A Spatially Coupled Data-Driven Approach for Lithology/Fluid Prediction

Prediction of lithology/fluid (LF) characteristics is always the bottleneck problem and difficulty of reservoir characterization. Deep-learning-based data-driven methods can review data and find specific trends and patterns that would not be apparent to humans, and have been successfully used in many geophysical applications including LF prediction (LFP). However, the above methods mostly predict LF point-by-point, which means that the spatial correlation of LF is not considered. When the predicted LF results are combined to form a 2-D/3-D image, the resulting image will be noisy or even geologically unreliable. To overcome these issues, we proposed a spatially coupled data-driven (convolutional neural network, CNN) approach for LFP from the poststack seismic data and well observations. Here, the vertical couplings of the LF are modeled by a Markov chain (MC) prior and the lateral continuity of the LF is further defined by a Markov random field (MRF) prior. We also proposed to perform spectral decomposition via inversion strategies (ISD) to get a time-frequency (TF) spectrum as the input of CNN. ISD helps make full use of the information hidden in the frequency domain of the poststack seismic data. Well-logs and poststack seismic data are integrated in a consistent manner to obtain predictions of the LF classes with the associated uncertainty statements. The LFP results of the proposed approach are more laterally continuous and geologically reliable than the LFP results of the point-by-point. We determined the effectiveness of this methodology on a 2-D synthetic model and a 3-D field seismic data set.