Predictive Analysis of Oil and Gas Using Well Log Data

The oil and gas sector constitutes a significant contributor to the worldwide economy. The industry is currently encountering various challenges that require immediate attention. One of the most pressing issues is the necessity to curtail expenses and enhance productivity. The utilization of predictive analytics has the potential to mitigate these challenges by enabling enterprises to gain deeper insights into their business processes and facilitate data-driven decision-making. The present study employs AutoML classification to construct a prognostic model for the detection of oil and gas. AutoML is a computational process that automates the various stages involved in the development of a machine learning model. These stages include data preparation, feature engineering, and model selection. The objective of AutoML is to streamline the machine learning model development process by automating the repetitive and time-consuming tasks that are typically performed by data scientists. The findings of our investigation indicate that automated machine learning (AutoML) was capable of identifying a group of classification algorithms that exhibited satisfactory performance on the novel dataset. The present study reports the top 5 algorithms that were selected by the automated machine learning (AutoML) process. This paper presents a comparative analysis of five popular machine learning algorithms, namely AdaBoostClassifier, BaggingClassifier, XGBClassifier, RandomForestClassifier, and DecisionTreeClassifier open experimentation. The predictive performance of the leading five algorithms was observed to range between 90 and 95%. The present findings indicate that automated machine learning (AutoML) can be leveraged to construct precise predictive models pertaining to the detection of oil and gas.