Coreflooding Simulations - ULisboa · – History Match – Sensitivity Analysis 3/May/2016 Instituto Superior Técnico 3. Presentation Overview Phase 2: Petro-Physical 3D Model (PETREL)

ANNUAL MEETING MASTER OF PETROLEUM ENGINEERING

Mahesh Avasare

Masters in Petroleum Engineering, IST Lisbon

Bachelors in Chemical Engineering, IIT Bombay

3/May/2016 Instituto Superior Técnico

Coreflooding Simulations

1

Chemical Coreflooding Simulations with Digital Rock Physics

Acknowledgement

I am grateful to my guide, Pedro Romero Fernandez,

and rest of Exploration and Production team at R&D

center of CEPSA for continuous guidance & support.

I am thankful to Prof. Maria João Pereira and Prof.

Leonardo Azevedo for offering such dynamic internship

opportunity. Also thankful to Prof. Amílcar Soares for

constant motivation during the internship.

3/May/2016 Instituto Superior Técnico 2

Presentation Overview

Introduction

– Study Motivation

– Basics of Chemical EOR

– Basics of Coreflooding

Phase 1: ASP Flooding in 1D Model

– ECLIPSE Model

– History Match

– Sensitivity Analysis


Presentation Overview

Phase 2: Petro-Physical 3D Model (PETREL)

– CT Scan Results (Digital Rock Physics)

– Density-Porosity-Permeability Models

Phase 3: Simulation 3D Model

– History Match

– Sensitivity Analysis

Further Work


Introduction: Study Motivation


Core analysis results are considered as most reliable data in

the Exploration & Production.

Properties derived from core analysis are extrapolated to

reservoir scale; assuming cores are homogenous.

In reality; cores are heterogeneous at micro level. This leads

to approximation of properties; leading to higher uncertainty.

The study is aimed at reducing uncertainty from core analysis

Introduction: Chemical EOR


Recovery Mechanisms

(Schmidt, 1990)

Introduction: Chemical EOR


Surfactant and Polymer Effects:

Chemical EOR Fundamentals, Delshad 2012

Introduction: Coreflooding


Injection of fluid(s) into the core, mainly to analyze the response of the core.

Chemical EOR techniques are always simulated on the core before advancing to piolet well test.

Image Courtesy: CEPSA-CIMNE



Standard Coreflooding Setup:

Courtesy: CEPSA R&D Center



Picture Courtesy: CEPSA R&D Center

Phase 1: 1D Model


Real core dimensions:

Diameter: 38.5 mm

Length: 242 mm

Cartesian Model: 100 X 1 X 1

Dx = 0.385mm

Dy = Dz = 242mm

Simulations are performed in ECLIPSE 2014.1

Phase 1: History Match




Phase 1: Sensitivity Analysis


Interfacial Tension (IFT) Variation:



Viscosity Variation (By varying polymer concentration):

Phase 1: Summary


Conclusion:

Homogenous 1D model was able to generate good history

match with lab data

Sensitivity analysis also showed expected trends.

Disclaimer:

Relative permeability curves

were modified unrealistically

to attain history match.

Phase 2: CT Scan of Core


Imported CT scan results into

Petrel as “seismic survey”

Model dimensions:

35.4x35.4x50.6 mm

Orthogonal grid

Resolution: 500x500x625 μm

Total Grid Cells: 408k

Active Grid Cells: 316k

Phase 2: Attenuation to Density


Correlation between average attenuation of complete core vs

bulk density of the same core was used.

Phase 2: Density to Porosity


Core is assumed to be made of “Grain” & “Fluids”:

- “Grain” component is assumed to be of mainly quartz (98% wt/wt)

and other impurities. [Average 𝜌𝐺𝑟𝑎𝑖𝑛 = 2.60 gm/cc ]

- “Fluids” component is assumed to be of oil + water with known

composition from material balance. [Average 𝜌𝐹𝑙𝑢𝑖𝑑 = 0.84 gm/cc ]

“Grains” are supposed to be distributed uniformly in core

and “liquid” is uniformly distributed in pore spaces.

Local porosity was determined with following

correlation:

Porosity = 1 −ρBulk − ρFluidρGrain − ρFluid

Phase 2: Density to Porosity


Phase 2: Porosity to Permeability


‘Mercury Injection Method’

is used to find pore throat

distribution.

With past data of several

cores, the core was

categorized into category

Rock Type 2: ‘RT2’.

(There are 4 rock types.)

Permeability – porosity

correlation for RT2 was

extrapolated to generate

permeability model.

Phase 2: Summary


Conclusion:

Porosity Model generated through above method largely follows

experimental trend.

Permeability model can not be verified with experimental

conditions due to complex nature.

Permeability distribution is generated with single rock-type

curve, generating homogeneity across the core.

Phase 3: 3D Simulation Model


Simulation Issues Resolved:

Well Geometry Integration

Convergence Error

Simulation Time Optimization



Simulation Targets:

Regeneration of un-swept oil zones

Restraining realistic relative permeability curve

Achieving theoretical history match



Simulation Targets:

Regeneration of un-swept oil zones

Restraining realistic relative permeability curve

Achieving theoretical history match







Sensitivity Analysis Targets:

- To create heterogeneity in model within realistic boundaries

- To dominate heterogeneity with extremity effect

Sensitivity Models*:

- Permeability Distribution Alterations

- Relative Permeability Curve Alterations

(*only basic 3 models are mentioned as representative)

Phase 3: Sensitivity – Permeability Dist.


According to “Genetic Hydraulic Unit”, permeability distribution follows log-normal distribution.

To maintain the natural distribution trend, and increase the standard deviation, the permeability distribution was co-krigedwith another reference lognormal distribution

Reference normal distribution can be described as:

Mean = 2700 Std. Deviation = 3000

Minimum = 0.025 Maximum = 25000


The resulting distribution:

(Original distribution: Dark blue | Model 2: Light blue)

Original

Distribution

(mD)

Resulting

Model

(mD)

Min: 3 31

Max: 50521 24994

Mean: 3597 2741

Standard

Deviation:2445 2711







Phase 3: Sensitivity – Rel. Perm. Curve

The original relative permeability curve used till now, was based on data from past experience from field

End points of the relative permeability curve were achieved from lab experiments, giving reliable data points. “Corey Exponent” (n) of these curves altered as follows:

Original

Curve

(kr Original)

Modified

Curve

(Kr1 curve)

Swi 0.21 0.21

Sro 0.23 0.23

n water 2 1

n oil 1.3 5

krw,ro 0.24 0.24

Kro,wi 1 1






Phase 3: Summary

Conclusion:

Alterations in Permeability Distribution modifies initial oil

production trend, but final saturation levels remains same.

Alterations in Relative Permeability Curve significantly altered oil

production curve, but deviates slightly from reality.

Both sensitivity analysis were not able to generate oil un-swept

zones.


Further Work

High heterogeneity was

generated with applying

different permeability vs

porosity curves for

different rock types.

Modified Permeability Distribution

*Distribution was generated on the

basis of percentage of each rock

type in the reservoir.


Further Work

Each rock type exhibits different relative permeability curve.


Further Work

Combination of above

different permeability

distribution and

corresponding

relative permeability

models generated un-

swept oil zones.

Further studies will be targeted at running experimental

Chemical EOR flooding and corresponding simulations.


Bibliography

• Abadli F.; Simulation Study of Enhanced Oil Recovery by ASP Flooding for Norne

C-Field; NTNU, 2012

• Bozorgzadeh M., Romero Fernandez Pedro; Strategy for Calibrating a Field-Scale

Numerical Simulation Study; SPE Conference; Abu Dhabi, 2015

• Mojdeh D.; Chemical Enhanced Oil Recovery Fundamentals; Madrid, 2012

• Sadegh K.; Numerical approach for enhanced oil recovery with surfactant flooding;

Petroleum, 2015.

• Pope G., Chemical Flooding Overview, UT Austin, 2007

• Schmidt R. L.; Thermal Enhanced Oil Recovery - Current Status and Future

Needs; Chemical Engineering Progress, 1990


Thank You !

Documents

Coreflooding Simulations - ULisboa · – History Match – Sensitivity Analysis 3/May/2016 Instituto Superior Técnico 3. Presentation Overview Phase 2: Petro-Physical 3D Model (PETREL)