Here’s the latest result of our Artificial Intelligence team at Instituto de Computação – Unicamp in collaboration with Petroleum Engineers at the UNISIM , Unicamp just published at the Springer Journal of Petroleum Exploration and Production.
History matching is an important reservoir engineering process whereby the values of uncertain attributes of a reservoir model are changed to find models that have a better chance of reproducing the performance of an actual reservoir.
In this work, we introduce a learning-from-data approach with path relinking and soft clustering to the history-matching problem. The algorithm is designed to learn patterns of input attributes that are associated with good matching quality and has two stages that handle different types of reservoir uncertain attributes.
The method is competitive regarding the quality of solutions and offers a significant reduction (up to 30 × less) in the number of simulations.
Data and article available: https://rdcu.be/ckExf