Neuquen EOR workshop - November 2010

1. Neuquen EOR workshop - November 2010 Application of an Advanced Methodology for the Design of a Surfactant Polymer Pilot in CentenarioP. Moreau1, M. Morvan1; B. Bazin2, F. Douarche2, J-F. Argillier2, R. Tabary21 – Rhodia2 – IFP Energies Nouvelles

2. 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 2

3. 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

4. Oil Water Waterflood w = 0.1 oil Water Saturation w = oil 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 Optimized surfactant formulations • The only realistic way is to drastically decrease the interfacial tension () 100 m Illustration of capillary trapping in micromodels (developed at Rhodia LOF). • Surfactant slug integrity is secured by controlling mobility ratio Polymer

5. Step 1 Step 2 Step 3 Step 4 An integrated workflow... A reservoir engineering approach from lab to pilot simulation Expertises 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 Towards pilot simulation with a commercial simulator

6. …With integrated solutions • EOR methods screening • Integrated reservoir analysis • Selection of EOR methods • Laboratory design – A 4 steps methodology • 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 6

7. Step 1 Step 1: Process and Material Selection Step 2 Step 3 Step 4 • Critical information for process selection from reservoir data • 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. I II III Step 1 Step 2: Chemical formulation optimization Step 2 Step 3 Step 4 • Microemulsion phase behavior • Winsor classification 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. Salinity (g/L) Microemulsion Morvan et al. SPE113705 (2008) Optimal Salinity Solubility water/microemulsion oil/microemulsion A fully automated formulation and optimization workflow Data generation for improved simulations Step 1 Step 2: Chemical Formulation Optimization Step 2 Step 3 Step 4 • Automated formulation and analysis • Automated formulation • Imaging & Image processing • Selection of the best formulations • Further optimization of chemicals concentrations and ratios Morvan et al. SPE113705 (2008)

10. Step 1 Step 2: Chemical Formulation Optimization Step 2 Step 3 Step 4 • 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 1 Step 2 Step 3 Step 4 ΔP Step 3: Coreflood validation with dedicated tools • Formulation injectivity/plugging is assessed • Millifluidic setup with calibrated cores • Single phase flow injectivity test prior to coreflood 1.05 cp solution 74 mD 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 1 Step 2 Step 3 Step 4 Step 3 : Core flood validation and strategy • 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 1 Step 4: Simulation from Lab to Reservoir scale Step 2 Step 3 Step 4 • 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 • SaripCHsimulations 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 commercial simulator forsimulations at pilot scale Validation of simplified physics

14. SARIPTM simulation: comparison with other simulators prototype commercial

15. An illustrative case study • 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 • 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 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)

16. Surfactant slug Surfactant slug salinity Reservoir brine An illustrative case study - Formulation Optimal salinity Solubilization ratios • 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 Polymer Chase water salinity Solubility Salinity Microemulsion

17. Cumulative oil Oil cut pH Surfactant conc. An illustrative case study – Core Flood • Injection strategy in a salinity gradient • Oil Recovery • 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)

18. 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

19. An illustrative case study – Simulation at pilot scale Simulations 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: 267.75 m • Irreducible water saturation: 0.35 • Residual oil saturation: 0.32 Injection - Input data • Simulation • Quarter 5-spot • Grid: 75x75x3 (16875) • Wells: 1 injector, 1 producer

20. An illustrative case study – Simulation at pilot scale Simulations at pilot scale – Sensitivity study • 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 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 IFT = f (Composition) Optimization of a pilot injection

21. Methodology deployed for multiple customers worldwide Conclusions • 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

22. Thank you for your attention