The influence of overburden on quantitive time-lapse seismic interpretation

Abstract

Time-lapse seismic data quality has improved over the past decade, which makes dynamic interpretation of the reservoir changes possible. To push the limits of this technique further, this thesis studies the time-lapse seismic noise generated by overburden heterogeneities, as well as its influence on quantitative seismic interpretation. This is done by testing the accuracy of a multi-attribute pressure and saturation inversion method in this context to gain insight into its performance in the case of seismic acquisitions not being perfectly repeated. Extensive seismic modelling studies are conducted in order to quantify the accumulated error for three different overburden complexities. Channels in the overburden above the Nelson Field, North Sea, are found to cause errors in the time-lapse amplitudes. The magnitude of these amplitude errors decreases with increased repeatability of the monitor survey’s source and receiver positions. On average, saturation change is estimated to an accuracy of less than 6% when affected by amplitude errors only. However, these mean errors significantly increase to more than 20% if the residual time shifts caused by the channels are not removed from the seismic data. Moreover, the maximum saturation change estimation error can exceed the production induced signal locally. In addition, a major finding of this study is that the shape of the channel in conjunction with the acquisition direction has a significant impact on the spatial distribution of the errors at the reservoir level. It is also shown that the commonly used repeatability measures of NRMS or Δsource+ΔReceiver do not correlate well with the spatial distribution of areas with increased saturation change estimation error. Consequently, a layer stripping method is presented which reduces the amplitude errors caused by the overburden channel and the acquisition non-repeatability by a factor of two. Nevertheless, the limits of using post-stack data to invert for timelapse changes become apparent and, as a result, it is strongly advised to do further research into applying this method to pre-stack seismic data. Production-induced amplitude changes inside the stacked reservoirs of a deepwater West of Africa field constitute the second overburden complexity studied. These changes imprint on the lower reservoir channel and reduce the time-lapse amplitude change locally by up to 42%. Furthermore, time-lapse amplitude errors are as large as 38% in case that the velocity change inside the upper reservoir is not included in the monitor migration velocity model. In addition, an important conclusion of this study is that due to the high frequency assumption ray-tracing based seismic modelling does not perform well for cellular models such as this West of Africa example. Finite-difference modelling methods are strongly advised to be used instead. Finally, the effect of overburden changes above the highly compacting Ekofisk chalk reservoir, North Sea, is investigated by combining reservoir simulation, geomechanical and ray-tracing models. The velocity change of the overburden rocks reduces the time-lapse amplitudes at the top reservoir predominantly in the zone of vertical displacements greater than six metres. In this zone, the mean time-lapse amplitude errors in the full and far offset stack data are 9.4% and 4.23%, respectively. These errors decrease below 2.3% in areas of less than six metres vertical displacement. Consequently, the full and far offset stack amplitudes are not suited for quantitative time-lapse interpretation. The time-lapse amplitudes for the near and mid offset stacks are significantly less affected and the mean errors are smaller than 1.5% across the entire reservoir. Therefore, these two partial stacks are recommended for quantitative time-lapse interpretation. Three different overburden complexities in the North Sea and West of Africa are studied and prove to have a measurable impact on the time-lapse amplitudes. It is shown that these errors affect the ability to estimate the saturation change and in a way that is not entirely predictable from inferences using commonly used repeatability measures.