Automated cementing quality detection using a domain-specific, multi-scale convolutional neural network.

ABSTRACT: Cementing quality is a key factor in ensuring the long-term safe production of oil and gas wells and preventing defects. Traditional cementing quality evaluation mainly relies on logging interpreters manually analyzing acoustic logging data, such as Variable Density Logging (VDL) images and acoustic amplitude curves. This process is highly dependent on personal experience, labor-intensive, and inefficient. To address these issues, this paper proposes an automated cementing quality detection method, CemQ-CNN, based on a Convolutional Neural Network (CNN). In this context, "intelligent" refers to the model's ability to perform automatic classification from raw data, thereby increasing efficiency and consistency. This method constructs a multimodal input CNN model that can simultaneously proc

SUBMITTER: Yang W 

PROVIDER: S-EPMC12688122 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature