3D porosity prediction from seismic inversion and neural networks

• Published in: Computers & geosciences Vol. 37; no. 8; pp. 1174 - 1180
• Main Authors: Leite, Emilson Pereira, Vidal, Alexandre Campane
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.08.2011; Elsevier
• Subjects: Acoustic impedance; algorithms; Applied geophysics; Areal geology; Areal geology. Maps; computers; Earth sciences; Earth, ocean, space; Exact sciences and technology; Feed-forward neural network; gamma radiation; Geologic maps, cartography; Hydrocarbons; impedance; Internal geophysics; Inversions; kriging; Mathematical models; Matlab; Neural networks; Porosity; prediction; Reservoir characterization; Sedimentary rocks; Seismic engineering; Seismic inversion; Seismic phenomena; Three dimensional; Reservoir characterization; Feed-forward neural network; Seismic inversion; Matlab; reservoir rocks; algorithms; gamma rays; kriging; neural networks; reservoirs; well logs; amplitude; reservoir properties; acoustical impedance; seismic reflection; correlation coefficient; computers; models; inverse problem; maps; hydrocarbons; porosity; data processing; interpolation; wavelet; prediction; three-dimensional seismics
• ISSN: 0098-3004; 1873-7803
• DOI: 10.1016/j.cageo.2010.08.001

In this work, we address the problem of transforming seismic reflection data into an intrinsic rock property model. Specifically, we present an application of a methodology that allows interpreters to obtain effective porosity 3D maps from post-stack 3D seismic amplitude data, using measured density and sonic well log data as constraints. In this methodology, a 3D acoustic impedance model is calculated from seismic reflection amplitudes by applying an L 1-norm sparse-spike inversion algorithm in the time domain, followed by a recursive inversion performed in the frequency domain. A 3D low-frequency impedance model is estimated by kriging interpolation of impedance values calculated from well log data. This low-frequency model is added to the inversion result which otherwise provides only a relative numerical scale. To convert acoustic impedance into a single reservoir property, a feed-forward Neural Network (NN) is trained, validated and tested using gamma-ray and acoustic impedance values observed at the well log positions as input and effective porosity values as target. The trained NN is then applied for the whole reservoir volume in order to obtain a 3D effective porosity model. While the particular conclusions drawn from the results obtained in this work cannot be generalized, such results suggest that this workflow can be applied successfully as an aid in reservoir characterization, especially when there is a strong non-linear relationship between effective porosity and acoustic impedance.