Data Conditioning of 4D Seismic Timelapse Data for Improved Inversion of Reservoir Pressure and Saturation

Recent progress in 4D seismic history matching benefits greatly from the use of 4D seismic timelapse data. Several methods for inversion of 4D seismic timelapse data for reservoir pressure and saturation have been developed with mixed success. Most of these methods work well on synthetic data or real data with low noise levels, but degrade in performance using data with high noise levels or artefacts. Improvements on the algorithmic side as well as on the data side are necessary to enforce further progress in 4D seismic timelapse inversion and 4D seismic history matching.

A seismic timelapse inversion method specifically aimed at direct inversion for reservoir pressure and saturation from AVO timelapse data is investigated on performance. This method performs robust on noisy timelapse data from which timelapse AVO intercepts and gradients are inverted to changes in pressure and saturation. Considerable uncertainties and ambiguities remain however in the inversion results in terms of interpretation of pressure and saturation fronts, fault transmissibility and reservoir compartmentalisation. Next-generation image-denoising and edge-preserving algorithms are employed to condition the seismic timelapse data prior to AVO computation, timelapse differences and inversion thereof. Two algorithms in particular are used: the Non Local Means (NLM) algorithm and the Alternating Guided Filter (AGF). These two algorithms have been shown to perform exceptionally well on both image-denoising and edge-preservation, two key properties of successful data conditioning of timelapse data ultimately for 4D seismic history matching. To create stable and smooth pressure and saturation fronts while preserving sharp boundaries in these fronts, the AGF algorithm is found to be most effective. In this study, we demonstrate this by application of our technique to the 4D seismic data in the Norne field in offshore Norway. Inversion for reservoir pressure and saturation from Norne AVO timelapse data is performed with data conditioning by the two algorithms applied in several stages of the inversion. It is found that data conditioning with the AGF algorithm posterior to the AVO timelapse differences gives best results.

Reservoir pressure and saturation fronts are obtained with great detail, honoring reservoir compartments and fault boundaries. These results are of much use for subsequent 4D seismic history matching.