1. Neuquen EOR workshop - November 2010
Application of an Advanced Methodology for the Design of a Surfactant Polymer Pilot in Centenario P. Moreau1, M. Morvan1; B. Bazin2, F. Douarche2, J-F. Argillier2, R. Tabary – Rhodia 2 – IFP Energies Nouvelles

2. Outline Introduction Chemical EOR (ASP/SP) – Basics
Rhodia-IFP énergies nouvelles & partners
An integrated workflow
Process & material selection
Chemical formulation optimization
Coreflood validation
Simulation
An Illustrative Case study
Conclusion & Perspectives

3. Chemical EOR (ASP/SP) - Basics
After waterflood, oil remains trapped in reservoirs because of capillary trapping at Sor
Oil displacement (typical Residual Oil Saturation  70%)
Capillary trapping
Mobility control to drive the surfactant slug and bank the oil to the production well
Water
Oil
Waterflood
The only realistic way is to drastically decrease the interfacial tension ()
Optimized surfactant formulations
100 m
Illustration of capillary trapping in micromodels (developed at Rhodia LOF).
Water Saturation
w = oil
w = 0.1 oil
Surfactant slug integrity is secured by controlling mobility ratio
Polymer

4. Bring together the capabilities required for Chemical EOR…
World-class geosciences public-sector research
Global leader in specialty chemicals and formulation
Independant E&P consulting and software editor (IFP subsidiary)
Polymer technologies for IOR and well performance 4

5. …With integrated solutions
EOR methods screening
Integrated reservoir analysis
Selection of EOR methods
Laboratory design
Process & Material selection
Chemical formulation optimization
Coreflood validation
Lab-scale simulation
Impact on water management
Pilot design
Numerical simulation at pilot scale
Pilot economics
Surface facility conceptual design
Pilot implementation / Full field extension
Field management and monitoring
Expertise and assistance to operations
Full-field surface facility design
Dedicated supply-chains
High-volume logistics
Large-scale manufacturing 5

6. An integrated workflow
Step 1
An integrated workflow
Step 2
Step 3
Step 4
Chemistry & Reservoir engineer competencies for selecting appropriate process and chemicals
High Throughput Screening (HTS) capabilities are critical to test large number of chemical combinations & provide optimized and robust formulations
Increase in oil recovery and minimum adsorption must be demonstrated in cores.
Lab-scale simulations are required before Up-scaling and injection strategy definition – Physics from SARIPCH implemented to full field simulators

7. Step 1: Process and Material Selection
Critical information for process selection
Reservoir temperature
Brine composition (divalent ions, TDS...)
Salinity distribution inside the reservoir
Oil properties (API, viscosity, acid number)
Rock properties (clay content, permeability)
Ca2+ (ppm)
Calcium concentration distribution calculated after waterflooding
Alkali:
Different alkalis are available depending on salinity and temperature.
Divalent ions concentration is critical for the use of alkali. Possible hurdles at very high temperature.
Surfactants:
Surfactant portfolio: olefin sulfonates, alkoxylated alcohols, sulfated/sulfonated alkoxylated alcohols, alkyl aryl sulfonates.
Raw material selection and process are critical.
Industrially representative samples are essential to guarantee pilot performances.
Polymer:
Polymer is a case by case selection with permeability, temperature and salinity limitations.
The most promising EOR chemicals are pre-selected
according to reservoir conditions

8. Step 2: Chemical formulation optimization
Microemulsion phase behavior
Winsor classification
III I II
Salinity (g/l)
Optimal formulation
Interfacial tension vs microemulsion
Variability for different reservoirs
Oil (composition, viscosity)
Reservoir parameters (T, P…)
Heterogeneities in a given reservoir
Salinity, temperature gradients
Oil and rock properties
Chemicals selection & Formulation optimization is necessary for each reservoir
Robustness of the formulation must be evaluated
4000+ formulations are required for a small design study.
HTS tools are necessary

9. Step 2: Chemical Formulation Optimization
Automated formulation and analysis
Automated formulation
Imaging & Image processing
Selection of the best formulations
Further optimization of chemicals concentrations and ratios
A fully automated formulation and optimization workflow
Data generation for improved simulations
water/microemulsion
oil/microemulsion
Robotic platform
Salinity (g/L)
Optimal Salinity
Microemulsion
Solubility
Morvan et al. SPE (2008)

10. Step 2: Chemical Formulation Optimization
Adsorption tests
Adsorption depends mainly on pH
Alkali can be used in soft brines
Compatibility with hard brines could be challenging
A specific evaluation (pH vs. solubility) is necessary depending on reservoir conditions
Static adsorption of an olefin sulfonate
on Na-Kaolinite as a function of pH
pH of hard brines with alkali
Dynamic adsorption in sandpacks or cores
Surfactant adsorption from breakthrough time
Hydrodynamic retention from plateau chemicals concentration DV
Surfactant adsorption profiles in different brines

11. Step 3: Coreflood validation
Formulation injectivity/plugging is assessed
Millifluidic setup with calibrated cores
Single phase flow injectivity test prior to coreflood
1.05 cp solution
74 mD ΔP
Formulation injectivity test
Oil recovery experiments
Characterization of core material (CT scan, RMN, HPMI...)
Petrophysics data
Relative permeabilities vs saturation
Capillary desaturation curve
Analysis
Oil recovery efficiency
Surfactant mass balance
Alkali propagation
Mobility control
Pressure monitoring…
Recovery experiments at reservoir conditions (live oil, pressure, temperature)

12. Step 3 : Core flood validation
Design – Injection with Salinity Gradient
A salinity window is defined in a range of salinity extending from the produced water to the injection water
Surfactant formulation optimum salinity is optimized inside the salinity window to meet the three phase region during displacement.
Additional advantages
Surfactant desorption with salinity gradient at the rear
Good mobility control at the rear of surfactant slug
Preparation of the surfactant formulation in low salinity water improves solubility.
The injection strategy depends on:
Field conditions
Brines & water management issues (river or sea water and production brine; water treatment)
Available ground facilities
A specific injection strategy must be optimized for each pilot

13. Step 4: Simulation from Lab to Reservoir scale
SaripCH is a prototype simulator for chemical EOR
Black oil simulator with mass balance equations for chemicals (Alkaline, Surfactant, Polymer)
Physics implemented
Capillary desaturation curve and Kr, Pc curves
Surfactant IFT with salinity gradient
Surfactant adsorption with salinity gradient and pH
Polymer physics
Additional options: ion exchange with clays,calcium carbonate dissolution/precipitation
SaripCH simulations at lab scale
Modeling of coreflood experiments
Model calibration prior to pilot simulations
Optimization of injection strategy & sensitivity analysis
Experimental tables or
analytical expressions are validated with core displacements
Cumulative oil
Oil cut
PumaFlow (Beicip Franlab) simulations at pilot scale (same physics)

14. An illustrative case study (1&2/4)
Model reservoir characteristics
Temperature: 60°C
Production brine: 50 g/L NaCl
Injection brine: mixture production/fresh water
Model oil: EACN 12
Rock: sandstone
Permeability: medium
Formulation design
Surfactants mixture:
Olefin sulfonate
Alkyl Ether Sulfonate
Cosolvent: short chain alcohol
Alkaline: Na2CO3 (10 g/L)
Polymer: HPAM (MW  6MD)
Optimum salinity: 36 g/L
x1000 formulations
Process & material selection
Process: ASP
Alkali: Sodium carbonate/metaborate
Surfactants: Sulfonates
Polymer: HPAM
Formulation performances
Ultra-low interfacial tension (10-3 mN/m)
Excellent solubility/injectivity
Acceptable adsorption (150 g/g)

15. An illustrative case study - Formulation
Optimal salinity
Solubilization ratios
Surfactant slug
Formulation design for a salinity gradient strategy
Injection is done with a salinity gradient in order to promote WII-WIII-WI transition during flooding
The scenario is illustrated here
Polymer drive
Formation brine
Surfactant slug
salinity
Polymer
Chase water
salinity
Solubility
Salinity
Microemulsion

16. An illustrative case study – Core Flood
Injection strategy in a salinity gradient
Slug size
(PV)
Salinity NaCl
(g/L)
Surfactant Conc.
Alkaline Conc.
Polymer Conc.
Waterflooding - 50
Alkaline Surfactant
0.5 30 8 10
Polymer 2 25 1
Chase water 5
Oil Recovery pH
Surfactant conc.
Oil cut
Cumulative oil
The oil bank occurs at 0.3 PV
Oil saturation after surfactant flooding is 18% (65% of the oil remaining after waterflooding has been recovered)
Excellent pH propagation
No formation damage (mobility reduction compared to relative viscosity)

17. An illustrative case study – Simulation at lab scale
Use of in-house SaripCH simulator to reproduce coreflood results
Input data
Extensive data from
petrophysics & formulation
Simulation results
Excellent oil recovery prediction
Good surfactant adsorption
profile
IFT = f (Composition)
Accurate predictive simulations with a limited number of adjustable parameters
Same physics implemented for pilot design

18. An illustrative case study – Simulation at pilot scale
Use of in-house SaripCH simulator at pilot scale with input data from lab steps
Reservoir - Input data
Geometry: 3 layers (layer cake)
Reservoir Thickness: 13 m
X-Y linear extension: m
Irreducible water saturation: 0.35
Residual oil saturation: 0.32
Injection - Input data
Slug size
(PV)
Surfactant Conc.
(g/L)
Polymer Conc.
(mg/L)
Alkaline preflush
0.5 -
SP formulation
2.5
200
Polymer
0.3
500
Chase water
2.3
Simulation
Quarter 5-spot
Grid: 75x75x3 (16875)
Wells: 1 injector, 1 producer

19. An illustrative case study – Simulation at pilot scale
Use of in-house SaripCH simulator at pilot scale – Sensitivity study
Sensitivity to surfactant concentration
Surfactant concentration is a critical parameter
and must be optimized together
with the surfactant slug size to achieve
the best economical design
Sensitivity to adsorption
Surfactant consumption by adsorption is
extremely costly in terms of oil recovery
Sensitivity to slug size
Base case: 0.3 PV with 20% ROIP recovery
Low additional oil recovery with higher slug size:
22 % ROIP with 0.5 PV of ASP injection
25 % ROIP with 1.0 PV of ASP injection
IFT = f (Composition)
Optimization of a pilot injection

20. Conclusions Methodology deployed for multiple customers worldwide
The integrated workflow presented here is based on:
A fast identification of the best chemicals for given field conditions
An extensive optimization study thanks to robotized techniques
Core flood experiments for adsorption and oil recovery determination
Optimization at pilot scale with simulations using extensive experimental input data
Next step: development at reservoir scale
Chemical reservoir model available (PumaFlow)
Sensitivity analysis
Optimization of injection strategy
The integrated workflow presented here is based on:
A fast identification of the best chemicals for given field conditions
An extensive optimization study thanks to robotized techniques
Core flood experiments for adsorption and oil recovery determination
Optimization at pilot scale with simulations using extensive experimental input data
Methodology deployed for multiple customers worldwide

21. Thank you for your attention