About This Item

Share This Item

The AAPG/Datapages Combined Publications Database

AAPG Bulletin

Abstract

DOI: 10.1306/05162221022

Hierarchical machine learning workflow for conditional and multiscale deep-water reservoir modeling

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 Previous HitmodelNext Hit 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 Previous HitmodelNext Hit accuracy. In addition, the reduced Previous HitmodelNext Hit parametric representation and efficient Previous HitmodelTop calculation available with the hierarchical stochastic pix2pix workflow is critical to the practical solutions of reservoir inverse problems.

Pay-Per-View Purchase Options

The article is available through a document delivery service. Explain these Purchase Options.

Watermarked PDF Document: $14
Open PDF Document: $24

AAPG Member?

Please login with your Member username and password.

Members of AAPG receive access to the full AAPG Bulletin Archives as part of their membership. For more information, contact the AAPG Membership Department at [email protected].