Compressional and Shear Wave Velocities Estimation from Well Log Data Using a Genetic Algorithm Approach

Geophysical measurements in bore-holes are frequently affected by gaps in the recording of one or more logs, or else, the recording of certain logs, such as S-wave velocity logs, may be performed along limited depth extensions. It is therefore of interest to be able to estimate the missing log intervals from a certain set of recorded logs. We propose to do that by making use of a Genetic Algorithm (GA) optimization that is capable of extracting the linear or exponential relations linking the sought parameter to the other available logs. We test this technique on different sets of logs (gamma ray, resistivity, density, neutron, sonic and shear sonic) from four wells drilled in different geological contexts and through different lithologies (sedimentary and intrusive). We focus particularly on P and S-wave velocity predictions. As it is demonstrated by a series of blind tests, it results that the GA optimization yields quantitative relations that are very effective and reliable in predicting the missing logs.