A global optimization approach for the factor analysis of wireline logging data sets is presented. Oilfield well logs are processed together to give an estimate to factor logs by using an adaptive genetic algorithm. Nonlinear relations between the first factor and essential petrophysical parameters of shaly-sand reservoirs are revealed, which are used to predict the values of shale volume and permeability directly from the factor scores. Independent values of the relevant petrophysical properties are given by inverse modeling and well-known deterministic methods. Case studies including the evaluation of hydrocarbon formations demonstrate the feasibility of the improved algorithm of factor analysis. Comparative numerical analysis made between the genetic algorithm-based factor analysis procedure and the independent well log analsis methods shows consistent results. By factor analysis, an independent in-situ estimate to shale content and permeability is given, which may improve the reservoir model and refine the results of the reserve calculation.


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