Research Article

Examining the Boundaries of Machine Learning and Deep Learning: A Thorough Review of the Main Obstacles in Missing Well Log Estimation

Abstract: In geotechnical engineering, environmental research, and the discovery and extraction of minerals, oil, natural gas, groundwater, and subsurface thermal energy, well logging is an essential technique for describing geological formations and evaluating resources. However, since well logs can only be measured through a drilling process that involves expensive and time-consuming field testing, computing well logging data is a challenging issue that will never be solved. This paper investigates the well-logging problems in predicting missing well-log data and gives a quick introduction to Deep Learning (DL) models. It also talks about a review of the literature that focuses on employing DL models for well-log estimation. As a result of this exploratory effort, appropriate design and implementation requirements are required.