GNGTS 2016 - Atti del 35° Convegno Nazionale

492 GNGTS 2016 S essione 3.1 sand. The statistical characteristics of this a-priori GM distribution are obtained by applying the expectation maximization algorithm to the well log data. Fig. 1 shows the a-priori distribution for the petrophysical properties. Fig. 1a represents the a-priori distribution projected onto the Sw-φ plane, together with the associated two marginal prior probability density functions ( PDF ) computed along the Sw and φ directions. As expected, the shale correspond to high Sw values and low porosity, whereas both brine sands and gas sands are characterized by higher porosity, with the gas sands at lower water saturation values than brine sands. Fig. 1b illustrates the a- priori distribution projected onto the Sw-Sh plane. Similarly to Fig. 1a the marginal distributions are also represented. As expected, the shales are characterized by higher shaliness values than brine sands and gas sands. In the inversion I have considered a subset of CMP gathers extracted from the whole 3D seismic dataset and centered on the spatial location of an exploration well that reached the reservoir zone. The well log data associated to this well will be used to validate the final results. No a-priori information on the spatial correlation of petrophysical parameters has been used to constrain the inversion of adjacent CMPs. Therefore, the lateral continuity of the final results is mainly related to the lateral correlation of seismic data that is dependent on the Fresnel zone and the corresponding migration operator. A close-up of the CMP gather closest to the well location around the target interval together with the associated AVA response, are represented in Fig. 2a. This AVA response have been extracted from the negative peak amplitude of the considered reflection and normalized to the normal-incidence reflection coefficient derived from borehole logs. Note the clear class III AVA anomaly around 2.46 s that is generated by the interface separating the overlying shale from the underlying gas sand. Also note the good S/N ratio of the seismic data on the whole angle range from 0 to 60 degrees that allows me to extract a reliable AVA response over a wide-angle range . Fig. 2b shows the final results (the posterior probability distributions for both the litho-fluid facies and for the petrophysical properties of interest) obtained from the inversion of the AVA response shown in Fig. 2a. The inversion correctly attributes this AVA response to a shale-gas sand interface and the predicted petrophysical properties are in good agreement with the average petrophysical properties measured in the gas sand interval: porosity around 25%, shaliness 10% approximately and water saturation around 30-35%. From Fig. 2b emerges the higher uncertainty that characterizes the saturation estimate and conversely the good resolution on the shaliness and particularly on the porosity that reveals to be the best resolvable parameter. The petrophysical properties estimated along the interpreted top of the reservoir are represented in Fig. 3a. This figure shows the maximum a posteriori (MAP) solution of the final Fig. 1 – Gaussian mixture a-priori distribution for the petrophysical properties. a) A-priori distribution projected onto the Sw-φ plane and the associated marginal distributions (PDF). b) Same as a) but considering the Sw-Sh plane. Black, yellow and red colors code the shales, brine sands and gas sands, respectively.