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

W-02

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

W-02

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

W-01

W-02

W-03

RES1

RES2Transmissibility Barriers

W-01

W-02

W-03

FIELD

Sand lobes modelled

artificial barriers needed to match history

RES1 Net to Gross

RES1

RES1 Net to Gross

W-03

FIELD FIELD

W-03

RES2

RES2 Net to Gross RES2 Net to Gross

W-01

W-02

FIELDFIELD

W-01

W-02

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

W-02

W-01

W-05

W-03

W-04

W-02

W-01

W-05

W-03

W-04

W-02

W-01

W-05

W-03

W-04

Net Oil Thickness (m)Oct 1987

Net Oil Thickness (m)Dec 2018

Sweep EfficiencyDec 2018

FIELD_AHM_v10&v12 History Match #007

W-02

W-01

W-05

W-03

W-04

W-02

W-01

W-05

W-03

W-04

W-02

W-01

W-05

W-03

W-04

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?