Rock Physical Model and AVO Patterns for the Mud-Rich Source Rock

Rock physical model and amplitude variation with offset (AVO) patterns considering the content of organic matter and the composition of minerals have a wider significance for guiding the identification and prediction of the mud-rich source rock. A rock physical model is proposed for describing the elastic properties of kerogen in different maturity stages. The proposed rock physical model builds an intrinsic connection between the elastic properties and physical parameters of the mud-rich source rock, thereby providing a theoretical basis for a seismic inversion and a seismic forward modeling. To overcome the limitations of laboratory measurement, a combination-four-parameter regression (CFPR) method is further proposed to estimate the continuous total organic carbon (TOC) values for the verification and analysis of the rock physical model. The modeling results reveal that the P-wave velocity and P-wave impedance will decrease with an increase in TOC, and the Poisson ratio and Poisson impedance will increase as the mud content increases, which are consistent with the conclusions of the cross plot using the actual well data. Based on the proposed rock physical model, the seismic responses of the mud-rich source rock are further modeled. The synthetic seismic records are consistent with the well-side seismic records, the top reflection of the mud-rich source rock behaves as a stronger negative event dimming with an incident angle corresponding to a class IV AVO pattern, and the bottom reflection exhibits a class I AVO anomaly. In addition, a two-layer model is constructed to analyze an effect of the TOC content and mud content on the AVO characteristics. The results indicate that increasing the TOC content and mud content will significantly increase the interceptions and slightly change the gradients of the P-P reflection coefficients. These results help to guide the identification and evaluation of the mud-rich source rock.

Time-lapse amplitude variation with offset inversion using Bayesian theory in two-phase media

In time-lapse seismic analysis, the Zoeppritz equations are usually used in the time-lapse amplitude variation with offset (AVO) inversion and then combined with a rock-physical model to estimate the reservoir-parameter changes. The real-life reservoir is a two-phase medium that consists of solid and fluid components. The Zoeppritz equations are a simplification, assuming a single-phase solid medium, in which the properties of this medium are estimated by effective parameters from the combined components. This means that the Zoeppritz equations cannot describe the characteristics of the seismic reflection amplitudes in the reservoir in an accurate way. Therefore, we develop a method for time-lapse AVO inversion in two-phase media using the Bayesian theory to estimate the reservoir parameters and their changes quantitatively. We use a reflection-coefficient equation in two-phase media, a rock-physical model, and the convolutional model to build a relationship between the seismic records and reservoir parameters, which include porosity, clay content, saturation, and pressure. Assuming that the seismic-data errors follow a zero-mean Gaussian distribution and that the reservoir parameters follow a four-variable Cauchy prior distribution, we use the Bayesian theory to construct the objective function for the AVO inversion, and we also add a model-constraint term to compensate the low-frequency information and improve the stability of the inversion. Using the objective function of the AVO inversion and the Gauss-Newton method, we derived the equation for time-lapse AVO inversion. This result can be used to estimate the reservoir parameters and their changes accurately and in a stable way. The test results from the feasibility study on synthetic and field data proved that the method is effective and reliable.

Attenuation and velocity estimation using rock physics and neural network methods for calibrating reflection seismograms

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 ([Formula: see text]) 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 [Formula: see text]. Then, the NN was applied to predict attenuation logs in the old wells. The [Formula: see text] 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 [Formula: see text] 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 [Formula: see text] 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.