The Value and the Danger of Complex Reservoir Simulations
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The weakness of reservoir simulations is the lack of quantity and quality of the required input; their strength is the ability to vary one parameter at a time. Therefore, reservoir simulations are an appropriate tool to evaluate relative uncertainty but absolute forecasts can be misleading, leading to poor business decisions. As recovery processes increase in complexity, the impact of such decisions may have a major impact on the project viability. A responsible use of reservoir simulations is discussed, addressing both technical users and decision makers. The danger of creating a false confidence in forecasts and the value of simulating complex processes are demonstrated with examples. This is a call for the return of the reservoir engineer who is in control of the simulations and not controlled by them, and the decision maker who appreciates a black & white graph of a forecast with realistic uncertainties over a 3-D hologram in colour.
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The Value and the Danger of Complex Reservoir Simulations
- 1. Society of Petroleum Engineers Distinguished Lecturer Program www.spe.org/dl 1 Daniel Yang The Value and the Danger of Complex Reservoir Simulations
- 2. Contents 2 • Introduction • Complexity • Simplicity • Matching Field Data • Examples • Discussion • Simulation Workflow • Slide Rule Criterion • Tips for Decision Makers • Summary • Conclusions National Geographic, 1952
- 4. By Definition ... 4 sim·u·late ’sim-yə-,lāt transitive verb to pretend, often with the intention to deceive pretend: all required input is available deceive: this is going to happen SAGD: Steam Assisted Gravity Drainage
- 5. Complexity 5 properties depend on pressure properties depend on each other P conventional chemicals solvents gas heat fracking multi-porosity systems geo-mechanicsP T s T P
- 6. Simplicity 6 grid blocks • one property value only • size matters: simulation results depend on grid size 1 m hotplate T 'Error' made by simplification is compensated by using non-realistic 'pseudo' values for input. Well Model • well is a sink/source in a grid block • well bore hydraulics PVT • phase behavior of components depends on pressure and temperature • compositional dependency
- 8. History Match 8 ideal approach constraint match BHP rate BHT BHP oil water gas BHT BHP BHT WHP - well head pressure BHP - bottom hole pressure BHT - bottom hole temperature field data common approach constraint match injection rate WHP BHP BHT rate WHP (BHP) production rates - liquid -- oil -- water - gas BHP BHT liquid oil (BHP) observation BHP BHT 4D seismic m - seismic surface heave forecast assumption?
- 9. History Match and Forecast 9 history match with fluid rate constraint forecast with BHP constraint History Match: constrained by production rates reservoir can have a large potential Forecast: constrained by BHP unrealistic rates
- 10. History Match Summary 10 • INCOMPLETE If any of the field data is ignored (e.g., gas production, observation well temperature) the match is incomplete, and can not be used to forecast. • Matches were achieved, despite: • the field data turned out to be incorrect • input data was incorrect • numerical calculations were incorrect • NON UNIQUE One history match is far from unique; there are many other combinations of input parameters that result in similar agreement to the data A history match is only relevant for the process that was matched.
- 11. EXAMPLES 11
- 12. Example: Misleading 12 • gas-condensate field with bottom aquifer • produce gas and move the condensate rim through the reservoir • how much condensate gets 'stuck'? • user: 20% • simulator: 0% • simulation results unrealistic Field Development Plan had to be changed gas-liquid transformation by simulator gas oil water water-oil user input
- 13. Example: Understanding 13 In-Situ Upgrading IUP: • reservoir to > 300 ºC with heaters • 6-10 API bitumen 30-50 API oil; in-situ • > 400 temperature and 15 pressure points With appropriate input, complex processes can be simulated • Simulation of • bitumen H2S, H2, light & coke • dolomite CO2 • evaporation of saline connate water • 18 components • 11 chemical reactions • History Match and Forecast • constraint: heat injection • O/W/G rates • P&T • oil composition, API • gas composition, incl. CO2, H2S, H2 • The pilot was executed in a sandstone for practical reasons. • Learnings from modelling were transferred to application in oil shales.
- 14. DISCUSSION 14
- 15. Ideal Workflow 15 build sensitivity analysis history match optimization uncertainty analysis forecast THINK objective? static dynamic KPIs? single parameters fixed input? all data non-unique analogue process constraints wells operation development deterministic probabilistic start simple; add complexity only if necessary (Occam's Razor)
- 16. Generation Slide Rule 16 Nintendo Engineer Reservoir Engineer • as complex as possible • worried about run time • pride high number of runs • pretty pictures • miss unphysical results • lack of reality check thinking is steered by simulations • no ownership of input • full ownership of input • as simple as possible • accept long run times • pride low number of runs • B&W X-Y plots • catch unphysical results • frequent reality check thinking steers the simulations
- 17. For Decision Makers 17 • Did you use 5-point or 9-point spatial discretization? • Were your convergence criteria equation residuals or variable changes? • What was your maximum material balance error? • Is the critical gas saturation temperature dependent? • What mixing rule for viscosity did you use? • Did you use STONE I for 3-phase rel perms? A few questions for the Decision Maker that will impress the Model Maker, and help to determine the value of the simulation results for business decisions: if the answer to any of these questions is then 'I don't know' or 'It doesn't matter' Nintendo Alarm! The simulations are not reliable. 'Good points - I have to check' Give them another 2-3 months ... Short and to the point Forecast can be basis for decision.
- 18. For Decision Makers 18 • Check for ownership of input: Every single value of input is relevant! • Reality check with analogs: Non-reservoir impacts on projects are not modelled! • Challenge the history match: Pressure should be used as constraint! All available data needs to be considered!
- 19. For Decision Makers 19 1st CSS model avg. 11 m pay avg. 25 m pay bitumen, 8-12 API Cyclic Steam with hor wells Are simulations really required for a successful development? No, but do they make us miss opportunities? first model first model 2018 blue asset sold to red company
- 20. Summary 20 Value of reservoir simulations • capture complex interactions of physical, chemical and mechanical processes in the reservoir • enable development optimization by identifying critical parameters through isolation and improving the conceptual model Danger of reservoir simulations • create a false confidence in predictions, forming the basis for business decisions • lack of control over input • underestimation of uncertainty and sensitivity • inappropriate interpretation of history match • incomplete assumptions for forecast
- 21. Here it comes ... 21
- 22. Conclusions 22 1. Usually, the available information is not sufficient as input for realistic reservoir simulations. Complex models are not suitable for absolute forecasts 2. Only in reservoir simulations, one parameter can be changed at a time. Complex models are ideal for uncertainty determination
- 23. Society of Petroleum Engineers Distinguished Lecturer Program www.spe.org/dl 23 Your Feedback is Important Enter your section in the DL Evaluation Contest by completing the evaluation form for this presentation Visit SPE.org/dl
Editor's Notes
- The real weakness of reservoir simulations is the lack of availability, quality, resolution of the required input. Pretending this has no impact leads to the deception that reservoirs are simulated realistically.
- With growing complexity of reservoir processes, the level of dependencies between properties increases dramatically. The strength of reservoir simulations is the ability to consider these dependencies, albeit in simplified forms. The weakness is the lack of understanding all dependencies (rules), and providing the necessary input (numbers). conventional is 1-dimensional injection adds complexity of composition heat increases complexity to 2 dimensions fractures and multi-porosity add complexity of flow regimes geo-mechanics increases complexity to 3 dimensions
- To allow for reasonable computing times, physical processes have to be calculated in simplified ways. This diminishes the goal of simulating reality but still allows comparative studies, where the impact of the variation of a single parameter is evaluated.
- Matching historical injection and/or production data to BHP constraints is more difficult but necessary because any forecast of production has to assume a future BHP; a match with production as constraint can not be extrapolated into the future.
- One development option for this field was to produce the gas at the top of the structure, move the condensate rim through the reservoir, and recover the condensate at the end. The simulator transformed the two-phase relative permeability with 20% residual oil saturation into a three phase relative permeability with zero residual oil. Hence, the simulation results were unrealistically optimistic. Once the error was corrected, the concept was abandoned because too much of the rim oil remains behind.
- The pilot was carried out in a sandstone reservoir because existing facilities allowed a fast execution. Unique and elaborate laboratory work made it possible to simulate this very complex process. The learnings from history matching were transferred to the initial application for IUP in oil shales. The reservoir models were also used to evaluate IUP application in sandstone versus steam injection.
- Modeling is an iterative process. The objective has to be defined first, since it impacts the model construction Before performing a sensitivity analysis the relevant KPIs have to be defined; also, the parameters that can vary have to be determined Some input data needs to be fixed for the history match; compare results to all available field data (e.g., observation wells) The forecast should be anchored to analogues; Only the recovery process that was matched can be forecasted. Well configuration and mode of operation can be optimized with simulations. Remaining uncertainties results in a range of expected performance.
- Engineers who have learned to use the slide ruler have to know the magnitude of the result before doing the calculation. Nintendo engineers punch in numbers and take the result without critique
- The reservoir simulations operating the blue curve field suggest the development of the red curve field is uneconomic.