Porosity from seismic data, a geostatistical approach

Abstract

A geostatistical modelling technique called cokriging is used to describe the lateral variations of porosity, f, in a synthetic and a real reservoir. Using this method, an error-qualified porosity model is estimated for each of the two reservoirs from sparse well porosity measurements and seismically derived velocities. The method capitalizes on the high spatial density of the seismic measurements and on their correlation with f. Compared with conventional reservoir models derived solely from sparse well control, the seismically consistent models are better spatially constrained and, hence, provide more detailed and accurate reconstructions of the porosity variations.

Using stochastic crosshole seismic velocity tomography and Bayesian simulation to estimate Ni grades: Case study from Voisey's Bay, Canada

“…In this framework, an optimal estimate of the mineralization grades, and thus the available resources, is likely achieved by integrating these different types of complementary data. The importance of integrating "hard" and "soft" spatial data has long been recognized in the petroleum industry, where reservoir properties such as permeability and porosity need to be inferred from a limited number of drill-holes (Doyen, 1988;Journel and Alabert, 1990;Le Ravalec-Dupin et al, 2001;Xu et al, 1992). Integrated modeling has also been used in the mining industry in ore reserve estimation (David, 1988;Journel and Huijbregts, 1978).…”

Local adaptive parameterization for the history matching of 3D seismic data

“…. Acoustic data can be directly integrated as trends in simulation processes from cokriging [7] or preprocessed in terms of facies proportions [6]. In this last case, they are used to generate facies realizations from the sequential indicator simulation [12], the truncated Gaussian or Pluri-Gaussian methods [3] for instance.…”

Collocated cokriging and neural-network multi-attribute transform in the prediction of effective porosity: A comparative case study for the Second Wall Creek Sand of the Teapot Dome field, Wyoming, USA

“…In CK, the secondary variable is not required at all nodes of the estimation grid. Doyen (1988) showed that the porosity of an oil-bearing sand of Alberta, Canada, predicted from CK is 20% more accurate, in a mean square sense, than that predicted from the standard singlevariate regression based on the well-log porosity and acoustic impedance (the product of density and velocity).…”