Forecasting the production of Distillate Fuel Oil Refinery and Propane Blender net production by using Time Series Algorithms
This work addresses forecasting challenges for reservoir engineers to reduce risky investments, but it is incremental as it uses standard methods on specific oil production data.
The study applied Seasonal Naive, Exponential Smoothing, and ARIMA time series algorithms to forecast Distillate Fuel Oil Refinery and Propane Blender net production for the next two years, aiming to improve cost control and management in petroleum reservoirs.
Oil production forecasting is an important step in controlling the cost-effect and monitoring the functioning of petroleum reservoirs. As a result, oil production forecasting makes it easier for reservoir engineers to develop feasible projects, which helps to avoid risky investments and achieve long-term growth. As a result, reliable petroleum reservoir forecasting is critical for controlling and managing the effective cost of oil reservoirs. Oil production is influenced by reservoir qualities such as porosity, permeability, compressibility, fluid saturation, and other well operational parameters. Three-time series algorithms i.e., Seasonal Naive method, Exponential Smoothening and ARIMA to forecast the Distillate Fuel Oil Refinery and Propane Blender net production for the next two years.