Neural network and rock physics for predicting and modeling attenuation logs

P-wave attenuation (Q), a powerful attribute, can be used as an indicator of lithology and fluid saturation in the characterization of oil and gas reservoirs. The estimations of Q from a new well at Waggoner reservoir were used to perform data analysis via the Gamma test, a mathematically non-parametric nonlinear smooth modeling tool, to choose the best input combination of well logs to train an artificial neural network (NN) for estimating Q. Then the NN was applied to predict attenuation logs in two nearby old wells. The Q logs detect oil saturated sand that was verified with a forward rock physical model. This is a significant result that shows for the first time that oil, gas, and water saturations of sand can be quantified from an attenuation anomaly estimated from full waveform sonic data. In addition, water, oil, and gas saturations of the sand were determined from Q anomalies observed in the old wells. This confirms the production of the Upper Milham oil-saturated sand intercepted by the three wells. This study demonstrates that attenuation logs combined with rock physical models can be used to discriminate between those anomalies associated with lithology and those associated with oil and gas saturations.