Joint use of multiseismic information for lithofacies prediction via supervised convolutional neural networks

Abstract

Lithology prediction from seismic data is of great significance for sweet-spot detection, reservoir structure delineation, and geologic model building, hence it is important in reducing the risk of exploration and development. Traditional lithofacies prediction methods often are limited by the seismic inversion accuracy and reliability of the rock-physics relationships, which are challenging to be applied in complex reservoirs (such as those containing coal-bearing strata or thin layers). Convolutional neural networks (CNNs) can represent the coupling relationship of seismic characteristics in the time domain through multilayer convolution and effectively manipulate multitype and multidimensional seismic data. Under the framework of a supervised CNN, we jointly integrate prestack seismic gathers (Pre-SGs), seismic inversion results (P-impedance and [Formula: see text] ratio), multiseismic attributes (amplitude-variation-with-offset [AVO] intercept, AVO gradient, instantaneous amplitude, instantaneous frequency, and instantaneous phase), and spectral decomposition attributes (SDA) to predict lithofacies in a complex clastic reservoir interbedded with thin-layer coal. We determine that the fusion model with multiseismic information containing different perspectives and complementary information of seismic data is capable of achieving better prediction performance than only using one type of input feature. In particular, using the proposed methodology, the angle-dependent Pre-SG is essential to decipher the rich information of lithologic details. The models using only poststack seismic data or inversion results cannot reliably describe lithologic details (especially the thin-coal layers). In addition, by including the SDA into model inputs, the model’s ability to recognize thin layers has been further improved but lead to the slight sacrifice of overall prediction accuracy.