Sub‐sample time shift and horizontal displacement measurements using phase‐correlation method in time‐lapse seismic

Abstract

Hydrocarbon production and fluid injection affect the level of subsurface stress and physical properties of the subsurface, and can cause reservoir‐related issues, such as compaction and subsidence. Monitoring of oil and gas reservoirs is therefore crucial. Time‐lapse seismic is used to monitor reservoirs and provide evidence of saturation and pressure changes within the reservoir. However, relative to background velocities and reflector depths, the time‐lapse changes in velocity and geomechanical properties are typically small between consecutive surveys. These changes can be measured by using apparent displacement between migrated images obtained from recorded data of multiple time‐lapse surveys. Apparent displacement measurements by using the classical cross‐correlation method are poorly resolved. Here, we propose the use of a phase‐correlation method, which has been developed in satellite imaging for sub‐pixel registration of the images, to overcome the limitations of cross‐correlation. Phase correlation provides both vertical and horizontal displacements with a much better resolution. After testing the method on synthetic data, we apply it to a real dataset from the Norne oil field and show that the phase‐correlation method can indeed provide better resolution.

Review Paper: Methods of measurement for 4D seismic post‐stack time shifts

“…Comparison of time‐shift measurements applied to field data set B (see Appendix A for a field description) for eight different methods. (a) Fast cross‐correlation (FCC; Rickett et al ., 2006); (b) phase correlation (PCC; Tomar et al ., 2016); (c) Taylor series expansion (TSH; Hatchell et al ., 2003); (d) correlated leakage method (CLM; Whitcombe et al ., 2010); (e) non‐linear inversion (NLI; Rickett et al ., 2007); (f) amplitude‐coupled non‐linear inversion (WFI; Williamson et al ., 2007); (g) dynamic time‐warping (DTW; Hale 2013); and (h) higher‐order statistics (HOS; Nikias and Pan 1988). …”

“…To improve on the definition of the maximum in the cross‐correlation, Tomar et al . (2016) employed the phase correlation approach originally developed for satellite imaging. The phase correlation method constructs the inverse Fourier transform of the cross‐power spectrum with the amplitude spectrum set to unity.…”

Reflection tomography by depth warping: a case study across the Java trench

“…NRM or warping applications were first introduced for 3-D time-lapse seismic data by comparing two seismic cubes acquired at different acquisition times with a special focus on depth formation changes resulting from hydrocarbon production (e.g. Rickett and Lumley, 2001;Aarre, 2008;Tomar et al, 2017). The image displacement warping method of Hall (2006) estimates a full 3-D local vector deviation employing an iterative search of maximum correlation us-ing a deformable mesh for sensitivity and quality analysis, whereas Hale (2009Hale ( , 2013 based his dynamic image warping (DIW) on 1-D cross-correlation optimization schemes in each dimension to estimate the vector displacements.…”

Two‐dimensional shape‐adaptive windowing functions for image analysis

“…Another example of application, in which the 2D DFT needs to be calculated from non-rectangular regions may be that of estimating the shift between (possibly parts of) images using the phase correlation method, e.g. in registration technique widely used in image processing [16][17][18][19][20][21]. Suppose that only an irregularly shaped subset of an image to-be registered that is shifted with respect to the reference image is known, and the task is to estimate the shift by the phase correlation method.…”