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AAPG Bulletin, V.
Unconventional reservoir characterization by seismic inversion and machine learning of the Bakken Formation
1Department of Geological Sciences, Jackson School of Geosciences, The University of Texas at Austin, Austin, Texas; [email protected]
2Department of Geological Sciences, Jackson School of Geosciences, The University of Texas at Austin, Austin, Texas; [email protected]
3Department of Geological Sciences, Jackson School of Geosciences, The University of Texas at Austin, Austin, Texas; [email protected]
4Department of Geological Sciences, Jackson School of Geosciences, The University of Texas at Austin, Austin, Texas; Hildebrand Department of Petroleum Engineering and Geosystems Engineering, Cockrell School of Engineering, The University of Texas at Austin, Austin, Texas; [email protected]
Characterization of the critical features of the unconventional Bakken Formation reservoir, specifically within northwestern North Dakota, using three-dimensional (3-D) seismic and well-log data are essential for resource management. We propose a new workflow for the estimation of the features, total porosity, and total organic carbon (TOC) using the output from prestack seismic inversion. Seismic inversion, when integrated with well-log data, can be used to predict these features by first establishing a relationship between the two, and then predicting petrophysical values away from the well-log data. The prediction problem is solved by employing a Bayesian neural network model that uses Markov chain Monte Carlo via Langevin dynamics to sample from the probability distribution and to estimate uncertainty in the desired reservoir features. This method establishes a correlation between estimated P-impedance from seismic inversion and total porosity from well data. Cluster analysis between elastic and petrophysical features is performed to understand the behavior of the reservoir and support the identification of a paleocurrent given the onsite of the Bakken Formation with the initial low surge of P-impedance values. This procedure ultimately allows for the prediction of total porosity and TOC away from the wells at all locations over an inverted 3-D volume, aiding in the assessment of the risk associated with drilling a well. Thus, by integrating our proposed workflow, a better understanding of the features useful for reservoir characterization is possible given a degree of uncertainty, thereby improving oil and gas exploration and risk assessment.
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