Abstract
The kashir and podolsk horizons of the S oilfield are represented by carbonate deposits. They are characterized by limestone and dolomite facies. The kashir and podolsk deposits petrophysical model is developed taking into account the variety of void space types and lithological composition. The success of a petrophysical model testing depends not only on the model correctness. It also depends on quality and volume of well logging data. The paper presents a methodology for creating a synthetic curve of gamma-gamma density log using the intelligent analysis module «Neural Networks» of the «Prime» software. The presence of a gamma-gamma density log synthetic allows to clarify the lithology of the section. It will affect the quality of petrophysical model application for well log data analysis. The result of this approach is an additional extraction of effective oil saturated thicknesses, not previously accounted for, due to incomplete well logging data in oilfield S. Comparison of this approach results and development allows to propose an involving priority areas strategy based on the results of clarifying potential zones for drilling due to additional separation of effective oil-saturated reservoirs.
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