Decision Tree and Random Forest Applications for the Oil and Gas Field

Predicting the generation of hydrocarbons involves estimating the final recoveries and well lives, which are important decision-making criteria in the oil and gas sector since they have a big influence on field planning and economic assessment. This paper proposes a decision tree (DT) and random forest (RF) applications for the oil and gas field. In data acquisition process the input dataset are gathered to save, clean, preprocess, and utilize data for other mechanisms. The input data is transferred for preprocessing. The dataset is preprocessed to handle missing values, cleaning, and filtered the values. The preprocessed image is further move to data visualization. It is the process of displaying information and data through maps, graphs, charts, and other visual aids. We can quickly identify any patterns, trends, or outliers in a data collection thanks to these visualizations. These raw data are transformed into features that are used to create a predictive model by using feature engineering. Selected features are subsequently classified using decision tree regressor and random forest regressor. After that, hyper-parameter tuning is generally the final step, which allows the model to be more stable and perform well. Additionally, it accurately predicts continuous numerical values and also comparatively more accurate. This project is implemented using Python software.