Improvement in facies discrimination using multiple seismic attributes for permeability modelling of the Athabasca Oil Sands, Canada*
Koji Kashihara 1 2 Takashi Tsuji 11 1-2-1 Hamada, Mihama-ku, Chiba 261-0025, Japan.
2 Corresponding author. Email: koji.kashihara@japex.co.jp
Exploration Geophysics 41(1) 80-87 https://doi.org/10.1071/EG09055
Submitted: 17 September 2009 Accepted: 22 December 2009 Published: 19 February 2010
Abstract
This study was conducted to develop a reservoir modelling workflow to reproduce the heterogeneous distribution of effective permeability that impacts on the performance of SAGD (Steam Assisted Gravity Drainage), the in-situ bitumen recovery technique in the Athabasca Oil Sands. Lithologic facies distribution is the main cause of the heterogeneity in bitumen reservoirs in the study area. The target formation consists of sand with mudstone facies in a fluvial-to-estuary channel system, where the mudstone interrupts fluid flow and reduces effective permeability. In this study, the lithologic facies is classified into three classes having different characteristics of effective permeability, depending on the shapes of mudstones. The reservoir modelling workflow of this study consists of two main modules; facies modelling and permeability modelling. The facies modelling provides an identification of the three lithologic facies, using a stochastic approach, which mainly control the effective permeability. The permeability modelling populates mudstone volume fraction first, then transforms it into effective permeability. A series of flow simulations applied to mini-models of the lithologic facies obtains the transformation functions of the mudstone volume fraction into the effective permeability.
Seismic data contribute to the facies modelling via providing prior probability of facies, which is incorporated in the facies models by geostatistical techniques. In particular, this study employs a probabilistic neural network utilising multiple seismic attributes in facies prediction that improves the prior probability of facies. The result of using the improved prior probability in facies modelling is compared to the conventional method using a single seismic attribute to demonstrate the improvement in the facies discrimination. Using P-wave velocity in combination with density in the multiple seismic attributes is the essence of the improved facies discrimination. This paper also discusses sand matrix porosity that makes P-wave velocity differ between the different facies in the study area, where the sand matrix porosity is uniquely evaluated using log-derived porosity, P-wave velocity and photographically-predicted mudstone volume.
Key words: geostatistics, heavy oil, probabilistic neural network, reservoir modelling, SAGD, seismic attribute.
Acknowledgments
The authors thank Japan Petroleum Exploration Co., Ltd. for permission to publish the results derived from this study. We also thank Japan Oil, Gas and Metals National Corporation and Japan Canada Oil Sands Ltd. for permission to disclose the core, log and seismic data in this paper. The authors are also grateful to Dr Futoshi Tsuneyama, Dr Lindsay Thomas, and the anonymous reviewer for their valuable advice on the manuscript.
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1 *Part of this paper was presented at the 9th SEGJ International Symposium (2009).