4D signal enhancement using singular‐value decomposition: application to mapping oil–water contact movement across the Nelson Field

Abstract

A B S T R A C TA new method for time-lapse signal separation and enhancement using singular-value decomposition is presented. Singular-value decomposition is used to separate a 4D signal into its constituent parts: common geology, time-lapse response and noise. Synthetic tests which demonstrate the advantages of the singular-value decomposition technique over traditional differencing methods are also presented. This signal separation and enhancement technique is used to map out both the original and moved oil-water contacts across the Nelson Field. The singular-value decomposition technique allows the oil-water contact to be mapped across regions which would have been missed using traditional differencing methods. In particular, areas toward the edges of the field are highlighted by the technique. The oil-water contact is observed to move upwards across the field, with the largest movements being associated, as anticipated, with natural production. The results obtained are broadly consistent with those predicted by the reservoir simulator model. Singular-value decomposition is demonstrated to be a useful tool for enhancing the time-lapse signal and for gaining confidence in areas where traditional differencing fails. I N T R O D U C T I O NThe differencing of seismic volumes to investigate time-lapse changes poses a variety of problems: simply subtracting one data set from another often has the effect of enhancing noise, with the true reservoir changes being swamped as a consequence. Numerous attempts to combat these problems have been made previously by, for example, Landrø et al. (1999), who computed the robust difference (effectively smoothing prior to differencing), which reduces the rms difference level

Repeatability enhancement in deep‐water permanent seismic installations: a dynamic correction for seawater velocity variations

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Computation of time‐lapse differences with 3D directional frames

“…[9] calculated the robust difference, which is effectively smoothing prior to differencing. [11] achieved a measurable improvement over differencing in the time domain by separating out the time-lapse signal through singular value decomposition.…”