Paleokarst caves recognition from seismic response simulation to convolutional neural network detection

Abstract

Paleokarst systems, found in carbonate rock formations worldwide, have potential for creating vast reservoirs and facilitating hydrocarbon migration. Thus, studying these systems is essential for the exploration and development of carbonate reservoirs. An approach using convolutional neural networks (CNNs) is introduced to automatically and precisely identify cave features within 3D seismic data. An efficient technique is outlined for generating ample amounts of 3D training data, which is comprised of synthetic seismic data and labels for cave features contained in the seismic data, as a solution to bypass the labeling task for training the CNN. This workflow uses point-spread functions to simulate the cave response in the seismic data and allows us to easily generate realistic and diverse synthetic training data sets with different geologic structures and cave features. By training a CNN with these synthetic data sets, it can effectively learn to detect cave features in field seismic volumes. Upon evaluation using multiple examples, this approach outperforms earlier techniques like seismic attributes and other CNN-based paleokarst characterization methods.