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The AAPG/Datapages Combined Publications Database
AAPG Bulletin
Abstract
DOI: 10.1306/05162221022
Hierarchical machine learning workflow for conditional and multiscale deep-water reservoir
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Wen Pan,1 Honggeun Jo,2 Javier E. Santos,3 Carlos Torres-Verdín,4 and Michael J. Pyrcz5
1Hildebrand Department of Petroleum and Geosystems Engineering, The University of Texas at Austin, Austin, Texas; [email protected]
2Hildebrand Department of Petroleum and Geosystems Engineering, The University of Texas at Austin, Austin, Texas; present address: BP plc, Houston, Texas; [email protected]
3Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico; [email protected]
4Hildebrand Department of Petroleum and Geosystems Engineering and Jackson School of Geosciences, The University of Texas at Austin, Austin, Texas; [email protected]
5Hildebrand Department of Petroleum and Geosystems Engineering and Jackson School of Geosciences, The University of Texas at Austin, Austin, Texas; [email protected]
ABSTRACT
Unconfined deep-water lobe deposits are among the most important targets in deep-water oil field exploration and production. Accurate stochastic simulations of the sedimentary architectures and petrophysical properties of deep-water lobe deposits require robust seismic
and well data integration. The reservoir heterogeneity at scales below
seismic
resolution generally exhibits important and predictable hierarchical architectures that control the vertical and horizontal connectivity of the reservoir, affecting hydrocarbon recovery rate during development. Current geostatistical simulation algorithms such as variogram– and multiple-point–based methods readily perform property
modeling
conditioned to well data and trends informed from
seismic
data at and above
seismic
resolution. Yet, these common geostatistical models are limited in their ability to reproduce essential, multiscale heterogeneities below
seismic
resolution between wells, including nested, multiscale architectures and trends. Rule-based methods are commonly used for
modeling
the hierarchical architectures, but conditioning the models to well logs and
seismic
data is still limited, difficult, and time-consuming. Either a prohibitive degree of expert intervention is employed resulting in overly deterministic models or the common “cookie-cutter” approach is applied, resulting in inconsistency over the multiple scales.
To address the well and seismic
data conditioning and multiscale
modeling
limitations of current geostatistical
modeling
methods, we propose a new workflow based on the hierarchical application of the newly developed stochastic pix2pix machine learning algorithm. The hierarchical stochastic pix2pix workflow allows us to extract stacking and geometrical patterns of architectural elements in deep-water lobe deposits by learning from rule-based training models, and stochastically calculate a diverse ensemble of conditional, multiscale, architectural and petrophysical, property model realizations reproducing the patterns. We demonstrate the hierarchical stochastic pix2pix workflow to calculate an ensemble of multiscale, deep-water lobe depositional systems conditioned by (1) geostatistical rule-based training and testing models, and (2) bounding surfaces and property estimates from
seismic
and well log interpretations.
With the hierarchical stochastic pix2pix workflow, we can efficiently and accurately calculate diverse three-dimensional reservoir models, which are conditional to the well-log and seismic
interpretations and reproduce multiscale heterogeneity. We also use quantitative measures, such as a raster-based compensational index and the dynamic Lorentz coefficient to validate the model accuracy. In addition, the reduced model parametric representation and efficient model calculation available with the hierarchical stochastic pix2pix workflow is critical to the practical solutions of reservoir inverse problems.
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