A deep-learning framework for borehole formation properties prediction using heterogeneous well-logging data: A case study of a carbonate reservoir in the Gaoshiti-Moxi area, Sichuan Basin, China

The properties of borehole formations, such as porosity, permeability, and water saturation, play a crucial role in characterizing and evaluating subsurface reservoirs. Although core sample experiments offer precise measurements, they are time consuming and cost intensive. An alternative method is to use the logging data to construct an empirical model that predicts formation properties, which is widely studied due to its speed and affordability. Nevertheless, because the response of a logging point reflects its surrounding formation, conventional logging methods relying on point-to-point (P2P) mapping perform poorly in complex reservoirs. Furthermore, the resolution of conventional logging is lower compared with imaging logging. To address these limitations, this study presents a novel approach to predict formation properties based on a deep-learning framework using heterogeneous well-logging data. Our neural network framework takes short sequences of conventional logging data and windowed imaging logging data as inputs. The neural network applies 1D convolution to extract features from the conventional logging sequences and 2D convolution to extract features from the resistivity imaging data. Then, these two feature vectors are fused and fed into a multilayer fully connected neural network to predict formation properties. A case study of a carbonate reservoir demonstrates that our method delivers more accurate predictions of formation porosity, permeability, and water saturation than the P2P, sequence-to-point, and image-to-point prediction methods. Moreover, it is expected that our paradigm will serve as a source of inspiration for forthcoming research endeavors aimed at enhancing the accuracy of predicting borehole formation properties in complex reservoirs.