Abstract

Velocity logs are the most important data used to evaluate rock, fluid, and geotechnical properties of hydrocarbon reservoirs. As a complementary physical property, P-wave attenuation (Q1) can be used as an indicator of lithology and fluid saturation in oil and gas reservoir characterization. We implemented an inversion self-consistent rock physical model to predict P- and S-wave velocities in two old wells near a new well containing a complete suite of logs at the Waggoner Ranch oil reservoir in northeast Texas. We selected a training data set from the new well to test the algorithm that was subsequently applied to predict velocity data in the two old wells. We used an attenuation log from the new well to perform data analysis via the Gamma test, a mathematically nonparametric nonlinear smooth modeling tool, to choose the best input combination of well logs to train an artificial neural network (NN) for estimating Q1. Then, the NN was applied to predict attenuation logs in the old wells. The Q1 logs detected oil-saturated sand that was modeled with a rock physical model. This is a significant result that revealed 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 Q1 anomalies observed in the old wells. This confirms the productivity of the Upper Milham oil-saturated sand intercepted by the three wells. The velocity, density, and Q1 logs were used to generate synthetic seismograms to calibrate seismic data to verify and evaluate the work flow for predicting velocity and attenuation logs in older wells. This demonstrated that attenuation logs can discriminate between anomalies due to lithology and those due to oil and gas saturation.

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