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The Uncertainty Quantification in Optimizing Reactive Intelligent Well Control
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Conference Paper · January 2014
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The Uncertainty Quantification in
Optimizing Reactive Intelligent Well Control
Strategy
Benjamin Kyeremeh, Kwame Nkrumah University of Science and Technology (KNUST);
Kwame Sarkodie, KNUST; and Bennet Nii Tackie-Otoo, KNUST
Abstract
Production and NPV forecast from oil fields are affected by different forms of uncertainties;
geological (static and dynamic) and economical. This study investigates the impacts of these
uncertainties to quantify them and to prove the fact that intelligent well (IW) technology under an
appropriate control strategy reduces the impacts of these uncertainties better that conventional
well (CW).
First, an optimized base case was defined for both CW and IW in the same trajectory. The IW
performed better than the CW in terms of productivity and profitability due to controlled water
production in the IW.
Second, different reservoir scenarios were created by varying reservoir parameters; (porosity,
permeability and NTG) for static case and (rel perms, OWC, skin and aquifer permeability) for
dynamic case. In each case, a pessimistic and an optimistic case were investigated. The impacts
of the various uncertainties were analysed deterministically and stochastically.
In both the static and dynamic case under the deterministic analysis, the CW offered a better
productivity than the IW in the pessimistic cases nevertheless the IW was better in terms of
profitability. In the optimistic cases, the IW performed better than the CW both in terms of
productivity and profitability. However, the dynamic optimistic case presented a platform for
clear distinction between the performance of IW and CW.
Moreover, the economic uncertainties were also scrutinized and the margin between their
performances increased as conditions become favourable.
Lastly, the stochastic analysis showed that the IW reduces uncertainty range in all cases and the
oil production is the most sensitive parameter in NPV forecast.
Introduction
The exploration and subsequent discovering of oil in reservoirs gave rise to the need for
development of measures of extracting and producing this oil. Conventional wells (CW’s), which
are vertical or slightly deviated, were the first types of wells used by oil producing companies.
Due to the heterogeneous nature of most oil-bearing formations, the need arose for better means
of recovering oil. These included drilling of highly deviated, multilateral and horizontal wells
known as non-conventional wells (NCW’s).
An intelligent well is best drilled in reservoirs where wellbore hydraulics (water coning or
cusping) and heterogeneity (fractures causing early water
breakthrough) exist (Zeid M. Al-
Ghareeb, June 2009).
An Intelligent well (IW) is an advanced type of non-conventional well. It is made up of a
combination system of down-hole sensors which are used to monitor the well and reservoir
conditions; and inflow control valves (ICV’s) which control the inflow of reservoir fluids to the
well bore. These sensors, valves and inflow control devices are installed on the production tubing
and they permit the continuous monitoring of fluid flow rates and pressure and periodic
adjustments of ICV’s.
In optimizing production from an intelligent well, two control strategies are followed by reservoir
engineers. These control strategies include the proactive (defensive) control strategy and the
reactive control strategy.
The proactive control strategy is a “preventive measure”. In this approach, down-hole inflow
control devices are used together with a predictive reservoir model to obtain the best
configuration of ICV’s which will yield a maximum oil recovery over a given period of time.
The reactive control strategy is a “corrective measure”. Here, a production problem (example;
water coning) is allowed to occur then instrumentation is reset to mitigate the problem. That is,
ICV’s operate only when there is an adverse change in flow (such as the influx of unwanted
fluids).
The degree of variation in rock properties with location in a reservoir or formation is termed
heterogeneity. This heterogeneity makes petroleum system modeling, formation evaluation and
reservoir simulation critical to maximizing production and NPV forecast. These uncertainty
variations are mainly geologic (static and dynamic) and economic.
The main objective of this study is to quantify the effect of uncertainties involved in the
optimization of reactive intelligent well control strategy as it affects productivity and profitability
and to validate that intelligent wells reduce uncertainty imparts on NPV and production forecast
Literature Review
A few authors have previously addressed the optimization of IWs. We shall briefly discuss some
of these works but throw more light on what Burak Yeten et al did, and introduce the gap we seek
to close in their works.
Brouwer et al started it all when they presented a static optimization methodology that
maximized sweep in a water flood study. They focused on smart horizontal injection by
penetrating it fully. They looked at the segments of the well with the highest productivity index
and shared the production from these segments to the other well segments.
Gai H. introduced a new dimension to the works. He did an optimization method for multi-zone
flow control completions. Inflow performance relationships were used to model valve
performance relationships to optimize valve settings. He further proposed the use of graphical
applications to performance curves and said optimal application of IWs should be addressed.
Nyhavn et al made similar argument.
The choice of an efficient control strategy is always a difficult problem. F. Ebadi
and D. R.
Davies (2006) made a study on determining which technique should be chosen when specifying
the requirements for an effective Intelligent Well Management System. They specified the
objectives of zonal flow control using ICVs as to maximize the oil production and NPV,
minimize the unwanted fluid productions or a combination of these objectives.
Yeten et al (2002) presented a general method for the optimization of a well equipped with ICVs.
Their method entails the use of an optimization tool based on a conjugate gradient algorithm.
This optimization tool was linked to a commercial reservoir simulator containing a wellbore flow
model capable of modeling ICVs.
Aitokhuehi et al (2005) combined IW optimization on the basis of Yeten et al work and history
matching techniques.
Naus in 2004 came out with a sequential linear programming approach. This was basically
aimed at optimizing production in heterogeneous reservoirs. This however suffered from the
convergence problems caused by non – linearities and oscillations and could not fully tackle the
problems of production optimization and uncertainties.
Sarma and Aziz also conducted a work in 2005. This work was geared towards a more efficient
well optimization.
They used an algorithm for the control of intelligent well. They also
considered increasing the value of the recovered hydrocarbons. This was primary aimed at
establishing a proactive strategy that could change the invading front’s behaviour, delaying the
breakthrough time and increasing the sweep efficiency.
João Paulo Q. G. da Silva et al. said smart wells were able to improve oil production and reduce
water production but the net present value (NPV) indicated that the use of conventional well was,
on average, slightly more advantageous.
Yeten and Jalali (2001) studied an optimum allocation method to smart wells, considering the
reactive control strategy, in a field limited by aquifer and gas cap. They both compared the
performance of smart and conventional wells. As results, it was showed that intelligent
completions are good to be used in horizontal wells with high pressure drop. They also
demonstrated the benefits of smart wells to prevent the unwanted fluids production and,
consequently, increase the oil production over the use of conventional wells.
This work investigates the uncertainties involved in intelligent well optimisation using the
reactive method. It compares the performances of both an intelligent well and a conventional well
to validate that with intelligent wells the effects of these uncertainties are reduced better.
Methodology
Our approach in the study is to simulate the flow of reservoir fluids in a conventional well as well
as a multi-segment well with ICV’s using ECLIPSE 100. We also made use of crystal ball
software which uses Monte Carlo Simulation to quantify uncertainties.
In this study, the expected outcome would be to quantify uncertainties (static, dynamic and
economic) and to prove the fact that intelligent wells, under the reactive control strategy, tend to
reduce the uncertainties associated with the productivity and profitability forecast of oilfields.
Therefore a range of reservoir situations were created to investigate the effects of various
parametric uncertainties on the well control strategies
This methodology was basically divided into two parts;

The first part which focuses on production strategy optimization (base case)

The second part which involves the quantification of uncertainties with the application of
deterministic and stochastic (Monte Carlo Simulation) to generate probability density
functions (PDF) for the NPV forecast for both CW and IW
Optimized Base Case Definition
The definition of the optimized base case involved;

Making use of data available for horizontal wells completed in the first layer of the
PUNQ-S3 reservoir model, the best horizontal well was chosen based on maximum NPV
and well productivity (FOE).

With the chosen optimal well in it preferred trajectory, a production strategy was
introduced.

Introduction of a reactive control strategy by using pressure drop and water cut data from
the segmented well to place ICVs in the segment modeled in eclipse.

With an optimized production and estimated NPV for the IW, it was compared with the
CW.
Production Strategy
The objective of the production strategy introduced for both the IW and the CW was to reduce
water production and maximize hydrocarbon recovery therefore providing a better NPV for the
field.
For both the CW and IW, the production strategy was to commingle flow from the first layer
under maximum liquid flow rate constraint of 1000 sm3. This was specified in eclipse 100 using
the COMPDAT and WCONPROD keywords
In this study, the conventional well completion used for the optimised base case was only
controlled using a surface choke. It was designed to produce till vertical lift die out then it shut in
the well.
The intelligent well completion uses binary (on/off) ICV for the segments completed in the first
layer. After every time step of sixty days, the design of the ICVs, is for the ICV with the highest
segment water cut (SWCT) to shut. Modeling this was achieved in eclipse using the ACTIONX
keyword. This was to offer an optimal reactive control strategy for the intelligent well by
delaying breakthrough of water. The figure below is a flow schematic diagram showing the
intelligent well reactive control strategy
Figure 1: Flow schematic illustrating the IW reactive control strategy for a comingled flow
Economic Model
The relationship used in this study for the NPV calculation is given by;
NPV = (FOPR*Po + FGPR*Pg – FWPR*Pw – CAPEX) *
⁄
⁄
……… (1)
Note: Each variable in the above equation represents its meaning as stated in The List of
Abbreviations.
Economic Inputs
Economic parameters used for the NPV calculation is summarized in table 1 below. Assumptions
made are as follows:

The price of oil (80$/bbl) assumed is based on current oil price but a bit lower to
compensate for taxes and tariff charges.

A CAPEX of 10.5 MMUS$ is assumed for the intelligent well which include the cost of
well completion plus ICV installation cost.

The CAPEX for the conventional well include 10 MMUS$ initial well completion cost
and a workover cost of 3 MMUS$ which is based on the assumption that there will be a
workover when water production rate exceeds that of oil.

It is assumed that produced oil and gas would be sold and the produced water treated at
the surface.
Economic parameters
Oil price ($/bbl)
Gas price ($/MSCF)
Water handling cost ($/bbl)
Discount rate (%)
Cost of IW completion (MMUS$)
Cost of CW completion (MMUS$)
Well workover cost (MMUS$)
Table 1: Summary of economic inputs used for NPV calculation
Estimates
80
4
7
10
10.5
10
3
Uncertainty Quantification Process
The purpose of this part of our methodology is to investigate how the various uncertainties affect
the production strategies defined for both wells (IW and CW). This is done by using the
deterministic and stochastic approaches.
Deterministic Approach
In this approach we;

The various uncertainties (static, dynamic and economic) were taking into consideration.

Suitable variations of these parameters for a pessimistic and an optimistic case made for
CW and IW.

Variation of these parameters was put in ECLIPSE to generate new production data for
various cases.

Production data generated by ECLIPSE were fed into the economic model to generate
discrete NPV estimates.

The various reservoir situations created by the variation of the uncertain parameters
offered a platform to compare the CW and IW based on incremental NPV and well
productivities.
Stochastic Approach
This approach involves the;

Selection of average production data for pessimistic and optimistic from which most
likely estimates for the base case are chosen for the NPV forecast.

PDFs of NPV are created by random sampling using Crystal Ball Software (Monte Carlo
simulation).

Quantification of the impacts of the well types on the NPV forecast by overlying the NPV
PDFs for each uncertainty range for the CW and IW.

Uncertainty ranking using tornado charts and trend charts to investigate the level of risk
for each simulated case and the parameters that affect the NPV distribution more.
Results and Discussion
Optimized Base Case
With the optimized production strategy defined for both CW and IW, the IW performed better
than the CW in terms of both productivity and profitability as shown by the figure below
(a)
(b)
Figure 2: Comparison of IW and CW – (a) Based on FOE and (b) Based on NPV
The reactive control strategy for the IW impeded the intake of water and enhanced the flow of oil
thereby providing less water handling cost and more revenue
Parameters
CW
IW
FOPR (SM3/D)
440.88
464.41
5320000
5539888
FGPR (SM /D)
43543.47
45450.28
FGPT (SM3)
524750.2
543310.5
FWPR (SM3/D)
554.14
498.23
FWPT (SM3)
6682721
6077302
FOE
0.41
0.43
FLPT (SM3)
12000000
11617190
FOPT/FLPT (SM)
0.44
0.48
NPV (MM$)
492.941
573.9396
3
FOPT (SM )
3
ΔNPV (MM$)
Table 2: Summary of optimized base case results
80.9983
Static Uncertainties
The static uncertainty parameters in this study are: porosity, permeability (in the X, Y and Z
direction) and the net-to-gross (NTG).
We have varied these parameters in two ways; the pessimistic case and the optimistic case. These
variations helped to acknowledge the possible worst outcome (pessimistic case) and the best
outcome (optimistic case). Details of the results obtained are discussed below.
Effects of Static (Geological) Uncertainties
Uncertainties
Static parameters
NTG
POROSITY
PERM (X, Y)
PERM Z
Case 1 (pessimistic)
Multiplier
0.5
0.5
0.4
0.32
Base case
Multiplier
0.9
1
1
1
Case 2 (optimistic)
Multiplier
1
2
1.6
1.28
Table 3: Static Uncertainty Parameters Variation
Static Case 1 (Pessimistic Case)
In the pessimistic case, a reduction in the values of the porosity and permeability distribution and
net-to-gross (NTG) led to the CW performing better than the IW in terms of productivity. This
was as a result of the IW not producing till lift die out because all the valves had equal water cut
at 7380 days causing all the valves to shut. The IW still performed better economical because the
CW produced significant amount of water which was to be handled and workover was also
required in the course of the wells life.
(a)
(b)
Figure 3: Comparison of wells in static case 1 – (a) FOE verses time and (b) NPV verses time
(a)
(b)
Figure 4: (a) SWCT verses time for IW and (b) FWPT verses time for both wells
Static Case 2 (Optimistic Case)
In the optimistic case, increasing the porosity and permeability distribution and net-to-gross
(NTG) values led to the IW performing better both in productivity and profitability as in the base
case. This is as a result of reduced water production by ICVs allowing more production of oil.
Parameters
CW
IW
630.632
660.94
7605450
7970947
FGPR (SM /D)
71844.92
74176.01
FGPT (SM3)
866362.9
894469.4
FWPR (SM3/D)
364.39
334.08
FWPT (SM )
4394550
4029053
FOE
0.29
0.31
FLPT (SM )
12000000
12000000
FOPT/FLPT (SM)
NPV (MM$)
ΔNPV (MM$)
0.63
875.2525
0.66
901.6422
3
FOPR (SM /D)
3
FOPT (SM )
3
3
3
26.3897
Table 4: Summary of the static case 2 results
Dynamic Uncertainties
Dynamic uncertainty deals with the parameters within the reservoir that are likely to change with
time during the reservoir producing life, some of which include aquifer permeability, fluid
contacts, relative permeability of fluids and skin effects.
Effect of Dynamic Uncertainties
Uncertainties
AQUIFER PERM (mD)
SKIN FACTOR
REL PERMS
OWC
Case 1 (pessimistic)
229
8
Shaly sand model
2390
Base case
137.5
0
BASE
2395
Case 2 (optimistic)
46
-1
Sand model
2405
Table 5: Dynamic Uncertainty Parameters Variation
Dynamic Uncertainties Case 1(Pessimistic)
An increase in the aquifer permeability causes an increase in the water production total at the
surface. An early water breakthrough was then realized for the CW whilst the ICV’s in the IW
controlled the production of water hence delaying water breakthrough.
(a)
(b)
Figure 5: (a) Early water breakthrough in CW and (b) Delayed water breakthrough in IW
A positive skin effect (8) indicates additional drawdown pressure due to extra flow resistance
near the wellbore. This causes an increase in reservoir pressure decline but it is masked by the
two strong aquifers which support reservoir pressure. The shaly sand model used for the relative
permeability reduced the Krw and increased Kro making the IW’s total water production less
with the aid of the ICV’s controlling the water production.
This case also resulted in the IW performing poorly in terms of productivity yet the it was again
better in terms profitability which was due to the handling of water production.
Dynamic Uncertainties Case 2 (Optimistic)
Low aquifer strength (permeability of 26mD) resists the flow of water into the reservoir resulting
in a reduction in the water saturation (Sw) and increasing the Kro within the reservoir causing a
late water breakthrough for both the CW and IW. The sand model used for relative permeability
also led to a reduction in the Krw and hence contributed to late water breakthrough observed for
both CW and IW. However the IW presented the best water production control in this case as
compared to all the other cases discussed earlier.
Figure 6: Delayed water breakthrough for the Dynamic case 2
Economic Uncertainties
Analysis was made on the impact of economic uncertainties on NPV by varying the various
economic parameters (oil price, gas price and water handling cost) for a pessimistic and an
optimistic case from the base case. Results are summarized on Table 6 below.
Uncertainties
OIL PRICE
GAS PRICE
WATER COST
IW NPV (MM$)
CW NPV (MM$)
∆NPV (MM$)
Case 1 (pessimistic)
60
1
10
218.6067
144.0916
74.5151
Table 6: Summary results for economic uncertainties
Base case
80
4
7
573.939
492.91
81.029
Case 2 (optimistic)
110
7
5
1073.734
980.7404
92.9936
Stochastic Analysis (Monte Carlo Simulation)
Production data from the various scenarios created were used to generate deterministic estimate
values for a pessimistic, optimistic with the average serving as the most likely estimate.
Triangular distributions which are able to estimate central tendency for three values served as the
stochastic assumptions for the NPV distribution forecast. 10,000 simulations runs of the Monte
Carlo simulation per scenario in crystal ball were carried out. NPV distributions were generated
which have uncertainty ranges and their impact on each well completion on the distribution were
analyzed using overlay, tornado and trend charts. The results were as follows:
Static NPV Distribution
Inputs
FOPR (sm3)
FGPR (sm3)
FWPR (sm3)
Oil Price ($/bbl)
Gas Price($/bbl)
Water Cost ($/bbl)
NPV (MM$)
Pessimistic
51.27
3481.34
592.34
80
4
7
Conventional well
Most likely
384.86
42337.11
371.53
80
4
7
102.8498767
Optimistic
995.597
185633.9
4.4
80
4
7
Assumptions
Triangular
Triangular
Triangular
Triangular
Triangular
Triangular
Forecast
Optimistic
995.6
185697.4
4.41
80
4
7
Assumptions
Triangular
Triangular
Triangular
Triangular
Triangular
Triangular
Forecast
Table 7: Stochastic inputs for static NPV distribution of CW
Inputs
FOPR (sm3)
FGPR (sm3)
FWPR (sm3)
Oil Price ($/bbl)
Gas Price($/bbl)
Water Cost ($/bbl)
NPV (MM$)
Pessimistic
274.71
16323.34
300
80
4
7
Intelligent well
Most likely
383.65
42397.94
229.885
80
4
7
103.0549161
Table 8: Stochastic inputs for static NPV distribution for IW
Setting a minimum NPV forecast value of about $100MM, CW gives a 68.84% confidence level
of achieving it. With a large range of uncertainty, CW has a very low rate of coping with adverse
uncertainties in the reservoir. IW on the other hand gives a 90.45% confidence level of achieving
it which means under appropriate reactive control strategy it is able to deal better with adverse
uncertainties in the reservoir.
(a)
(b)
Figure 7: NPV distribution for static uncertainty – (a) CW and (b) IW
Dynamic NPV Distribution
Inputs
FOPR (sm3)
FGPR (sm3)
FWPR (sm3)
Oil Price ($/bbl)
Gas Price($/bbl)
Water Cost ($/bbl)
NPV (MM$)
Pessimistic
116.26
6883.87
883.74
80
4
7
Conventional well
Most likely
367.35
55069.7
546.13
80
4
7
98.55220711
Optimistic
1000
425025
11.22
80
4
7
Assumptions
Triangular
Triangular
Triangular
Triangular
Triangular
Triangular
Forecast
Table 9: Stochastic inputs for Dynamic NPV distribution for CW
Inputs
FOPR (sm3)
FGPR (sm3)
FWPR (sm3)
Oil Price ($/bbl)
Gas Price($/bbl)
Water Cost ($/bbl)
NPV (MM$)
Pessimistic
118.54
8234
350.81
80
4
7
Intelligent well
Most likely
443.105
60419.5
310.335
80
4
7
103.0549161
Optimistic
1000
419143
0.001
80
4
7
Table 10: Stochastic inputs for Dynamic NPV distribution for IW
Assumptions
Triangular
Triangular
Triangular
Triangular
Triangular
Triangular
Forecast
(a)
(b)
Figure 8: NPV distribution for dynamic uncertainty – (a) CW and (b) IW
With the dynamic uncertainties same analysis as in the static case was made with CW having a
confidence level of 76.46% for achieving a minimum NPV of $100MM with wider range of
uncertainties whiles the IW gives a confidence level of 82.27% of achieving the same minimum
NPV value with a narrow range of uncertainties.
Impact of Intelligent Wells on Uncertainties
(a)
(b)
Figure 9: An Overlay of NPV Distributions (CW, IW) – (a) static and (b) dynamic
From figure 9, IW has a higher mean than CW in both cases and the range of uncertainties for the
IW fell within that of CW showing that the IW has a narrower range of uncertainty therefore
reduces the impact of reservoir uncertainties.
Ranking of Uncertainties
Ranking of the uncertainties using to tornado chart is now done to know which parameter is most
sensitive to the project so that much attention will be given to it to get a better NPV forecast.
Figure 10: Tornado chart for the wells in various reservoir scenarios
In all the cases, it was observed that the Oil Production is the most sensitive parameter to the
NPV distribution followed by gas production, because we intend to sell the gas this parameter is
also very important to our NPV forecast. Having noticed this trend, it therefore means that in
order to have a better oil production and NPV forecast, an appropriate reactive control strategy
must be adapted to the IW. In much the same way, if an appropriate work over strategy is adapted
to the CW it could perform the similar to the IW.
Comparison of NPV Trend for all Cases
Figure 11: NPV Trend Charts for all Cases
Both the NPV STATIC IW and NPV DYNAMIC NPV IW have the highest certainty bands,
meaning they cover a wider range of certainties, thereby reducing the entire range of
uncertainties. IWs are therefore able to lower level of risk as compared to their counterparts CWs
which have the lowest certainty bands and hence ,a wider range of uncertainty. CW is therefore
very poor in reducing the impact of risk associated with project NPV evaluation. It is interesting
to note that the certainty bands of the IW correspond with the high NPV forecast whereas the
certainty bands of the CW correspond with the low NPV estimates.
Conclusions
As it was expected, the IW was able to reduce the reservoir uncertainties to a large extent under
the proposed reactive control strategy but faired averagely under severe reservoir uncertainties.
In terms of profitability, IW offset CW in almost all the scenarios meaning, IW is able to deal
better with adverse economic uncertainties.
In the stochastic analysis, it was observed that the oil production is a very sensitive parameter and
both IW and CW could do better depending on the control strategy adopted by each.
Acknowledgements
This paper is based on the work conducted in a BSc final year project from the Kwame Nkrumah
University of Science and Technology (KNUST), Department of Chemical Engineering. We
would like to thank all lecturers and colleges who aided in making this project a success.
Nomenclature
CAPEX = Capital Expenditure
IW = Intelligent Well
CW = Conventional Well
Kro = Oil relative permeability
FGPR = Field Gas Production Rate
Krw = Water relative permeability
FGPT = Field Gas Production Total
NPV = Net Present Value
FLPT = Field Liquid Production Total
NTG = Net to Gross Ratio
FOE = Field Oil Efficiency
OWC = Oil-Water Contact
FOPR = Field Oil Production Rate
PERM X, Y, Z = Permeability in x, y, z
FOPT = Field Oil Production Total
FWCT = Field Water Cut Total
FWPR = Field Water Production Rate
FWPT = Field Water Production Total
GOC = Gas-Oil Contact
ICV’s = Inflow Control Valves
direction
Pg = Gas Price
PO = Oil Price
Pw = Water handling cost
Sw = Water Saturation
SWCT = Segment Water Cut
t = Total time in days
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