Petrophysical seismic inversion based on lithofacies classification to enhance reservoir properties estimation: a machin
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ORIGINAL PAPER-EXPLORATION GEOPHYSICS
Petrophysical seismic inversion based on lithofacies classification to enhance reservoir properties estimation: a machine learning approach Amir Abbas Babasafari1 · Shiba Rezaei1 · Ahmed Mohamed Ahmed Salim1 · Sayed Hesammoddin Kazemeini2 · Deva Prasad Ghosh1 Received: 23 May 2020 / Accepted: 22 September 2020 © The Author(s) 2020
Abstract For estimation of petrophysical properties in industry, we are looking for a methodology which results in more accurate outcome and also can be validated by means of some quality control steps. To achieve that, an application of petrophysical seismic inversion for reservoir properties estimation is proposed. The main objective of this approach is to reduce uncertainty in reservoir characterization by incorporating well log and seismic data in an optimal manner. We use nonlinear optimization algorithms in the inversion workflow to estimate reservoir properties away from the wells. The method is applied at well location by fitting nonlinear experimental relations on the petroelastic cross-plot, e.g., porosity versus acoustic impedance for each lithofacies class separately. Once a significant match between the measured and the predicted reservoir property is attained in the inversion workflow, the petrophysical seismic inversion based on lithofacies classification is applied to the inverted elastic property, i.e., acoustic impedance or Vp/Vs ratio derived from seismic elastic inversion to predict the reservoir properties between the wells. Comparison with the neural network method demonstrated this application of petrophysical seismic inversion to be competitive and reliable. Keywords Petrophysical inversion · Nonlinear optimization · Reducing uncertainty · Lithofacies class
Introduction Different approaches for petrophysical properties prediction from elastic properties are routinely employed in oil and gas reservoirs (Doyen 2007; Bosch et al. 2010; Grana and Della Rossa 2010; Figueiredo et al. 2018). Empirical equations, geostatistical methods, multi-attribute regression and neural network, petrophysical seismic inversion and co-simulation after stochastic inversion are the predominant procedures (Doyen 1988; Bortoli et al. 1993; Russell et al. 2011; Lang and Grana 2018). Each methodology for reservoir properties prediction possesses deficiency in terms of demonstrating proper relations between elastic and petrophysical properties * Amir Abbas Babasafari [email protected] 1
Geoscience Department, Center of Seismic Imaging and Hydrocarbon Prediction, University Teknologi PETRONAS, 32610 Tronoh, Malaysia
AlphaReservoir Plus, Houston, TX, USA
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per each lithofacies class. For instance, fitting an experimental polynomial equation to acoustic impedance (AI) versus porosity (Phi) cross-plot, will not always result in a proper match between the measured and the predicted porosity due to low correlation in some lithofacies classes, e.g., shale and coal. To overcome this issue in the industry methods such as geostatistical methods
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