Artificial Neural Networks applied to estimate permeability, porosity and intrinsic attenuation using seismic attributes and well-log data

• Published in: Journal of applied geophysics Vol. 107; pp. 45 - 54
• Main Authors: Iturrarán-Viveros, Ursula, Parra, Jorge O.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.08.2014
• Subjects: Artificial Neural Networks; Attenuation; Estimates; Learning theory; Mathematical models; Neural networks; Permeability; Porosity; Reservoirs; Seismic attributes; The Gamma test; USA, Texas; USA, Florida; Attenuation; The Gamma test; Permeability; Porosity; Artificial Neural Networks; Seismic attributes
• ISSN: 0926-9851; 1879-1859
• DOI: 10.1016/j.jappgeo.2014.05.010

Permeability and porosity are two fundamental reservoir properties which relate to the amount of fluid contained in a reservoir and its ability to flow. The intrinsic attenuation is another important parameter since it is related to porosity, permeability, oil and gas saturation and these parameters significantly affect the seismic signature of a reservoir. We apply Artificial Neural Network (ANN) models to predict permeability (k) and porosity (ϕ) for a carbonate aquifer in southeastern Florida and to predict intrinsic attenuation (1/Q) for a sand–shale oil reservoir in northeast Texas. In this study, the Gamma test (a revolutionary estimator of the noise in a data set) has been used as a mathematically non-parametric nonlinear smooth modeling tool to choose the best input combination of seismic attributes to estimate k and ϕ, and the best combination of well-logs to estimate 1/Q. This saves time during the construction and training of ANN models and also sets a lower bound for the mean squared error to prevent over-training. The Neural Network method successfully delineates a highly permeable zone that corresponds to a high water production in the aquifer. The Gamma test found nonlinear relations that were not visible to linear regression allowing us to generalize the ANN estimations of k, ϕ and 1/Q for their respective sets of patterns that were not used during the learning phase. •Estimation of seismic sections of porosity and permeability using Neural Networks•Estimation of intrinsic attenuation at well-log scale using Neural Networks•Selection of seismic attributes and well-logs via the Gamma test