IOP PUBLISHING JOURNAL OF GEOPHYSICS AND ENGINEERING

J. Geophys. Eng. 7 (2010) 16–29 doi:10.1088/1742-2132/7/1/002

Seismic monitoring of in situ combustionprocess in a heavy oil fieldHossein Mehdi Zadeh1,2, Ravi P Srivastava3, Nimisha Vedanti3 andMartin Landrø2

Department of Petroleum Engineering and Applied Geophysics, NTNU, 7491, Trondheim, Norway

E-mail: hossein.mehdi-zadeh@ntnu.no, ravi prakash@ngri.res.in, nimisha@ngri.res.in andmartin.landro@ntnu.no

Received 19 February 2009Accepted for publication 9 November 2009Published 1 December 2009Online at stacks.iop.org/JGE/7/002

AbstractThree time-lapse 3D seismic surveys are analysed to monitor the effect of in situ combustion, athermal-enhanced oil recovery process in the Balol heavy oil reservoir in India. The baselinedata were acquired prior to the start of the in situ combustion process in four injection wells,while the two monitor surveys were acquired 1 and 2 years after injection start, respectively.We present the results of baseline and second monitor surveys. Fluid substitution studies basedon acoustic well logs predict a seismic amplitude decrease at the top reservoir and an increaseat the base reservoir. Both the amplitude dimming at the top reservoir and the brightening atthe base reservoir are observed in the field data. The extent of the most pronounced 4Danomaly is estimated from the seismic amplitude and time shift analysis. The interesting resultof seismic analysis is that the anomalies are laterally shifted towards the northwest, rather thanthe expected east, from the injector location suggesting a northwest movement of the in situcombustion front. No clear evidence of air leakage into other sand layers, neither above norbelow the reservoir sand, is observed. This does not necessarily mean that all the injected air isfollowing the reservoir sand, especially if the thief sand layers are thin. These layers might bedifficult to observe on seismic data.

Keywords: time-lapse seismic, heavy oil

1. Introduction

Heavy oil is commonly characterized by high viscosity andlow API gravity which results in a low primary recovery.The in situ combustion process is a thermal recovery method,used for heavy oil reservoirs in which a part of the oil isburned to generate heat. This heat reduces the viscosity ofthe oil leading to improved mobility and hence increased oilproduction rate. A typical combustion front moves throughthe reservoir matrix by consuming the fuel as it moves ahead,thereby leaving practically no oil behind the burning front(Burger 1976, Tadema and Weijdema 1970). In this process,

1 Author to whom any correspondence should be addressed.2 Present address: Norwegian University of Science and Technology, S.P.Andersens vei 15 A, 7491 Trondheim, Norway.3 Present address: National Geophysical Research Institute, Uppal Road,Hyderabad 500 007, India.

typical seismic parameters such as P, S wave velocity, densityand attenuation factor within the reservoir matrix changeunder the influence of the movement of the thermal front.Seismic monitoring of the in situ combustion can help tobetter understand the process and optimize the strategy forenhanced oil recovery project (Nur et al 1984, Lumley 1995).The heat produced in the reservoir will cause a decrease inthe seismic velocity and one can expect push-downs (travel-time increases) in the subsequent monitor surveys. Therefore,one way to seismically monitor the movement of the thermalfront in the reservoir is to perform a time shift analysis ofthe 4D seismic data (Landrø and Stammeijer 2004). On theother hand, the amplitude of the top and bottom reservoirs isalso affected by the influence of the thermal front; therefore,amplitude analysis can also show consistent anomalies (Hedlinet al 2001). However, in a heterogeneous reservoir, the thermalfront direction, rates and efficiency of the process can be

1742-2132/10/010016+14$30.00 © 2010 Nanjing Institute of Geophysical Prospecting Printed in the UK 16

Seismic monitoring of the in situ combustion process in a heavy oil field

unpredictable and may need combination of different methodsto monitor. Greaves and Fulp (1987) were able to monitor thecombustion burnt area and sweep efficiency with confirmationby borehole measurements. In fact, one of the earliest worksin seismic monitoring of steam injection is by Macrides et al(1988). Monitoring of the Duri field, Sumatra, Indonesia,also showed a successful improvement in monitoring of steamflood (Jenkins et al 1997, Waite and Sigit 1997, Sigit et al1999). Another interesting attempt was made by Zou et al(2003) to study the effects of substitution of thermal fluidsin a synthetic heavy oil reservoir that is based on the PikesPeak field, Canada. Indeed, there are many interesting workson Canadian’s heavy oil (e.g. Bianco et al 2008, Chi and Xu2007, Dumitrescu and Lines 2008, Gray et al 2004, Isaac andLawton 2006, Li et al 2001, Mayo 1996, Schmitt 1999). Inthis study we test both travel time and amplitude analysis onBalol 4D seismic data for monitoring the in situ combustionfront movement.

2. Field description

The Balol field, operated by Oil and Natural Gas Corporationof India Ltd (ONGC), is located in the heavy oil belt in thenorth-western part of the Cambay Basin, India. Balol is oneof the major fields within this belt with a density of oil around960 kg m−3 at 15.5 ◦C. In terms of API gravity index, the Baloloil is about 15.5◦ API. This index is a measure of oil density incomparison to water at 15.5 ◦C. Heavy oil is classified in theAPI range from 22 to less than 10 (Han et al 2008). The Balolcrude is asphaltic in nature (6–8%) with viscosity varyingfrom 100 to 450 centipoise. The reservoir temperature andpressure are 72 ◦C and 10.2 MPa, respectively. The averageporosity and permeability of this sandstone reservoir are 28%and 3–8 Darcy, respectively (Kumar and Mohan 2004, Kumaret al 2006). Balol was initially operated under active waterdrive (Doriah et al 2007). The primary recovery was only10–12%, due to a high mobility difference between oil andwater. Therefore, the composition of the produced fluids hadincreased to 95–100% water in some wells and forced the wellsto be closed. This was the main motivation for using a thermalEOR processes.

The base map of the 4D area and the positions of the activeair injector wells B-145, B-147, B-153 and B-162, whichare in operation after November 2003 (i.e. soon after thebaseline survey), are shown in figure 1. The major fault,close to the indicated injectors, is reported to be a sealing fault(Mukherjee et al 2006), and therefore it is likely to assume thatthe injectors have no significant effect west to the fault. Themajor pay zone sand within the reservoir has a northwest up-dip direction with an average pay thickness of 2–15 m whilethe oil–water contact (OWC) varies from 990 to 1025 m depth.

3. Rock physics modelling

In the case of in situ combustion, air or pure oxygen is injectedand then it is ignited in the reservoir zone. Subsequent airinjection propagates the burning front through the reservoir.

Figure 1. Top reservoir structural map. The contours show thedepth of the top of the reservoir in meters. The thick red lines showmajor faults at the top of the reservoir. Rectangle shows the 4D areawith in-line (vertical) and cross-line (horizontal) numbers.

The burning front is thin (roughly a meter, much less than theseismic resolution limit) but high temperatures are generatedwithin it (600 ◦C in Balol field) that vaporize the connatewater and a portion of the crude (phase change from liquid togas). The vaporized connate water forms a steam zone thatbehaves like a steam drive. The vaporized oil consists of thelight components that form a miscible displacement. The hightemperature combustion can also form in situ CO2 flood (Lake1989).

In the in situ combustion process, oxygen is consumed intwo stages, one during a low-temperature oxidation at about300 ◦C and other at high-temperature oxidation at about 400 ◦C(Lake 1989). In low-temperature oxidation, the oil viscosityis lowered and the oil is converted into alcohols, ketones andaldehydes (no phase change from liquid to gas). In high-temperature oxidation, the combustion proceeds entirely tocarbon dioxide or monoxide, this involves a phase changefrom liquid to gas.

The heavy oil behaves as solid at high frequencies andlow temperatures (laboratory condition), due to non-negligibleshear modulus. This solid-like properties of the heavy oilviolates Gassmann’s fluid substitution (Das and Batzle 2008).The velocity of heavy oil is a function of temperature, APIgravity (density), viscosity and seismic frequency. However,at low (seismic) frequencies and high temperatures, heavy oildoes not support shear wave propagation and have liquid-likeproperties. The liquid point is the temperature at which theshear rigidity vanishes. This point depends on the API andwave frequency. When the reservoir has a temperature abovethe liquid point, heavy oil properties are similar to that of lightoil. In this case, the oil bulk modulus shows a linear trend withincreasing temperature and depends mainly on API gravity(Han et al 2008). The expected behaviour of the liquid point asa function of oil density (API) and the frequency measurementis given by Han et al (2008). For the Balol field, with

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Table 1. Initial properties used for fluid replacement modelling (FRM). Reproduced from Kumar and Mohan (2004).

Fluid Matrix

Component Brine Oil Gas Quartz Clay Rock matrix Fluid mixture

Density (g cc−1) 1 0.92 0.007 2.56 2.58 2.56 0.93Bulk modulus (GPa) 2.48 1.79 0.021 38 20.9 36.5 1.95Shear modulus (GPa) 0 0 0 44 6.9 34.7 0Saturation (%) 30 70 0 95 5 72 28

Figure 2. P-wave velocity (top) and density (bottom) changesversus gas saturation.

15.50 ◦API gravity oil, the reservoir temperature (72 ◦C) ishigher than liquid point (lower than 5 ◦C in seismic frequencyrange) and the shear modulus of the oil is negligible. Thus,for this case Gassmann’s fluid substitution equation remainsvalid.

The rock properties for Gassmann’s fluid substitution aresummarized in table 1 and are reproduced from Kumar andMohan (2004). They calculate the bulk modulus and densityof the rock matrix from assumed mineral composition (i.e.95% quartz and 5% clay). The fluid properties are calculatedfrom available PVT data and rock physics equations (Batzleand Wang 1992, Vasquez and Beggs 1980).

The changes in P-wave velocity and density versus gassaturation are shown in figure 2. The maximum drop inP-wave velocity is 8% which corresponds to a 15% increase ingas saturation and a 1.5% decrease in density. This representsthe maximum change in P-wave velocity (corresponding tothe minimum in figure 2). However, if the reservoir is fullysaturated with gas (100% gas case), the drop in P-wave velocityis 3.6% which corresponds to a 12% decrease in density. Thisrepresents the maximum change in density (see figure 2).

A fluid replacement modelling (FRM) using available welllog and field seismic data was done. The initial physicalproperties of the matrix and fluid used for the FRM are shownin table 1 for the initial case. In this study we use a porosityof 28% and assume it is constant after in situ combustion.There is no S-wave log available in our study. Therefore,we use Castagna’s mud-rock line to generate the S-wave log.Castagna’s relation is a statistical relationship between P- andS-wave velocities (Castagna et al 1985). We assume a constant

Poisson’s ratio (0.1) for dry rock and estimate the dry rockbulk modulus from Gregory’s method (Gregory 1977). InGregory’s method, the dry bulk modulus is obtained by solvingthe following quadratic equation:

y2(s − 1) + y

{φs

(ks

kf

− 1

)− s +

M

ks

}

−φ

(s − M

ks

) (ks

kf

− 1

)= 0 (1)

where

y = 1 − kdry

ks

(2)

s = 3(1 − σ)

1 + σ(3)

and M is given by

M = ρv2p (4)

where ϕ, ks , kf are porosity, rock matrix bulk modulus andfluid mixture bulk modulus, respectively. In these equations,Poisson’s ratio and dry rock bulk modulus are noted by σ andkdry, respectively. In equation (4), ρ and vp are density andP-wave velocity of the saturated rock, respectively. Thedensity of the saturated rock can be calculated by

ρ = (1 − φ)ρs + φρf (5)

where ρs and ρf are solid and fluid part densities of saturatedrock, respectively. The assumption behind this method is fromthe fact that for most dry rocks and unconsolidated sands,Poisson’s ratio is about 0.1 and is independent of pressure aslong as the effective pressure is non-zero. As the effectivepressure approaches zero, it is shown by Duffaut and Landrø(2007) that the Poisson’s ratio will change. However, ifthe shear-wave velocity is available, Poisson’s ratio can becalculated directly.

The moduli of the mineral mixture can be predicted usingHashin–Shtrikman bounds (Hashin and Shtrikman 1963).We use these bounds due to the fact that they give thenarrowest possible range of moduli, without specifying thegeometry of the constituents (Avseth et al 2005). Sincethe Gassmann model assumes a homogenous mineral modulus,it is useful to represent this mixed mineralogy with an averagemineral modulus, equal to the upper and lower bound average.Therefore, we use an average of lower and upper Hashin–Shtrikman bounds as frame bulk and shear modulus. Weuse the Reuss lower bound (Mavko et al 1998) to estimatethe effective elastic modulus of the fluid mixture, assuminghomogeneous saturation of fluid components.

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Seismic monitoring of the in situ combustion process in a heavy oil field

Figure 3. Synthetic modelling result of different gas saturation scenarios. From left to right: resistivity log, natural gamma ray log,synthetic seismic data (five repeated traces for 0, 15 and 100% gas saturation), P-wave velocity log and density log. The horizontal linesshow the approximate top (955 ms) and base (975 ms) of the reservoir. The time axes are calculated from depth conversion using thebaseline velocity model.

Figure 4. Comparison of real and synthetic data for a realistic changes in P-wave velocity (−4%) and density (−0.75%). From left to right:natural gamma ray log, acoustic impedance log, synthetic seismic data and real seismic data. The approximate top (955 ms) and base(975 ms) of the reservoir, for baseline, are indicated in logs. The time axes are based on the baseline velocity model.

The reservoir pressure change is negligible according tothe field history (Kumar and Mohan 2004). This is due tostrong aquifer support. Hence, we keep the pressure constantbefore and after combustion to 10.2 MPa.

Nur and Simmons (1969) and Devilbiss et al (1979) showthat the temperature-related changes in the velocity are largelydue to the effect of pore fluids while the skeleton propertiesremain approximately constant. We, therefore, consider theeffect of temperature in terms of change in fluid phase, i.e.from heavy oil to vapour.

Synthetic, zero offset traces corresponding to 0, 15 and100% gas saturation are shown in figure 3. These gas

saturation values were chosen judiciously, as they correspondto pre-combustion (0%), maximum vp change (15%, seefigure 2) and post-combustion (100%) cases. The well logsare from a production well (B-183) that was logged beforethe in situ combustion process started (baseline case). Theresult from FRM shows an amplitude decrease at the top andan amplitude increase at the base of the reservoir for 15%and 100% gas saturations compared to 0.0% gas saturationcase. Since there are no log data available for the post-combustion case, we estimated the realistic change in P-waveand density within the bounds obtained from FRM whichsatisfies the monitor seismic data. The estimated change in

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Figure 5. Schematic geological models for base (left) and monitor (right) cases. The changed area (in monitor case) has 4% and 0.75%decreased in P-wave velocity and density, respectively.

Figure 6. Seismic section of unmigrated synthetic data, base (top), monitor (middle) and difference (bottom). The data are stacked. Thediffraction curves due to sharp velocity change around the fault are visible in all sections. The top (red line) and base of the reservoir arelocated around 955 ms and 990 ms, respectively.

vp and density is around −4.0% and −0.75%, respectively.A synthetic trace of such changes is shown in figure 4. Thesynthetic result is compared with real traces which are post-stack traces from baseline survey (B) and monitor (M) surveysat injector well B-153 (situated approximately 635 m southof production well B-183). A good correspondence betweenthe synthetic and the real trace is observed. The normalizedRMS (NRMS) amplitude change in this case is around 30%for a 50 ms time window including both top and base of thereservoir.

4. Synthetic data study

A major challenge for the Balol 4D seismic is that the expected4D changes are close to a major fault. The typical vertical

displacement of the major fault in this area is reported to bearound 300–400 m. We study the effect of fault to 4D analysisby a synthetic 2D elastic finite difference modelling. Twosets of synthetic data are generated. The source and receiverseparation are 25 m and 12.5 m, respectively, with a maximumoffset of 2500 m. The source and receiver depths are 6 m and8 m, respectively. The models for baseline and monitor casesare shown in figure 5. For simplicity, we add a water layer tothe model to avoid ground roll noise with the expense of addingmultiples. In the monitor case, we introduce an anomalyclose to the fault with 50 m extension. The anomaly has a4% decrease in P-wave velocity and a 0.75% decrease in thedensity in comparison to the base model, which was estimatedfrom the rock physics model. A stacked seismic sectionfor baseline, monitor and difference is shown in figure 6.The diffraction curves are evident in the difference section.

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Seismic monitoring of the in situ combustion process in a heavy oil field

Figure 7. The corresponding base (top), monitor (middle) and difference (bottom) synthetic seismic sections after post-stack timemigration. The multiples are present. The top (red line) and base of the reservoir are located around 955 ms and 990 ms, respectively.

Figure 8. Amplitude analysis for the synthetic data in a 50 ms time window (950–1000 ms), including both top and base reservoir primaries.

This suggests that migration may improve the 4D image.The results after post-stack migration are shown in figure 7.The 4D signal is clear and interpretable in this case. Notethat the time shift is not observable due to small extensionof the 4D anomaly that limits the resolution. Therefore,amplitude analysis helps to detect such a small anomaly. Theamplitude analysis of the baseline, monitor and the differenceis shown in figure 8. The RMS amplitude analysis of thedifference gives the most pronounce 4D effect in this case.Based on this analysis, we chose to focus on amplitudeanalysis of the real data as time shifts are expected to beless significant. Since the injector wells are close to the

fault in the field, we think that migrated field data are abetter candidate to monitor the fire front, despite the factthat the migration may introduce artefacts. Additionally,we perform a resolution test to find the minimum detectableextension of anomaly for the field case. According to thistest, the first Fresnel zone is 180–190 m. Hence, the expectedanomalies for the Monitor-1 survey (100 diameter) might bebeyond the noise level, while for the Monitor-2 survey (200m diameter) might be detectable. However, we emphasizethat the migration collapses the Fresnel zone to a muchsmaller area.

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Figure 9. The location of the sources for baseline (red) and monitor line (blue).

5. Time-lapse 3D seismic acquisition

The main goal of the time-lapse 3D seismic surveys wasto monitor changes in the reservoir condition due to in situcombustion and track the in situ combustion front movement.To achieve this goal, P-wave time-lapse seismic data wereacquired at regular intervals, keeping the same acquisitionparameters. The baseline data were acquired duringOctober–November 2003, before the in situ combustionprocess started. After the base data acquisition, fourinjection wells were put on in situ combustion successivelyfrom north to south. Wells B-147, B-162, B-145 andB-153 were ignited respectively on November 2003, (19)December 2003, (29) December 2003 and January 2004 andcontinuous air injection is going on in all these wells tosustain the fire. Subsequent to the baseline survey, twomonitor surveys (Monitor-1 and Monitor-2) were carriedout at the interval of 12 months under similar climateconditions with the same survey parameters (Kumar et al2006). The acquisition consists of 12 receiver lines with72 channels in each line. Group and inline shot intervals are20 m with a minimum near offset of 20 m. The data arerecorded for 5 s with a 2 ms time sampling interval usingthe end-on shooting method. Shot and receiver locations forbaseline and monitor data are shown in figures 9 and 10,respectively. The fold map of the data sets after matchingis shown in figure 11. Note that the injectors B-162 and B-152are in the low fold area.

6. 3D processing

Consistent 3D processing flows are applied to both baselineand monitor data. Since the data are processed by ONGC, wehave limited knowledge on specific details related to the 3Dand 4D processing. The available data processing details aregiven in the appendix. The data are contaminated by groundroll noise. FK-filtering, in view of amplitude preservation,is avoided for attenuation of this noise. Instead, surfacenoise attenuation is performed. Despite applying the noiseattenuation process, the high amplitude of ground rolls in shortoffset traces could not be fully eliminated and caused noisystack in shallower zones. Surface noise attenuation, consistentamplitude balancing and deconvolution are the processes thatare in favour of 4D. However, approximately 10% of tracesare relinquished in the noise editing process that can weaken4D processing.

Since the injectors are close to the major fault, as shownin figure 1, it is expected that the time migrated data are morereliable than the unmigrated due to scattering and the shadoweffect of fault on the anomalies. This is confirmed by thestudy of both synthetic data and processing of unmigratedfield data. Therefore, we use time migrated data for the time-lapse seismic analysis in this case. The reservoir engineeringestimate of the thermal front movement was reported to be50 m/year, thus we focused our 4D study on baseline andsecond monitor data which has time interval of 2 years andexpected to bear the signature of thermal front movementby 100 m.

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Seismic monitoring of the in situ combustion process in a heavy oil field

Figure 10. Receiver positions for baseline (red) and monitor line (blue).

7. 4D processing

In order to look for injection-related time-lapse changes andnon-processing artefacts, both data sets are treated the sameway, when possible. The pre-stack time migrated data sets arefully stacked using the same velocity field.

Despite the best effort to acquire and process the datasets in the same way, systematic differences are observed.To remove such a difference, a process that we call pre-4D is applied to the monitor data. The idea of the pre-4Dprocess is to minimize the difference in the overburden (staticwindow). This can be achieved by removing the time andwavelet differences in common trace pairs within the staticwindow. In this study, the static window is assumed to befrom 600 ms to 750 ms, since there is a strong reflectorin this window. No changes are expected above 600 ms;hence, we do not include it in static window. The windowbelow 750 ms is not contributed in the static window, sincethere might be temperature changes right above the reservoir.We do understand that the in situ combustion process affectsthe overburden and underburden; however, the calculation ofheat losses to overburden and underburden shows negligibleeffects in this case (Lake 1989, Farouq Ali 1966). All pre-4D processes are designed in the static window and appliedto the whole volume. Therefore, the pre-4D process correctstime invariant changes. To quantify the repeatability issues,we use the normalized root mean square (NRMS) concept. Inthis method, the percentage-normalized RMS difference of the

two traces (say at − bt ) from two different surveys within agiven window t1 − t2 is computed using the formula (Kraghand Christie 2002)

NRMS = 200RMS(a − b)

RMS(a) + RMS(b)(6)

where NRMS is measured in per cent, and the RMS operatoris defined as

RMS(at ) =√√√√ t2∑

t1

(at )2

/N (7)

where N is the number of samples in the time interval t1 − t2.The value of NRMS is not limited to the range 0–100%.

If both the traces contain random noise, the NRMS is 141%(√

2). If both traces are uncorrelated, the NRMS error attainsits maximum value i.e. 200%. Typical NRMS values for someof the early 4D studies, like for instance the Gullfaks 4D study(Landrø 1999), are 60–80%. For more recent 4D studies usingsteerable streamer technology (Goto et al 2004) typical NRMSvalues might be between 10 and 30%. However, for land data,the NRMS values are often higher, due to acquisition problemsand seasonal changes within the near surface layers.

In this study, the pre-4D process consists of three differentsteps. First, a cross-correlation-based time shift is applied tothe monitor data. Second, a shaping filter is applied thattries to match the wavelet of the data sets. Third, a cross-normalization is applied to the monitor data to ensure the rootmean square (RMS) amplitude of common traces is similar.

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H M Zadeh et al

The cross-correlation time-shift process computes a singletime shift in the static window (600–750 ms), for each trace,and tries to align the monitor data with baseline. Thecorrelation value is used as a quality control. If the maximumvalue of correlation is small (less than 0.85), then the calculatedtime shift is considered unreliable. Only time shifts withcorrelation values more than 0.85 are applied to the monitordata. The maximum time shift with this criterion reveals thatthe baseline data are in average 2 ms earlier in time relativeto monitor data. The reason for this time shift might bethe variation of near surface velocity that is not completelycorrected by prestack statics. The NRMS improved to 48%,in NRMS scale. This means that the repeatability of the datais increased approximately by 10%.

Shaping filter is designed in the static window and appliedto the entire data. The purpose of this Wiener–Levinson filteris to construct a similar wavelet for monitor data to baselinedata in a least-squares sense (Yilmaz 1987). Since volumesare assumed to be from a same subsurface and the waveletis consistent through the time, the filter matches the phaseand amplitude in the area with changes (reservoir) withoutequalizing them (Rickett and Lumley 2001). In this study, weuse a global filter in the static window and include only thetrace pairs with correlation values above 0.85 in designing. Inthis way, we estimate a more stable shaping filter and includemore repeatable and less noisy traces. A study of the NRMSmap of the differences in the static window, after applyingshaping filter, shows that the NRMS value decreased to 43%.This indicated the effectiveness of the shaping filter in thepre-4D process.

Cross normalization is a process that applies amplitudecorrection on trace-by-trace or average basis. Here, we usea global scaling factor extracted from the RMS amplitude ofthe static window. This process decreased the NRMS value to42%.

The NRMS map of the static zone after applying pre-4Dis shown in figure 12. The repeatability is improved afterapplying the pre-4D process. However, areas with low fold,i.e. mainly the survey corner, show lower repeatability.

8. Time-lapse data analysis

The data after applying the pre-4D process are analysed bothwith respect to time shift and amplitude. To correct the timeshifts associated with injection velocity changes, we apply atime-variant (TV) time shift process. This process implementsa cross-correlation-based time shift and tries to remove thetime differences between monitor and baseline for multiplewindows. A 40 ms sliding window which starts from 600 msand moves down one sample in time is used to cross-correlatethe monitor with baseline data. The result of the TV time shiftsin this setup is shown in figure 13. The line is chosen in a waythat passes all four injectors. There are some big time shiftsfor late arrival times that are probably caused by noise in thedata. There are time shifts presents in the overburden of theinjector B-162. There might be two reasons for the presence of

overburden time shift around this injector. First, the section,line A–A′ in figure 12, passes the major fault near injectorB-162. Since the time shift associated with fault is affected byrelative movement of layers around the fault plane, it is lesslikely that the time shift represents a real 4D anomaly. Second,the injector B-162 is located in the low fold area (see figure 11)that has a lower signal-to-noise ratio. Therefore, the time shiftin this area is more affected by noise. To remove the big anduncorrelated time shifts that are associated with noise, the timeshifts with correlation coefficients larger than 0.2 are appliedto the monitor data. Note that the low correlation coefficientmight also be due to injection. A map of time shift aroundthe top reservoir is revealed in figure 14. The map shows theaverage time shifts in an 80 ms time window surrounding bothtop and bottom of the reservoir. The anomalies around theinjectors (mainly B-147 and B-145) are noticeable and exhibitapproximately −2 ms time shift that is feasible. It is worth tomention that the NRMS value in the static window improvesto 38% after TV time shift.

Amplitude analysis of the reservoir event after applyingTV time shift is shown in figure 15. This map shows the RMSamplitude of differences in an 80 ms time window (includingboth top and base reservoir). Despite the fact that the mapis noisy, there are clear anomalies around the injectors. Theextension of the biggest anomaly (injector B-147) correspondswell with the reservoir engineering expectation of a 100 mnorthwest movement of the front. Even an injector on theleft side of the fault (B-169) shows an anomaly. However,we do not have enough information about this injector toconfirm the result. The anomalies in the middle (aroundproducers B-146, B-163 and B-191) which dominate the mapare probably due to production activities in this area. There isan anomaly in the northeast corner of the map that we believeis noise.

9. Discussion

From the analysis of the data, we find that the 4D anomaliesaround the injectors are better imaged on migrated data. Thesynthetic modelling study from well B-183 shows that theair injection creates an amplitude dimming (due to a velocitydecrease) at the top and an amplitude brightening at the baseof the reservoir. This corresponds well with the observedamplitude anomalies, as well as with the observed travel timeshifts, up to 2 ms push down between baseline and monitorsurvey.

Despite the fact that the RMS difference maps are noisy,they show promising results. We see anomalies northwest ofinjector B-147 and northwest and northeast of injector B-145that were ignited first. Even though the fold close to B-153 islow, we observe the same type of anomaly also for this injector.The noise in the map may be due to production activities inthe field of which we do not have detailed information. Weobserve that the anomaly is strongly shifted towards the northand northwest. This result is confirmed by the increase ofproduction in two wells of the neighbouring field (Lanwafield), located in north of Balol, shortly after the start of

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Seismic monitoring of the in situ combustion process in a heavy oil field

Figure 11. The fold map of the Balol area. The injectors are indicated in the map by stars. The fold is low around 2 of the injectors (southin the map). The distance between adjacent inline numbers is 10 m (as for crossline).

Figure 12. NRMS map of overburden (600–750 ms time window) after applying the pre-4D process shows the repeatability of the data.The repeatability is improved to 42% in the NRMS scale. The distance between adjacent inline numbers is 10 m (as for crossline). Theposition of a random line (A-A′) that is passing all the injectors is shown (see figure 13).

injection (Kumar et al 2008). A similar result is obtained byseparate inversion of pre-stack migrated data volumes (Vedantiand Sen 2009). The expectation of the in situ combustion front

movement was towards production wells in the east. However,the unexpected movement, as delineated by seismic study, isconfirmed by other studies done in the field. The cause for

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Figure 13. A random line, passing all the injectors, shows the time shifts (in ms). The position of the line is shown in figure 12. Some of thelarge time shifts in the late time are probably noise. Time shifts in the overburden, south of B-162 are probably caused by the fault zone.

Figure 14. Map of average time shifts around the top reservoir. An 80 ms window is chosen that include both top and bottom of thereservoir and time shifts are averaged in the window. The distance between adjacent inline numbers is 10 m (as for crossline).

this northwest movement might be explained in at least twodifferent ways. The first possible cause is that the structuredips towards northwest, and hence the gas will tend to migrate

in the up-dip direction. The second possible cause mightbe that the horizontal permeability is higher in the northwestdirection.

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Seismic monitoring of the in situ combustion process in a heavy oil field

Figure 15. Map of RMS difference for the reservoir level (80 ms time window) after applying time variant (TV) time shift to monitor data.The anomalies are consistent with the injectors and producers. The major fault in the map is confirmed to be sealed. The distance betweenadjacent inline numbers is 10 m (as for crossline).

10. Conclusions

We observe prominent 4D amplitude anomalies near all fourinjectors. The areal extent of the mapped anomalies variesfrom well to well. The clearest anomalies have a radius ofapproximately 100 m. The reservoir engineering expectationis around 50 m/year, and since the second monitor surveywas acquired after 2 years, the 4D seismic observation is inagreement with the reservoir engineering expectation.

The repeatability level of the monitor survey comparedto the base survey over the Balol field is relatively low:approximately 60% measured in NRMS error. Despite thisfact, it is possible to interpret anomalies close to four ofthe injectors that have been active between the base and themonitor survey. The same anomalies are also found whencomparing the base and the first monitor, but these anomaliesare comparable to the background noise. Due to increasedgas saturation within the reservoir zone, a decreased P-wavevelocity is expected. Fluid substitution combined withconvolution modelling predicts a 4D amplitude decrease at thetop reservoir and a 4D amplitude increase at the base reservoir.This is also found when a detailed comparison between thebase and the second monitor survey is performed. However,the magnitude of the observed changes in the field data issignificantly larger than the synthetic values. This differencein amplitude of synthetic and field data might be due to noise inthe field data. A time shift of approximately 2 ms is expectedbased on the well modelling. Comparison with time shiftestimation from the time-lapse seismic data shows time shifts

in the same order, although the estimated time shifts are noisyand not as consistent as the amplitude changes. There is noclear evidence of air leaking into sand layers below or abovethe reservoir sand. This does not necessarily mean that suchleakage is not occurring, since the 4D detectability is limited,mainly due to the low repeatability level.

Acknowledgments

The financial support from the Indo-Norwegian program forinstitutional cooperation is highly appreciated. Without thissponsorship, the cooperation between NGRI and NTNU wouldnot have been possible. We also thank ONGC for providingthe field data from the Balol Field, and for permission topresent the result of our analysis. We are greatly indebtedto the kind help and assistance especially from Apurba Saha,D.P. Sinha, M.P. Singh and Asit Kumar, all of ONGC. AsmundSjøen Pedersen and Andrew Morton are acknowledged fortheir kind support during the loading of the field data. TheResearch Council of Norway (NFR) is acknowledged forfinancial support to the ROSE project at NTNU. We wouldlike to acknowledge Tom Davis for useful discussions andkind help. Finally, the anonymous referees are acknowledgedfor useful and constructive comments.

Appendix. 3D seismic processing sequence

The processing sequence applied on both baseline and monitordata is

27

H M Zadeh et al

• SEGY input.• 3D binning with bin size of 10 × 10 m.• Field statics.• True amplitude recovery (TAR).• Bandpass filter (8–12–90–120 Hz).• Editing.• Surface wave noise attenuation to attenuate the ground

rolls and not damage the signal amplitude. This processtransforms the data from the time-space domain to thefrequency-space domain, where it performs a frequency-dependent mix of adjacent traces.

• Surface consistent amplitude balancing.• Surface consistent deconvolution.• Bandpass filter (8–12–80–100 Hz).• Editing.• Sorting.• Velocity analysis (200 × 200 m).• Residual statics.• Velocity analysis.• Pre stack time migration (PSTM).• RMS velocity refinement over PSTM gathers at every

10th line (100 m).• Pre stack time migration with refined velocity.

The same velocity field is used for stacking both baselineand monitor data.

References

Avseth A P, Mukerji T and Mavko G 2005 Quantitative SeismicInterpretation: Applying Rock Physics Tools to ReduceInterpretation Risk (Cambridge: Cambridge University Press)p 359

Batzle M and Wang Z 1992 Seismic properties of pore fluidsGeophysics 57 1396–408

Bianco E, Kaplan S and Schmitt D 2008 Seismic rock physics ofsteam injection in bituminous oil reservoirs TLE 27 1132–7

Burger J 1976 Spontaneous ignition in oil reservoirs SPE 16 73–81Castagna J P, Batzle M L and Eastwood R L 1985 Relationship

between compressional wave and shear wave velocities inclastic silicate rocks Geophysics 50 571–81

Chi G and Xu Q 2007 Petrography and microthermometry of fluidinclusions in oil sands and heavy oil deposits from WesternCanada Sedimentary Basin CSEG Expanded Abstracts 660–1

Das A and Batzle M 2008 Modeling studies of heavy oil-in betweensolid and fluid properties TLE 27 116–23

DeVilbiss J, Ito H and Nur A 1979 Measurement of compressionaland shear wave velocities of water filled rocks during watersteam transition Geophysics 44 407

Doriah A, Ray S and Gupta P 2007 In-situ combustion technique toenhance heavy oil recovery at Mehsana, ONGC- a successstory SPE 105248

Duffaut K and Landrø M 2007 VP/VS ratio versus differential stressand rock consolidation—A comparison between rock modelsand time-lapse AVO data Geophysics 72 C81–94

Dumitrescu C C and Lines L 2008 Seismic attributes used forreservoir simulation: application to a heavy oil reservoir inCanada CSEG Expanded Abstracts 27 1471–4

Farouq Ali S M 1966 Marx and Langenheim’s model of steaminjection Producers monthly 30 2–8

Goto R, Lowden D, Smith P and Paulsen J O 2004 Steered streamer4D case study over the Norne field SEG Expanded Abstracts 232227–30

Gray D, Anderson P and Gunderson J 2004 Examination ofwide-angle, multi-component, AVO attributes for prediction ofshale in heavy oil sands: a case study from the Long LakeProject, Alberta, Canada SEG Expanded Abstracts23 1599–602

Greaves R J and Fulp T J 1987 Three-dimensional seismicmonitoring of an enhanced oil recovery processGeophysics 52 1175–87

Gregory A R 1977 Aspects of rock physics from laboratory and logdata that are important to seismic interpretation AAPG Mem.26 15–46

Hashin Z and Shtrikman S 1963 A variational approach to theelastic behavior of multiphase materials J. Mech. Phys.Solids 2 127–40

Hedlin K, Mewhort L and Margrave G 2001 Delineation of steamflood using seismic attenuation SEG Expanded Abstracts20 1572–5

Han D, Liu J and Baztle M 2008 Seismic properties of heavyoils—measured data TLE 27 1108–15

Isaac J H and Lawton D C 2006 A case history of time-lapse 3Dseismic surveys at Cold Lake, Alberta, CanadaGeophysics 71 B93

Jenkins S D, Waite M W and Bee M F 1997 Time-lapse monitoringof the Duri steamflood: a pilot and case study TLE 16 1267–73

Kragh E D and Christie P 2002 Seismic repeatability, normalizedrms, and predictability TLE 21 640–7

Kumar A., Bhattacharya N and Kharoo H L 2008 Time-lapseinversion for monitoring in-situ combustion process in Balolfield, India SPG Expanded Abstracts 7th International Conf. &Exposition on Petroleum Geophysics p 293

Kumar A, Bhattacharyya N and Mohan S 2006 Cross-equalizationfor time lapse study in Balol field, India SPG ExpandedAbstracts 6th International Conf. & Exposition on PetroleumGeophysics pp 1143–48

Kumar A and Mohan S 2004 Feasibility assessment of a time lapseseismic survey for thermal EOR in Balol field, India, based onrock physics and seismic forward modeling SPG ExpandedAbstracts 5th International Conf. & Exposition on PetroleumGeophysics pp 688–95

Lake L W 1989 Enhanced Oil Recovery (Englewood Cliffs, NJ:Prentice-Hall) p 498

Landrø M 1999 Repeatability issues of 3-D VSP dataGeophysics 64 1673–9

Landrø M and Stammeijer J 2004 Quantitative estimation ofcompaction and velocity changes using 4D impedance andtravel time changes Geophysics 69 949–57

Li G, Purdue G, Weber S and Couzens R 2001 Effective processingof nonrepeatable 4-D seismic data to monitor heavy oil SAGDsteam flood at East Senlac, Saskatchewan, Canada LeadingEdge 20 54

Lumley D E 1995 4-D seismic monitoring of an active steamfloodSEG Expanded Abstracts 14 203–6

Macrides C G, Kanasewich E R and Bharatha S 1988 Multiboreholeseismic imaging in steam injection heavy oil recovery projectsGeophysics 53 65–75

Mavko G, Mukerji T and Dvorkin J 1998 The Rock PhysicsHandbook (Cambridge: Cambridge University Press)p 329

Mayo L 1996 Seismic monitoring of foamy heavy oil, Lloydminster,Western Canada SEG Expanded Abstracts 15 2091–4

Mukherjee T K, Chattopadhyay S K and Panchanan G K 2006 TheImpact of sealing fault on enhanced oil recovery—a case studyfrom northern part of Balol field 6th Int. Conf. and Expo. onPetroleum Geophysics (Kolkata, India) pp 1338–41

Nur A and Simmons G 1969 The effect of viscosity of afluid phase on velocity in low porosity rocks EPSL7 99–108

Nur A, Tosaya C and Vo-Thanh D 1984 Seismic monitoring ofthermal enhanced oil recovery processes SEG ExpandedAbstracts 3 337–40

28

Seismic monitoring of the in situ combustion process in a heavy oil field

Rickett J E and Lumley D E 2001 Cross-equalization dataprocessing for time-lapse seismic reservoir monitoring:a case study from the Gulf of Mexico Geophysics66 1015

Schmitt D R 1999 Seismic attributes for monitoring of a shallowheated heavy oil reservoir: a case study Geophysics64 368–77

Sigit R, Morse P J and Kimber K D 1999 4-D seismic thatworks: a successful large scale application, Duri Steamflood,Sumatra, Indonesia SEG Expanded Abstracts18 2055–8

Tadema H J and Weijdema J 1970 Spontaneous ignition of oil sandOil Gas J. 68 77–80

Vasquez M and Beggs H D 1980 Correlations for fluid physicalproperty predictions JPT 32 968–70

Vedanti N and Sen M K 2009 Seismic inversion tracks in situcombustion: a case study from Balol oil field, IndiaGeophysics 74 B103–12

Waite M W and Sigit R 1997 Seismic monitoring of the Duristeamflood: application to reservoir management TLE 161275–8

Yilmaz O 1987 Seismic Data Processing (Tulsa, USA: Society ofExploration Geophysicists) vol 2 pp 103–5

Zou Y, Bentley L R and Lines L R 2003 Seismic modelling for aheavy oil reservoir time-lapse study SEG Expanded Abstracts22 1442–5

29