Hydrocarbon detections using multi-attributes based quantum neural networks in a tight sandstone gas reservoir in the Sichuan Basin, China

Abstract

A direct hydrocarbon detection is performed by using multi-attributes based quantum neural networks with gas fields. The proposed multi-attributes based quantum neural networks for hydrocarbon detection use data clustering and local wave decomposition based seismic attenuation characteristics, relative wave impedance features of prestack seismic data as the selected multiple attributes for one tight sandstone gas reservoir and further employ principal component analysis combined with quantum neural networks for giving the distinguishing results of the weak responses of the gas reservoir, which is hard to detect by using the conventional technologies. For the seismic data from a tight sandstone gas reservoir in the Sichuan basin, China, we found that multi-attributes based quantum neural networks can effectively capture the weak seismic responses features associated with gas saturation in the gas reservoir. This study is hoped to be useful as an aid for hydrocarbon detections for the gas reservoir with the characteristics of the weak seismic responses by the complement of the multi-attributes based quantum neural networks.