Combining Absorption and AVO Seismic Attributes Using Neural Networks to High-Grade Gas Prospects

摘要

A seismic attribute is a transformation of the original seismic data that can help determine the rock type and fluid characters. AVO attribute analysis is a proven method for confirming seismic amplitude anomalies associated with gas anomalies. AVO analysis examines the intensity of seismic reflections at varying source-receiver distances (offset). The major risk with AVO analysis is whether seismic amplitude anomalies represent commercial or non-commercial gas accumulations. Combining AVO and absorption attributes using ANN reduces this risk. Further risk reduction is accomplished thorough investigation of the different types of absorption attributes and enhancing the ANN training with petrophysical information derived from well logs. We demonstrate that the high frequency content of the seismic response attenuates more as it propagates through gas- bearing reservoirs and, unlike AVO, the absorption attribute is impacted by the amount of gas saturation. Furthermore, we demonstrate how the correlation between shallow gas and shallow seismic amplitudes improves as we include different types of well logs with proper calibration. The ANN, trained by the suite of logs and different frequency-related attributes, enhances the ability to detect undeveloped pockets of shallow gas. Gas has a very marked effect on both density and neutron logs, resulting in lower bulk density and lower apparent neutron porosity. Therefore, combining neutron and density logs and training neural networks based on the well logs with AVO and AQF attributes, instead of just training based on attributes alone, increases the certainty of suspected gas pockets. The real power comes from being able to tie absorption and AVO anomalies with other frequency attributes.