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.
Biology for Computer Engineers Course Handout.pptx
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
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.
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.
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
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
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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.