Porosity and water saturation predicting beyond boreholes from electromagnetic sounding and core sample data: Soultz-sous-Forêts (France) case study

Abstract

Porosity predicting beyond boreholes is carried out by means of neural network taught by correspondence of the mercury porosity values determined from core samples and electrical resistivity estimated by magnetotelluric sounding in its vicinity. Testing of the artificial neuronet porosity prediction beyond boreholes indicated that the total relative error of the forecasts is about 7% that is slightly less than the prediction errors (∼10%) obtained in laboratory using seismic attributes. Comparative analysis of the prediction accuracy in boreholes with porosity values determined by direct and indirect ways showed that building of the porosity model of the section using simultaneously porosity data determined by direct and indirect techniques is inexpedient. At the same time, accuracy of electromagnetic porosity predicting using neural networks calibrated with data measured by the mercury porosimetry turns to be about three times higher than the accuracy of porosity estimation by indirect methods (for example, neutron gamma logging). A new approach to building of a water saturation section is proposed. It is based on a comparison of total open porosity determined using porosity values estimated by mercury porosimetry in a reference well and fluid volume fraction determined from electromagnetic sounding data. The latter is estimated taking into account the dependence of the brine electrical resistivity on the temperature determined using electromagnetic geothermometry. The errors of predicting water saturation beyond boreholes are around 20–22%, which are comparable with its estimation from seismic data in laboratory conditions.