Upcoming Division Talk
Practical Facies for Digital 3D Models
Speaker: David Garner, TerraMod Consulting
Location: Husky Conference Room A, 3rd Floor, +30 level, South Tower, 707 8th Ave SW, Calgary, Alberta
March 28th, 2019 | 12:00 Noon
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A key impact on success in reservoir studies is a sound strategy around facies for modeling. The modeled facies provide local geological features, patterns and properties. Facies are derived from many sources with varied definitions and purposes. Classically, facies are a visual interpretation of the face of a rock driven by concepts. For example, from outcrops, we derive an understanding of depositional architecture and stacking patterns. In petroleum reservoirs, we commonly use these surface observations of analogues in addition to sparse subsurface information to determine facies logs. Is this adequate?
Figure 1. Visually interpreted depo-facies inputs are not
consistent with logs. Electrofacies results provides consistency necessary for
geomodeling processes. Figure is from Martinius, et al., 2017
Figure 2. Permeability based on electrofacies and
micromodeling illustrates non-linear relationships and a percolation effect.
Electrofacies can provide petrophysical distinctness. (Garner et al.,
2014; Manchuk et al., 2015).
For modeling purposes, the input facies each represent consistent statistical properties across a study area. Visually interpreted facies must be checked for petrophysical consistency, i.e. the distinctness of petrophysical distributions which is not guaranteed. Application of electrofacies, a multivariate classification can improve consistency and is beneficial for the hierarchy of modeling workflows (Figure 1; Martinius et al. 2017). The result of electrofacies is to enforce the lithological characteristics based on distinct rock properties measured and to be distributed in models (Figures 2 and 3; Garner et al., 2014; Manchuk et al., 2015). A brief discussion of five assumptions underlying an application of electrofacies provides practical guidance on checking and improving useful facies inputs (Davis, 1986; Nivlet, et al., 2001).
There are rules and checks for sampling facies logs and associated properties into discrete grids to maintain model fidelity. The bigger the scale, the greater the uncertainty on individual facies and the more mixed the properties become. Across larger scales, facies probabilities and proportions are introduced, similar to the concept of net-to-gross for two categories.
Trends, both vertical and lateral, must be taken into account to fairly represent the large scale reservoir features and connectivity in local areas of the 3D model. Seismic attributes sample from a relatively large scale, yield facies probabilities and can be introduced to update spatial trends for facies proportions. Fluid distributions as well as flow and mechanical properties are dependent on the characterization by each facies. Accounting for known physical behavior, percolation and capillarity, when distributing properties facilitates reasonable physical responses in flow models.
Modeling strategy strongly benefits and depends on questions to be addressed mainly by reservoir engineering, from well understood to complex systems. Additional criteria derive from availability of data from multiple disciplines e.g. petrophysics, geophysics, geomechanics. Resource extraction for a mature reservoir waterflood generally requires a different type of model than a thermal gravity-driven extraction, i.e. SAGD-based. The scale of geological features, spatial trends, physical properties, size and architectural arrangement are all significant in the modeling process and are derived from the modeled facies. Handling facies digitally from concept to engineering is one of the most critical foundations of a successful reservoir study using geomodels. A number of techniques and examples will be noted to establish context.
Figure 3. Water
saturation trends for each electrofacies are illustrated using conditional
expectation curves. This captures the capillary effect of the variable grain
sizes. An empirical irreducible water saturation, Swirr, can be estimated from
each curve for the reservoir simulator. (Garner, et al., 2014)
David Garner is an internationally recognized consulting advisor in applied geostatistics and geomodeling with more than 35 years of diverse technical experience in the hydrocarbon industry. He has taught numerous public and private courses in various countries during his career. He is currently an associate of Geovariances in Fontainebleau, France and TerraEX Group in Denver. He has over 23 years of work directly in geostatistical studies in petroleum and mining. He has published and presented numerous papers, many of which were peer-reviewed.
Previously Mr. Garner held positions in applied R&D with Halliburton and Statoil, as a hands-on geomodeling advisor for Chevron and specialist at ConocoPhillips. He was president of TerraMod Consulting for 6 years applying geostatistics and geomodeling techniques mainly for large international reservoir studies and mining resources. As an active volunteer, Mr. Garner currently serves as a co-chair for the CSPG Geomodeling Technical Division committee. He was chairman/convener for the 2018 Gussow conference entitled Closing the Gap III - Advances in Geomodeling for Hydrocarbon Reservoirs, and was the chair for the CSPG 2011 and 2014 Gussow conferences, co-editor of the special edition December 2015 BCPG on Geomodeling Advances and the 2013 CSPG Memoir 20.
Mr Garner is registered as a Professional Geophysicist (P.Geoph) through the Alberta’s Association of Professional Engineers and Geoscientists (APEGA).
- Davis, John, 1986. Statistics and Data Analysis in Geology, 2nd Edition, New York, John Wiley & Sons, 646 pages.
- Garner, D., A. Lagisquet, A. Hosseini, K. Khademi, B. Jablonski, R. Strobl, M. Fustic, and A. Martinius, 2014. The Quest for innovative technology solutions for in-situ development of challenging oil sands reservoirs in Alberta, WHOC14-139, 2014 World Heavy Oil Congress
- Manchuk, J.G., Garner, D.L., C.V. Deutsch, 2015. Estimation of permeability in the McMurray formation using high resolution data sources, Petrophysics, Vol. 56, No. 2.
- Martinius, A.W., M. Fustic, D.L. Garner, B.V.J. Jablonski, R.S. Strobl, J.A. MacEachern, S.E. Dashtgard, 2017. Reservoir characterization and multiscale heterogeneity modeling of inclined heterolithic strata for bitumen-production forecasting, McMurray Formation, Corner, Alberta, Canada, Marine and Petroleum Geology, Vol. 82, pages 336–361.
- Nivelet, P., F. Fournier, and J.J. Royer, 2001. Propagating Interval Uncertainties In Supervised Pattern Recognition For Reservoir Characterization, SPE 71327, presented at the SPE Annual Technical Conference and Exhibition, New Orleans, USA, 30 September - 3