Cold production footprints of heavy oil on time-lapse seismology: Lloydminster field, Alberta

Similar documents
Time-lapse seismic modelling for Pikes Peak field

Four-D seismic monitoring: Blackfoot reservoir feasibility

Integration of seismic methods with reservoir simulation, Pikes Peak heavy-oil field, Saskatchewan

Seismic methods in heavy-oil reservoir monitoring

Integrating reservoir flow simulation with time-lapse seismic inversion in a heavy oil case study

Reservoir Characteristics of a Quaternary Channel: Incorporating Rock Physics in Seismic and DC Resistivity Surveys

Delineating a sandstone reservoir at Pikes Peak, Saskatchewan using 3C seismic data and well logs

A look into Gassmann s Equation

The Hangingstone steam-assisted gravity drainage

Interpretation of baseline surface seismic data at the Violet Grove CO 2 injection site, Alberta

Time-lapse seismic monitoring and inversion in a heavy oilfield. By: Naimeh Riazi PhD Student, Geophysics

ROCK PHYSICS DIAGNOSTICS OF NORTH SEA SANDS: LINK BETWEEN MICROSTRUCTURE AND SEISMIC PROPERTIES ABSTRACT

AVO responses for varying Gas saturation sands A Possible Pathway in Reducing Exploration Risk

Partial Saturation Fluid Substitution with Partial Saturation

Reservoir Characterization of Plover Lake Heavy-Oil Field

Heriot-Watt University

Hydrogeophysics - Seismics

The Weyburn Field in southeastern Saskatchewan,

Summary. Seismic Field Example

Time-lapse seismic modeling of CO 2 sequestration at Quest CCS project

Rock physics and AVO applications in gas hydrate exploration

Geological Classification of Seismic-Inversion Data in the Doba Basin of Chad*

Fluid-property discrimination with AVO: A Biot-Gassmann perspective

RC 1.3. SEG/Houston 2005 Annual Meeting 1307

Seismic Rock Physics of Steam Injection in Bituminous-oil Reservoirs

Heavy Oil Field, Saskatchewan

Seismic modelling and monitoring of carbon storage in a shallow sandstone formation

Shear Wave Velocity Estimation Utilizing Wireline Logs for a Carbonate Reservoir, South-West Iran

IDENTIFYING PATCHY SATURATION FROM WELL LOGS Short Note. / K s. + K f., G Dry. = G / ρ, (2)

Laboratory experiments and numerical simulation on Bitumen Saturated Carbonates: A Rock Physics Study for 4D Seismology

Edinburgh Anisotropy Project, British Geological Survey, Murchison House, West Mains

Integrating rock physics and full elastic modeling for reservoir characterization Mosab Nasser and John B. Sinton*, Maersk Oil Houston Inc.

6298 Stress induced azimuthally anisotropic reservoir - AVO modeling

Velocity Measurements of Pore Fluids at Pressure and Temperature: Application to bitumen

Time lapse view of the Blackfoot AVO anomaly

V Sw =1 * V (1-S w ) 2* (V Sw =1 -V (1-Sw )) * (TWT 99 -TWT 94 ) under reservoir conditions (Rm 3 ) V Sw =1

Rock Physics Modeling in Montney Tight Gas Play

Practical Gassmann fluid substitution in sand/shale sequences

SRC software. Rock physics modelling tools for analyzing and predicting geophysical reservoir properties

Techniques for determining the structure and properties of permafrost

Workflows for Sweet Spots Identification in Shale Plays Using Seismic Inversion and Well Logs

Bandlimited impedance inversion: using well logs to fill low frequency information in a non-homogenous model

RESEARCH PROPOSAL. Effects of scales and extracting methods on quantifying quality factor Q. Yi Shen

We apply a rock physics analysis to well log data from the North-East Gulf of Mexico

Reservoir Characterization using AVO and Seismic Inversion Techniques

The role of seismic modeling in Reservoir characterization: A case study from Crestal part of South Mumbai High field

4-D seismic monitoring of an active steamflood

Methane hydrate rock physics models for the Blake Outer Ridge

The elastic properties such as velocity, density, impedance,

UNIVERSITY OF CALGARY. Reservoir property prediction from well-logs, VSP and. Natalia Soubotcheva A THESIS

Seismic anisotropy in coal beds David Gray Veritas, Calgary, Canada

Feasibility study of time-lapse seismic monitoring of CO 2 sequestration

Seismic applications in coalbed methane exploration and development

AFI (AVO Fluid Inversion)

An empirical study of hydrocarbon indicators

Measurement of elastic properties of kerogen Fuyong Yan, De-hua Han*, Rock Physics Lab, University of Houston

Coupled seismoelectric wave propagation in porous media. Mehran Gharibi Robert R. Stewart Laurence R. Bentley

SPE These in turn can be used to estimate mechanical properties.

Feasibility and design study of a multicomponent seismic survey: Upper Assam Basin

Reservoir simulation and feasibility study for seismic monitoring at CaMI.FRS, Newell County, Alberta

A021 Petrophysical Seismic Inversion for Porosity and 4D Calibration on the Troll Field

BPM37 Linking Basin Modeling with Seismic Attributes through Rock Physics

Sections Rock Physics Seminar Alejandra Rojas

ScienceDirect. Model-based assessment of seismic monitoring of CO 2 in a CCS project in Alberta, Canada, including a poroelastic approach

An overview of AVO and inversion

Key Laboratory of Geo-detection (China University of Geosciences, Beijing), Ministry of Education, Beijing , China

ROCK PHYSICS MODELING FOR LITHOLOGY PREDICTION USING HERTZ- MINDLIN THEORY

Uncertainties in rock pore compressibility and effects on time lapse seismic modeling An application to Norne field

Baseline VSP processing for the Violet Grove CO 2 Injection Site

Downloaded 11/20/12 to Redistribution subject to SEG license or copyright; see Terms of Use at

Temperature Dependence of Acoustic Velocities in Gas-Saturated Sandstones

Evaluation of Rock Properties from Logs Affected by Deep Invasion A Case Study

Keywords. Unconsolidated Sands, Seismic Amplitude, Oil API

4D stress sensitivity of dry rock frame moduli: constraints from geomechanical integration

PRELIMINARY REPORT. Evaluations of to what extent CO 2 accumulations in the Utsira formations are possible to quantify by seismic by August 1999.

An empirical method for estimation of anisotropic parameters in clastic rocks

Seismic Velocity Dispersion and the Petrophysical Properties of Porous Media

SEG/New Orleans 2006 Annual Meeting

Linearized AVO and Poroelasticity for HRS9. Brian Russell, Dan Hampson and David Gray 2011

Th LHR2 04 Quantification of Reservoir Pressure-sensitivity Using Multiple Monitor 4D Seismic Data

Rock physics and AVO analysis for lithofacies and pore fluid prediction in a North Sea oil field

AVO Attributes of a Deep Coal Seam

THE USE OF SEISMIC ATTRIBUTES AND SPECTRAL DECOMPOSITION TO SUPPORT THE DRILLING PLAN OF THE URACOA-BOMBAL FIELDS

Estimating the hydrocarbon volume from elastic and resistivity data: A concept

Elements of 3D Seismology Second Edition

Post-stack inversion of the Hussar low frequency seismic data

Recent advances in application of AVO to carbonate reservoirs: case histories

Laboratory Rock Physical and Geological Analyses of a SAGD Mannville Heavy Oil Reservoir, Senlac, West-Central Saskatchewan 1

Integration of Geophysical and Geomechanical

Reservoir properties inversion from AVO attributes

Crosswell tomography imaging of the permeability structure within a sandstone oil field.

Modelling of 4D Seismic Data for the Monitoring of the Steam Chamber Growth during the SAGD Process

Kondal Reddy*, Kausik Saikia, Susanta Mishra, Challapalli Rao, Vivek Shankar and Arvind Kumar

SUMMARY INTRODUCTION EXPERIMENTAL PROCEDURE

Rock physics of a gas hydrate reservoir. Gas hydrates are solids composed of a hydrogen-bonded ROUND TABLE

SENSITIVITY ANALYSIS OF THE PETROPHYSICAL PROPERTIES VARIATIONS ON THE SEISMIC RESPONSE OF A CO2 STORAGE SITE. Juan E. Santos

Gas Shale Hydraulic Fracturing, Enhancement. Ahmad Ghassemi

Th LHR2 08 Towards an Effective Petroelastic Model for Simulator to Seismic Studies

Sensitivity Analysis of Pre stack Seismic Inversion on Facies Classification using Statistical Rock Physics

A E. SEG/San Antonio 2007 Annual Meeting. exp. a V. a V. Summary

Transcription:

Cold production footprints of heavy oil on time-lapse seismology: Lloydminster field, Alberta Sandy Chen, Laurence R. Lines, Joan Embleton, P.F. Daley, and Larry Mayo * ABSTRACT The simultaneous extraction of oil and sand during the cold production of heavy oil generates high permeability channels termed as wormholes. The development of wormholes causes reservoir pressure decrease below the bubble point, resulting in dissolved gas out of solution to form foamy oil. The foamy oil could fill depressurized drainage regions (production footprints) around the borehole, leading to fluid phase changes. In this paper, the upper bound and lower bound models on the fluid mixture bulk moduli have been used to detect the sensitivity of seismic P-wave velocity. Then the Gassmann Equation has been employed to calculate the bulk modulus variations of the drainage rocks, where the velocity and density decrease dramatically due to some exolved gas. A 2D cold production model has been built to examine the seismic responses of drainage regions based on well logs in the Lloydminster cold production pool. The seismic modelling responses indicate amplitude anomalies and traveltime delays in these regions upon comparison of pre and post production results. However, because most heavy oil cold production reservoirs are very thin, with less than 10 m of net pay, seismic resolution is a challenge. Different seismic frequency bandwidths have also been tested to look at how vertical seismic resolution determines the detection of the drainage regions. INTRODUCTION The extraction of heavy oil by cold production is a non-thermal process, in which sand and oil are produced simultaneously to enhance the oil recovery. This process has been economically successful in several unconsolidated heavy oilfields in Alberta and Saskatchewan. The reason for this is that producing sand creates a wormhole network and a foamy oil drive. These two effects are key to boosting oil recovery. Wormhole networks During the cold production process, both heavy oil and sand are transported to the surface using a progressive cavity pump, generating sharp pressure gradients in the reservoir. This results in a failure of the unconsolidated sand matrix. The failed sand flows to the well, triggering the development of wormholes. Sawatzky and Lillico (2002) believe that wormholes grow in unconsolidated, clean sand layers within the net pay zone, along the highest pressure gradient between the borehole and the tip of the wormhole. Wormholes usually can grow to a distance of 150 m or more from the original boreholes, forming approximately horizontal high permeability channels in the reservoirs. Based on the laboratory experiments conducted by the Alberta Research Council (ARC), Tremblay et al. (1999) observed that the porosity of the mature wormholes could reach * EnCana Corporation CREWES Research Report Volume 15 (2003) 1

Chen et al. 50% or more. He also determined wormhole diameters could range from the order of 10 cm to 1 metre as the maximum size. Wormholes play an important role in the improvement of the reservoir permeability. However, Sawatzky believes that the wormhole density cannot exceed 5% of the total net pay. In this case, the wormhole contribution to the average porosity of the net pay is relatively small. Foamy oil drives The cold production process involves the depressurization of the reservoir while wormholes grow. The pressure falls below the bubble point, resulting in the exsolution of dissolved gases as bubbles within the oil. The viscosity and specific gravity of heavy oil restrict the gas bubbles from coalescing into a separate phase. Thus, the trapped gas bubbles within the heavy oil form the foamy oil (Figure 1a). The formation of foamy oil increases the fluid volume within the reservoir, generating the high pressure, forcing grains apart and driving both oil and gas to the wells through the wormholes (Figure 1b). Therefore, the foamy oil drive within the reservoir enhances oil recovery. (a) (b) FIG. 1. a) Foamy oil (Greenidge, ESSO); b) Single propagating wormhole in sand reservoirs (Dusseault, 1994), where foamy oil drives the oil and gas to the low pressure wormhole. Drainage footprints The drainage footprints could be considered as regions that are depressurized. In cold production, the extent of the drainage region is strongly related to the growth of wormholes, where reservoir pressure has decreased. The drainage region can expand at least as far as the length of wormholes through the entire net pay. Sawatzky and Lillico (2002) showed a likely drainage footprint scenario for the cold production wells in the small Southwest Saskatchewan heavy oil pool (Figure 2), which helped engineers design infill drills and development plan. Engineers also believe that 3D time-lapse seismology could be a tool implemented in the imaging of the drainage region, caused by foamy oil and the presence of wormholes. Mayo (1996) showed some amplitude anomalies caused by foamy oil effects in a 3D time-lapse seismic survey from cold production in the Lloydminster area (Figure 3). The 3D survey was required 9 years after production began. Mayo believes that the amplitude anomalies around the boreholes could represent the drainage regions. 2 CREWES Research Report Volume 15 (2003)

FIG. 2. Most likely depletion (drainage) footprint scenario for the cold production wells in the small southwest Saskatchewan heavy oil pool.(sawatzky, 2002). FIG. 3. Seismic amplitude extraction from the MacLaren horizon (Mayo, 1999). The 3D seismic survey shot 9 years after the pool was discovered shows amplitude anomalies. Based on the characteristics of the heavy oil cold-production footprints, we built a simplified 3D model as a cylinder around the borehole, representing the drainage region of heavy oil cold production (Figure 4), which shows a 3D plane view and a 2D crosssection of the drainage region. Before production, most of the heavy oil cold-production reservoirs are highly saturated with oil, and a small partial of water. After the start of the production, with sand extraction and wormhole growth, the fluid phases change into oil, water, and foamy oil with gas bubbles. Therefore, the drainage regions are highly disturbed with the presence of foamy oil, and can be significantly different from the initial reservoir state of oil and water only. From what we saw in Figure 3, the question was raised of whether or not the foamy oil effects can explain seismic amplitude anomalies. This is the key study of this paper. CREWES Research Report Volume 15 (2003) 3

Chen et al. wormholes Drainage Undisturbed well Undisturbed Drainage Undisturbed FIG. 4. 3D plane view (above) and 2D cross-section of the drainage region METHODS OF COMPUTING PHYSICAL PROPERTIES OF DRAINAGE REGIONS Before production, the drainage regions show no difference in velocity and density with the ambient reservoir rocks. However, during or after the start of production, the reservoir fluids in the drainage regions change to three phases of oil, gas (in foamy oil), and water from the two phases of oil and water there were before production. With the fluid phase changes, the Gassmann equation (1951) is employed to calculate the saturated K, rock bulk modulus ( ) u K u = K + d 2 (1 Kd Ks) φ 1 φ K + K K K d 2 f s s, (1) where, Kd, Ks, K f,andφ are the dry rock bulk modulus, the solid grain modulus, the fluid bulk modulus and the porosity. The physical aspects of this equation are reviewed by Russell et al. (2003). The saturated rock bulk density can be calculated as: ρ = ρ (1 φ) + ρ φ. (2) u s f where ρs, ρu and ρ f are the densities of solid grains, saturated reservoir rock and fluid mixture at reservoir conditions. To approximate the bulk modulus K f of the fluid mixture, an assumption has to be made that the fluids are mixed uniformly at a very fine scale. So the different waveinduced increments of pore pressure in each phase have time to diffuse and equilibrate during a seismic period. Then the saturation-weighted harmonic average is appropriate (Reuss average, 1992; Bentley et al., 1998): 4 CREWES Research Report Volume 15 (2003)

1 Sg So Sw = + +, (3) K K K K f g o w Where, Sg, So and S w are the gas, oil and water saturations, respectively. In this situation, any small amount of gas will cause the estimate of the fluid mixture bulk modulus to be very low. In order to compute the bulk moduli of gas ( g ) K, oil ( K ) and water ( ) o K, the methods demonstrated by Batzle and Wang (1992) are applied. The fluid mixture density is a volume average given by: ρ = S ρ + S ρ + S ρ. (4) f g g o o w w In order to estimate the dry bulk modulus, we have to use the two equations: w V p K + 4µ / 3 ρ u =, (5) u V s µ = (6) ρ u Here µ is the shear modulus of saturated rock. As V P is known from the sonic log (Figure 5) and the VP V S ratio of 1.9 derived from one of the dipole sonic logs in Pikes Peak area, we can obtain the values of K u and µ based on equations. (5) and (6). Applying them to equation (1), the dry rock bulk modulus can be derived. In this case, we assume that the dry bulk modulus of rocks remains constant before and after production although slight differences of porosities exist between the drainage region and the undisturbed region. Finally, the Gassmann Equation can be employed to calculate the saturated rock bulk modulus Ku and shear modulus under in-situ reservoir conditions after production. Table 1 shows the in-situ reservoir parameters before and after a 3-year production period provided by Sawatzky (ARC). Table 2 contains the physical properties of reservoir rock in the drainage regions before and after production calculated using the above formula. With a 10% gas saturation, the saturated rock bulk modulus dramatically drops from 10.6 Gpa to 5.2 Gpa, while the shear bulk modulus shows only a slight increase. Hence the P- wave velocity has decreased from 2795 m/s to 2325 m/s, and the S-wave velocity has increased slightly due to the density decrease. CREWES Research Report Volume 15 (2003) 5

Chen et al. The sensitivity of seismic velocities to pore fluids depends not only on saturations but also on the spatial distributions of the phase within the rock (Mavko et al., 1998). Therefore, another assumption is made that the fluids are patchy at a scale that is smaller than the seismic wavelength, but larger than the scale at which the scale fluids can equilibrate pressures through local flow. The effective fluid bulk modulus is then larger, which defines the upper bound on the fluid mixture bulk modulus. The upper bound of the fluid mixture bulk modulus is an isotrain average, known as the Voigt model: K f Sg Kg SoKo SwKw = + +. (7) Table 1. In-situ reservoir parameters in drainage regions Production: Pre Post V p (m/s) from logs 2795 unknown V p /V s (from logs) 1.9 unknown density from log (g/cm 3 ) 2.16 unknown density of grain sand (g/cm 3 ) 2.65 2.65 oil API 11.3 11.3 specific gravity of methane 0.56 0.56 gas pseudocritical temperature (F) -116.5-116.5 gas pseudocritical pressure (Psi) 667 667 solution Gas-Oil ratio 7.5 7.5 reservoir temperature (C) 20 20 reservoir pressure (kpa) 3000 1500 water saturation (%) 20 20 oil saturation (%) 80 70 gas saturation (%) 0 10 water salinity (ppm) 44000 44000 porosity (%) 30 30 Table 2. Physical properties of drainage region before and after production using lower bound on fluid bulk modulus Before production After production S g =0, S o =0.8 S g =0.1, S o =0.7 saturated rock bulk modulus (Gpa) 10.616 5.2252 saturated shear modulus (Gpa) 4.6726 4.6777 saturated bulk density (kg/m 3 ) 2156.5 2126.6 V p (m/s) 2795 2325 V s (m/s) 1472 1483 6 CREWES Research Report Volume 15 (2003)

Table 3 shows the variations of bulk moduli and seismic velocities when applying the equation (7), where the saturated rock bulk modulus and P-wave velocity are only slightly different from that in Table 2. So, equation (3) actually defines the lower bound of the fluid mixture bulk modulus. And any other distribution of fluid phases gives the moduli falling between these two bounds (Mavko et al., 1998). Table 3. Physical properties of drainage region before and after production using upper bound on fluid bulk modulus Before production After production S g =0, S o =0.8 S g =0.1, S o =0.7 saturated rock bulk modulus (Gpa) 10.616 10.113 saturated shear modulus (Gpa) 4.6726 4.6777 saturated bulk density (kg/m 3 ) 2156.5 2126.6 V p (m/s) 2795 2773 V s (m/s) 1472 1483 Considering the uncertainty of the fluid state, the average fluid bulk moduli from equations (3) and (7) is applied to the Gassmann Equation. Then the average P-wave velocity and density (Table 4) will be employed to the following seismic modelling. Table 4. Physical parameters of in-situ drainage region using the average of upper bound and lower bound on fluid bulk modulus Saturated rock Preproduction Postproduction S g =0, S o =0.8 S g =0.1, S o =0.7 bulk modulus (Gpa) 10.616 7.807 shear modulus (Gpa) 4.671 4.676 bulk density (kg/m 3 ) 2156 2126 V p (m/s) 2795 2570 V s (m/s) 1472 1483 GEOLOGICAL MODELS Before production, the geological model was built based on the logs from one well drilled by EnCana Corporation in the Lloydminster heavy oil cold production pool. The production zone is the McLaren Formation consisting of unconsolidated sandstones in the Upper Mannville BB Pool. According to the density, gamma ray, and P-wave velocity (converted from sonic) logs, a geological model of the initial reservoir has been built as shown in Figure 5. This model is 1000 m by 1000 m in the horizontal and vertical directions, consisting of four layers matched the variations of well logs. From top to CREWES Research Report Volume 15 (2003) 7

Chen et al. bottom, layer 1 is defined from the surface to the top of Mannville Formation at 726 m with a velocity of 2496 m/s and a density of 2.26 g/cm 3 ; layer 2 is from the top of the Mannville Formation to the top of the McLaren Formation at 742 m with a velocity of 3227 m/s and a density of 2.37 g/cm 3 ; layer 3, the production formation, with a velocity of 2795 m/s and a density of 2.16 g/cm 3, is from the top of McLaren Formation to 760 m, instead of the bottom of the McLaren Formation. The reason for this is because there are sharp variations in both density and velocity logs at the bottom of Maclaren Formation, which are probably due to the highly cemented sand. Layer 4 starts from 760 m to the bottom of the model at 1000 m with a velocity of 3261 m/s and a density of 2.4 g/cm 3. The interval velocities and densities of each layer except layer 4 are calculated from logs corresponding to their depths. The interval velocity and density of layer 4 are the averages from 760 m to 800 m on the logs. Well Top Mannville Top McLaren Bottom McLaren FIG. 5. Geological model of the initial reservoir before production from well logs (density, gamma ray, and V p from left to right) From the porosity, gamma ray, and resistivity logs (Figure 6), we can see that there are two main preferred net pay zones in the MacLaren Formation. One is from 745 m to 750 m and the other from 755 m to 758 m. During production, wormholes would likely grow within the two pay layers, where the drainage regions can be expanded and filled with foamy oil. Assuming that the wormholes can extend 100 m and 150 m from the well in each pay zone respectively, the diameters of the drainage region will be 200 m and 300 m through the entire pays. We have considered a drainage region as a cylinder in 3D domain, and a 2D model is illustrated in Figure 7. The P-wave velocity and density are respectively 2570 m/s and 2.13 g/cm3 as shown in Table 4. 8 CREWES Research Report Volume 15 (2003)

FIG. 6. Two main net pays (745-750 m, 755-758 m) in MacLaren reservoir sand, the upper one with 5 m net pay, and the lower one with 3 m Well FIG. 7. The drainage region model after production within two net pays (upper one 5 m thick with a diameter of 200 m; the lower 3 m thick, a diameter of 300 m (in purple). Both have a velocity of 2570 m/s, a density of 2.13 g/cm 3 MODELLING RESULTS Based on the models shown in Figure 5 and 7, zero-offset seismic traces with and without drainage regions were generated using the exploding reflector, then a Kirchhoff depth migration was applied in the Promax software package. The velocity and density grid is 1 m by 1 m. CDP intervals are 1 metre. Figure 8 illustrates the migrated sections of pre- and post-production, as well as the difference between the two at a frequency bandwidth of 200 Hz. We can observe the amplitude anomalies and traveltime delays caused by the two drainage regions, with lower velocity and density compared to the ambient rocks. In order to detect seismic resolution effects on thin-bed drainage regions, we changed the frequency bandwidth from 200 Hz to 100 Hz and 50 Hz. Figure 8 illustrates that only CREWES Research Report Volume 15 (2003) 9

Chen et al. the cumulative responses of the two drainage regions can be observed due to lower resolutions. And it is almost unidentifiable in the 50 Hz case (shown in Figure 9). Compared to Figure 8, where the two drainage regions are clearly separated, we believe that the seismic frequency bandwidth plays an important role in imaging the drainage regions. (a) Without the drainage regions (b) With the drainage regions (c) Difference between (a) and (b) FIG. 8. Time-lapse migrated sections with a frequency bandwidth of 200 Hz. (a) With a frequency bandwidth of 100 Hz (b) With a frequency bandwidth of 50 Hz FIG. 9. Migrated sections with the drainage regions. CONCLUSIONS The cold production process in heavy oil is a unique procedure of producing oil and sand simultaneously, while generating high-permeability wormhole channels within the 10 CREWES Research Report Volume 15 (2003)

reservoir. The presence of wormholes with depleted pressures leads to significant changes in reservoir characteristics from the initial state of the reservoir. This makes seismic detection of drainage regions (production footprints) practical using time-lapse seismology techniques. The length of the wormholes determines the extent of the drainage regions, also called production footprints. With the growth of wormholes, the reservoir pressures drop below the bubble point, the dissolved gas comes out of solution from the live oil, forming foamy oil. Hence, the exolved gas changes the fluid phases in the drainage regions, leading to P- wave velocity and density decreases. These physical properties cause bright spots on the synthetic seismic traces. Since most net pays in cold production are less than 10 m thick, vertical seismic resolution is key on detecting the images of drainage regions, which are determined by the velocity and the frequency bandwidth. ACKNOWLEDGEMENTS The authors thank the COURSE project and CREWES for their financial and technical support. We acknowledge the contributions to our research made by Alberta Research Council. We also would like to thank Dr. Ron Sawatzky for his valuable suggestions and discussions. REFERENCES Batzle, M. and Wang,Z., 1992, Seismic properties of pore fluids: Geophysics, 57, No. 11, P1396-1408. Bentley, L. and Zhang, J., 1999, Four-D seismic monitoring feasibility: CREWES Research Report, 11. Dusseault, M. 1994, Mechanisms and management of sand production: C-FER Project 88-06, Vol. 4. Gassmann, F., 1951, Elastic waves through a packing of spheres: Geophysics, 16, 637-685. Mayo, L., 1996, Seismic monitoring of foamy heavy oil, Lloydminster, Western Canada: 66th Ann. Internat. Mtg. Soc. Of Expl. Geophys, 2091-2094. Russell, B.H., Hedlin, K., Hilterman, F., and Lines, L.R., 2003, Fluid-property discrimination with AVO: A Boit-Gassmann perspective: Geophysics, 68, 29-39 Sawatzky, R.P. and Lillico, D.A., 2002, Tracking cold production footprints: CIPC Conference, Calgary, Alberta, June11-13. Tremblay, B., 1999, A review of cold production in heavy oil reservoirs: EAGE 10th European Symposium on Improved Oil Recovery, Brighton, UK. CREWES Research Report Volume 15 (2003) 11