Reservoir Engineer and Artificial Intelligence Techniques for Data Analysis

Abstract

Abstract Different knowledge areas on the petroleum industry require to solve problems related with data transformation processes needed to generate information and knowledge. This paper describes data analysis steps using Artificial Intelligent techniques which include the problem exploration, space analysis, surveying data source, data preparation and building the appropriate data mining model. Predictive and inferential models are illustrated by applications implemented using Unsupervised Artificial Neural Networks and a Fuzzy Rule Diagnosis System. (1) The first application is able to identify well zones potentially producing hydrocarbons in Colombian PETROLEA field. This reservoir is mainly a fractured and calcareous formation. The knowledge predictive model uses Fuzzy Learning Vector Quantization (FLVQ) built from historical production tests data and spontaneous potential, short and long resistivity well logs used for training, testing and validating the model. The final Bravais & Pearson correlation factor obtained is 0.95. (2) Finally a Fuzzy Rule Diagnosis System is applied to enhanced oil recovery screening comparing technical information from reservoir, well and oil properties. The model uses heuristic and lab results for knowledge base implementation adapted to Colombian oil fields.