co plume prediction at the top surface using history ... · masoud ahmadinia 1, seyed shariatipour...

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CO2 PLUME PREDICTION AT THE TOP SURFACE

USING HISTORY MATCHING TECHNIQUE

Masoud Ahmadinia1, Seyed Shariatipour1, Odd Andersen2, Mahdi Sadri1

1 Centre for Fluid and Complex Systems, Coventry University2 SINTEF Digital, Mathematics and Cybernetics

UKCCSRC NETWORK CONFERENCE,

CARDIFF UNIVERSITY, APRIL 2019

ABOUT ME

Education

• Ph.D. Flow Measurement and Fluid Mechanic, Coventry University

• M.Sc. Petroleum Engineering, Polytechnic University of Turin

• M.Sc. Environmental and Land Planning Engineering, Polytechnic University of Milan

• B.Sc. Petroleum Engineering, Shiraz University

Work Experience

• TNO. UTRECHT, NETHERLANDS (Internship)

• SINTEF. OSLO, NORWAY (Research placement)

CO2 PLUME PREDICTION AT THE TOP SURFACE USING HISTORY MATCHING TECHNIQUE | Masoud AhmadiniaUKCCSRC NETWORK CONFERENCE,


CENTRE FOR FLUID AND COMPLEX SYSTEMS

• Effects of multi-phase fluid flow on CO2 storage and security

Michael Onoja, 4th year

• Improving oil and gas recovery through optimal flow measurement

Mahdi Sadri, 3rd year

• Numerical and experimental analysis of CO2 leakage through well cements

M. Reza Bagheri, 2nd year

• Developing new techniques to accelerate CO2 dissolution in brine in downhole conditions

Mohsen Abbaszadeh, 2nd year

• Structural trapping mechanisms in CO2 storage capacity and security

Azadeh Pourmalek, 2nd year



HISTORY MATCHING - OBJECTIVES

• Improve and validate the reservoir simulation model

• Better understanding of reservoir processes

• Improve the reservoir description and data acquisition program

• Identify unusual operating conditions



HISTORY MATCHING - METHODS

Manual

• Run simulation for

historical period

• Compare results to

actual field data

• Adjust simulation input

to improve match

• Selection of input data

based on knowledge

and experience



Automatic

• Minimizes the objective

function; i.e., difference

between observed reservoir

performance and simulation

results

HISTORY MATCHING – COMMON PARAMETERS



• Aquifer size

• Vertical permeability barriers

• Flow capacity, kHh

• kV/kH ratio

• Pore volume

• Relative permeability

CO2 PLUME DEVELOPMENT @ SLEIPNER

• World's first industrial-scale GCS project.

• Time-lapse seismic monitoring data are available from 1996 to 2010.



CURRENT STUDY

• The numerical simulation is performed on a synthetic model with a

specific slope and rugosity pattern.



𝑧 𝑥, 𝑦 = A[sin 𝜔1𝑥 + sin ሿ𝜔1𝑦 + 𝑨𝒙 sin 𝜔2𝑥 +𝑨𝒚 sin 𝜔2𝑦 + 𝑥 tan 𝑺𝒙 + 𝑦 tan 𝑺𝒚

CURRENT STUDY



𝑧 𝑥, 𝑦 = A[sin 𝜔1𝑥 + sin ሿ𝜔1𝑦 + 𝑨𝒙 sin 𝜔2𝑥 +𝑨𝒚 sin 𝜔2𝑦 + 𝑥 tan 𝑺𝒙 + 𝑦 tan 𝑺𝒚

Observed Initial guess Calibrated

CURRENT STUDY

• The results of the plume shape is recorded and subsequently

reinterpreted as "observed" data.

• A new synthetic model is generated, without knowledge of the first.

• This model could be regenerated to change rugosity magnitude and

slope directions.



MODEL PARAMETERS



Parameter Value

Reservoir dimensions (NX×NY×NZ) 101×101×4

Reservoir size (km) (LX×LY×LZ) 15×15×0.1

Cell size (m)DX×DY 148.5

DZ 25

Rock compressibility (1/bars) 4.35E-5

Water density (kg/m3) 1000

CO2 density (kg/m3) 745.6

Residual water saturation (Srw) 0.27

Residual CO2 saturation (Src) 0.20

Permeability (mD) 5

Porosity 0.2

Simulation period (years) 1010 (100 × 0.1 years + 100 × 10 years)

Number of time steps 200

Water viscosity (Pascal-second) 8.0E-4

CO2 viscosity at 150 bar (Pascal-second) 6.4E-5

VERTICAL EQUILIBRIUM



• Large disparity in lateral and vertical scales, plus differences in density

between the supercritical CO2 plume and the resident brine vertical fluid

segregation will be almost instantaneous compared with the up-dip

migration.

• The flow of a thin CO2 plume in 3D can be approximated in terms of its

thickness to obtain a 2D simulation model

INITIAL GUESSES & SEARCH RANGE FOR EACH OF THE PARAMETERS



Ax

(meter)

Ay

(meter)

Sx

(meter)

Sy

(meter)

Limits for scenario a 0-10 0-10 0-5 0-5

Limits for scenario b 0-20 0-20 0-10 0-10

Observed 9 7 2 3

Case# of

iterations

Initial guess

Ax

(meter)

Ay

(meter)

Sx

(meter)

Sy

(meter)

1a 12

0 0 0 0b 17

2a 6

5 5 1 2b 11

3a 7

15 12 0 0b 14

4a 8

15 12 6 7b 7

5a 9

20 20 10 10b 9

• Simulations are performed on the

new synthetic model, and coupled

with a nonlinear optimization

routine, where the investigated

parameters (slope, rugosity

magnitude) are varied, with a view

to match the original "observed"

data.

OPTIMIZED VALUES FOR ALL THE CASES



case 1a 1b 2a 2b 3a 3b 4a 4b 5a 5b

Error (%) 16.41 1.01 1.37 1.78 5.33 1.81 0.49 0.45 0.54 0.36

RESULTS & CONCLUSION

• The results were able to reproduce the impact of caprock

topography on the plume evolution.

• Uncertainties in caprock slope and rugosity may impact the

simulation outcome.

• Setting tighter bounds for the parameter ranges did not result

in a better match, which can be explained by the presence of

local optima.



16

Thank You

UKCCSRC NETWORK CONFERENCE,


co plume prediction at the top surface using history ... · masoud ahmadinia 1, seyed shariatipour...

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