Predicting Oil Production After Enhancement Techniques Using Multidimensional Feature Representation Learning: A Case Study of Profile Control Technique

Abstract

The prediction of oil production following enhancement techniques has garnered widespread attention, leading scientists to explore this area using machine learning. However, field data collection constraints and single model accuracy limitations mean few models can precisely predict daily oil production after technique implementation. Building upon previous research, this paper introduces a model that predicts oil production after enhancement operations, utilizing multidimensional feature representation learning. It thoroughly examines three characteristic categories affecting the effectiveness of oil production enhancement techniques: geological static parameters, production dynamic parameters, and enhancement technique process parameters. The model comprehensively explores these features with an emphasis on global spatial, local spatial, and temporal information. A complete machine learning prediction process is established, which includes data preprocessing, model training, cross-validation, and oil production prediction after implementing enhancement techniques. The first part of the model involves representation learning on processed data, producing three sets of new features: global spatial, local spatial, and temporal information. These features are fused with the original data, serving as input for the advanced ensemble learning model XGBoost, which predicts daily oil production after implementing the technique. Following the construction of the model, actual field data from profile control techniques are selected to conduct various evaluations based on the model’s performance on validation and test sets. Compared with traditional machine learning regression algorithms, this model demonstrates significantly higher predictive accuracy. The prediction accuracy for oil production using given enhanced techniques reached 96% in the validation set and 94% in the test set. This research provides a technical foundation for selecting appropriate production enhancement techniques in oil fields by accurately predicting oil production after implementing enhancement techniques, which offers guidance for actual oilfield production.