Assessment of predictive learning methods for the completion of gaps in well log data

• Tipo: Artigo em Revista Científica Internacional
• Data: 2018

Autores

Lopes, RL (Autor) Outra; Jorge, AM (Autor) FCUP

Revista

Título: Journal of Petroleum Science and Engineering

Vol. 162

Páginas: 873-886

ISSN: 0920-4105

Editora: Elsevier

Indexação

ISI Web of Knowledge - 8 Citações

Scopus - 12 Citações

Outras Informações

ID Authenticus: P-00N-A05

DOI: 10.1016/j.petrol.2017.11.019

Abstract (EN): Well logs are records of petro-physical data acquired along a borehole, providing direct information about what is in the subsurface. The data collected by logging wells can have significant economic consequences in oil and gas exploration, not only because it has a direct impact on the following decisions, but also due to the subsequent costs inherent to drilling wells, and the potential return of oil deposits. These logs frequently present gaps of varied sizes in the sensor recordings, that happen for diverse reasons. These gaps result in less information used by the interpreter to build the stratigraphic models, and consequently larger uncertainty regarding what will be encountered when the next well is drilled. The main goal of this work is to compare Gradient Tree Boosting, Random Forests, Artificial Neural Networks, and three algorithms of Linear Regression on the prediction of the gaps in well log data. Given the logs from a specific well, we use the intervals with complete information as the training data to learn a regression model of one of the sensors for that well. The algorithms are compared with each other using a few individual example wells with complete information, on which we build artificial gaps to cross validate the results. We show that the ensemble algorithms tend to perform significantly better, and that the results hold when addressing the different examples individually. Moreover, we performed a grid search over the ensembles parameters space, but did not find a statistically significant difference in any situation.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 14