Dip Constrained Migration Velocity Analysis and Interpretative processing

In pre-stack depth migration processing f low, there is the important step of v elocity estimation. Migration v elocity Analy sis (MVA) is one of most used method in the seismic industry . In this work we describe the MVA workf low that takes into account the dip of lay ers to automatically increase geologic inf ormation as a regularization procedure in inv ersion. We apply the workf low in a real data set f rom Campos Basin detailing the shallow region. The experiment clearly shows the importance of interpretation team interacting during processing. Particularly f or the studied area, MVA deliv ers v elocity field consistent with geologic ev olution of the site. Introduction Seismic workf low in oil industry is composed of three main phases: acquisition, processing and interpretation. Usually those phases hav e sharp limits. In pre-stack depth migration processing f low, there is the important step of v elocity estimation. Migration v elocity Analy sis (MVA) is one of the most used method in seismic industry . The objectiv e of MVA workf low (Figure 1) is the estimation of a v elocity f ield f or depth migration f or best imaging. Sometimes best imaging does not mean geologically f easible v elocity model. This subject has been addres s ed by some authors. Delprat-Janaud and Lailly (1992) compute numerical uncertainties that approximate the phy sical uncertainties. They limit the study to Hilbertian model spaces and deriv e necessary and suf f icient condition which y ields the desired result the norm chosen in model space has to bind the Frechet deriv ativ e of the f orward map. Clapp et al. (2002) use nonstationary operators that tend to spread inf ormation along structural dips of lay ers in tomographic process. Costa et al. (2008) propose a ref lection-angle-based kind of smoothness constraint as regularization in slope tomography . Santos et al. (2013) quantif y the gradients dif f erences of veloci t y and amplitude v olumes in a parameter called Geological Incoherence Index (GII). Luo et al. (2017) propose anisotropic dif f usion smoothing operators into the conjugate gradient algorithm to precondition tomography . We dev eloped and apply the structure tensor based regularization in MVA process using a workf low that includes av ailable geological inf ormation. With an example of Campos Basin dataset we show adv antages and limitation of this process.