Stochastic surface-based modeling of turbidite lobes

Pyrcz, Michael J.; Catuneanu, Octavian; Deutsch, Clayton V.

doi:10.1306/09220403112

Cited by 89 publications

(59 citation statements)

References 20 publications

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“…The recognition that many important geologic features are often associated with depositional and erosional surfaces [5] has led to the development of stochastic surface modeling approaches [32][33][34]. These methods use explicit surfaces to delineate depositional or erosional boundaries.…”

Section: Parametrization Methods: a Brief Overviewmentioning

confidence: 99%

“…We show that this can be done locally near the wells to preserve the characteristics of well-driven flows. Our parametrization is similar to surface-based modeling methods (e.g., [32][33][34]) in the use of volumetric trend functions. However, a significant difference is that we use trend functions as a way to reduce model parameters.…”

Section: Heuristic Multiscale Parameterizationmentioning

confidence: 99%

“…However, a significant difference is that we use trend functions as a way to reduce model parameters. This is reflected in our use of permeability trend functions instead of stochastically generated permeability field as in, e.g., [32].…”

Section: Heuristic Multiscale Parameterizationmentioning

confidence: 99%

See 2 more Smart Citations

Effective Parametrization for Reliable Reservoir Performance Predictions

Kalla

2012

Int. J. UncertaintyQuantification

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Section: Parametrization Methods: a Brief Overviewmentioning

confidence: 99%

Section: Heuristic Multiscale Parameterizationmentioning

confidence: 99%

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Effective Parametrization for Reliable Reservoir Performance Predictions

Kalla

2012

Int. J. UncertaintyQuantification

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“…A stochastic, 3-D, surfacebased modeling approach is required to address these issues. Similar approaches have been demonstrated for other depositional environments (e.g., Xie et al, 2001;Pyrcz et al, 2005;Zhang et al, 2009) and to create models of generic, dipping barriers to flow (e.g., Jackson and Muggeridge, 2000), but at present, there are no tools available to automate the generation of multiple 3-D clinoforms using a small number of parameters. The aims of this paper are to develop an efficient, quick, and practical method for incorporating clinoforms into models of shallow-marine reservoirs and to validate its application through building both geologic and fluid-flow simulation models.…”

Section: A C K N O W L E D G E M E N T Smentioning

confidence: 85%

Three-dimensional modeling of clinoforms in shallow-marine reservoirs: Part 1. Concepts and application

Graham¹,

Jackson²,

Hampson³

2015

Bulletin

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“…In this example of a weakly confined channel system, the authors record the mean aspect ratio and other characteristics of elements at a range of stratigraphic scales (channel element, complex and complex set), and also investigate the variability of these parameters between both the axial and marginal parts of the depositional system and the up-dip and down-dip areas. If such systematic variations in the dimensions of sediment bodies can be derived from outcrop, together with their stacking patterns, it may be possible to incorporate this information in 'rule-based' subsurface geological models (Pyrcz et al 2005(Pyrcz et al , 2008McHargue et al 2011).…”

Section: Deep-water Depositional Systemsmentioning

confidence: 99%

The application of outcrop analogues in geological modelling: a review, present status and future outlook

2014

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Analogues, especially outcrop analogues, have played a central role in improving understanding of subsurface reservoir architectures. Analogues provide important information on geobody size, geometry and potential connectivity. The historical application of outcrop analogues for understanding geobody distributions in reservoirs is reviewed, from the pioneering work of the 1960s to the high-tech virtual outcrop methodologies of today. Four key types of analogue data are identified: hard data, which describe the dimensions and geometry of the geobody; soft data, which describe the conceptual relationships between different geobody types; training images, which record the dimensions, proportions and spatial relationship; and analogue production data, which are taken from direct subsurface production analogues. The use of these different data types at different stages of the geomodelling workflow is discussed and the potential sources of error considered. Finally, a review of geobody and analogue studies in different clastic environments is discussed with reference to selected previous work and the range of papers in the current volume.Over the last 30 years, computer-based, geocellular models have become a routinely used tool for understanding subsurface reservoirs (Budding & Inglin 1981; review in Keogh et al. 2007). Such models are typically built to aid field development and reservoir management business decisions, but also serve the purpose of integrating disparate scales and types of subsurface data, and visualizing complex three-dimensional (3D) distributions of rocks and fluids. Since the application of early modelling tools, to the present day, it has been common practice to supplement sparse subsurface datasets (e.g. wells and seismic) with data and concepts derived from reservoir analogues. The intention of this process is to generate more accurate representations of the subsurface than would otherwise be possible. Considerable effort has been expended in recent years by industry and academia on the description of reservoir analogues for this purpose. This paper examines the challenges inherent in selection and application of appropriate analogue data, particularly quantitative datasets, during construction of geological models.The most commonly used type of analogue is the outcrop, where information on geometric data that are limited in the subsurface is more readily available in cliff sections. Analogues may also include subsurface data but this is less common. This aspect is considered briefly in this review. The perspective on the application of analogues as discussed here is with regard to sedimentology and stratigraphy applied to hydrocarbon reservoir analysis, and the insights that 3D facies models bring to the distribution of petrophysical properties that control hydrocarbon flow. The discussion is based on clastic systems, but many of the aspects described are equally as applicable to carbonates, although they are not discussed explicitly here. Other branches of subsurface modelling, no...

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Stochastic surface-based modeling of turbidite lobes

Cited by 89 publications

References 20 publications

Effective Parametrization for Reliable Reservoir Performance Predictions

Effective Parametrization for Reliable Reservoir Performance Predictions

Three-dimensional modeling of clinoforms in shallow-marine reservoirs: Part 1. Concepts and application

The application of outcrop analogues in geological modelling: a review, present status and future outlook

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