Pore type identification in carbonate rocks using convolutional neural network based on acoustic logging data

Existing methods of well logging interpretation often contain uncertainties in the exploration and evaluation of carbonate reservoirs due to the complex pore types. Based on the time–frequency analysis of array acoustic logging data, the identification of pore types based on a convolutional neural network (CNN) was established. The continuous wavelet transform was first used to transform the 1-D acoustic wave data into 2-D time–frequency spectra as the input data of the CNN based on the pore aspect ratios. According to the acoustic logging data obtained from numerical simulations, a three-type (vug, interparticle-pores, and crack) prediction was established to validate the identification method. The noise-sensitivity analyses demonstrate that our method is stable for noise mixed signals. The CNN-based identification method was used to analyze the field acoustic logging data in a carbonate reservoir. According to the description of the pore structures from the core analysis, the formation pores were divided into two types (cracks and interparticle-pores). The accuracy of the method using field acoustic logging data can reach 90%. This work provides promising means for pore type identification from complex acoustic logging data by applying deep learning technologies, which can be easily extended into other similar neighboring carbonate reservoirs.