Hybrid AI Models for Predicting Inshore and Offshore Oil and Gas Exploration Fields

The oil and gas industry faces significant challenges and opportunities in the era of digital transformation, with rising energy demand driving the need for improved production efficiency, cost reduction, and environmental impact minimization. This study presents a hybrid AI learning model aimed at enhancing predictions for inshore and offshore oil and gas exploration fields to address these challenges. The process begins with raw data collection, followed by data cleaning, transformation, and visualization to gain insights into its complexity. Feature engineering extracts meaningful attributes, preparing the data for model input. A hybrid CNN-BILSTM model is employed to improve classification accuracy, and the system implemented using Python, established significant improvements. The model's accuracy and precision is 90% and 92%. Highlighting its effectiveness in handling complex data and enhancing classification results. The system demonstrates robust performance in refining accuracy iteratively for better predictive results.