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Uncertainty analyses for thermal development in heavy oil fields

Master in Petroleum Engineering 2010-2011. Uncertainty analyses for thermal development in heavy oil fields. Author: Riccardo Sabatino. San Donato Milanese, 19-20 October 2011. Master In Petroleum Engineering 2010-2011.

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Uncertainty analyses for thermal development in heavy oil fields

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  1. Master in Petroleum Engineering 2010-2011 Uncertainty analyses for thermal development in heavy oil fields Author: Riccardo Sabatino San Donato Milanese, 19-20 October 2011

  2. Master In Petroleum Engineering 2010-2011 Stage SubjectUncertainty analyses for thermal development in heavy oil fields Author Ph.D. Ing. Riccardo Sabatino Company Tutors Ing. Filomena M. Contento Dott. Ivan Maffeis Dott. Alice Tegami UniversityTutor Prof. Ing. Francesca Verga Division Exploration & Production Dept. TENC/MOGI San Donato Milanese, 19-20 October 2011

  3. List of Content Stage SubjectUncertainty analyses for thermal development in heavy oil fields • Project Scope • Introduction • Thermal EOR techniques • Case study • Operating parameter definition • Risk Analysis • Conclusions

  4. Project Scope Uncertainty analyses for thermal development in heavy oil fields • Study the feasibility of thermal EOR techniques for the development of a real extra-heavy oil Venezuelan field • Select the best operating parameters for steamflooding and electrical heating • Perform a Risk Analysis, highlighting the main uncertainties on reservoir development • ComparetwoRiskAnalysisworkflows: Monte Carlo vs. Experimental Design and ResponseSurfaceModelling • Study the feasibility of thermal EOR techniques for the development of a real extra-heavy oil Venezuelan field • Select the best operating parameters for steamflooding and electrical heating • Perform a Risk Analysis, highlighting the main uncertainties on reservoir development • Comparetwo Risk Analysis workflows: Monte Carlo vs. Experimental Design and Response Surface Modelling

  5. List of Content Stage SubjectUncertainty analyses for thermal development in heavy oil fields • Project Scope • Introduction • Thermal EOR techniques • Case study • Operating parameter definition • Risk Analysis • Conclusions

  6. Introduction Heavy Oil Classification Heavy Oil °API 10-20 10-20 cP Extra-Heavy Oil °API <10 100-10,000 cP Tar Sands and Bitumen °API 7-12 >10,000 cP Low gravities and high viscosity reduce the mobility within a reservoir.

  7. Introduction Heavy Oil Worldwide

  8. List of Content Stage SubjectUncertainty analyses for thermal development in heavy oil fields • Project Scope • Introduction • Thermal EOR techniques • Case study • Operating parameters definition • Risk Analysis • Conclusions

  9. Thermal EOR Techniques • Thermal techniques are based upon the oil viscosity reduction due to a thermal power input • Typical thermal EOR techniques • adopted in oil & gas industry: Viscosity [cP] • CSS (Cyclic Steam Stimulation) • Steamflooding • SAGD (Steam Assisted Gravity Drainage) • In-situ combustion • Electrical Heating Temperature [°F]

  10. Thermal EOR Techniques Steamflooding Steam is injected through injection wells. Steam bank spreads away and begins to condense in hot water. Heat is transferred from steam to oil reducing its viscosity.

  11. Thermal EOR Techniques Downhole electrical heating A heating element is run inside the wellbore; the electric current flowing in the cable produces heat according to Joule’s law. Control Panel Producer Well Downhole heater

  12. List of Content Stage SubjectUncertainty analyses for thermal development in heavy oil fields • Project Scope • Introduction • Thermal EOR techniques • Case study • Operating parameters definition • Risk Analysis • Conclusions

  13. Case Study • Approx. 424 km2 • About 20 appraisal wells • No production data • Average porosity: 0.29 • Average permeability: 4000 mD • Average net-to-gross: 0.64 • Oil viscosity: 2000-4500 cP • Reservoir zones thickness: 50 ft • 8-10 °API extra-heavy oil • Hydraulically separated units • Criticalities • No fluid samples available • Major Issues • High viscosity Depth [ft]

  14. Case Study Development Plan • 1st phase: Cold Production (RF=6-8%) • 2nd phase: EOR techniques to enhance recovery factor • Horizontal 1000 m long wells, 400 m spaced, grouped in clusters • Producers reconverted into injectors (according to pattern) Steamfloodingscheme Cluster configuration

  15. List of Content Stage SubjectUncertainty analyses for thermal development in heavy oil fields • Project Scope • Introduction • Thermal EOR techniques • Case study • Operating parameters definition • Risk Analysis • Conclusions

  16. Operating parameter definition Thermal sector Depth [ft] • Due to runtime, a smaller “thermal” sector has been extracted from full field model for dynamic analyses • Steamflooding screening criteria → Connected Net Pay Map • Connected pay thickness > 15-20 ft • Permeability > 1000 mD • The sector is completely included in a single unit • Approx. 2.3 km2 • Number of active gridblocks: 14,096 • Block dimensions: 81 x 81 x 22 ft3 • Average initial pressure: 593 psi • Average porosity: 0.319 • Average permeability: 5244 mD • Average net-to-gross: 0.72 • Average oil viscosity: 3279 cP • Average reservoir temperature: 117 °F Connected Net Pay [ft]

  17. Operating parameter definition Steamflooding • Sensitivity analyses • Injection pressure (BHP inj., psi) [650-750 psi] • Steam Rate (STW, bbl/d) [1500-2000 bbl/d] • aimed at maximizing and anticipating production, minimizing the cumulative steam-oil-ratio [CSOR], which is defined as the ratio between injected steam (equivalent water volume) and produced oil (economical threshold 4.0). • CSOR is the main parameter affecting the success or failure of a steamflooding project. Permeability [mD] Producer 1 Injector 1 Producer 2 Injector 2 Producer 3

  18. Operating parameter definition Steamflooding Steam Rate 1500-2000 bbl/d +98% Cum. Oil Production [bbl] At the beginning, cold production is moreconvenient than steamflooding (in fact two out of five wells are reconverted into injectors). cold X-point Time [date] 2012 start of simulation 2016 start of steamflooding 2035 end of risk analysis simulation

  19. Operating parameter definition Steamflooding Injectionpressure 650-750 psi CSOR=4 CSOR [bbl/bbl] Time [date] 2012 start of simulation 2016 start of steamflooding 2035 end of risk analysis simulation

  20. Operating parameter definition Steamflooding - Summary • Injectors • Injection pressure: 700 psi • Injection Temperature: 502 °F • Steam Rate (STW): 1800 bbl/d • Steam Quality: 0.8 • Producers • BHP min: 200 psi • max surface liquid rate: 3150 bbl/d • minimum surface oil rate: 50 bbl/d

  21. Operating parameter definition Steamflooding – Temperature profiles After 5 years After 10 years Producer 1 Producer 1 Injector 1 Injector 1 Producer 2 Producer 2 Injector 2 Injector 2 Producer 3 Producer 3 Temperature [F] Temperature [F]

  22. Operating parameter definition Electrical heating subsector – Grid refinement Gridsize: 80 x 80 x 22 ft3 Local Grid refinement is a major issue in Electrical Heating simulations Cum. Oil Production [bbl] Permeability [mD] Time [days]

  23. Operating parameter definition Electrical heating subsector – Grid refinement Grid size: 80 x 80 x 22 ft3 Permeability [mD] Grid size: 80 x 27 x 7 ft3 Grid size: 80 x 7 x 7 ft3

  24. Operating parameter definition Electrical heating subsector – Power Input Power Input 100-200 W/m 200 W/m: +12.2% @10 yrs (39.2 kWh/bbl) 150 W/m: +9.7% @10 yrs (30.1 kWh/bbl) +12% Cum. Oil Production [bbl] Time [days] 2022 end of simulations

  25. Operating parameter definition Electrical heating subsector – Summary • Power Input: 150 W/m • BHP min: 200 psi • max surface liquid rate: 3150 bbl/d • minimum surface oil rate: 50 bbl/d

  26. List of Content Stage SubjectUncertainty analyses for thermal development in heavy oil fields • Project Scope • Introduction • Thermal EOR techniques • Case study • Operating parameters definition • Risk Analysis • Conclusions

  27. Risk Analysis Monte Carlo Workflow 1. Uncertaintyidentification 2 3 1 RiskRegister X1=Contacts X2=PVT X3=Aquifer size … 2. StochasticSampling 3. RunNsimulations 4. Stabilizationcheck 4 5 6 5. SensitivityAnalysis 6. Analysisofresults

  28. Risk Analysis ED & RSM Workflow 1. Uncertaintyidentification 3 2 1 RiskRegister X1=Contacts X2=PVT X3=Aquifer size … 2. Define N Experiments 3. RunNsimulations 4 4. Build and validate proxy 6 5. Monte Carlo Sampling 6. Analysisofresults y=a0+a1x1+a2x2+a3x1x2+a4x2x3++a5x1x3+a6x1x2x3+...

  29. List of Content Risk Analysis Outline • Uncertainty identification • OOIP • Cold Production • Np @2035 • Steamflooding • Np @2035, CSOR @2035 • Steamflooding vs. Cold production @2035 • Electrical Heating • Np@2022 • Electrical Heating vs. Cold Production @2022

  30. Risk Analysis Uncertainty Identification

  31. Risk Analysis – Monte Carlo Workflow Oil in place StabilizationCheck Frequency and Cumulative Distribution

  32. Risk Analysis – Monte Carlo Workflow Cold production: Np @ 2035 StabilizationCheck Frequency and Cumulative Distribution Sensitivity Analysis

  33. Risk Analysis – Monte Carlo Workflow Cold production: Cum. Oil Profiles Base Case Cum. Oil Production Cum. Oil Production Time 2035 Allprofiles Time 2035

  34. Risk Analysis – ED&RSM Workflow Cold production: Np @ 2035 Proxy Validation Proxy terms Monte Carlo vs. ED&RSM √ • VOLMOD • VISO • MODPERM • NW 100 runs Predicted [bbls] 53 runs Observed [bbls]

  35. Risk Analysis – Monte Carlo Workflow Steamflooding: Np @ 2035 StabilizationCheck Frequency and Cumulative Distribution Sensitivity Analysis

  36. Risk Analysis – Monte Carlo Workflow Steamflooding: Cum. Oil Profiles Base Case Cum. Oil Production Cum. Oil Production Time 2035 Allprofiles Time 2035

  37. Risk Analysis – ED&RSM Workflow Steamflooding: Np @ 2035 Proxy Validation Proxy terms Monte Carlo vs. ED&RSM √ • MODPERM × VOLMOD • VISO × VOLMOD • NW • MODPERM2 • MODPERM × VISO • KRWMAX • KRWMAX2 88 runs 200 runs P50 -3.90% Predicted [bbls] Observed [bbls]

  38. Risk Analysis – Monte Carlo Workflow Steamflooding: CSOR @ 2035 StabilizationCheck Frequency and Cumulative Distribution Sensitivity Analysis

  39. Risk Analysis Steamflooding vs. Cold Production ED&RSM Workflow @P50 Steamflooding gives +89% Cumulative Oil Recovery Monte Carlo Workflow @P50 Steamflooding gives +96% Cumulative Oil Recovery In both cases, Steamflooding can be an effective way to enhance oil recovery +96% Oil viscosity is the most impacting unknown @P10 the economic convenience should be properly evaluated

  40. Risk Analysis Electrical Heating: Np @ 2022 Frequency and Cumulative Distribution Monte Carlo vs. ED&RSM √ 100 runs 41 runs Sensitivity Analysis

  41. Risk Analysis Electrical Heating vs. Cold Production ED&RSM Workflow @P50 Electrical heating gives +11% Cumulative Oil Recovery Monte Carlo Workflow @P50 Electrical Heating gives +11% Cumulative Oil Recovery In both cases, Electrical Heating can be an effective way to enhance oil recovery +11% Oil viscosity and pore volume are the most impacting unknowns

  42. List of Content Stage SubjectUncertainty analyses for thermal development in heavy oil fields • Project Scope • Introduction • Thermal EOR techniques • Case study • Operating parameters definition • Risk Analysis • Conclusions

  43. Conclusions • In this work, the feasibility of thermal EOR techniques has been investigated, within a real extra-heavy oil reservoir • Operating parameters for steamflooding (steam rate, injection pressure) and electrical heating (power input) have been investigated and best cases have been selected • ED&RSM Risk Analysis workflow proved to be an effective alternative to Monte Carlo workflow, although proxy models have to be properly checked • Steamflooding proved to be an effective way to improve oil recovery although for pessimistic scenarios its convenience should properlybe evaluated • Electrical heating can cheaply provide additional oil recovery, also with low power input, and it is particularly convenient in pessimistic scenarios • In this work, the feasibility of thermal EOR techniques has been investigated, within a real extra-heavy oil reservoir • Operating parameters for steamflooding (steam rate, injection pressure) and electrical heating (power input) have been investigated and best cases have been selected • ED&RSM Risk Analysis workflow proved to be an effective alternative to Monte Carlo workflow, although proxy models have to be properly checked • Steamflooding proved to be an effective way to improve oil recovery although for pessimistic scenarios its convenience should properlybe evaluated • Electrical heating can cheaply provide additional oil recovery, also with low power input, and it is particularly convenient in pessimistic scenarios

  44. Recommendations and future activities • Collect reservoir fluid samples and perform oil viscosity analyses • Extend simulations to a larger thermal sector, in order to get more representative results (steam flooding) • Introduce economic analyses to assess applicability of thermal recovery methods • Collect reservoir fluid samples and perform oil viscosity analyses • Extend simulations to a larger thermal sector, in order to get more representative results (steam flooding) • Introduce economic analyses to assess applicability of thermal recovery methods

  45. Acknowledgements I would like to acknowledgeeni e&p division managementfor permission to present this work and related results and TENC/MOGI colleagues (particularly Alice, Ivan, Michela and Micla) for the technical support and needed assistance. San Donato Milanese, 19-20 October 2011

  46. Master in Petroleum Engineering 2010-2011 Uncertainty analyses for thermal development in heavy oil fields Author: Riccardo Sabatino San Donato Milanese, 19-20 October 2011

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