Nicolas Bousquié

4D-repeatability of Reservoir Illumination - New Quality Indicators for Marine Acquisition

A target-oriented approach is developed to provide support for in-fill and re-shoot decisions during time-lapse (4D) marine seismic acquisitions. Repeatability quality of surveys positioning is determined from their respective illumination on selected reservoir horizons. Quality control of seismic repeatability is carried out from processing standpoint, through the check of 4D differences in target illumination fold and shifts in reflection-points. Corresponding support to re-shoot decisions is provided onboard from a dedicated indicator. It is defined as the similarity between time-lapse illumination of individual shots or navigation lines, evaluated from an adapted image registration metric. This indicator provides a user-friendly tool to qualify the acquisition, or identify and rank any re-shoot options. Evaluation of 4D mismatches at target depth enables to derive re-shoot decisions driven from the repeatability of reservoir illumination. At the end of acquisition, monitor data are delivered that have been validated in terms of seismic illumination repeatability with respect to base data.

From Time to Depth Imaging: an Accurate Workflow

We present a new strategy for depth velocity model building from pre-stack time migrated gathers. It is based on a dense volumetric dip and residual move-out (RMO) picking in the prestack time migrated domain. The kinematic information is demigrated to compute multioffset un-migrated attributes – called seismic invariants – used as input data for a multi-offset depth tomography. Compared to the corresponding existing strategy based on picking in the depth migrated domain, our new strategy allows to fully take advantage of a previous time imaging project. It bypasses the initial depth model building and initial prestack depth migration required for picking RMO in depth. Furthermore it takes advantage of the quality of a picking phase carried out on already reasonably focused time migrated dataset, with no compromise on the accuracy. We demonstrate the relevance of the approach on a real dataset.