1887

Abstract

Summary

The constructing of volumetric petrophysical models is in demand for the deposits exploitation and wells drilling tasks. This is due to the fact that petrophysical parameters, such as porosity or permeability, storing important information about porous reservoirs presence or absence, the migration of fluids in the geological environment, and so on. Initially, petrophysical parameters are measured using well logging and core analyses methods, which is why they are known only in borehole or around them. The interpolation of such a limited data set within a three-dimensional region can be carried out using various stochastic and interpolation methods: deterministic interpolators (linear, spline, Newton’s formulas, etc.), sequential Gauss simulation, direct conversion of seismic attributes into GWL parameters, geostatistics, neural networks etc. Recently, the last two methods have being developed most intense, since they are based on the joint use of seismic and GWL data and allow obtaining statistically optimal results. In this article, a new petrophysical parameter predicting method that using two statistical models is presented. The advantages and disadvantages of this method are discussed. Approbation results relying on real materials from the fields of Western Siberia are presented and discussed.

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/content/papers/10.3997/2214-4609.201801985
2018-08-11
2020-05-26
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