A comprehensive machine learning workflow for classifying production profiles in unconventional reservoirs

Accurate determination and prediction of production profile types in unconventional reservoirs is a critical task for optimizing petroleum exploration and development. While machine learning (ML) methods have been increasingly applied in petroleum engineering, a comprehensive and generalizable workflow for classifying and predicting production profiles remains lacking. To support unconventional reservoir decision-making, we propose an ML workflow integrating geological, completion, and production data. The workflow begins with univariate and multivariate analyses to explore data relationships, followed by feature selection using multiple ranking methods: maximum relevance–minimum redundancy, variance inflation factor, partial correlation coefficient, recursive feature elimination, and model-based random forest importance. This yields six key features selected from the initial dataset. K -Means clustering applied to a dataset of 189 Permian Basin wells identifies two distinct production patterns (high and low), with normalized oil production histories showing clear separation. Multidimensional scaling (MDS) is used to reduce feature dimensionality to two components while preserving pairwise sample distances, enabling improved visualization and interpretability. A random forest classification model trained on these MDS components achieves a higher test accuracy (97.5%) and stability (lower variance) compared to a reference model using the original high-dimensional features (95.0%) and the model using two principal components of principal component analysis (90.0%). This workflow facilitates the automated classification of production profiles and provides a data-driven foundation for optimizing completion designs in unconventional reservoir development.