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A comprehensive anisotropic velocity model building – Cusiana-Cupiagua Sur TTI PSDM

Uzi Egozi, Matt Yates, Jose Omana and Bruce Ver West, Veritas DGC Nick Burke, Mario Mesa, Eduardo Moreno, Jaime Checa and Mónica Martinez, BP Héctor Alfonso and Jose E. Calderón, Ecopetrol

Abstract The structural complexity of this Colombian land data and the resulting challenge of the velocity model building required leading edge technologies. Using the well data from this producing field, we are demonstrating in this work the use of a Kirchhoff tilted transverse isotropy (TTI) prestack depth migration (PSDM), a vertical transverse isotropy (VTI) wave equation (WE) de-migration, refraction tomography, TTI multi azimuth reflection tomography and TTI well mis-tie tomography to produce a good velocity model.

Introduction The Cusiana Cupiagua Sur project is a merged land dataset. The Cupiagua 3D program was acquired in 1995 and 1996 with 4 cables along SE-NW direction. The natural bin size was 20x20 meter, nominal fold was 20, far offset along the cables was 3160 meter and across the cables was 600 meter. The Cusiana 3D program was acquired in 900 to the Cupiagua program, in SW-NE direction. It was acquired in 1997 and 1998 with 6 cables. The natural bin size was 15x15 meter, nominal fold was 12, far offset along the cables was 3585 meter and across the cables was 1635 meter. There are 165 exploration and production wells some of which have sonic logs and checkshots, many have gamma logs and most have well tops. There was a RMS velocity field available from previous processing. The complexity of the structure (figure 1) and the resulting challenge of the model building required leading edge technologies. These include a Kirchhoff TTI PSDM, a VTI WE migration and de-migration, refraction tomography, TTI multi azimuth reflection tomography and TTI well mis-tie tomography. On the other hand, some simplifications were employed. Only the two SW-NE major faults, Cusiana and Yopal, and four major reflectors were used for the model building workflow. There are several ways to determine the vertical velocity and the anisotropy parameters. Grechka et al. (2005) describe velocity and anisotropic parameters estimation using multicomponent wide-azimuth data. Their tomography is applied to unmigrated media. The work presented here is applied to a single component migrated data. Following the assumption that most of the anisotropy is a result of the sediment bedding (Uhrig and Van Melle, 1955), the anisotropy was assumed to be TTI above the Yopal fault and VTI

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below it. The model building workflow was then divided into four major parts. The area right below the surface (topography), the area above the Yopal fault, the area below the Yopal fault and above the Cusiana fault, and the prospect zone. The low velocity area just below the surface was derived using Hampson-Russell refraction tomography. This was incorporated into a velocity model which was converted using Dix formula (Dix, 1955) from the RMS velocity field above the Yopal fault. Below the Yopal fault, the initial model was one function calculated from the sonic logs. The velocity model was then updated with residual curvature analysis (RCA) tomography (Zhou et al., 2003) over isotropic depth common image gathers (CIGs). Next it was calibrated to the well tops, only above the Cusiana fault and above the prospect zone, using well mis-tie (WMT) tomography. The initial δ was calculated using Thomsen equation 26a (Thomsen, 1986). Elliptical model was initially assumed for ε. The anisotropic parameters θ and φ were extracted from the reflectors picks above the Yopal fault. The ε parameter was adjusted by η scans following Pech et al. (2003). The velocity below the Cusiana fault, which is around and below the prospect area, was derived by using velocity scans. The velocity was then calibrated with WMT tomography and ε and δ were adjusted accordingly.

Figure 1: Depth structural NW-SE cross section

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Time Processing The time processing included standard time processing procedure like Deconvolution, Band Pass filtering, ground roll attenuation, Common Mid Point (CMP) sorting, static and residual static corrections and Automatic Gain Control (AGC). Due to the sparse offset distribution and the low signal to noise ratio some additional special processing steps were applied. First, the data was sorted to “super” CMP gathers and FX Deconvolution was applied in CMP-offset domain. Second, data was interpolated by borrowing traces from nearby offsets. The data was then sorted to common offset volumes followed by FXY Deconvolution in the inline-crossline domain, for each offset. The interpolated traces were then removed.

Construction of the Velocity Model The velocity model was designed to meet the main objectives of: (1) Building a velocity model that will produce a good depth image and good fault termination with minimal fault shadow issues and with no velocity artifacts. (2) Determining the anisotropy velocity model parameters, V0, ε, δ, θ and φ, that will lead to flat depth migrated gathers after Kirchhoff TTI PSDM. (3) Using grid based reflection tomography, constrained by horizons, for the velocity inversion. (4) Tie all the well-tops with mis-ties of 1% or less. The velocity model was constructed by integrating well data, prestack time migration (PSTM) RMS velocity field, depth interpreted horizons and velocity scans. The anisotropy parameters were extracted directly from the well tops and the depth horizons. The anisotropy parameter δ within the shale layers was free to be either positive or negative as necessary for flattening the CIGs. Both positive and negative δ exist in shale layers (Thomsen, 1986) or in any finely layered media (Berryman, 1997). Ryan-Grigor (1977) suggested that the δ is negative where the P wave velocity is equal or larger than twice the S wave velocity. Berryman (1997) presented sign δ as a function of average P wave and S wave velocities. In this work we did not have enough well information to calculate sign δ with these methods. The velocity model was constructed in five major steps and two iterative updating steps. The first two were isotropic and the other three were TTI (figure 2). The construction was done from top to bottom by first constructing isotropic velocity model that tied the wells and continue to isotropic velocity model that flattened the CIGs. The velocity model was then transformed to a TTI velocity model that both tied the wells and flattened the CIGs.

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Figure 2: The velocity model construction overall flowchart

Refraction tomography The refraction tomography was calculated using the first break picks with initial static two-layer model. The velocity from the refraction tomography was extracted. The long wavelength part of the static solution was removed so only the short wavelength static was applied to the prestack data. The velocity was smoothed in slowness with 1500 meter lateral and 150 meter vertical moving operator. The refraction tomography velocity model was trimmed deeper than 200 meter below sea level.

Initial velocity model above the Yopal fault The PSTM RMS velocity field was converted to interval velocity in depth using Dix formula (Dix, 1955). This model is shown in figure 3a. The refraction tomography velocity model was merged together with the Dix converted RMS velocity model by replacing the converted RMS velocity with the overlaid refraction tomography velocity model. This is shown in figure 3b.

Figure 3a: The Dix converted RMS velocity model Figure 3b: Merge with refraction tomography model

Down to 200m above sea level – refraction tomography

Down to Yopal fault – isotropic velocity model (well calibrated PSTM) Below Yopal fault – single function from cross-plot of sonic logs

Below C5 and Cusiana fault – Update velocity with velocity scans

Down to C5 and Cusiana fault – Determine the TTI parameter

Down to Mirador – Update velocity with well tomography

RCA tomography

Well tomography

TTI

Isotropic

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There were only six sonic logs with valuable information for the model building. From these six logs, one was located outside to the north of the project area and one was mostly below the Yopal fault. On the other hand, there were eighty six well depth tops of horizon C1 and seventy three well depth tops of horizon C5. The velocity adjustment was done then based on well tops. The sonic logs profiles were used afterward, to quality assured (QA) the velocity model (figure 6). The procedure flowchart was done according to figure 4 using equations 1 through 6. Equation 1 defines the slowness calculation of any grid cell of the depth velocity volume model.

(1) 2

111

−−− +

≡ iii

VVSl ,

where iSl is the average slowness in sec/m for a given grid cell in the velocity

volume, 1−iV is the velocity at the top of the cell and iV is the velocity at the base of the cell.

Equation 2 defines the number of complete cells, of the velocity volume, between the surface (topography) depth and a given depth horizon, for each surface location.

(2) [ ]SRZINTNSR ≡ , where NSR is an integer number of the number of depth samples, Z is the horizon depth in meters for a given surface location and SR is the depth sample rate in meters of the velocity volume.

Equation 3 is showing the two-way vertical time of a given depth horizon, for each surface location.

(3) ( ) ( )

⋅−⋅+⋅= +=∑ SRNSRZSlSRSlt NSR

NSR

ii 1

1

*2 ,

where t is the two-way vertical time in seconds for a given surface location.

Equation 4 defines the depth mis-tie for a given surface well top location.

(4) seismicwelltopmistie ZZZ −≡ ,

where mistieZ is the depth mis-tie in meter, welltopZ is the true vertical depth top in

meters, located along the well trajectory and the seismicZ is the depth in meters of the depth horizon at the well-surface intersection location.

Equation 5 defines an isochron. This is the time difference between the vertical time of a given horizon and the horizon above it, for each surface location.

(5) topbaseiso ttt −≡ ,

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where isot is the isochron in seconds, baset is the two-way time in seconds of the

horizon and topt is the two-way time in seconds of the horizon above it.

Equation 6 is providing the residual velocity value that needs to be added to each one of the grid cells of the depth velocity volume, between the horizon and the horizon above it. It has a different value for each well location.

(6) isomistie tZyxV *2),( =∆ ,

where V∆ is the velocity adjustment at the well and the depth surface subsurface intersection location.

Figure 4: The flowchart of the C1 well mis-tie initial velocity update

The inputs were the merged velocity model (figure 3b), the time datum and C1 horizons and the well depth tops. The depth map of C1 was derived by vertical stretch of the time migrated map. The well mis-ties were calculated along the well trajectories. The velocity adjustments at the subsurface well top locations were calculated down to the C1 horizon using equation 6. The ∆V at the well locations were gridded to a map by minimum curvature (Smith and Wessel, 1990). The velocity model volume was adjusted according to equation 7 by adding the ∆V map to the velocity model below the datum and above the well-calibrated C1 depth map

Time migrated C1 map

Depth horizon map C1

Isochron map horizon C1

Well (locations) mis-ties C1

∆V horizon map C1

Well depth tops C1

Merged velocity model

Time datum map

Well adjusted velocity model

Well adjusted depth map C1

Depth datum map

7

(7)

≤<

≤<∆+

≤≤

=

max);,,(

);,(),,(

);,,(

),,(

ZZZzyxV

ZZZyxVzyxV

ZZZzyxV

zyxV

baseinput

bassetopinput

toptopographyinput

where ),,( zyxV is the updated velocity model volume, ),,( zyxVinput is the input

velocity model volume, Z is the depth, topZ is the well calibrated depth map of the

horizon above, baseZ is the well calibrated depth map of the current horizon. The well adjustment of the C5 horizon was followed. The input was changed so the datum horizon was replaced by C1, horizon C1 was replaced by horizon C5 and the merged velocity model was replaced by the adjusted velocity model to the C1 well tops.

Initial velocity model below the Yopal fault A velocity as function of depth was picked from the sonic log of well CSK2A (figure 5a). This well trajectory was mostly on the south-east side of the Yopal fault. The Yopal fault was stretched from time migrated to depth using the well adjusted velocity model. The velocity model was then replaced by the picked single velocity function below the Yopal fault, to complete the initial velocity model (figure 5b).

Figure 5a: Pick (solid line) from sonic log (dots) Figure 5b: Initial velocity model

The initial velocity model was plotted against the sonic logs for QA (figure 6). Figure 6 is showing each well sonic log, the extracted function from the PSTM RMS Dix converted velocity model and the initial velocity model for the TTI PSDM work. Both velocities were extracted at the well surface location. It shows that the initial velocity model vertical profile had a much better correlation with the sonic logs velocity than the Dix converted PSTM RMS velocity model.

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Velocity[m/sec]

Depth[m

]

Figure 6: Sonic logs (dark blue), PSTM RMS converted to interval velocity in depth (magenta)

and the initial velocity model for the TTI PSDM work (light blue)

Wide azimuth Residual Curvature Analysis (RCA) tomography The initial velocity model was updated using RCA tomography (Zhou et al., 2003). This is a grid based reflection tomography (Stork and Clayton, 1991). It is a global solution algorithm that updates the entire velocity volume. Non-flatness of the CIGs is associated with velocity errors along the path of the corresponding Common Reflection Point (CRP) ray pair (figure 7). The velocity model is updated by a weighted least squares fit, where the objective is to produce a new velocity model which will yield flatter CIGs in the next migration iteration. The inputs for the RCA tomography were the events dips, picked over the depth migrated stack volume and the correspondent non-hyperbolic moveout of the CIGs, picked over the CIGs volume. Both picking were done automatically.

RCE1 BAC3 BAA1Z CSK2A CSU5Z

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Figure 7: Schematic 2-D extraction of RCA tomography ray tracing procedure for a velocity

model which is too fast. Land acquisition in general has wider azimuth than marine acquisition. This project was a special case for land data since it was a merge of two different acquisitions that were acquired perpendicular to each other. Since the velocity errors could effect the migration of traces with different azimuth, it required a wide azimuth tomographic update. Nevertheless, the CIGs were no longer containing the azimuth information of the input CMP gathers. The RCA tomography was programmed and parameterized to account for velocity errors in any surface azimuth. Figure 8 shows (a) a CIG from Kirchhoff PSDM, which was the input for the RCA tomography with residual velocity error of about 7%, (b) the same CIG after single azimuth RCA tomography followed by Kirchhoff PSDM, and (c) the same CIG after multi azimuth RCA tomography followed by Kirchhoff PSDM. It is notable that the CIG after multi azimuth RCA tomography (8c) is more focused and “flat” than the one after single azimuth RCA tomography (8b).

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Offset Offset Offset

Depth[m

]

Depth[m

]

Depth[m

]

Figure 8: Depth migrated CIG. (a) Input to RCA tomography. (b) After single azimuth RCA tomography followed by

Kirchhoff PSDM. (c) After multi azimuth RCA tomography followed by Kirchhoff PSDM The entire velocity model was updated four times by RCA tomography, until the CIGs were “flat”. The first iteration was a single azimuth RCA tomography while the following three iterations were multi azimuth RCA tomography. Each of the iterations was followed by a Kirchhoff PSDM. The only geological constrains were the topography layer and the auto-picked dips. Since tomography is a mathematical tool that yields a geological solution the geological details of the initial velocity model were very beneficial.

Determine the TTI axis of symmetry parameters While isotropic velocity model requires only a velocity volume, a TTI velocity model requires four additional volumes. These are the Thomsen’s δ and ε (Thomsen, 1986) and the two angles, θ and φ, which define the tilted axis direction. Assuming that the anisotropy is mainly due to layering (Uhrig, 1955; Backus, 1962), we set the tilted axis of symmetry to be perpendicular to the bedding direction (Vestrum, 2004). Since the bedding direction below the Yopal fault (figure 1) was mostly horizontal, we used TTI above the Yopal fault and VTI below it. Russell (1944) showed that gamma ray is a good indicator for shale. Figure 9 is showing two gamma log curves above the Yopal fault (BAB2W and RCE1) and one below it (CSK7). The gamma ray is measured in GAPI units (Belknap et al., 1959). Blum (1997) indicated that gamma ray values above 75 GAPI are good indication of shale. From these logs the layering below the Leon reflector and above the C1 reflector were made of shale.

2590 2590 2590

3590 3590 3590

4590 4590 4590

5590 5590 5590

6590 6590 6590

a b c

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Gamma ray[GAPI] Gamma ray[GAPI] Gamma ray[GAPI]

De

pth[m]

De

pth[m]

De

pth[m]

Figure 9: Gamma ray logs of 3 different wells. The shaded area is shadowing gamma ray values of less than

75 GAPI. The well tops marker names are overlaying the log curves. The high gamma ray values are generally between the Leon and C1 tops.

Jones and Wang (1981) showed that shale layers are highly anisotropic. To extract the parameters θ(x,y,z) and φ(x,y,z), which define the axis of symmetry, we used the final-isotropic PSDM horizon-picks of the C1 reflector, which was indicated by the gamma-ray logs as the base of the shale. These two volumes, θ(x,y,z) and φ(x,y,z) remained unchanged through the entire velocity model building. Figure 10 is showing the definition of (a) θ and (b) φ, for one lateral location.

Figure 10a: The dip angle θ Figure 10b: The dip-azimuth angle φ

Determine the anisotropy parameter δ There are several ways to determine the initial anisotropy parameters. Most studies were done from surface data (i.e. Grechka et al,. 2005; Palmer, 2000; Xiao et al., 2005; Isaac

BAB2W RCE1 CSK7

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and Lawton, 2004; van der Baan and Kendall, 2002). Some studies were done from migrated data (i.e. Liu et al., 2004; Sarkar and Tsvankin, 2004). When Kirchhoff PSDM CIGs are “flat”, it is common to relate the mis-tie between the reflectors and the well tops to anisotropy (Banik, 1984). In areas with no major fault and minimal structure, a post migration velocity scaling can adjust for the mis-ties. With major fault but with no other structure, a velocity scaling followed by VTI migration is usually sufficient. In this TTI work, the Thomsen’s anisotropy parameter δ was calculated using a modification of Thomsen equation 26a (Thomsen, 1986). The VNMO from that equation was replaced by the final isotropic velocity model, which was the velocity that flattened the CIGs. The α0 of Thomsen equation was replaced by the velocity that tied the image to the well tops. The Thomsen’s parameter ε is usually set initially to be equal to δ and can be adjusted with more updating iterations. In this work there were major over-thrust faulting and steep dip reflectors of over 300. If anisotropy was the cause of the well tops mis-ties then the errors in the velocity could not be adjusted vertically. These errors had to be adjusted along a ray path that its normal depended on the anisotropy strength and the anisotropy axis of symmetry. Even more, the gamma logs profiles were showing a lithology-dependency of a thin shale layers. We assumed then that both δ and ε could vary in both lateral and vertical directions. In this work we invented and developed a new tool to minimize the well top mis-tie and to estimate the Thomsen’s parameter δ. This was the TTI WMT tomography. Figure 11 is showing the flowchart of this technique. The WMT tomography ray tracing engine is fat-ray (Sheng et al., 2006). It is using the depth picked reflectors to determine the ray tracing normal. The ray tracing is a high resolution zero offset TTI, using the TTI velocity model. It is then converting the depth mis-ties to times errors along the ray paths. This completes the tomography matrix. Next, the tomography is minimizing the errors by iterations of a weighted least squares fit and finally, extracting the updated velocity model.

Figure 11: The TTI well tomography flow chart. Note that this chart can be iterated by using the

updated velocity model as input for the next iteration

Depth horizons and faults

Calculated well tops mis-ties

Updated velocity model V0

Well depth tops of all markers

Anisotropic velocity model V0, δ, ε, θ, and φ

TTI Kirchhoff PSDM

TTI WMT tomography

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Using the input and output velocity model of the WMT tomography, after stretching each to vertical time using its own velocity model, a new Thomsen’s parameter δ is calculated:

(8) ( ) [ ]),,(2

),,(),,(21),,(,,

22

221

21

2 τττδττδ

yxV

yxVyxyxVyx

−+=

where δ2 is the new calculated Thomsen’s parameter δ volume in vertical time, V1 and δ1 are the WMT tomography input velocity and Thomsen’s parameter δ respectively, both stretch to vertical time, and V2 is the WMT tomography output velocity volume stretched to vertical time.

Note that if the input velocity volume is isotropic, δ1 is zero and equation 8 becomes Thomsen equation 26a (Thomsen, 1986). The WMT tomography is requiring depth horizons as input. To be able to iterate the WMT tomography, we needed an updated set of depth horizons before each of the iterations. Vertical stretch of time migrated horizon was not correct for this TTI velocity model. On the other hand, a full volume TTI PSDM and re-picking of all the depth horizons was not feasible. Nevertheless, wave equation modeling and TTI zero offset Kirchhoff remigration gave a consistent set of depth horizons for the different iterations. From the final isotropic migrated stack we picked the three depth reflectors, Leon, C1 and C5 and the two major faults, Yopal and Cusiana. Since it was an overhang data and there were no well-tops below the Cusiana fault, we broke each reflector to three segments. The first set of segments was above the Yopal fault, the second was between the Yopal and Cusiana faults and the third was below the Cusiana fault. We created a seismic depth volume with spikes along the depth picks and zeros elsewhere. We then modeled the data by a post stack wave equation de-migration, from topography, to create an un-migrated synthetic zero offset volume. We calculated the depth mis-ties and updated the velocity model with the WMT tomography. We stretched the two velocity volumes to vertical time and used equation 8 with δ1=0 to calculate the initial δ. We then stretched the δ volume back to depth using the updated velocity model. We set the initial velocity model to be an elliptical TTI model (ε=δ). We then applied a TTI Kirchhoff zero offset migration to the synthetic zero offset volume. We auto-picked the depth horizon and faults and used them to recalculate the new well-tops mis-ties. This enabled us to rerun the WMT tomography, this time as TTI, using the new inputs. We outputted a new updated velocity model. Figure 12a is showing the final isotropic velocity model and 12b is showing the velocity model after the second update iteration with the TTI WMT tomography.

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Figure 12a: The final isotropic velocity model Figure 12b: Velocity model after second TTI WMT

tomography update We adjusted δ (figure 17a) according to equation 8 and again set ε to be equal to δ. To QA the velocity model update we repeated the TTI Kirchhoff zero offset migration using the updated velocity model which followed by the well mis-tie calculations. Figure 13 is showing the well mis-ties histogram of the C5 segment above the Yopal fault at the well and the layer subsurface intersection locations after (a) isotropic migration which used the final isotropic velocity model, (b) after TTI Kirchhoff migration which used the first velocity update from the TTI WMT tomography and (c) after the second update with the TTI WMT tomography. Note in figure 13 that the mis-tie spread was shrinking when comparing the isotropic iteration to the first TTI and the first TTI iteration to the second one. Also the number of wells around the zero mis-tie was increasing from one iteration to the next. While about 5% of the wells had a mis-tie close to zero after we migrated with the isotropic velocity model, more than 40% of the wells had around zero mis-tie, after we migrated with the second velocity update from the TTI WMT tomography. Number of wells [%]

Depth M

is-tie [m]

Figure 13: The well mis-tie histograms of C5: (a) after Kirchhoff migration using isotropic velocity

model. (b) after TTI WMT tomography velocity update and TTI Kirchhoff migration and (c) after second TTI WMT tomography update and TTI Kirchhoff migration. Note that negative mis-tie

means that the imaged reflector is deeper than the well top

(a) (b) (c)

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The WMT tomography should have no effect on the flatness of the CIGs. It should have only effected the mis-ties. Since the TTI WMT tomography was updating the velocity model without any prestack data information we implemented some QC steps. First, by calculating γ (Rietveld et al., 1998) cubes we compared the CIGs flatness after the isotropic Kirchhoff PSDM (figure 14a) to the CIGs after the Kirchhoff TTI PSDM using the velocity model from the second TTI WMT tomography (figure 14b). The values of gamma, which were close to 1, indicated that the CIGs were reasonably flat after both migrations. The overall look of the two gamma volumes was similar, which implied that the TTI WMT tomography did not change much the flatness of the CIGs.

Figure 14a: γ crossline section (about 11km from left to

right) calculated from the isotropic migration. Superimposed are the topography and the Yopal fault

Figure 14b: γ crossline section calculated from the TTI migration after second WMT tomography.

Superimposed are the topography and the Yopal fault The second QC step was to compare the two migrated stacks and to verify that the velocity update from the TTI WMT tomography, as well as the introduction of the other TTI parameters, did not create any structural artifacts. Figure 15 is showing a crossline stack (a) after PSDM using the isotropic velocity model and (b) after TTI PSDM using the TTI velocity model from the WMT tomography second velocity model update. There were no structural artifacts associated with velocity anomalies on both images. It is noticeable that the TTI migration gave a better image than the isotropic migration. The prospect (Mirador) reflector, at about 4 km depth in the center of figure 15, was more continues in the TTI image than in the isotropic image.

Figure 15a: depth stack from the isotropic Kirchhoff

PSDM Figure 14b: depth stack from the TTI Kirchhoff

PSDM after second WMT tomography

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Determine the anisotropy parameter ε The Thomsen’s parameter ε is representing the difference between vertical and horizontal P velocities (Thomsen, 1986). In the TTI model of this work it was showing the difference between the velocities along the axis of symmetry and perpendicular to it. If large-offset data was available, it could be used to determine ε from the far offsets (offset/depth > 1.5) moveout (Alkhalifah, 1997). Alas, in this Colombian’s data, the effective (high enough fold) far offset was only 3200 meter. This offset distance was in most places less than the depth of the C1 horizon, which was the most anisotropic reflector. To determine the Thomsen’s parameter ε we performed η scans with four different constants η values. Following Pech et al. (2003) we defined:

(9) ),,(),(),,( zyxyxzyx δηε += The four ε volumes were: (a) ε=δ, (b) ε=0.03+δ, (c) ε=0.065+δ and (d) ε=0.1+δ. The other four TTI model volumes, (i.e. V, δ, θ and φ) remained unchanged. We applied a full volume TTI Kirchhoff PSDM to each one of the four TTI velocity models. We then stacked the data and scanned through the different migrated volumes. We were looking for better fault positioning, layering continuity and structure. For each lateral location (e.g. one pick from top to bottom) we picked one η value. We then gridded the picks into a η map (figure 16).

Figure 16: The η gridded horizon that was picked from

the η scans By using equation 9 we added the η map to the δ volume (figure 17a) to create the ε volume (figure 17b)

Figure 17a: The δ volume after the second TTI

WMT tomography update Figure 17b: The ε volume after η scans

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Updating the velocity model around and below the prospect area Similar to the problem in analyzing ε, due to the lack of far-enough offsets, additional velocity update around the prospect area could not be determined from the flatness of CIGs. As is shown in figure 14b, the CIGs were flat. For the velocity update we “borrowed” the sub-salt velocity scans technique (Wang et al., 2004) that is used in the deep water Gulf of Mexico. We created a reference horizon that was made of a merge of the segments of the C5 reflector above the Cusiana fault and the Cusiana fault itself. In the overlap areas we used the C5 horizon and smoothed the transition zone. We then generated five velocity models, where the velocity above the reference horizon was unchanged from the TTI WMT tomography second velocity update. Below the reference horizon we scaled the velocity model to be (a) V100=WMT tomography second velocity update, (b) V80= 0.8V100, (c) V90= 0.9V100, (d) V110= 1.1V100 and (e) V120= 1.2V100. The other four anisotropic parameters (i.e. ε, δ, θ and φ) volumes remained unchanged. We then applied five full-volume TTI Kirchhoff PSDM using each of the velocity models. We scan through the depth migrated stacked volumes and picked the lateral and vertical velocity variations, below the reference horizon. The picking was done based on focusing and imaging quality. Figure 18 is showing the velocity model after the update from the scans. There were many more details in the deeper part of the velocity model after the velocity scans update. The main change was a slow down compared to the input velocity model (figure 12b). Since the anisotropy in this deep zone was close to zero, no adjustments were applied to the anisotropy parameters in this stage.

Figure 18: Velocity model after updating from the scans

Determine the anisotropy parameter δ around the prospect area To determine the anisotropy parameter δ in the deeper part we used the same approach as we did for the shallower part. We applied a full volume TTI Kirchhoff PSDM using the latest velocity model from the velocity scans (figure 18). We then picked the Mirador reflector (the prospect) from the depth stacked migrated data. We calculated the well tops mis-ties for all the horizons, this time including the Mirador. We then applied a TTI WMT tomography to generate the final velocity model volume (figure 19). We allowed the tomography to update the velocity model for all the reflectors, including the shallower ones. This helped to farther reduce the shallow residual mis-ties that might have left from previous iterations.

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Figure 19: Final velocity model after third TTI WMT

tomography update Using equation 8 we calculated the final δ volume (figure 20a). Using equation 9 and the η map from the η scans (figure 16) we computed the final ε volume (figure 20b)

Figure 20a: The final δ volume after the third TTI

WMT tomography update Figure 20b: The final ε volume calculated from η grid

using equation (2)

Final velocity model QA and migration results comparisons As a final QA step we compared the velocity model vertical profile to the well sonic logs, we calculated the final mis-ties and compared the image quality in reference to previously done PSTM work. Figure 21 is showing a plot of the sonic logs together with vertical functions that were extracted from the final velocity model at the wells surface locations. It showed a good correlation between the sonic logs and the velocity functions that were extracted from the final velocity model, at the wells surface locations.

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Velocity[m/sec]

Depth[m

]

Figure 21: Sonic logs (yelow), PSTM RMS converted to interval velocity in depth (light blue), the initial

isotropic velocity model (black) and the final velocity model (mgenta) all the same surface location Using the final TTI velocity model we applied full volume TTI Kirchhoff PSDM. Figure 22 is showing a comparison between (a) the previous PSTM work stretch to depth with the final velocity model and (b) the final TTI PSDM image. Note that the Mirador structure and continuity, the fault positioning and the well depth-tops tie were all much better on the TTI PSDM image.

Figure 22: (a) The previous PSTM stretched to depth with the final velocity model and (b) The final TTI PSDM We re-picked all the horizons and check the mis-ties. Figure 23 is showing the Mirador final mis-ties. Note that almost all the mis-ties are less than 1%.

(a) (b)

RCE1 BAC3 BAA1Z CSK2A CSU5Z

20

Figure 23: The Mirador final mis-tie (a) in meters and (b) in percentage of Mirador depth

Summary We presented here a complete workflow for a land data TTI velocity model building. We used all the well information that was available to us. We used first break and refraction tomography to determine the near surface velocities. We used wide-azimuth reflection tomography for the isotropic velocity determination. We invented a new tomography tool to determine the anisotropy from the well tops mis-tie. These techniques enabled us to build a reliable TTI velocity model for this relatively deep complex data, which had much shorter offset than usually used for this type of work. The prospect’s depth image that we produced, with its well-tie, continuity and fidelity, is reducing the risk for future well drilling decisions. It had already made its impact on some of these decisions.

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