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 (Q −1 ) can be used as an indicator of lithology and fluid saturation in oil and gas reservoir characterization. We implemented an inversion selfconsistent 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 Q −1 . Then, the NN was applied to predict attenuation logs in the old wells. The Q −1 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 Q −1 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 Q −1 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. IntroductionThe characterization of oil and gas fields relies on knowledge of the distribution of rock-physics properties of the reservoir based on well log data. P-and S-wave velocities are the most important data used to evaluate reservoir rock and fluid properties, as well as geotechnical rock properties. Similarly, P-wave attenuation (Q −1 ) can be used as an indicator of lithology; pore structure; clay, sand, and fluid content; and hydrocarbon saturation. For most old wells, P-and S-wave velocity logs and attenuation data are missing. When new wells are drilled at such sites, service companies provide complete suites of logs to small oil and gas producers. The producers can then integrate new data with existing data to better delineate the reservoir and to estimate new reserves. To facilitate data integration, several investigators have developed regression analysis methods to predict P-and S-wave data at the vicinities of new wells where the complete suite of logs is available (Augusto and Martins, 2010). These authors extend

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Surface reflection seismic inversion techniques are currently applied by the industry for mapping the rock physical properties of oil reservoirs. This information permits speeding up the interpretation process to ultimately provide well locations. At present, many companies require the inversion to be completed before any well is drilled. Inversion techniques can be applied to prestacked and poststacked seismic data. Prestacked data inversion is more complex than poststacked, but it provides more information for the interpreter (e.g., P-wave and S-wave impedances). On the other hand, poststacked inversion provides only the acoustic P-wave impedance. However, the main outcome of prestacked inversion is the increase in resolution when full waveforms are inverted. Currently, poststacked seismic inversion is used to correlate P-wave impedance with rock physical properties obtained from well logs. The logs are provided by a well near the survey line, allowing images of different rock properties to be processed and analyzed. We extend the use of the acoustic P-wave impedance by constraining it with the well lithology, consequently categorizing the impedance by classes (i.e., sand, shale, and limestone) and converting the impedance to earth properties using well logs and regression models. This process allows us to build a single initial estimate of the earth property model, which is iteratively refined to produce a synthetic seismogram (by means of forward modeling) to match the observed seismic data. The inversion algorithm that minimizes the misfits between observed and synthetic full-waveform data improves the P-wave velocity resolution. The interpreter can thus delineate thin channels (flow units saturated with hydrocarbons) that are undetected using current techniques.

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The state of the art in predicting tunnel-induced subsidence settlements is based on empirical and analytical methods. Empirical methods are useful when the equations are implemented with host medium properties where tunnels have been excavated. Analytical solutions can predict tunneling-induced ground movements, with the predictions accounting for tunnel radius and depth as well as ground-loss parameters in soft soils. The drawback is that these methods require human intervention, as each model must be adjusted manually by the interpreter until the model signature fits the observed data. It would take tremendous effort to evaluate displacement anomalies detected by remote sensing methods using such forward-modeling methods. Therefore, we present a method based on an inversion algorithm that automatically inverts subsidence signatures for tunnel radius, depth, Poisson's ratio, and the gap parameter. It is an advancement over conventional methods because it does not require a first guess, and it can invert several subsidence signatures in a matter of minutes. The algorithm, coupled with remote sensing-based displacement maps, is a cost-effective solution in operational characterization of displacement anomalies. We demonstrate that observed and predicted subsidence signatures are in good agreement with existing tunnel data in uniform clay and that the inversion parameters correspond to those predicted with forward modeling alone.