Accurate prediction of formation tops and lithology plays
a critical
role in optimizing drilling processes, cost reduction, and risk mitigation
in hydrocarbon operations. Although several techniques like well logging,
core sampling, cuttings analysis, seismic surveys, and mud logging
are available for identifying formation tops, they have limitations
such as high costs, lower accuracy, manpower-intensive processes,
and time or depth lags that impede real-time estimation. Consequently,
this study aims to leverage machine learning models based on easily
accessible drilling parameters to predict formation tops and lithologies,
overcoming the limitations associated with traditional methods. Data
from two wells (A and B) in the Middle East, encompassing drilling
mechanical parameters such as rate of penetration (ROP), drill string
rotation (DSR), pumping rate (Q), standpipe pressure
(SPP), weight on bit (WOB), and torque, were collected for real-field
analysis. Machine learning models including Gaussian naive Bayes (GNB),
logistic regression (LR), and linear discriminant analysis (LDA) were
trained and tested on the data set from well A, while the data set
from well B was utilized for model validation as unseen data. The
formations of wells A and B consist of four lithologies, namely, sandstone,
anhydrite, carbonate/shale, and carbonates, necessitating the development
of multiclass classification models. The drilling parameters, specifically
the WOB and ROP, exhibited a strong influence on lithology identification.
Among the models, GNB demonstrated exceptional performance in predicting
formation lithology from the drilling parameters, achieving accuracy
and nearly perfect precision, recall, and F1 score for the different
classes. LDA and LR models accurately predicted sandstone and carbonate
lithologies, although some misclassifications occurred in approximately
5% of points for anhydrite and around 20% in carbonate/shale formations.
During validation, the models demonstrated accuracies of around 0.96,
0.95, and 0.92 for the GNB, LR, and LDA, respectively. The study highlights
the efficacy of the developed machine learning models in accurately
predicting the formation lithology and tops in real time. This is
achieved by utilizing readily available drilling parameters, making
the approach highly accurate and cost effective by leveraging existing
real-time drilling data.