Numerical modeling of EM logging signals in sand-shale reservoir using clay distribution models for study of low-resistivity oil reservoirs

Ivanov Y.S. Glinskikh V.N.

A.A. Trofimuk Institute of Petroleum Geology and Geophysics SB RAS, Novosibirsk, Russia

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The problem of evaluation of reservoir characteristics for water and oil reservoirs is usually being solved using petrophysical measurements of core. The basic rock physical parameter that is used for evaluation of rock water saturation is electrical resistivity (inverse value - electrical conductivity). The relation between petrophysical parameters of rock and electrical resistivity of fluid-saturated sandstones is being usually described using Archie [1] and Dahnov [2] equation for partially water saturated rocks:

where R_t - observed bulk resistivity, R_w - formation water resistivity, S_w - water saturation, φ - porosity, a - a constant, m - cementation factor, n - saturation exponent. However this model does not take into account several factors such as clay content that strongly affects the bulk resistivity of reservoir rock. For correct estimate of shale effect to be accomplished clay part volume and content and spatial distribution of clay particles in the rock must be considered.

Present work uses three types of clay distribution models to describe the electrical properties of a sand-shale reservoir. These are structural clays, coated clays and dispersed clays. Each model refers to special genesis conditions. In the first case the clay grains act as framework grains without altering the reservoir properties [3], in the second the clay grains actually coat the sand grains, and in the third case clay grains fill the pore space between sand grains [4]. Behavior of electrical conductivity versus clay part plot for first two models is very similar, however the dispersed clay model shows absolutely different relation.

For all these models the conductivity of formation essentially relates on porosity and saturation fluid electrical conductivity. The last is determined by water content, its salinity and temperature. One important characteristic of sand-shale reservoirs must be marked out – raise of water salinity leads not only to raise of fluid electrical conductivity but also to raise of additional electrical conductivity of shale material [5]

This study can be applied to the data observed by high frequency induction logging tool for evaluation of reservoir properties. The same ideas were developed before [6] with the respect to the sea-bed logging tool.

For induction logging tool the numerical modeling and comparison of EM signals in typical models of terrigenous reservoirs of Western Siberia was accomplished using clay distribution models.

Jurassic oil reservoirs of Western Siberia are characterized by low value of electrical resistivity close to the typical values of water reservoir resistivity. In present work author studies the influence of shale material in sandstone reservoir on bulk electrical reservoir conductivity. Direct one-dimensional modeling of induction logging signals within the three-layer model “well-invaded zone-layer” based on mixing models shows the possibility of explanation of low electrical resistivity of oil reservoir by presence of clay material (Fig.).

Fig. The results of one-dimensional numerical modeling of signals of induction log for water and oil saturated rocks using mixing models.

References:

1. G.E. Archie: “The electrical resistivity log as an aid in determining some reservoir characteristics”, Tran. AIME, Vol. 146, P 54-62, 1942.

2. V.N. Dahnov: “Well logging and interpretation of well logs”, M.–L. (in Russian), 1941

3. A.E. Bussian: “Electrical conductance in a porous medium”, Geophysics, Vol. 48, P 1258-1268, 1983.

4. O.A.L. de Lima, M. M. Sharma: “A grain conductivity approach to shaly sandstones”, Geophysics, Vol. 55, P 1347-356, 1990.

5. W.O. Winsauer, W.M. McCardell: “Ionic double-layer conductivity in reservoir rock”, Petroleum transactions, AIME, Vol. 198, P 129-134, 1953.

6. Z. Wang, L.J. Gelius. “Modeling of seabed logging data for a sand-shale reservoir”, Piers online, Vol. 3, P 236-240, 2007.

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