This review covers the vast field of machine learning (ML), and relates to weak artificial
intelligence. It includes the taxonomy of ML algorithms, setup diagram of machine learning …

Abstract Machine learning techniques combined with multi-seismic attributes and well logs
datasets have been successfully used in reducing the risk of drilling operations and …

Lithology changes significantly affect the drilling program and the total cost of drilling an oil
well, therefore, it is very important to detect the lithology variation and formation tops while …

Field characterization in oil industry is a challenging task which aims to determine or
estimate some of the petrophysical properties of a reservoir. These properties are expected …

The accurate prediction of underground formation lithology class and tops is a critical
challenge in the oil industry. This paper presents a machine-learning (ML) approach to …

We present a neural network approach to invert surface wave data for a global model of
crustal thickness with corresponding uncertainties. We model the a posteriori probability …

With the expansion of machine learning algorithms, automatic lithology classification that
uses well logging data is becoming significant in formation evaluation and reservoir …

A method based on fuzzy logic inference can be used to identify lithological and
depositional facies from wire-line logs. Fuzzy logic is inherently well suited to characterizing …

Borehole lithology discrimination is the foundation of formation evaluation and reservoir
characterization. Due to the limitation of costing or accuracy, direct discrimination methods …

Fuzzy logic has been used for lithology prediction with remarkable success. Several
techniques such as fuzzy clustering or linguistic reasoning have proven to be useful for …