generated during SHF analysis.
From
Petrel
A Field In West Africa – CMOST Workflow Example
• Engage CMOST-Petrel-IMEX
engine
• Use assisted history matching
(AHM)
• Different algorithms available for
parameter generation
• Converge towards single solution
quickly, or
• Multiple history matches based on a
wider range of input parameters →
provides better forecast ranges, but
requires more runs
CMOST
objective
function
A Field In West Africa – CMOST-Petrel Process Overview
Many models with good History Match!
All from significantly different geological
realizations.
“Randomly” generate
geo realizations
Run simulations,
A lot of them…
Some will match
history!
RE
FIELD
A Field In West Africa – Results
W-01
W-03
W-02
W-03 W-03 W-03
W-02W-02W-02
W-01 W-01 W-01
29
67 65
31
51
16
122
10
8582
201
105
0
50
100
150
200
250
1996TennecoUpdate
2002Geological
Model
2010 MBAL 2016Geological
Model
2018 MBAL 2019DynamicModel
MM
stb
Octopus STOIIP Evolution (MMstb)
LOW CASE
BASE CASE
HIGH CASE
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
40 50 60 70 80 90 100 110 120 130
MMstb
Octopus STOIIP Cumulative Probability
Normal Probability DistributionAverage : 85 MMstbS. Deviation : 15.9 MMstb
65 MMstb
85 MMstb
105 MMstb
Field Field
A Field In West Africa – Results
Net Oil Thickness (m)Oct 1987
Net Oil Thickness (m)Dec 2018
Sweep EfficiencyDec 2018
FIELD_AHM_v10&v12 History Match#001
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W-04
W-02
W-01
W-05
W-03
W-04
W-02
W-01
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W-04
Net Oil Thickness (m)Oct 1987
Net Oil Thickness (m)Dec 2018
Sweep EfficiencyDec 2018
FIELD_AHM_v10&v12 History Match #007
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Nearly 15,000 runs performed
3 major versions of Petrel workflow implemented including
geophysical input
10 equally probable history matches achieved: STOIIP range
55 – 108 MMstb
No artificial barriers implemented
Examples of realizations
Conclusion
Conclusion
Uncertainty is captured and a range of possible outcomes were presented for more
robust decision making.
• No need to pick and select reservoir properties, eliminate user bias
• Increased reliability and predictability of the final model
Approach allows automatic link between geological/geophysical/engineering input,
model generation and feedback from dynamic output
• But requires more scrutiny to prevent unrealistic realisations
• More geological variations can be investigated resulting in more equally probable history matches and
better forecast capabilities
• Less need for arbitrary manual parameter modification outside of G&G description
Thank You
Questions?