upstream and technical services - UiS Laura Dovera IOR_Norway.pdf · upstream and technical services Robust (on multiple models) optimization ! EnKF output: ensemble of 100 matched

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Polymer injection optimization using ensemble methods Laura Dovera

IOR Norway 2015 28-29 April, Stavanger


§ Reservoir modelling study to implement a chemical Enhanced Oil Recovery (EOR) pilot in a giant on-shore brown field

§  quantification of EOR benefit: incremental oil and associated cash flow

§  confidence interval accounting for geological/engineering

uncertainties

§  Integrated workflow with Ensemble Kalman filter (EnKF) and Ensemble Optimization (EnOpt) §  Automatic integration of production data (history match)

§  Forecast optimization of chemical injection parameters

§ Uncertainty quantification with multiple models

Introduction


The field and the pilot area

§  Giant brown field §  Moderate recovery factor §  Onshore sandstone

environment §  Oil viscosity 3-8 cP §  About 200 prod. and 40 inj. §  Unfavorable mobility ratio

§  Multilayered field with 12 stacked reservoirs §  Turbiditic and deltaic sandstones with intercalations of shales and evaporites §  Production start-up in 1953 §  Current production strategy is peripheral water injection

IOR/EOR actions Why IOR/EOR?


Polymer flooding

§ Polymer flooding is one of the selected EOR techniques polymer vs. water injection

−  Higher viscosity µ polymer >> µW

−  Rock adsorption i.e. Krw reduction

à Lower mobility ratio

§ Actual mobility 6.5 à expected 1-0.5, with 3-6 cP target polymer

viscosity


Project activities

§  Selection of suitable area for pilot test §  Good petrophysical properties §  Current low recovery factor §  Possibility of implementing a dispersed injection scheme §  Short producers-injectors distance

§  New core data acquisition

§  Laboratories analyses §  Routine and special core analyses §  Chemical study for ad hoc polymer

§  Sector model construction §  Geological and dynamic model §  History match §  Definition and optimization of forecast strategy

wellspolymer injectorpolygon for local updating in the sector model

Legend

The pilot area


Sector model

Pilot area sector §  High resolution model

§  22 total wells

§  14 wells in commingle

§  6 producers, 2 injectors to match

§  Complex history match

§  60 years production

§  Few pressure data

§  Communication with the rest of the field

§  Challenges:

§  Define multiple models to represent uncertainty

§  Automatic multiple history match with EnKF


§  The Ensemble Kalman filter is a sequential data assimilation method to update dynamic systems

§ Monte Carlo approach: representation of initial uncertainty using an ensemble of models § Geostatistics and/or parameters sampling

§  Forward step: evolution of uncertainty by forward ensemble models in time § Eclipse simulation

§ Assimilation of measurements using an error minimizing update § Update parameters and state variables

The Ensemble Kalman Filter (Evensen, 2006)

SEQUENTIAL ASSIMILATION


The EnKF idea

Water Cut

Time

Measurements with errors

Original prediction

Models prediction

Updated estimate

New models prediction

Updated estimate

New models prediction

Updated estimate


EnKF on pilot area – Ensemble generation

§  NTG

§  Porosity

§  Permeability

§  Water Saturation = fun(Porosity)

Multiple porosity grid Multiple permeability grid

•  Spatial variables •  Generated using geological workflow

§  Water rel. perm.

§  Faults Transmissibility

•  Scalar variables •  Sampled within uncertainty ranges


§  EnKF updating: realizations are adjusted adding data mismatch weighted by the Kalman Gain matrix

Localization

Permx‘ = Permx + (dobs-dsim)

Kalman gain weights for permeability in layer 5 Probable spurious

correlations

Probable true correlations

Pressure data Assimilation well I2


§  EnKF updating: realizations are adjusted adding data mismatch weighted by the Kalman Gain matrix

Localization

Kalman gain weights for permeability in layer 5

= *

Localization region Localized Kalman gain

Pressure data Assimilation well I2


EnKF on the pilot area – Summary

Multiple porosity realizations Multiple permeability realizations §  Ensemble size: 100

§  Ensemble generation using 3 spatial and 6 scalar parameters

§ Match of 8 wells §  Localization for each well and each measurements

§ Run EnKF for 42 assimilation steps § Rerun of the final updated realizations


EnKF on the pilot area - History match for PROD P4 PROD P4 PROD P4

PROD P4


EnKF application – Final properties update

Initial average Lnk map - layer 5 Final average Lnk map - layer 5

§ Geological coherence is preserved

§ No manual modifications


Robust (on multiple models) optimization

§  EnKF output: ensemble of 100 matched models that represent the current geological uncertainty

§ Single scenario optimization: major risk of underestimating uncertainty, even the P50 could be a very unlikely model!

§ Can we perform a robust optimization - where the target is to improve the statistical outcome of the models ?

Optimization of a single profile

Robust Optimization of a profile distribution

p90

p10

p50


Ensemble Optimization (Chen, 2008)

§  Define optimization parameters and objective §  polymer concentration, rates, … §  cash flow, cumulative oil, …

§  Generate an ensemble of controls and couple it with the ensemble of realizations

§  Use the ensemble to investigate the objective

§  Compute gradient with the ensemble

§  Move the ensemble towards the maximum

),( jujNPVg xy=

),,( 1 cNxx …=xeNjj

uj 1},{ =xy


Polymer flooding optimization with EnOpt

Polymer injector

§ Optimize polymer injection to increment Cash Flow § Over 10 years §  For the TARGET wells

§ Optimization parameters:

§  Polymer slug concentrations (kg/sm3) § Slug cycles (time) §  3 different slugs

§ Comparison to § Natural depletion scenario § Water injection scenario

Target wells


Polymer flooding optimization – Results

Actual Development Strategy

Water Injection Strategy

Polymer Injection Strategy

Polymer injection profiles guarantee the highest oil

rates



P50 profiles

1st Polymer slug viscosity 3 cP

Initial water pre-flush

2nd Polymer slug viscosity 6.5 cp Driving water

injection



P50 profiles Polymer injection average incremental recovery equal to 16%

Water injection average incremental recovery equal to 10%



P50 profiles Produced water is reduced by 11% using polymer with respect to

the water injection scenario


Polymer flooding optimization – Summary

Development scenario

Additional Oil Production at 2023

(P50)

Additional technical Cash Flow at 2023

(P50)

% %

No Injection - -

Water Inj. + 10 % + 3 %

Polymer Inj. + 16 % + 8 %

§ Reference values for technical cash flow evaluation § Oil price: 90 $/bbl §  Treated water cost: 1 $/bbl §  Polymer cost: 4.25 $/kg § CAPEX polymer pilot: 2.0 $ million


Current status of project

§ Dispersed water injection implemented in the pilot area

§  Polymer plant commissioned

§ Start-up of polymer injection planned for Q3/2015

§  Injection strategy will be implemented according to optimization results

§ A proper monitoring plan is on going


Conclusions

§ Multiple sector models §  Integrated workflow from static to dynamic

§ Multiple history matching using the EnKF § Good quality history match §  No manual modifications

§ Robust optimization of polymer flooding §  Polymer flooding is cost-effective §  Average cumulative oil = +16% §  Average Cash Flow = +8%

§ Closed loop workflow §  First iteration: ensemble HM and optimization §  Interesting way forward - Close the loop: update the ensemble with

most recent data and use it for continuos real time monitoring and optimization

The closed loop

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upstream and technical services - UiS Laura Dovera IOR_Norway.pdf · upstream and technical services Robust (on multiple models) optimization ! EnKF output: ensemble of 100 matched