Characterization of facies within a hydrocarbon reservoir is essential for potential prospect identification and evaluation. We have developed a practical workflow that integrates poststack seismic attributes and well-log facies analysis to understand the development and depositional setting of the Triassic-age Akekule Formation in Tahe field, Northwest China. The workflow begins with sequence and sedimentary cycle analysis on selected benchmark wells. We then identify sand bodies within each sedimentary cycle using well logs. The analysis from well logs and drilling cuttings together illustrate that there exist five sand bodies within the fourth member of Akekule Formation. We next predict the horizontal distribution of each sand body based on the facies analysis on well logs. We finally perform multiple poststack seismic attributes analyses to identify seismic facies. Multiattribute analysis is implemented either through corendering multiple seismic attributes or neural network clustering on multiple seismic attributes. The lithofacies obtained from seismic attribute analysis is calibrated with facies identified using well logs.
Using neural networks and the Markov Chain approach for facies analysis and prediction from well logs in the Precipice Sandstone and Evergreen Formation, Surat Basin, Australia
