Anzeige
Erschienen in:
2007 | OriginalPaper | Buchkapitel
Estimating Monthly Production of Oil Wells
verfasst von : Rajani Goteti, A. Tamilarasan, R. S. Balch, S. Mukkamala, A. H. Sung
Erschienen in: Theoretical Advances and Applications of Fuzzy Logic and Soft Computing
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
This paper describes results concerning the capability of supervised machine learning techniques to predict production potential for a single formation, prior to drilling, over a 16,000 square mile area of SE New Mexico. In this paper a neural network is used to predict production potential for a single formation of SE New Mexico region. The process involved gathering data for use as potential inputs, collecting production data at known wells, selecting optimal inputs, developing and testing various network architectures, making predictions, analyzing and applying the results. This predicted production was further refined by excluding production at locations where the Woodford shale was not present. Results were evaluated by inspecting a map of predicted production and performing statistical testing, including a correlation of predicted and actual production, which produced a correlation coefficient of 0.79. The results were then used by the Devonian FEE Tool, an expert system designed to reduce exploration risk.