Multivariate correlations developed via radial-basis function (RBF) network analysis of a modern suite of logs are used to estimate crossplot density-neutron porosity and pseudo-deep resistivity from 1950's vintage well logs. Historically, porosity has been estimated with the logarithm of the neutron count rate standardized with a 2% porosity tight spot and a maximum porosity of ~40% in a shale stringer. Despite a frequently observed visual correlation of the gamma ray track with the neutron count rate, the gamma ray log information is not included in conventional porosity estimates.

The visual comparison of the openhole gamma ray and the neutron porosity to the shape of the crossplot porosity from a new well, Foster 4, is similar to the pattern seen between the old gamma ray and the neutron-count rate logs. The new log data were investigated with various RBF networks to correlate the gamma ray and neutron-count rate with the crossplot porosity. An optimal RBF network was then used to estimate the crossplot porosity of the Penrose sand for 22 wells in the Reed Sanderson Unit where only cased-hole gamma ray and neutron-count rate logs are available. An equation converts the modern neutron log to the old style neutron count rate while providing a normalized input to the network training. The "goodness" of the RBF network predictions was validated via exclusion testing.

A similar procedure was used to estimate a pseudo-deep resistivity in old wells. The study indicated that a carefully trained RBF network could identify the high resistivity intervals (shale stringers) with only gamma ray and neutron count rate used as the network inputs. The methodology presented should prove useful to others faced with characterizing old fields.

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