Chevron Gulf of Mexico Faulted Data

Figure is an image of faulted Gulf of Mexico data. When a significant discontinuity occurs within a data-set, local dip estimators return incorrect estimates. Faults with significant displacement contaminate the dip estimation at the faults. This, in turn, results in unsatisfactory flattening results.

We use the crude binary weight cube shown in Figure to identify areas where the inversion should ignore incorrect dips. This weight was created by manual picking. It should be noted that a suitable weight can also, in principle, be made from an automatic fault detector.

The flattening results are shown in Figure . Notice that the horizons are flat, even directly across from the fault. Also, notice the presence of a channel in the horizon slice. Figure shows the original data with one unflattened horizon overlying it. It successfully tracks the horizon even across the fault. The time slice at the top of Figure shows a horizon that is being partially cut by the fault at about x=$2600\ m$.

 

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Figure 10 Faulted Chevron Gulf of Mexico data. The 2D vertical section shows fault with significant displacement (enough to cause our dip estimator to return erroneous values). (a) The time slice at time=$1.32\ s$. (b) An in-line section at y=$2693\ m$. (c) A cross-line section at x=$2348\ m$.

 

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Figure 11 A weight identifying the faults data displayed in . (a) The time slice at time=$1.32\ s$. (b) An in-line section at y=$2693\ m$. (c) A cross-line section at x=$2348\ m$.

 

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Figure 12 The result of flattening of Figure . (a) The horizon slice at time=$1.32\ s$. (b) An in-line section at y=$2693\ m$. (c) A cross-line section at x=$2348\ m$.

 

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Figure 13 The result of overlaying a tracked horizon on the image in Figure . (a) The time slice at time=$1.28\ s$. (b) An in-line section at y=$2693\ m$. (c) A cross-line section at x=$2348\ m$.