Uncertainty analyses for thermal development in heavy oil fields Author: Riccardo Sabatino San Donato Milanese, October 2011 Master in Petroleum Engineering

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

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

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  Compare two Risk Analysis workflows: Monte Carlo vs. Experimental Design and Response Surface Modelling  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  Compare two Risk Analysis workflows: Monte Carlo vs. Experimental Design and Response Surface Modelling

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

6 Introduction Heavy Oil Classification Heavy Oil °API cP Extra-Heavy Oil °API < ,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  Project Scope  Introduction  Thermal EOR techniques  Case study  Operating parameters definition  Risk Analysis  Conclusions List of Content Stage Subject Uncertainty analyses for thermal development in heavy oil fields

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: Temperature [°F] Viscosity [cP]  CSS (Cyclic Steam Stimulation)  Steamflooding  SAGD (Steam Assisted Gravity Drainage)  In-situ combustion  Electrical Heating

10 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. Thermal EOR Techniques

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

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

13 Case Study  Approx. 424 km 2  About 20 appraisal wells  No production data  Average porosity: 0.29  Average permeability: 4000 mD  Average net-to-gross: 0.64  Oil viscosity: cP  Reservoir zones thickness: 50 ft  8-10 °API extra-heavy oil  Hydraulically separated units Depth [ft] Criticalities  No fluid samples available Major Issues  High viscosity

14 Case Study  1 st phase: Cold Production (RF=6-8%)  2 nd 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) Development Plan Cluster configuration Steamflooding scheme

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

16 Operating parameter definition 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 > ft  Permeability > 1000 mD The sector is completely included in a single unit Thermal sector Depth [ft] Connected Net Pay [ft]  Approx. 2.3 km 2  Number of active gridblocks: 14,096  Block dimensions: 81 x 81 x 22 ft 3  Average initial pressure: 593 psi  Average porosity:  Average permeability: 5244 mD  Average net-to-gross: 0.72  Average oil viscosity: 3279 cP  Average reservoir temperature: 117 °F

17 Sensitivity analyses  Injection pressure (BHP inj., psi) [ psi]  Steam Rate (STW, bbl/d) [ 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. Steamflooding Permeability [mD] Producer 1 Injector 1 Producer 2 Injector 2 Producer 3 Operating parameter definition

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

CSOR=4 19 Steamflooding Operating parameter definition 2016 start of steamflooding 2012 start of simulation 2035 end of risk analysis simulation Injection pressure psi CSOR [bbl/bbl] Time [date]

20 Steamflooding - Summary Producers  BHP min: 200 psi  max surface liquid rate: 3150 bbl/d  minimum surface oil rate: 50 bbl/d Injectors  Injection pressure: 700 psi  Injection Temperature: 502 °F  Steam Rate (STW): 1800 bbl/d  Steam Quality: 0.8 Operating parameter definition Steamflooding sensitivity Operating parameterReservoir response Injection Pressure↑CSOR↑ Injection Pressure↑X-point↓ Steam Rate↑ Cum. Oil Production slightly ↑ Steam Rate↑CSOR↑

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

22 Electrical heating subsector – Grid refinement Local Grid refinement is a major issue in Electrical Heating simulations Permeability [mD] Grid size: 80 x 80 x 22 ft 3 Operating parameter definition Time [days] Cum. Oil Production [bbl]

23 Permeability [mD] Grid size: 80 x 80 x 22 ft 3 Grid size: 80 x 7 x 7 ft 3 Grid size: 80 x 27 x 7 ft 3 Electrical heating subsector – Grid refinement Operating parameter definition

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

25 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 Operating parameter definition Electrical Heating sensitivity Operating parameter Reservoir response Power Input↑ Cum. Oil Production ↑ Power Input↓Efficiency↑ Block Dimension↑ Cum. Oil Production Negligible Block Dimension↓Runtime↑↑

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

27 Monte Carlo Workflow 1. Uncertainty identification 2. Stochastic Sampling 3. Run N simulations 4. Stabilization check 5. Sensitivity Analysis 6. Analysis of results Risk Register X 1 =Contacts X 2 =PVT X 3 =Aquifer size … Risk Register X 1 =Contacts X 2 =PVT X 3 =Aquifer size … Risk Analysis

28 ED & RSM Workflow Risk Analysis 1. Uncertainty identification 2. Define N Experiments 3. Run N simulations 4. Build and validate proxy 5. Monte Carlo Sampling 6. Analysis of results Risk Register X 1 =Contacts X 2 =PVT X 3 =Aquifer size … Risk Register X 1 =Contacts X 2 =PVT X 3 =Aquifer size … y=a 0 +a 1 x 1 +a 2 x 2 +a 3 x 1 x 2 +a 4 x 2 x 3 + +a 5 x 1 x 3 +a 6 x 1 x 2 x

29  Uncertainty identification  OOIP  Cold Production  Steamflooding  Steamflooding vs. Cold  Electrical Heating  Electrical Heating vs. Cold List of Content Risk Analysis Outline

30 Uncertainty Identification Risk Analysis IDUncertaintyDistributionRange VISOOil viscosity Discrete (equiprobable) [ ] cP VOLMODPore Volume multiplier User assigned (cumulative) [ ] MODPERMAbsolute Permeability multiplierNormal  =1,  =0.13, [ ] THCONRRock thermal conductivityUniform[35-85] Btu/day-ft-F KRWMAX Water relative endpoint Uniform[ ] NWWater Exponent in Corey's EquationUniform[1-5]

31 Risk Analysis – Monte Carlo Workflow Oil in place Original Oil In Place [bbls] P10P50P90meanst. dev. 2.00E E E E E+06 Stabilization CheckFrequency and Cumulative Distribution

32 Risk Analysis – Monte Carlo Workflow Cold production: 2035 Cumulative Oil [bbls] P10P50P90meanst. dev. 1.81E E E E E+05 Stabilization Check Sensitivity Analysis Frequency and Cumulative Distribution

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

34 Risk Analysis – ED&RSM Workflow Cold production: 2035 Proxy Validation Cumulative Oil [bbls] P10P50P90meanst. dev. 1.78E E E E E+05 √ √ 100 runs 53 runs  VOLMOD  VISO  MODPERM  NW Proxy termsMonte Carlo vs. ED&RSM Predicted [bbls] Observed [bbls]

35 Risk Analysis – Monte Carlo Workflow Steamflooding: 2035 Cumulative Oil [bbls] P10P50P90meanst. dev. 2.10E E E E E+06 Stabilization Check Sensitivity Analysis Frequency and Cumulative Distribution

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

37 Risk Analysis – ED&RSM Workflow Steamflooding: 2035 √ √ P % 200 runs 88 runs Observed [bbls]  MODPERM × VOLMOD  VISO × VOLMOD  NW  MODPERM 2  MODPERM × VISO  KRWMAX  KRWMAX 2 Proxy termsMonte Carlo vs. ED&RSM Cumulative Oil [bbls] P10P50P90meanst. dev. 2.56E E E E E+06 Proxy Validation Predicted [bbls]

38 Risk Analysis – Monte Carlo Workflow Steamflooding: 2035 [bbl/bbl] P10P50P90meanst. dev Stabilization Check Sensitivity Analysis Frequency and Cumulative Distribution

39 Risk Analysis Steamflooding vs. Cold Production ED&RSM Steamflooding gives +89% Cumulative Oil Recovery Monte Carlo 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 the economic convenience should be properly evaluated

40 Risk Analysis Electrical Heating: runs 41 runs √ √ Cumulative Oil [bbls] (Monte Carlo) P10P50P90meanst. dev. 2.11E E E E E+05 Sensitivity Analysis Monte Carlo vs. ED&RSMFrequency and Cumulative Distribution

41 Risk Analysis Electrical Heating vs. Cold Production ED&RSM Electrical heating gives +11% Cumulative Oil Recovery Monte Carlo 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  Project Scope  Introduction  Thermal EOR techniques  Case study  Operating parameters definition  Risk Analysis  Conclusions List of Content Stage Subject Uncertainty analyses for thermal development in heavy oil fields

 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 properly be evaluated  Electrical heating can cheaply provide additional oil recovery, also with low power input, and it is particularly convenient in pessimistic scenarios 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 properly be evaluated  Electrical heating can cheaply provide additional oil recovery, also with low power input, and it is particularly convenient in pessimistic scenarios

 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 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

45 Acknowledgements I would like to acknowledge eni e&p division management for 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, October 2011

Uncertainty analyses for thermal development in heavy oil fields Author: Riccardo Sabatino San Donato Milanese, October 2011 Master in Petroleum Engineering