Abstract:
As the world grapples with the challenges of sustainable energy, oil and gas remain the
main sources of the world's energy supply. While, global oil consumption has increased
significantly, the accessible reserves of oil and gas resources have decreased as a result of
the usage of common light oils. To maximize extraction from known sources, secondary
recovery procedures, often known as EOR, - enhanced oil recovery- are used when relying
exclusively on reservoir pressure is no longer adequate. EOR projects depend on variety of
factors including economic factors, crude oil market price, and the investment from
individuals and firms willing to take on the risk. To optimize extraction beyond reservoir
pressure and maximize reservoir recovery and profits, several methods for EOR have been
proposed including water injection and polymer and surfactant injection. This paper
examines the use of three distinct machine learning simulation models- the random forest
regressor, the gradient boost regression, and the KNN Regression- to investigate these EOR
methods. The three models were applied to over 1500 data points from field data and
extrapolated and expanded using Eclipse Simulator. Analysis of the techniques by these
methods show that surfactant injection achieved the greatest return in oil production with
an average oil rate of 27.27Mstb per day. Polymer Injection produced the second greatest
oil generation per day with an average rate of 25.19 Mstb per day, with water injection
generating the least amount of oil at 25 Mstb per day. These results suggest that surfactant
injection could be a promising method for enhancing oil recovery and extending the
lifespan of existing oil and gas reserves.
