Forecasting the production of Distillate Fuel Oil Refinery and Propane Blender net production by using Time Series Algorithms

TL;DR

This paper applies three time series algorithms to forecast the net production of Distillate Fuel Oil Refinery and Propane Blender over two years, aiding reservoir management and investment decisions.

Contribution

It introduces the use of Seasonal Naive, Exponential Smoothing, and ARIMA algorithms for petroleum production forecasting, providing a comparative analysis.

Findings

ARIMA outperforms other models in accuracy.

Forecasts enable better reservoir management.

Methodology supports long-term planning.

Abstract

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.