Abstract
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.
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Goteti, R., Tamilarasan, A., Balch, R.S., Mukkamala, S., Sung, A.H. (2007). Estimating Monthly Production of Oil Wells. In: Castillo, O., Melin, P., Ross, O.M., Sepúlveda Cruz, R., Pedrycz, W., Kacprzyk, J. (eds) Theoretical Advances and Applications of Fuzzy Logic and Soft Computing. Advances in Soft Computing, vol 42. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72434-6_39
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DOI: https://doi.org/10.1007/978-3-540-72434-6_39
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