1 prediction of oil production with confidence intervals* james glimm 1,2, shuling hou 3, yoon-ha...
TRANSCRIPT
1
Prediction of Oil Production With Confidence Intervals*
James Glimm1,2, Shuling Hou3, Yoon-ha Lee1,David H. Sharp3, Kenny Ye1
1. SUNY at Stony Brook2. Brookhaven National Laboratory3. Los Alamos National Laboratory
*Supported in part by the Department of Energy and National Science Foundation
2
Application of Prediction Theory
Reservoir Development Choices, for example Sizing of Production Equipment Location of Infill Drilling
During Early Development Stages Risk high Payoff high
3
Basic Idea: I
History match with probability of error
Start with geostatistical probability model for permeability, etc.
Observe production rates, etc.
4
Basic Idea: II
Multiple simulations from ensemble
(Re)Assign probabilities based on data, degree of mismatch of simulation to history
5
Basic Idea: III
Redefine probabilities and ensemble to be consistent with:
(a) data
(b) probable errors in simulation and data
6
Basic Idea: IV
New ensemble of geologies = Posterior
Prediction = sample from posterior
Confidence intervals come from
- posterior probabilities
- errors in forward simulation
7
New Idea: Arrival Time Error Models
Formulate solution error model in terms of arrival times, rather than solution values errors are equi-distributed relative to
solution gradients, ie relative to changes in solution values:1
0
1 0
( )( ) ( )
t
t
s tdt s t s t s
t
8
Arrival Time Model is Simple, Robust
Regard s as the new independent variable
and t as the new dependent variable. Thus
( ) t t s and the error is equidistributed in
units of .s This makes the error robust.
We compare arrival time and solution based error models
9
Illustration:Posterior reduces choices and uncertainty
Left: Oil cut curves for the complete ensemble.
Right: Oil cut curves for the reduced ensemble.
10
New Result: Predict outcomes and risk
Risk is predicted quantitatively
Risk prediction is based on
- formal probabilities of errors
in data and simulation
- methods for simulation error analysis
- Rapid simulation (upscale) allowing
exploration of many scenarios
11
Problem Formulation
Simulation study:
Line drive, 2D reservoir
Random permeability field
log normal, random correlation length
),( yxK
13
Ensemble
100 random permeability fields for each correlation length
lnK gaussian, correlation length
0. 1, 8. 0, 6. 0, 4. 0, 2. 0
14
Upscaling
Solution from fine grid
100 x 100 grid
Solution by upscaling
20 x 20, 10 x 10, 5 x 5
Upscaled grids
15
Upscaling by
Wallstrom, Hou, Christie, Durlofsky,
Sharp
1. Computational Geoscience 3:69-87
(1999)
2. SPE 51939
3. Transport in Porous Media (submitted)
Upscaling References
16
Examples of Upscaled, Exact Oil Cut Curves
Scale-up: Black (fine grid) Red (20x20)
Blue (10x10) Green (5x5)
17
Select one geology as exact.
Observe production for
Assign revised probabilities to all
500 geologies in ensemble based on:
(a) coarse grid upscaled solutions
(b) probabilities for coarse grid
errors.
Compared to data (from “exact” geology)
0ik
)(tsoi
)(0 0 PVItt
Design of Study
18
Bayes Theorem
K Permeability = geology
Observation = past oil cutO
dKKpKOp
KpKOpOKp
OKp posterior; Kp prior
19
Errors and Discrepanciesje scsf jj scsfsd jiij
usually iji de
but iij ed
impliesgeology geolog
yj i
Fine
Coarse
20
Example
Fig. 1 Typical errors (lower, solid curves) and discrepancies(upper, dashed curves), plotted vs. PVI. The two families of curves are clearly distinguishable.
iji de
21
Mean error
Sample covariance
Precision Matrix
Gaussian error model: has covariance C, mean
01
teN
te j
21
)()(1
1),(
N
j j seseN
tsC
1 C
.e
22
In Bayes Theorem, assume is exact.
Then, is an error, probability
jKK
ijd dd
e,
to
o ijij dtdssdtsCtddd )(),()(),( 1
23
For arrival time error models, the
formulation is identical, except that the
independent variables s and t now
denote the solution values, and not the
time values, while the error e(s)
denotes an error in the time of arrival
of the solution value s.
25
Prediction based on
(a) Geostatistics only, no history match (prior).
Average over full ensemble
(b) History match with upscaled solutions (posterior). Bayesian weighted
average over ensemble.
(c) Window: select all fine grid solutions “close” to exact over past
history. Average over restricted ensemble.
Three Prediction Methods
26
• Window prediction is best, but not
practical
• -uses fine grid solutions for
complete ensemble
• -tests for inherent uncertainty
• Prior prediction is worst
• - makes no use of production data.
Comparing Prediction Methods
27
Prediction error reduction, asper cent of prior prediction
choose present time to be oil cut of 0.6
Error Reduction
28
Window based error reduction: 50%(fine grid: 100 x 100)
Upscaled error reduction: 5 x 5 23%
10 x 10 32%
20 x 20 36%
Error Reduction
29
Confidence Intervals
5% - 95% interval in future oil production
Excludes extreme high-low values with 5%probability of occurrence
Expressed as a per cent of predicted production
30
s0 = oil cut at present time.
t0 = present time.
Compute 5%--95% confidence intervals for
future oil production, based on posterior and
forward prediction using upscaled simulation.
Result is a random variable. We express
confidence intervals as a percent of predicted
production, and take mean of this statistic.
Confidence Intervals
31
Confidence intervals in percent for three values
of present oil cut s0 and three levels of scaleup
with fine grid values included.
s0 100x100 20x20 10x10
5x5
0.8 [-13,22] [-21,36] [-24,35] [-
27,34]
0.6 [-14,20] [-18,20] [-22,22] [-
29,25]
0.4 [-14,17] [-18,18] [-24,21] [-
33,23]
Confidence Intervals
32
Arrival Time Error Analysis
Error Model defined by 5 solution values:s = 1- (Breakthrough), 0.8, 0.6, 0.4, 0.2.
Covariance is a 5 x 5 matrix, diagonallydominant, and neglecting diagonal terms,thus has 5 degrees of freedom. Thus it is simple.
Covariance is basically independent of thegeology correlation length. Thus it is robust.
33
Covariance Matrix: 10x10 Scaleup
0.00037 8.83 5 5.23 5 9.89 5 0.00023
8.83 5 0.00045 1.35 5 0.00014 0.00022
5.23 5 1.35 5 0.00059 8.71 5 0.00034
9.89 5 0.00014 8.71ij
E E E
E E
c E E E
E E
5 0.0011 0.00017
0.00023 0.00022 0.00034 0.00014 0.0065
34
( ) ( / )ij ij ii jjd c c c
1 0.22 0.11 0.14 0.15
0.22 1 0.026 0.17 0.13
0.11 0.26 1 0.09 0.17
0.13 0.17 0.09 1 0.56
0.15 0.13 0.17
0.56 1
Correlation matrix =
36
Diagonal covariance matrix elements, three levels of scaleup, averaged over all correlation lengths
38
Diagonal covariance matix elements for 10x10scaleup, showing general lack of dependence on correlation length (except for s = 0.2 entry)
39
Covariance matrix diagonal entries
for arrival time error model are
independent of correlation length,
except for final (s = 0.2) entry.
40
Confidence intervals for arrival time error model (%)
0 20 20 10 10 5 5
0.8 [ 22,23] [ 24,24] [ 26,26]
0.6 [ 20,20] [ 23,22] [ 26,25]
0.4 [ 17,18] [ 20,20] [ 25,23
s ]
41
Arrival time error model vs. solution value model: confidence intervals (%) for s = 0.6 and 10x10 scaleup
Arrival time Solution value
0.2 [-23,11] [-22,11]
0.4 [-23,18] [-20,20]
0.6 [-22,23]
x
[-18,27]
0.8 [-22,27] [-16,36]
1.0 [-23,31] [-17,36]
mean [-23,22] [-19,26]
42
Summary and Conclusions New method to assess risk in
prediction of future oil production New methods to assess errors in
simulations as probabilities New upscaling allows consideration
of ensemble of geology scenarios Bayesian framework provides formal
probabilities for risk and uncertainty
43
References J. Glimm, S. Hou, H. Kim, D. H. Sharp, “A Probability
Model for Errors in the Numerical Solutions of a Partial Differential Equation”. Computational Fluid Dynamics Journal, Vol. 9, 485-493 (2001).
J. Glimm, S. Hou, Y. Lee, D. H. Sharp, “Prediction of Oil Production with Confidence Intervals”, SPE reprint SPE66350 (2001).
J. Glimm, S. Hou, H. Kim, D. H. Sharp, K. Ye, W. Zhu, “Risk Management for Petroleum Reservoir Production”, J. Comp. Geosciences, to appear.
J. Glimm, Y. Lee, K. Ye, “A Simple Model for Scale Up Error” Cont. Math. 2002 (to appear).