The Mara West Field reservoir in the Maracaibo Lake basin presents some challenges in petrophysical evaluation, which are related to the determination of the water saturations, porosities and permeabilities. The information extracted from conventional log data is not sufficient to fully evaluate the formation. This is due to the fact that these logs are affected by the presence of carbonates, complicating the evaluation of porosities and other properties. In order to overcome this problem, Nuclear Magnetic Resonance technique has been used in the evaluation of core plug samples and further core log correlations. Its advantages lie in the measurement of porosity independently of lithology and pore distribution as well as in the calculation of permeability using parameters such as Nuclear Magnetic Resonance porosity, T2 cutoff, free fluid index and bound water volume.
The present work shows a very good correlation between Nuclear Magnetic Resonance data and conventional data extracted from core plug samples. It also shows how the implementation of a new equation for the estimation of permeability provides an increment of the correlation coefficient from 0.36 to 0.81 obtained from the Nuclear Magnetic Resonance permeability vs. Klinkemberg permeability plot.
In order to complete the log information in the cored well and in a neighbor well, Artificial Neural Nets based on the back-propagation algorithm have been applied, generating the so called pseudologs (synthetic logs). To obtain the pseudologs of NMR porosity, gamma ray, resistivity and neutron logs have been used as input, with the core plugs value for NMR porosity and permeability as output. The results show a very good match between the existing data and the artificial neural net output in the validating data set.
Author(s): J.P. Salazar, P.A. Romero, PDVSA-INTEVEP
Paper Number: SPE 71701