To overcome the deficiencies of current porosity prediction methods, the XGBoost algorithm
is introduced to construct a model for porosity prediction, and the obtained model is …

The total organic carbon content usually determines the hydrocarbon generation potential of
a formation. A higher total organic carbon content often corresponds to a greater possibility …

To supplement missing logging information without increasing economic cost, a machine
learning method to generate synthetic well logs from the existing log data was presented …

Well logs are commonly used by geoscientists to infer and extrapolate physical properties of
subsurface rocks. However, at some depth intervals, well log values might be missing due to …

Geomechanical logs are of ultimate importance for subsurface description and evaluation,
as well as for the exploration of underground resources, such as oil and gas, groundwater …

Total organic carbon (TOC), volatile hydrocarbon (S 1) and remaining hydrocarbon (S 2) are
significant factors for shale oil and gas exploration and development. However, the TOC, S 1 …

The estimated ultimate recovery (EUR) of shale gas wells is influenced by many factors, and
the accurate prediction still faces certain challenges. As an artificial intelligence algorithm …

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 …

Abstract KNN (K-Nearest Neighbors) clustering in machine learning is a very efficient
clustering method applied in lithology identification, reservoir type recognition, flow unit …

Total organic carbon (TOC) estimation is significantly crucial for shale reservoir
characterization. Traditional TOC estimation methods (such as Passey and Schmoker …