Pre-stack Simultaneous Inversion of 3D Seismic Data for Velocity Attributes to Delineate Channel Sand Reservoir

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Pre-stack Simultaneous Inversion of 3D Seismic Data for

Velocity Attributes to Delineate Channel Sand Reservoir

Zubair Ahmed

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Deputy Manager (Geophysics), Bangladesh Petroleum Exploration and Production Company Ltd., BAPEX Bhaban, 4, Karwan Bazar, Dhaka-1215, Bangladesh

Abstract Channel sand acts as stratigraphic trap for hydrocarbon accumulation in many parts of the world. Delineation of this type of reservoir is crucial as channel sand may be scarce and inaccurate location of the drilling wells could lose a huge currency. To differentiate channel sand from the host rocks, attributes such as P and S wave velocity ratio plays a vital role in the study area. The velocity ratio showed a negative relationship with the sand percentage in the formation i.e. lower the ratio higher the percentage of sand. After proper data preconditioning, simultaneous inversion of pre-stack angle gathers is performed to get P and S wave impedances and VP/VS ratio. The output ratio from the

inversion follows the distribution of the channel sand which is evident from the blind wells penetrated the target reservoir. This finding on the extension of the channel sand reservoir in terms of velocity ratio can be used for quantitative interpretation of the reservoir properties which lead to reservoir management, future planning of the field, and setting location for new wells.

Keywords3D Seismic Data, Channel Sand Reservoir, Data Preconditioning, Pre-stack Inversion, VP/VS ratio.

I. INTRODUCTION

Channels filled with sand can be potential reservoir for hydrocarbons. In regular seismic data, they may not be differentiated unlike structural trap from the direct observation. Therefore attributes derived from the seismic are utilized to find a solution.

Most channel sands have higher porosity filled with fluids. As it takes more time for P wave to pass through the pore fluid, the velocity is reduced in the porous channel sand than usual compact sand or the surrounding rocks. On the other hand, S wave does not travel through the fluid hence does not affected by the presence of pore fluid in the formation. Rather it travels through the matrix of the formation and velocity remains more or less unchanged. Therefore, the ratio between P and S wave velocity in the porous channel sand filled with fluid shows lower values than the surroundings. Utilizing this relationship between the velocity ratio and lithology, seismic inversion for velocity ratio can be a useful method for delineating channel sand reservoir.

Pre-stack seismic data pose angle dependant or offset related information unlike the stacked data where averaging of the traces in a common midpoint location is done hence information from amplitude variation with angle or offset is lost. This pre-stack data can be used to get simultaneously P impedance, S impedance and P and S wave velocity ratio from a single inversion process with a greater accuracy level. The output velocity attribute volumes are good indicators of the porous sand distribution which is a vital point in any reservoir characterization.

II. THEORY

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Concerning the angle issue, it has been found that the inverted values of P and S impedances show realistic within 30 degree incident angle range whereas density could not give realistic value less than 45 degree. For that reason, in pre-stack inversion technique, density is excluded if the desired output would be P and S wave attributes.

III. DATA SET

Blackfoot 3C 3D seismic data was acquired over the Blackfoot area (near Strathmore, Alberta, Canada) in November, 1995. The seismic survey was planned and conducted by the CREWES (Consortium for Research in Elastic-Wave Exploration Seismology) project of the University of Calgary and Boyd exploration consultants. Dataset used in this study comprises raw shot gathers that are acquired from the field as vertical component seismic data covering around 7.65 square kilometer surface area having a total of 15 east-west trending receiver lines with 60 m group interval and 255 m line spacing, and 12 north-south trending source lines having 60 m source interval and 210 m line spacing (Figure-1).

Acquired data is of 2 ms sampling interval, and 2 seconds record length. The bin size is half of the source and receiver interval i.e. 30 m by 30 m.

Well log data from 9 wells (Figure-1 and TABLEI) are available from which two of them have dipole sonic log data to provide S wave velocity. The shot gathers of vertical component only are processed by the author for the inversion.

Figure-1: The seismic survey. Shot points are in red and receiver stations are in blue. Purple circles are the bottom hole well locations

with their respective names.

TABLEI

TYPES OF WELL LOGS AVAILABLE TO STUDY

Well Name Log type P w a v e so n ic S w a v e so n ic D en si ty Ga m ma ra y N eu tr o n p o ro si ty D en si ty p o ro si ty In d u ct io n d ee p SP

9-17-23-23

4-16-23-23 _ _ _ _ _

12-16-23-23 _ _ _ _ _ _

8-8-23-23 _ _

1-8-23-23 _

11-8-23-23 _

5-16-23-23 _

14-9-23-23 _

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Figure-2: NMO corrected pre-stack offset gather after processing the raw seismic data. Data shows a considerable noise level which should be removed. Well location is displayed by the red curve of P wave

sonic log.

IV. DATA PRECONDITIONING

After following true amplitude preserving processing steps, NMO corrected pre-stack offset gathers are obtained (Figure-2). Before doing the inversion, noise should be substantially removed as any of the inclusion of noise in the data may ruin the inversion result significantly. The noise reduction was done by making common offset stacking, band pass filtering and radon transform. Then the substantial noise removed data is converted into angle gather so that the inversion procedure could find the angle based amplitude response in the data.

A. Noise Reduction

Two methods have been adopted for noise reduction in the input offset gather. The first one is making super gather by common offset stacking (Ostrander, 1984). In common offset stacking, traces inside a grid, which is defined in terms of particular offset range and CMP numbers, are averaged. By stacking like this, signal to noise ratio is improved while at the same time, the offset dimension is preserved. Therefore, anomalies due to the amplitude versus offset can be seen more clearly. But using too many traces in the CMP range could make smearing over the structure. Moreover, using too many traces in offset range could distort the amplitude response.

In this study, equal spaced 20 offset ranges are calculated by arithmetic mean for each CMP bin in the data set (Figure 3-2). Offset range is limited to 2400 meter so that far offset traces could not make any adverse effect in stacking. After common offset stacking, it has been found that signal to noise ratio (S/N) and the continuity of the events are improved. But still there are some problems such as presence of random noise and some bending effect at the far offset traces. To remove those, the second and last method used in this study is radon transform.

Figure-3: Offset gather after applying common offset stacking to improve S/N. Random noises are still present.

To remove random noise by radon transform, model of primary data is generated. This primary data is used to calculate the noise model and then subtracted from the input data. For model building, the algorithm used here is a parabolic radon transform. The process assumes that all the coherent events within a gather can be modeled as a linear combination of a series of parabolic shapes of constant amplitude. The algorithm is performed in frequency domain and it can be thought of as a set of parabolas located at each time sample. Each parabola can be defined by constant amplitude and its moveout. The moveout is the curvature of the parabola, measured as the difference in time between the parabola at the far offset and the parabola at the near offset.

Figure-4: Random noise is removed substantially after applying radon transform.

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The number of parabolas at each time sample is taken as 15. There is no disadvantage of using larger number here but the excessive run time. This minimum number depends on the low moveout time, high moveout time and maximum frequency in the range. Subtraction of the modeled noise from the input data is usually carried on in the time domain. To get rid of the overly synthetic result from radon transform model building, 10% noise has been incorporated as time variant scaled down version to the noise model and then subtracted from the input data (Figure-4).

Figure-5: Angle gather after converted from offset gather. The red curve shows the log at well location 16-8-23-23.

B. Angle Gather

Each of the time samples of the offset gather seismic trace is calculated as the angle of incidence. Since the inversion results are consistent and reliable within a range of incidence angle up to 30 degrees, the range is taken from 0 to 30 degree with 10 numbers of angles equally separated by 3 degrees (Figure-5).

V. PRE-STACK SEISMIC INVERSION

Before running the inversion algorithm into the pre-stack angle gathers, some basic preparatory steps such as well to seismic tie, horizon picking, incidence angle dependant wavelet extraction, model building and quality control of the inversion outputs are carried out.

A. Well to Seismic Tie

Well logs are to be correlated with the seismic data for getting the same time alignment of the events. First synthetic traces are generated from the convolution between the earth’s reflectivity and one suitable wavelet extracted from seismic data using statistical method. A single composite seismic trace comes from averaging the seismic traces around the borehole location to enhance the signal to noise ratio. Now the correlation between the synthetic and composite traces is performed.

After getting the optimum correlation between these two, corrected time-depth curve is the result which is also used by all other logs to get that optimum alignment with the seismic data at that well location.

Wavelet can be extracted by means of purely deterministic where wavelet is measured using surface receivers, marine signatures or VSP analysis; purely statistical where seismic data alone is used, and using the well log data. Wavelet is subjected to change from trace to trace hence is a function of travel time. But getting a large number of wavelets for analysis could create uncertainty in the analysis for further data processing steps. For generating synthetic traces at different well locations having similar subsurface environment within a particular survey area, it would be practical and useful to extract a single and average wavelet.

Figure-6: . Time response (up) and amplitude and phase response (down) of the extracted wavelet for well to seismic correlation.

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Then desired phase is added. Finally inverse Fast Fourier Transform produce the wavelet in time domain and summing up with the other wavelets calculated from other traces gives the resultant wavelet. The length of the wavelet is an important factor. To extract the wavelet (Figure-6), a number of wavelet lengths which are the multiples of the half of the autocorrelation length (which is found 60 ms here) of the processed seismic traces are tested to get the optimum correlation between the synthetic and seismic. The optimum parameters to get the desired wavelet are shown in TABLE II.

TABLEII

PARAMETERS TAKEN FOR WAVELET EXTRACTION

Analysis window starts 800 ms Analysis window ends 1100 ms Offset starts 0 m Offset ends 1500 m Wavelet length 180 ms Taper length 20 ms Sample rate 2 ms Phase rotation 0 degree Phase type Constant phase

Figure-7: Figure 3-6. Correlation between synthetic and seismic traces at the well location 4-16-23-23. Track 1 and track 2 show P wave sonic and density log respectively. Traces in blue and red presented 5 times

each represent the synthetic traces calculated using the mentioned well logs and composite trace averaged from the original seismic traces around the borehole, respectively. Black traces are original seismic traces showing inline and crossline numbers at their tops. Well location is shown as synthetic trace in blue superimposed on the original seismic data. Calculation window is shown in yellow line from

800 ms down to the end of the well log.

P wave velocity data from nine wells and density data from seven wells are available. Density logs are calculated from P wave log using Gardner’s equation for the well logs of 8-8-23-23 and 12-16-23-23 as the sampling rate of these wells for density log is about 75 meter which is quite low for creating the reliable output.

Composite traces are calculated from the well location to 1500 m offset range. Analysis window for calculating the correlation value is set from 800 ms to the bottom of the well log data. Correlation values are found more than 80% for all the wells. TABLE IIII shows the correlation values between the synthetic and seismic traces at each well location. Figure-7 shows well log correlation with the seismic trace at the well location of 4-16-23-23. To examine the accuracy of the correlation values obtained at each well between synthetic and seismic traces, multi-well analysis has been performed (TABLE IV). They are the total correlation values that indicate how well synthetic and seismic data are correlated for a given wavelet. Total correlation value is calculated by concatenating end to end analysis window of the well logs, thus creating a massive trace and finding the correlation value of that.

TABLEIII

CORRELATION VALUES BETWEEN SYNTHETIC AND SEISMIC TRACES AT THE WELL LOCATIONS.

Well name Correlation value

(fraction)

9-17-23-23 0.862 4-16-23-23 0.926 12-16-23-23 0.866 8-08-23-23 0.802 1-08-23-23 0.879 11-08-23-23 0.870 5-16-23-23 0.840 14-09-23-23 0.803 16-08-23-23 0.861

TABLEIV

CORRELATION VALUES BETWEEN SYNTHETIC AND SEISMIC TRACES AT THE WELL LOCATIONS.

Well name Correlation value (fraction)

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B. Horizon Picking

Appropriate horizons are important in both processing and interpretation stage. In processing, horizons are needed to build models for the inversion. In interpretation, referring the target events, calculating the quantitative measures, structural interpretation, and for so many other uses horizons are essential.

Three horizons have been picked from the seismic traces at the top of the events namely, Viking, Channel Top and Mississippian (Figure 3-7). Reference for these events has been got from the report of Potter et.al. (1996). In that report, events have been correlated with the well logs namely 8-8-23-23, 4-16-23-23, 12-16-23-23, and 9-17-23-23.

First these events are marked in the well logs mentioned above and then marked in the other well logs of the study area. To draw the horizons, corresponding wiggles have been followed throughout the seismic volume and checked with the other well logs. Picking top of Mississippian does not have confidence as this interface is unconformable and eroded channel base overlain by thin channel fill deposits that cause the interference effect in a single peak. It is tried to follow the bottom portion of the peak to pick this horizon.

C. Incidence Angle Dependant Wavelet Extraction

In this part, wavelets that depend on the incidence angles are extracted. Far offset traces face frequency absorption and some NMO stretches. To cope with these phenomena, wavelets are extracted with respect to their corresponding incidence angle. Two sets of wavelets are extracted statistically from the seismic volume for inversion purpose. One is from 0 to 15 degree averaged at 7.5 degree and the other one is from 15 to 30 degree averaged at 22.5 degree (Figure-8).

Figure-8: Angle dependent wavelets extracted for inversion purpose. Amplitudes at time (top) and frequency (bottom) response are shown.

D. Model Building

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Making an initial guess can give the way to which the inversion results should lead. Horizon guided inverse-distance weighting method is used to interpolate the values of the logs throughout the seismic volume. Horizons should be smoothed enough to create a geologically plausible model. These models are high-cut frequency filtered to get the smother results.

As two wells namely 4-16-23-23 and 9-17-23-23 have both P and S wave sonic logs together with density log, both of them have been used to model the initial P impedance, S impedance and density model (Figures 9, -10 and -11) to run the inversion process. Three picked horizons namely Viking, Channel Top and Mississippian have been used. A high cut frequency filter of 10/15 Hz has been used in the end to get the smoother models.

Figure-9: P impedance model used for simultaneous inversion. Red curve shows the P-wave log from well 4-16-23-23.

Figure-10: S impedance model used for simultaneous inversion.

Figure-11: Density model used for simultaneous inversion.

E. Quality Control

To check the quality of the inversion results, cross plots between the inverted results and the original logs have been prepared from the data at the well locations. Cross plots of original and inverted results of P impedance and S impedance show slopes of 0.99 and 1.04 respectively (Figure 3-14). In VP/VS case, it shows a slope of 0.72 (Figure 3-15). Inversion results at the well locations of 4-16-23-23 and 9-17-23-23 are displayed in the Figure-14 and Figure-15 respectively. Correlation values between the inverted and original seismic traces at all of the well locations are found as an average of 0.99 (TABLE V). RMS errors between them are also calculated at all of the well locations (TABLE VI).

Figure-12: Cross plots between the impedances of the inverted results and the original logs from wells of 4-16-23-23 and 9-17-23-23 used for

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Figure-13: Cross plots between the VP/VS ratios of the inverted results

and the original logs from the two wells used for inversion.

Figure-14: Inversion results at the well location of 4-16-23-23. Left three panels contain original log, inverted result and initial model of ZP, ZS and VP/VS ratio from left to right. Two wavelets represented as

the average of 0-15 degree and 15-30 degree incidence angle response, are shown in blue. Synthetic or inverted and observed seismic traces

are in red and blank respectively with their corresponding angles. The rightmost red traces are error found between measured synthetic

and original seismic trace. Yellow lines show the calculation window.

Figure-15: Inversion results at the well location of 9-17-23-23. The terminologies described in Figure 3-14a can also be applied here.

TABLEV

CORRELATION VALUES BETWEEN THE INVERTED AND ORIGINAL SEISMIC TRACE AT ALL OF THE WELL LOCATIONS.

Well Name Synthetic Correlation

1-8-23-23 0.9947 11-8-23-23 0.9924 12-16-23-23 0.9940 14-9-23-23 0.9958 16-8-23-23 0.9938 4-16-23-23 0.9965 5-16-23-23 0.9935 8-8-23-23 0.9921 9-17-23-23 0.9929

TABLEVI

RMS ERRORS CALCULATED FROM THE INVERTED AND ORIGINAL SEISMIC TRACE AT ALL THE WELL LOCATIONS.

Well Name Relative error

1-8-23-23 0.1063 11-8-23-23 0.1254 12-16-23-23 0.1101 14-9-23-23 0.0926 16-8-23-23 0.1132 4-16-23-23 0.0850 5-16-23-23 0.1149 8-8-23-23 0.1258 9-17-23-23 0.1208

F. Inversion Results

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Figure-16: Inverted P impedance section at the well location of 8-8-23-23. Note that low impedance value is evident below the Channel Top

horizon at the well location.

Figure-17: Inverted S impedance section at the well location of 8-8-23-23. Note that higher impedance value is evident below the Channel

Top horizon at the well location.

Figure-18: Inverted VP/VS ratio section at the well location of

8-8-23-23. Note that lower VP/VS ratio is evident below the Channel Top

horizon at the well location.

VI. DISCUSSION

By applying common offset stacking and radon transform in the pre-stack NMO corrected offset gather, substantial noise is reduced. Angle gather from 0 to 30 degree is produced so that the linearity of the reflectivity could hold.

Synthetic seismogram is tied with the seismic data at the respective well location with a high correlation value (more than 80%). The suitability of the wavelet needed to prepare the synthetic seismogram is also checked using multi-well analysis and found a higher total correlation value (around 82%) for all the nine wells.

Two wavelets from 0 to 15 degree and 15-30 degree incidence angle ranges are extracted from the seismic data statistically. These two wavelets represent the average of all the angles inside the range. Models are created for P impedance, S impedance and density by using two wells guided by the three horizons. These two wells are located at the outside of the main channel, northern portion of the survey area. It would have more accurate result if well controls would be from southern portion too.

Figure-19: Amplitude slice for VP/VS ratio at the channel level. The

channel shows north-south trending low VP/VS ratios.

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From the inverted sections, it is clear that the anomalous target zone can be distinguished by low P impedance, higher S impedance and lower VP/VS ratio, which are the characteristics of hydrocarbon potential zone.

The cross-sections at P impedance (Figure-16), S

impedance (Figure-17) and VP/VS ratio (Figure-18) show a

relatively low, medium to high and low values respectively. From the amplitude slice map, north-south trending elongated lower value of VP/VS ratio clearly depicts the channelized zone. This zone is evident in some wells namely 1-8-23-23, 8-823-23, 14-9-23-23 and 12-16-23-23 shown in the map at Figure-19. Here it is noteworthy that none of these wells are used to build models for inversion purpose.

VII. CONCLUSION

The studied inversion method to find compressional and Shear wave attributes is found a very robust one as it has higher level of accuracy, and takes less effort as only vertical component data is required. As these attributes have a relationship with the petrophysical properties of the formation, distribution of these throughout the survey area can give useful hints that are used in finding new well location, discovering new prospects and pay zones, estimating the reserves and resources. In addition, these volumes can also be used to take farm decisions regarding reservoir planning and management.

Moreover, these attributes after building geostatistical relationship with the respective petrophysical well log data can be used for quantitative interpretation to find reservoir characteristics such as sand-shale percentage, porosity, permeability, and hydrocarbon saturation and so on.

Acknowledgement

The author is deeply grateful to Associate Professor Dr. Pisanu Wongpornchai, Department of Geological Sciences, Faculty of Science, Chiang Mai University, Thailand for providing the dataset to study.

REFERENCES

[1] Ahmed, Z., 2013, Reservoir delineation of 3D seismic data by pre-stack simultaneous inversion, MS Thesis, Masters in Applied Geophysics, Department of Geological Sciences, Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand.

[2] Fatti, J.L., Smith, G.C., Vail, P.J., Strauss, P.J., and Levitt, P.R., 1994, Detection of gas in sandstone reservoirs using AVO analysis: A 3-D seismic case history using the Geostack technique, Geophysics 59 (9), 1362-1376.

[3] Gardner, G.H.F., Gardner, L.W., and Gregory, A.R., 1974, Formation velocity and density-The diagnostic basics for stratigraphic traps, Geophysics 39 (6), 770-780.

[4] Ostrander, W.J., 1984, Plane-wave reflection coefficients for gas sands at non-normal incidence, Geophysics, 49, 1637-1648. [5] Potter, C.C., Miller, S.L.M., and Margrave, G.F., 1996, Formation

elastic parameters and synthetic P-P and P-S seismograms for the Blackfoot field. CREWES Research Report-37.

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