probabilistic approach stochastic modelling modeling flow assurance reservoir uncertainties monte carlo cooldown time MEG injection

1. Integration of Reservoir Uncertainties
into Flow Assurance Strategies
Dr Martin J Watson
SPE Applied Technology Workshop
Bridging the Gap Between Reservoir Engineering and
Facilities Design
14th&15th February 2012, San Antonio

2. Facilities Thermal Design & the Reservoir
Many uncertainties in the thermal hydraulic design of Facilities lie in
the reservoir
How much is going to flow
At what temperature
The traditional approach to Facilities design is to design to a FWHT
and a “design rate”
But such decisions are arbitrary
Over conservative for some, under conservative for others
E.g. What FWHT is conservative for both wax and corrosion management?
Thermal Hydraulic IPM can help manage these risks more rigorously
Rigorous thermal hydraulic model from reservoir to processing facilities
Can investigate how the reservoir uncertainties effect Facilities Design
Without the need for arbitrary intermediate boundary conditions
What follows is an example in hydrate management
But equally applicable to wax, corrosion, chemical injection, etc 2

3. Hydrate Management for a Deepwater Oil System
Phi, Beta, Kappa fields
7 production wells planned
25mile daisy chained tieback in deep water 3

4. Deepwater Oil Hydrate Management & Cooldown Time
350
Hydrate
Require a good cooldown
Dissociation
time as a hold time before
300 Curve
the expensive hydrate
Hydrates No Hydrates
management operation
250 needs to be carried out
Typically >10hours is
Pressure (bara)
Normal
required, making most Hot Operation
200
shutdowns recoverable Restart
with a Hot Restart
Shut in
150
Cooldown
100
Blowdown
& Dead Oil Displacement
50
0
0 5 10 15 20 25 30 35 40 45 50
Temperature (°C) 4

5. Life of Field Cooldown Time
The Cooldown Time of a production system changes through life
As the watercut, GOR and relative rates from each well changes
On the FEESA website is described a method for turning a life of field
thermo hydraulic simulation and a transient simulation into an
estimate for cooldown time throughout life
http://www.feesa.net/consultancy/casestudies.html
Based on Newton’s Law of Cooling
Min Pipeline Temp at Steady State
Tmin SS − Tamb
t = Bln Ambient Temperature
Thyd − Tamb
Cooldown time Hydrate Avoidance Temperature
Fitting parameter
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6. Life of Field Cooldown Time
Oil Rate
Phi
Beta
Kappa
Year
Upside
Most Likely Temps
Cooldown Time
Downside
Target Cooldown Time [18hrs]
What’s the chance that all wells are on their downside? Year
6
What’s the probability that the cooldown time will be less than 18hours?

7. What is the probability the cooldown time is <18hours?
Make some assumptions about the probabilities of the uncertainties
I.e. Probability of upside, expected and downside, PI and Reservoir temp
Probability density function
Generate multiple combinations scenarios who’s probability we can
calculate
E.g. Kappa and Beta on expected, Phi on downside, etc
We could simulate each combination to calculate cooldown time
Generate a distribution of probability vs cooldown time
However, there are too many possible scenarios
Even if there we assumed there was only upside, expected and downside;
for six uncertainties (PI & Tres for three reservoirs)
36=729 combinations
729 life of field simulations is impractical, even with the fastest thermal
hydraulic simulator
In reality there are many more
We needed to choose the least number of runs to yield the most
information about how these uncertainties affect cooldown time
7

8. Design of Experiment (DOE)
Design of Experiment (a.k.a. Experimental Design) is the process of
reducing the number of experiments whilst still ensuring statistically
meaningful results
Various Experimental Design methods in the literature
Most famously the “Monte Carlo” method
This requires too many simulations to be run (tens of thousands)
We used the Taguchi method
Developed to improve product design and manufacturing methods
Procedural
In this case we used Taguchi to reduce the number of simulations to just
enough to develop a correlation for cooldown time versus our
uncertainties
Which we can then use in a Monte Carlo Simulation
A reference
Manivannan, S. et al, 2010, Taguchi Based Linear Regression Modelling of Flat
Plate Heat Sink, J Eng & App Sci, Vol 5, Issue 1, 36-44 8

9. Applying the Taguchi Method to this Problem
Requires the user to do sensitivity studies
Rate the uncertainties in order of most important to least
Phi PI, Phi Tres, Kappa Tres, Beta Tres, Kappa PI, Beta PI
The rest is procedural
In this case, there are 6 variables, a L8 orthogonal array was used
Selects 27 runs from the 729 possible
Phi Kappa Beta
Phi PI Temperature Temperature Temperature Kappa PI Beta PI
Run 1 0 0 0 0 0 0
Run 2 0 0 -1 -1 0 1
Run 3 0 0 1 1 0 -1
Run 4 0 -1 0 -1 1 -1 Keys
Run 5 0 -1 -1 1 1 0 -1 Downside
Run 6 0 -1 1 0 1 1 0 Most Likely
Run 7 0 1 0 1 -1 1 1 Upside
Run 8 0 1 -1 0 -1 -1
Run 9 0 1 1 -1 -1 0
Run 10 -1 0 0 -1 -1 -1
Run 11 -1 0 -1 1 -1 0
Run 12 -1 0 1 0 -1 1
Run 13 -1 -1 0 1 0 1
Run 14 -1 -1 -1 0 0 -1
Run 15 -1 -1 1 -1 0 0
Run 16 -1 1 0 0 1 0
Run 17 -1 1 -1 -1 1 1
Run 18 -1 1 1 1 1 -1
Run 19 1 0 0 1 1 1
Run 20 1 0 -1 0 1 -1
Run 21 1 0 1 -1 1 0
Run 22 1 -1 0 0 -1 0
Run 23 1 -1 -1 -1 -1 1
Run 24 1 -1 1 1 -1 -1
Run 25 1 1 0 -1 0 -1
9
Run 26 1 1 -1 1 0 0
Run 27 1 1 1 0 0 1

10. Method
Each of the 27 Maximus runs gives us a CDTmin. We then use this data
set to fit a correlation
2 2
CDTmin C0 aXn bXn 1 ... eXn fXn 1 ... R
C0 = Constant The values of these are
fitted by regression, e.g.,
Italics a, b, c etc. = Coefficients in Excel
Xn = The outcome of variable n, taking the value of +1 or 0 or -1. Variable
n refers to the set {Phi temperature, PI…etc.}
Xn2 = Square of Xn hence can only be 1 or 0
R = Residual term, assumed negligible
The effect of these parameters on CDT can now be predicted without
running a full simulation by entering +1, 0, -1 into the equation
10

11. Comparison of Correlation vs Simulation
40
35
30
25
CDT (hrs)
20
15
10
5
CDT calculated from Maximus results
CDT calculated using polynomial
0
1 3 5 7 9 11 13 15 17 19 21 23 25 27
Run number
The average difference was 2%
This was deemed acceptable, given the assumptions about the uncertainties
This correlation was then entered into Crystal Ball for the Mote Carlo Simulations 11

12. Results
Conclusion; given the reservoir uncertainties, the P90 CDT is 18hrs
90% chance that the CDT will be equal to or greater than 18 hrs throughout field life
On Normal Operation
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13. Other Uses - MEG Injection System Sizing
Montini et al (2011)
“A Probabilistic Approach to
Prevent the Formation of
Hydrates in Gas Production
Systems”
ICGH 2011 (Edinburgh)
West Nile Delta Project
Large offshore gas condensate
network
34 wells, ~380km of pipelines
Traditional design approaches
would lead to it being the
world’s largest MEG injection
system!
All the worst case scenarios
occurring at once
The Operator sought an
alternative approach!
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14. A Probabilistic Approach to MEG
An alternative approach to the traditional worst of the worst of the
worst……
A life of field thermo-hydraulic compositional evaluation
Proved that many of these worst cases can’t happen in the same year
A statistical investigation
Reduced the MEG design rates by a factor of four
Still 99% confident that hydrates are avoided
Why not 100%?
It takes hours to form a hydrate blockage in such systems
Remediation is possible
Scope for future work
A better quantification of the risk of forming blockages
A better cost estimate of remediation
A better justification for “99% certainty”
But BP were happy
Help make the project feasible
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Moved into FEED

15. Conclusion
Facilities Engineers have a lot to learn from Subsurface Engineers!
Life of Field Approach
Viewing issues on a life of field basis rather than “mass sensitivity studies”
Easier to understand the problem, easier to explain to others
Integrated Production Modelling
Removing arbitrary and erroneous boundary conditions (e.g. FWHT)
Investigating the impact of Reservoir Uncertainties
A statistical approach to uncertainties
Rather than assuming all worst cases happen at once
Build a picture of what is statistically likely to happen
“We believe this is good for 90% of all scenarios we expect, and have a plan for
the other 10%, do you want to go ahead or not?”
On real Flow Assurance projects, such methods have;
Reduced the number of excessive design margins
Have proved conventional (proven) insulation systems could work, where
traditional design methods pointed to less proven low U value solutions
Made MEG injection practical for a large gas network
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