Forecasting Drilling Mud Losses Using Python

Abstract

Introduction. Drilling mud losses are among the most common complications encountered during well drilling. Forecasting these losses is a priority as it helps minimize drilling fluid wastage and prevent wellbore incidents. Mud loss events are primarily influenced by the geological properties of the formations being drilled. Understanding the relationship between mud loss occurrences and the geological characteristics of the formations has both fundamental and practical significance. Given the complexity of predicting mud loss probabilities using traditional mathematical models, this study aims to develop a machine-learning-based system to predict the probability of mud losses based on well location and stratigraphic description.

Materials and Methods. Experimental data from 735 wells at the Shkapovskoye oil field, including well location coordinates, geological layer indices, and mud loss intensities, were prepared for computational analysis. The dataset was divided into training and testing subsets. The classification problem was addressed using four intensity classes with the following machine learning models: Decision Tree, Random Forest, and Linear Discriminant Analysis.

Results. Predictions generated by the three models were compared against the experimental data in the test set. The evaluation metrics included accuracy and recall. All three models achieved an average prediction accuracy of 91%. Linear Discriminant Analysis was identified as the most accurate model.

Discussion and Conclusion. High-accuracy predictions enable reliable forecasting of the probability and intensity of mud losses based on the location and stratigraphic description of new wells. The study presents three machine learning methods that demonstrated superior results in solving this problem.