Partial Domain Adaptation for Building Borehole Lithology Model Under Weaker Geological Prior

Abstract

Lithology identification plays a pivotal role in stratigraphic characterization and reservoir exploration. The promising field of intelligent logging lithology identification, which employs machine learning algorithms to infer lithology from logging curves, is gaining significant attention. However, models trained on labeled wells currently face challenges in accurately predicting the lithologies of new unlabeled wells due to significant discrepancies in data distribution among different wells caused by the complex sedimentary environment and variations in logging equipment. Additionally, there is no guarantee that newly drilled wells share the same lithology classes as previously explored ones. Therefore, our research aims to leverage source logging and lithology data along with target logging data to train a model capable of directly discerning the lithologies of target wells. The challenges are centered around the disparities in data distribution and the lack of prior knowledge regarding potential lithology classes in the target well. To tackle these concerns, we have made concerted efforts: 1) proposing a novel lithology identification framework, sample transferability weighting based partial domain adaptation (ST-PDA), to effectively address the practical scenario of encountering an unknown label space in target wells; 2) designing a sample transferability weighting module to assign higher weights to shared-class samples, thus effectively mitigating the negative transfer caused by unshared-class source samples; 3) developing a module, convolutional neural network with integrated channel attention mechanism (CG ${}^{2}$ CA), to serve as the backbone network for feature extraction; and 4) incorporating a target sample reconstruction module to enhance the feature representation and further facilitating positive transfer. Extensive experiments on 16 real-world wells demonstrated the strong performance of ST-PDA and highlighted the necessity of each component in the framework.