FIELD: geophysics.
SUBSTANCE: invention relates to exploration geophysics and can be used in prospecting and determining prospects of hydrocarbon deposit. Summary: based on the data of magnetotelluric probing in the nearest vicinity of the well, the one-dimensional profile of specific electric resistance up to the prediction depth is plotted. Porosity logging, electrical logging and resistivity logging data obtained by inversion of magnetotelluric sounding data are interpolated on the same grid to the bottomhole depth. First artificial neural net is trained at compliance of depths less than bottomhole depth, and corresponding values of specific electric resistance obtained as a result of inversion of magnetotelluric sounding data at input and data of electric logging at output. Using the first trained artificial neural network forecast of pseudo-electric logging from depth of bottomhole to target depth is made from values of specific electric resistance obtained as a result of inversion of data of magnetotelluric probing at these depths. Second artificial neural network is trained at compliance of depths less than bottomhole depth, and corresponding values of specific electric resistance of logging at the input and porosity logging at the output. Using the second trained artificial neuron, porosity forecast is made at depths from bottomhole depth to target depth from values of pseudo-electric logging at these depths.
EFFECT: possibility of constructing a predictive profile of porosity at depths from bottomhole to target depth.
1 cl, 1 tbl, 6 dwg
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Authors
Dates
2019-08-05—Published
2018-08-15—Filed