Copyright 2007, Society of Petroleum Engineers This paper was prepared for presentation at the 15th SPE Middle East Oil & Gas Show and Conference held in Bahrain International Exhibition Centre, Kingdom of Bahrain, 11–14 March 2007. This paper was selected for presentation by an SPE Program Committee following review of information contained in an abstract submitted by the author(s). Contents of the paper, as presented, have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material, as presented, does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Papers presented at SPE meetings are subject to publication review by Editorial Committees of the Society of Petroleum Engineers. Electronic reproduction, distribution, or storage of any part of this paper for commercial purposes without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of where and by whom the paper was presented. Write Librarian, SPE, P.O. Box 833836, Richardson, TX 75083-3836, U.S.A., fax 01-972-952-9435.

1. Abstract Increasing use of advanced (intelligent) completions has provided the facility for real time reservoir monitoring through use of permanent downhole instrumentation and control of production from different zones within the reservoir through use of surface operated inflow control devices. The potential for the real-time data generated by advanced completion solutions is now being exploited to a greater extent

The paper will demonstrate that trending analysis of the pressure drop (dP trending) using the data delivered by sensors located strategically in a multi-zone horizontal or multilateral intelligent completion can identify the time and location of water influx into a well. It will be shown that "pressure drop trend signatures" can identify the location of the water influx and application of this approach will be illustrated by use of two, three and four zone completions in synthetic reservoirs. A more complex, field based example will also be employed to illustrate the robustness of the technique. Prompt recognition of the influx of unwanted fluid and its location can be achieved using the technique described in this paper.

2. Introduction Advances in drilling and completion technology during the last twenty years have aided the delivery of increased well productivity and reservoir drainage efficiency. The advent of multilateral and maximum reservoir contact wells has added a further dimension to these recovery techniques 1, 2

Early detection of unwanted water or gas production and the knowledge of the point of influx allows for prompt remedial action, by for example, re-direction of injection water. The use of downhole instrumentation such as pressure and temperature sensors has provided one of the necessary tools to assist in real-time optimisation of well performance.

Intelligent well completions offer a combination of well performance monitoring, zonal productivity control and subsequent well production optimisation3, 4, 5 and are one of the steps in the realisation of the vision of Real-Time Optimisation in oil and gas field operations. Real-Time Optimisation enables operations to be transformed by downhole sensor and data transmission systems delivering real-time information and allowing rapid reconfiguration of the completion to optimise production. The resulting step change in the efficiency of the production process is the target that many companies are striving for as they introduce their variant of this vision under acronyms such as: Smart Fields, Digital Oil Field, i-field, e-field, Fields of the Future. The intelligent field concept has matured to such an extent in the last decade that several major operators now have fields where intelligent technologies have been deployed on a large scale6, 7

Field deployment has been facilitated by the rapid development of measurement and information technologies such as:

Fibre (non-electronic) optics sensing devices.

High capacity data transmission and storage systems

An increasing computing capability available at a decreasing cost.

The development of high-resolution visualization tools.

Field applications have been reported that illustrate the value that can be derived from the systematic use of real-time data 9,

10, 11

Permanent downhole pressure sensors installed in advanced completions can be used to determine differential pressures between any two adjoining zones. This paper will show how the analysis of the pressure drop trend or gradient can be used to determine information on the time and location of a water front arriving at a production well. Once field exploitation commences a pressure perturbation will propagate from the production well. The aquifer will start to move, with the speed of movement being, in general, dependent on the magnitude of the global pressure depletion and the aquifer properties (compressibility, porosity, permeability and the relative reservoir and volumes). Simplified transient and pseudo-steady state, analytical models (e.g. Carter-Tracy, Fetkovich12) have been developed to

SPE 105374

Completions Using Real-Time Downhole Pressure Data G.H. Aggrey and D.R. Davies, Heriot-Watt U., and L.T.Skarsholt, Statoil ASA

A Novel Approach of Detecting Water Influx Time in Multiz one and Multilateral

2 SPE 105374

describe the natural encroachment of aquifers into a reservoir. The reservoir heterogeneity and the injection well pattern will determine whether an irregular, water front develops. The oil-water displacement process is normally dominated by viscous forces when an unfavourable fluid mobility is present. These combined effects can result in water production at unexpected times and locations along the well. Determining the water influx locations in conventional wells can only be achieved through well intervention e.g. production logging. Interpretation of repeated, field seismic surveys can also suggest possible breakthrough locations13

Model scenarios of standard well and reservoir types using conventional modelling techniques14, 15 that allow determination of flow parameters (pressure, temperature, flow rate, water cut) at various points along the completion interval have been developed The modelling algorithms employed consider the multiple interactions present within the complex well geometry of an intelligent well. They also realistically capture the wellbore-reservoir formation flow processes. The underlying physical principals are honoured as far as the capabilities of the current versions of commercial software permit.

A project was initiated using simplified models to generate sensor pressure and temperature data at various times and locations in the well. Analysis of the data revealed that the change in the pressure drop trend between any two adjoining pressures sensors located in the tubing close to an inflow control valve can reveal information on the reservoir performance. This process will be referred to as dP trending across the sensors or zones.

3. The Study Methodology Various reservoir/well models were used to produce data analogous to real time data acquisition within a multi-zone, intelligent well equipped with downhole pressure, flow rates and phase cut sensors. The intention was to examine how combinations of sensor measurements could be used to detect the:

a) Time of water breakthrough b) Location of the water influx

for production at all times above bubble point.

Simulated, flowing downhole tubing pressure near the Inflow Control Valves (ICV) in a two zone, horizontal, intelligent well were examined. The dP trend changes due to the development a of water cut within the well were determined. The study was extended to examine how the pressure response would change if a three or four zone completion had been installed. Further, a more complex geological environment was employed in place of the simpler, synthetic reservoir models. It was investigated whether the same conclusions could be drawn when the total well production was constrained to either a constant, surface liquid rate or a constant, tubing head pressure with a declining production rate. The final model examined the performance of a tri-lateral well.

4. Reservoir and Well Model Construction

4.1 The Synthetic Reservoir Models A synthetic heterogeneous, water-drive, reservoir model was used in the investigation. The model contained three sand facies, each facies being modelled using different petrophysical parameters (permeability and porosity) to characterise the three different depositional environments. The middle sand was typified by relatively high permeability and porosity compared to the other two. The model had 75 grid elements in the X, Y and in Z direction (total 421,875 grid cells). Each grid cell being 80 ft x 80 ft x 10 ft in the X, Y and Z directions. A more complex reservoir model based on a real field was used in the latter stages of this project.

4.2 The Well Models. 4.2.1 The Two Zone, Three Zone and Tri-Lateral Intelligent Well Completion.

A horizontal well of approximately 6,000 ft with an intelligent completion was simulated. The well was placed in the reservoir at a stand-off of some 200 ft from the aquifer. A two zone; three zone and a tri-lateral completion were simulated in turn. The flow rates across each ICV were monitored (in this case, output from the simulations). Pressure (tubing and annulus), temperature (tubing and annulus) and flow rate data (inferred from the pressure drop across the ICV) were determined for each of the three completion cases. The wellbore model (annulus and tubing) was divided into 114 segments for the 3-zone completion case. The completion consists of a 3.6 inch ID tubing across the heel-zone and 2.99 inch ID tubing for the other two zones. This allowed a more accurate performance simulation of the intelligent wells. The segments were 110 ft long for each of the main flow paths with smaller segment lengths placed as required to mimic the sensors and the ICV locations. The smaller segments provided a more accurate evaluation of the wellbore pressure gradient.

The multi-segment well option and the Drift-flux flow model in the Eclipse 100/300 simulator was used to compute the pressure drop across each of the well segments. As the base case, the well with a two zone intelligent completion was controlled at a liquid rate of 20,000 STB/D and a tubing head pressure of 400 psi. Figure 1 shows the oil and water production profiles, with water influx into the well being observed after 606 days.

4.2.2: The Multi-lateral Completion. The concept of water detection was extended to multi-lateral completions with three laterals of length 2,200 ft, 2,800ft and 2,280 ft length. The mother bore had a cemented production casing of 8.7 in. ID with three ICVs located in the (3.6 in ID) tubing installed in the mother-bore to control the flow from each lateral into the tubing. This was identical to the inlet points in the three zone horizontal well completion. Simulated pressure sensors were installed in the mother bore near the inflow control valves at 7,000 ft, 8,200 ft and 9,600 ft. The well was produced at a constant fluid rate of 20,000 STB/D.

SPE 105374 3

4.2.3: A Four Zone, Intelligent Well Completion in a More Complex Reservoir.

The water detection concept was tested with a four zone, intelligent, horizontal completion placed in a model based on data from a fluvial North Sea type reservoir. The reservoir consisted of two facies (fluvial channel sands and floodplain siltstone) that had been modelled using the stochastic, object modelling method. The well was placed at the crest of the structure to intersect the meandering high permeability channels. The completion length was 7,470 ft and the distance between the installed sensors located close to the ICVs was 1,785 ft, 1,780 ft and 1,700 ft respectively. The well was completed with 3.6 inch ID tubing installed in a cemented, 8.7 inch ID casing. The well was produced at a constant liquid flow rate of 20,000 STB/D.

5. The Pressure Trend Data Fundamentals

5.1 The Pressure Drop (dP) Definition The absolute difference in pressure measured between any two points is the sum of three components: a. The Hydrostatic pressure term is calculated from the

density of the two phase fluid mixture. It is derived from the hold-up of the oil and water phases, their respective densities and the cosine of the well deviation angle.

b. The Frictional pressure term arises from the drag of the fluids on the walls of the pipe. It is a function of the flow rate (Q), tubing diameter (D), fluid density (ρ), tubing length (L) and friction factor (λ).

5

2

DiQLP iiii

iρλ

=Δ

c. The Acceleration pressure term is minimal for the water-oil environment studied here. It arises from the increasing kinetic energy of the fluids as they expand and accelerate with decreasing pressure.

The Hasan and Kabir16 and the Shi-0317 Drift flux model formulae are available within the Eclipse reservoir simulator to calculate pressure drop across each segment. The robustness of this approach was confirmed by analysing whether the dP trend response showed the same signature when five of the well-known, one-dimensional, multi phase slip correlations (e.g. Beggs and Brill18 and Duns and Ross19) were employed instead. The results of the new correlations were coupled to Eclipse via a Vertical Flow Performance (VFP) table generated for a representative segment which was then scaled up to the exact segmented length. The dP trending results showed a similar signature for all of the five multi-phase flow correlations studied.

5.2 The Inflow Performance The inflow fluid rate from the reservoir is calculated by the simulator as being proportional to the product of three quantities:

(a) The transmissibility between the reservoir and the well bore. (b) The fluid mobility near the wellbore. (c) The pressure drawdown between the reservoir and the well bore.

The simulator’s connection transmissibility remains constant at the breakthrough time. The mobility of any phase at any perforation will depend on the saturation conditions near the wellbore. The presence of increased water saturation around the wellbore will decrease the (total) fluid mobility. The effective permeability to oil is reduced at the same time, leading to a reduction in the total fluid delivery. 5.3 The Water Influx Detection Concept. The water detection concept is based on the interaction of the changes that occur in the fluid inflow from the affected reservoir zones and the outflow performance in the tubing. The tubing outflow pressure performance is thus an integral part of the dP trending technique. The main driver of the rate change that allows identification of the source of the water influx is the reduction of fluid mobility in the water affected zone and the consequent reduction in the zonal productivity.

Initially, a natural choking phenomenon results from the appearance of two mobile phases (water and oil) in a reservoir zone. The initial reduction in total fluid mobility (and hence flow rate) from the zone is a result of changes in the relative permeabilities of the mobile phases. The other zones, which are still producing dry oil, will increase their production rate so as to compensate for the reduced productivity of the zone affected by the water cut. This will be achieved as the well obeys the imposed total well production constraints (surface liquid rate or tubing head pressure).

The impact of this rate change is manifested in the outflow performance via a corresponding decrease or increase in the tubing pressure drop, which will itself be a function of the reservoir inflow flow rate. It will thus be dependent on the well’s inflow performance.

A water influx from the toe of the horizontal well shows a distinct decreasing pressure drop. Figure 2 shows a production scenario with water influx from the toe of a well with a two zone completion. The pressure drop (dP) between the sensors show a decrease after breakthrough of water. Similarly, when water breakthrough occurs higher up in the well, the pressure drop (dP) between zone one and two will increase.

Figure 3 is a plot of the fluid production rates as observed at the ICVs for a three zone completion. The dry oil production rates at ICV 2 and 3 increase in magnitude to satisfy the imposed fluid rate constraint of 20,000 STB/D as water breaks through at ICV 1.

In the three zone completion, water appearance at the toe (zone three) prompts an increase in flow rates from zones one and two.

The pressure drop measured across zone two (dP2) is a function of fluid flow from zone three and the zone two completion length. The flow rate reduction from zone three causes a reduced pressure drop across zone two.

The fluid flow rate from zones two and three (Qz2 + Qz3) before the water influx is greater than the flow rate from the same zones (Qwz2 + Qwz3) after water influx. This reduction in fluid rate is compensated for by an increase in the production

4 SPE 105374

from zone one. The reduced flow rates into zone one and the impact of the {Bo/Bw} ratio account for the reduction in dP1 measurement across zone one. The oil and water PVT properties will thus affect the changes in the dP measurement as well as the rate of water influx.

An influx from zone two (middle zone) can be detected because dP1 will decrease and dP2 will increase (Figure 4).

Simulations have shown that the concept of water detection by dP trending can be extended to multi-lateral completions The inflow into the mother-bore from the laterals can be treated in exactly the same manner as has been developed for inflow from the ICV’s of a multi-zone completion. A reduction in the production rate in any of the laterals caused by water influx will be compensated for by increased production from the others. Thus, dP trending analysis of data from pressure sensors located in the mother-bore close to the inflow points can identify pressure drop trends that correspond to fluid flow rate changes from the laterals.

5.4 Impact of Sensor Location in Undulating, Inclined and Multi-lateral Wells

One important requirement for the dP trend analysis technique to be able to detect small water influx rates is that the hydrostatic pressure drop must be small compared to the frictional pressure drop. This is particularly important in undulating and inclined wells. The location of pressure sensors in the well configuration is important as the vertical difference between adjoining sensors determines the hydrostatic pressure drop. There will be a pressure drop reduction measured in the tubing (the detection signal), after water influxes into a zone, associated with the flow due to fluid rate reduction. This reduced pressure drop will, however, also be affected by hydrostatic pressure changes. Locating all the sensors in the same horizontal plane in an undulating well will minimise the impact of hydrostatic head differences. However, even in this favourable situation the hydrostatic head differences can only be completely ignored for a homogenous flow regime in the undulating section.

6. Sensitivity analysis in the Eclipse model

6.1 Two-Zone with constant wellhead pressure

The flowing wellhead pressure was set to 200 psi. The initial production rate of some 24,000 STB/D decreased gradually to a stabilised fluid rate of 21,400 STB/D at 1,000 days (Figure 5). Water influx into the well occurred at 540 days and was confirmed by dP1 trending analysis (Figure 6) which indicated that the influx was located ahead at ICV 1 (the pressure drop trend had increased).

6.3 Two-Zone - Sensitivity to Relative Permeability Curve

The reservoir fluid mobility is controlled by the relative permeability curves which describe the behaviour of two phase fluid movement within the reservoir. Two different relative permeability curves (Figure 7) were used to evaluate the impact on the efficiency of water influx detection. Figure 8 shows the results for the two curves.

6.4 Three Zone A three zone completion was simulated at a constant fluid rate of 20,000 STB/D. An increase in dP1 (P2 - P1) and dP2 (P3 - P2) occurs when the water influx point is ahead of pressure drop measurement points (Figure 3). This is analogous to the earlier description of a two zone completion. 6.5 Three Zone – Water Coning at the middle and toe zones With water production in zone two, a dP plot across the three sensors (Figure 9) shows that the water influx after 1138 days. The pressure drop trend also identified the influx time based on the change in the pressure drop gradient trend. The pressure drop (dP2) is seen to be increasing while the pressure drop across zone one (dP1) reduces (Figure 4). The dP1 and dP2 trends declined with water influx from the toe (zone three) of the well (Figure 3).

6.6 Four Zone The technique has also been proven in a four-zone completion in the more complex reservoir model described in Section 4.2.3. Water production was confirmed by the decrease of dP1 and dP2 and increase of dP3 (Figure 10). This indicates that the water influx occurred behind zones one and two and ahead of zone four (influx into zone three). 6.7 Multilateral Well Figures 11 and 12 show the dP trending when water influxed into lateral two of the tri-lateral well. The resulting plots show that dP1 decreases while dP2 increases after the influx. The dP signature is similar to water influxing from zone two in a three zone completion. The dP trending technique can thus be extended to other multilateral completions.

7. Practical Applications of dP Trending A key screening parameter to determine if a horizontal well is suitable for the dP trending analysis is that the hydrostatic pressure drop should be small compared to the frictional pressure loss. The water detection methodology generally shows similar detection behavior in both transient and steady state production environments, however, a clearer influx is observed during steady state flow.

One important feature of the trending procedure is that the pressure data must be acquired at the same time for all the sensors.

Future water influxes from other zones will change the pressure drop gradient (increasing or decreasing depending on the influx location).

The size of the pressure drop across different zones can be significantly different (the pressure drop is proportional to the completion length, the flow rate across that zone and the diameter of the tubing).

The value of the information created by early water detection is further enhanced by prompt control of the water production by use of inflow control valves1, 3, 4, 6, 9.

SPE 105374 5

8. Conclusions Modelling studies have shown that it is feasible to identify the time and location of water influx into a multi-zone well simulation by use of a multi pressure drop trending algorithm (dP trending). Determination of the source of water influx is inherently linked to the inability of a given zone to sustain the previous flow rate once water appears at the wellbore in that zone. The decrease in the zonal productivity index reduces the flow rate which manifests itself as a reduction in the pressure drop trend across the zone downstream of the water influx point. An increase in the dP trend is indicative of water production in front of the sensing points while a decreasing trend suggests water influx from behind the sensing points. Utilisation of real time data with this technique will permit a more rapid response to water breakthrough in field situations, however, the accuracy of the method will depend upon the quality of the pressure data, stability of well production operations, knowledge of the well geometry and the ability to accurately model dynamic flow properties. Acknowledgements The authors would like to thank Geoquest for software provision. One of the authors also wishes to thank the Sponsors (BG, BP, ENI, ExxonMobil, Norsk Hydro, Statoil and WellDynamics) of the “Added Value from Intelligent Well System Technology” JIP at Heriot-Watt University, Edinburgh, UK for providing financial support. The authours also thank Anthony Mitchell of Statoil for his contribution.

Nomenclature Bo = volume formation factor of oil

Bw = volume formation factor of water ICV = Interval Control Valve ID = Internal Diameter Li = length ithzone, ft

Qi = Fluid Production rate from ith zone, STB/D STB/D = Stock Tank Barrels per Day

List of References

1. Saleri, N.G., et al; “SHAYBAH-220: A Maximum Reservoir Contact (MRC) Well and Its Implications for Developing Tight Facies Reservoirs” paper SPE 81487, presented at the Middle East Oil Show, 9-12 June, Bahrain.

2. Saleri, N.G., Al-Kaabi A.O. and Muallem, A.S. “Haradh III: A Milestone for Smart Fields” Technology Update, JPT 2004.

3. Elmsallati, S., Davies, D.R., Tesaker, O., and Reime, A.: "Optimization of Intelligent Wells - A Field Case Study" presented at the Offshore Mediterranean Conference and Exhibition, Ravenna, Italy, March 16-18, 2005.

4. Ebadi, F, and Davies D., “Should “Proactive” and “Reactive” Control Be Chosen for Intelligent Well Management?”, paper SPE 99929, presented at the SPE

IEC and Exhibition, Amsterdam, The Nederlands, 11-13 April 2006

5. Mackay, E. et al.: “Impact of In-Situ Sulfate Stripping on Scale Management in the Gyda Field” paper SPE 100516 presented at the SPE International Oilfield Scale Symposium, 31 May-1 June, Aberdeen, UK

6. Aggrey G.H. et al.: “Data richness and reliability in Smart field’s management – Is there value?” paper SPE 102867, presented at the SPE Annual Technical Conference and Exhibition held in Houston, Texas, USA, September 2006.

7. Digital Energy Journal. Report from IQPC. Future Fields conference, Amsterdam, June 13-14 2006.

8. Oberwinkler, C. and Studner, M. “From Real Time DTA to Production Optimisation” paper SPE 87008 presented at the SPE APC, Kuala Lumpar, Malaysia, 29-30 March, 2004.

9. Saputelli, L.A. et al.: “Promoting Real-Time Optimization Hydrocarbon Reducing Systems” paper SPE 83978, presented at Offshore Europe 2003, Aberdeen, UK, 2-5 September 2003.

10. Saputelli, L.A. et al.: “Real Time Optimization: Classification and Assessment” paper SPE 90213, presented at the SPE Annual Technical Conference and Exhibition held in September 2003. 26-29 September, Houston, Texas

11. Unneland, T., and Hauser, C. “Real-Time Asset Management: From Vision to Engagement - An Operator’s Experience.” paper SPE 96390, presented at the SPE Annual Technical Conference and Exhibition held in Houston, Texas, USA, October, 2005

12. GeoQuest (2005). ECLIPSE Reference Manual 2005A. Schlumberger.

13. Marschall, R. and Sherlock, D. “Some Aspects of 4-D Seismics for Reservoir Monitoring” paper SPE 83978, presented at Improved Oil Recovery Symposium, 13-17 April, Tulsa, Oklahoma

14. T.D. Bui, et al.: “In-situ Diagnosis of Inflow Behaviour in Horizontal Wells” SPE 84873, presented at Improved Oil Recovery Symposium, Asia Pacific, 20-21 October, Kuala Lumpur, Malaysia

15. Holmes, J.A., Barkve, T., and Lund, O.: “Application of a Multisegment Well Model to Simulate Flow in Advanced Wells,” paper SPE 50646 presented at the 1998 SPE European Petroleum Conference, The Hague, 20–22 October

16. Hasan, A. R. and Kabir, C. S. “A Simplified Model for Oil/Water Flow in Vertical and Deviated Wellbores” SPE Production & Facilities, Page 56-62, Feb. 1999

17. 89836, presented at the SPE Annual Technical Conference and Exhibition, Houston, Sept. 2004.

18. Beggs, H.D. and Brill, J.P.: “A Study of Two-Phase Flow in Inclined Pipes,” JPT (May 1973) 607; Trans., AIME, 255.

19. Duns, H., Jr. and Ros, N. C. J.: .Vertical Flow of Gas and Liquid Mixtures in Wells, Proc. Sixth World Pet. Congress, Frankfurt (Jun. 19-26, 1963) Section II, Paper 22-PD6.

6 SPE 105374

2400

2500

2600

2700

2800

2900

1 200 400 600 800 1000 1200 1400 1600 1800

Rat

e , S

TB

/D

0

3

6

9

12

15

18

21

103

Time, Days

Pres

sure

, Psi

a

Tubing Pressure at ICV1Tubing Pressure at ICV2Oil ProductionWater Production

Figure 1: Downhole Pressure Measurements with Oil and Water Production Profiles for the Base Case Well.

65

70

75

80

85

500 720 940 1160 1380 1600 1820 2040

Time, Days

Wat

er C

ut

0%

10%

20%

30%

40%

Pres

sure

Dro

p, P

sia

Pressure DropWater Cut at ICV2Water Cut at ICV1

Figure 2: dP Behaviour in a 2-zone completion as Water Influxed form the Toe of the Well.

SPE 105374 7

7000

7500

8000

8500

9000

9500

10000

10500

11000

20 64 111 190 291 420 620 804 844 884 924

Time, Days

Rat

e, S

TB

/D

3000

3500

4000

4500

5000

5500

6000

6500

7000

Rat

e, S

TB

/D

Fluid Rate at ICV 1Fluid Rate at ICV 2Fluid Rate at ICV 3

Figure 3: ICV Fluid Production Rates as a function of time for the 3-zone Completion.

Figure 4: Schematic of the dP1 and dP2 Trends as Water Influxes at one of the Three ICVs of a Three-zone Completion.

Water Influx Time

8 SPE 105374

21000

21500

22000

22500

23000

23500

24000

111

222

433

644

856

0

672

784

896

1008

1120

1232

1344

1456

1568

1680

1792

Time, days

Flui

d R

ate,

ST

B/D

2000

2200

2400

2600

2800

Tub

ing

Pres

sure

, Psi

a

Liquid Rate Tubing Pressure 1 Tubing Pressure 2

Figure 5: Well produced under reduced Tubing Head Pressure Control (Decreasing Surface Rate).

60

64

68

72

76

80

84

88

152 372 592 812 1032 1252 1472 1692

Time, days

Pres

sure

Dro

p, P

sia

0%

10%

20%

30%

40%

50%

Wat

er C

ut

Pressure Drop (dP1) Water Cut Valve 2 Water Cut Valve 1

Figure 6: Water Influx into Zone-1 identified by a increase in dP1.

Influx Time

Increase in dP

Influx Time

SPE 105374 9

0.0

0.2

0.4

0.6

0.8

1.0

20% 35% 45% 55% 65% 83%

Water Saturation

Rel

ativ

e Pe

rmea

bilit

y, F

ract

ion

KrwKroKrw (Base Case)Kro (Base Case)

Figure 7: The Relative Permeability Curves used to determine their importance

to the model results.

55

57

59

61

63

65

67

69

500 560 620 680 740 800 860 920 980

dP, P

sia

0%

1%

2%

3%

4%

5%

6%

7%

8%

Time, Days

Wat

er C

ut

dP1_KrdP1_Base CaseWater Cut for Kr at ICV1Water Cut for Base Case at ICV1

Water Influx Times

Influx Identification with dP

Figure 8: dP Trend the same with different Influx Times Identified.

10 SPE 105374

128

130

132

134

136

530

552

602

642

680

720

762

812

842

882

932

982

1032

1082

1132

1182

1232

1282

1332

Time, Days

dP1,

Psi

a

4546474849505152535455

dP2,

Psia

dP1 dP2

Figure 9: Water Influx from the middle zone in a 3-zone Completion

100

110

120

130

140

30 90 150 210 270 330 390

Time, Days

Pres

sure

, Psi

a

0%

2%

4%

6%

8%

10%

12%

14%

Wat

er C

ut

dP1dP2dP3Water Cut- ICV3

Figure 10: The dP trends identify the point of water influx in a 4-zone

Completion in a complex reservoir as being Zone-3.

Water Influx Time

Water Influx Time

SPE 105374 11

120

122

124

126

128

130

132

134

136

1 51 101 151 201 251 301 351 401 451 501

Time, Days

dP, P

sia

0

200

400

600

800

1000

1200

1400

1600

Wat

er P

rodu

ctio

n, S

TB

/D

dP1

Water Production from Lateral 2

Figure 11: dP1 reacting to Water Influx from lateral two in a Trilateral Well.

0

5

10

15

20

25

30

35

40

45

1 51 101 151 201 251 301 351 401 451 501Time, Days

dP, P

sia

0

200

400

600

800

1000

1200

1400

1600

Wat

er P

rodu

ctio

n, S

TB

/DdP2

Water Production from Lateral 2

Figure 12: Incease in dP2 as Water Influx from lateral two in a Trilateral Well.