The permeability and the porosity are the two most important petrophysical properties for qualification of oil and gas reservoirs. The porosity is related to the capacity of fluids storage and the permeability, with the production of these fluids. The estimates of the permeability and porosity are of fundamental importance for reservoir engineers and geophysics, once its values can define the completacion or not of an oil well. Its measures are, usually, accomplished in laboratory, through cores of the rock. The porosity log and its relationship with the density log, is very well-known in the well logging, however, it just exist a few qualitative relationships (Kozeny's relation, for instance) between the porosity and the permeability. This work search the establishment of the permeability log and of the porosity log, starting from information of the density log. For so much, we looked for the relationship among the physical property of the rock (density) and the petrophysical properties: permeability and porosity, using as methodology the technique of artificial neural networks with radial base function. To obtaining the permeability and the porosity, the artificial neural network possessing as input the information of the density that facilitates a smaller cost for the acquisition of those important petrophysical information, giving possibility to the well log analysts, to opt or not for the exploration of a studied unit, in addition, it facilitates a more complete vision of the reservoir. The procedures for the estimate of the permeability and of the porosity are addressed for an only formation, but the log interpreters can apply the guideline presented in the program of artificial neural network with radial base function, using the estimate of those properties for another formations, besides of another oil fields. Therefore, is recommended the use of a large data set of the same well in order to make possible the best interpretation.
|
