Integrating Advanced Big Data Analytics for Strategic Price Projections in the Oil and Gas Industry

Oil and gas resources, extracted from underground reservoirs through oil wells, are crucial for various industries as fuels and raw materials. Predicting their future prices accurately is essential for decision-making in procurement, production, and transportation due to their impact on costs. This study aims to create a robust forecasting model for precise oil and gas price predictions. Initially, it explores foundational theories related to oil and gas price forecasting. Then, it reviews two key forecast theories: the Target Capacity Utilization Rule (TCU) and Exhaustible Resources Theory. Using a Target Capacity Utilization Rule recursive simulation model and historical data from 1987 to 2017, the study projects oil and gas prices from 1991 to 2017. The optimal methodology results in Mean Absolute Deviation (MAD) of 12.676, Mean Squared Error (MSE) of 280.92, Mean Absolute Percentage Error (MAPE) of 0.2597, and Mean Percentage Error (MPE) of 0.028. The analysis extends to monthly forecasts, generating MAD of 5.66, MSE of 82.1163, MAPE of 0.1246, and MPE of 0.038. These findings confirm the model's effectiveness, especially at a monthly level.