Quantitative Clastic Reservoir Geological Modelling: Problems and Perspectives

Bryant, Ian; Flint, Stephen S.

doi:10.1002/9781444303957.ch1

Cited by 23 publications

(20 citation statements)

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“…Furthermore, as it is not possible to observe directly the processes of development of alluvial architecture, it is necessary to use models to interpret and predict alluvial architecture. Most quantitative models of alluvial architecture (reviewed by Bryant & Flint, 1993;Koltermann & Gorelick, 1996;North, 1996;Anderson, 1997; are either process-based (processimitating) or stochastic (structure-imitating), as discussed below.…”

Section: Deposits Of Alluvial Valleys (Alluvial Architecture)mentioning

confidence: 99%

“…sandstones, shales) from well logs, cores, seismic or GPR; (2) interpret the origin of the sediment bodies; (3) use outcrop analogues to predict more sediment-body characteristics; and (4) use stochastic (structure-imitating) models to simulate the alluvial architecture between wells, and the rock properties with sediment bodies such as channel-belt sandstones. Stochastic (structureimitating) models are either object-based (also known as discrete or Boolean) or continuous, or both (reviewed by Haldorsen & Damsleth, 1990;Bryant & Flint, 1993;Srivastava, 1994;North, 1996;Koltermann & Gorelick, 1996;Deutsch, 2002). A common combined approach is to use object-based models to simulate the distribution of channel-belt sandstone bodies and floodplain shales, and then use continuous models for simulating 'continuous' variables such as porosity and permeability within the objects.…”

Section: Prediction Of Alluvial Architecture Of Subsurface Depositsmentioning

confidence: 99%

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Numerical Modelling of Alluvial Deposits: Recent Developments

Bridge

2008

Analogue and Numerical Modelling of Sedimentary Systems: From Understanding to Prediction

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Alluvial deposits occur over a range of superimposed scales, dependent on the scales of associated topographic features (such as ripples, dunes, bars, channels, floodplains, valleys, river systems), the time and spatial extent over which deposition occurred, and the degree of preservation. Understanding and prediction of alluvial deposits is aided by numerical (forward) modelling of depositional forms and processes. The most desirable approach to such numerical modelling is through solution of the fundamental equations of motion for the fluid and the sediment (e.g. conservation of mass, momentum and energy) for all of the scales of deposits. Construction of the equations of motion requires an understanding of the interactions among unsteady, non-uniform, turbulent water flow, the supplied and transported sediment, and the topography of the sediment bed. This understanding is very incomplete.Simplified numerical forward models have been applied to relatively short-term, small-scale processes such as bed degradation and armouring downstream of dams, reservoir sedimentation, and sorting of sediment (e.g. downstream fining) during deposition in spatially decelerating flows. However, such models are rarely applied over long time periods, over large spatial scales, and where there are complicated temporal and spatial variations in the geometry, water and sediment supply. This is because of limitations to computing facilities, and because of lack of understanding of the workings of the alluvial system. As a result, long-term, large-scale alluvial processes are commonly treated using 'process-imitating' models. Process-imitating models do not necessarily represent processes accurately or completely. This review of process-based models demonstrates that they are generally undeveloped, and linkages between models for different scales are lacking. Process-based modelling is in its infancy. As a result, structure-imitating stochastic models are widely used for simulating alluvial hydrocarbon reservoirs and aquifers, given some initial data. However, such models cannot help understanding of alluvial deposits, nor can they predict their nature outside the data region.

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Section: Deposits Of Alluvial Valleys (Alluvial Architecture)mentioning

confidence: 99%

Section: Prediction Of Alluvial Architecture Of Subsurface Depositsmentioning

confidence: 99%

Numerical Modelling of Alluvial Deposits: Recent Developments

Bridge

2008

Analogue and Numerical Modelling of Sedimentary Systems: From Understanding to Prediction

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“…The traditional methods of observation are, however, largely paper-based for both data collection and interpretation, which provide predominantly qualitative data (Jones et al 2004;McCaffrey et al 2005). There are limitations in how this abundant descriptive data, that lacks a spatial attribute (Miall and Tyler 1991), can be utilized in reservoir models as an aid to geostatistical modelling (Bryant and Flint 1993;North and Prosser 1993;Caers and Zhang 2004).…”

Section: Introductionmentioning

confidence: 98%

Applications of digital outcrop models: two fluvial case studies from the Triassic Wolfville Fm., Canada and Oukaimeden Sandstone Fm., Morocco

et al. 2009

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The application and benefits of employing digital outcrop models (DOMs) are discussed using two Triassic fluvial case studies to demonstrate data collection and integration methods. Developments in data analysis techniques are examined to demonstrate their utility for collecting meaningful and reliable statistical information needed to build realistic stochastic reservoir models. To establish a significant geostatistical dataset a large number of accurate observations are required. It is difficult to get the necessary statistics using subsurface data alone, due to the limited resolution and/or areal coverage of respectively seismic and well data. Outcrop studies are, therefore, commonly utilized to provide analogue statistical information (e.g. channel width, length, thickness and thickness vs. width ratio). Traditional data collection methods used in the field are however largely restricted to areas with (easy) physical access, or using remote observations with limited accuracy, such as photographic methods.Digital data collection techniques such as LiDAR (Light Detection and Ranging) and differential GPS allow more accurate measurements, as well as from previously inaccessible locations, to be taken of sedimentary architecture. The technique generates much larger volumes of measurements, as the area from which accurate data can be extracted is increased. This offers a more meaningful statistical dataset, hence reducing uncertainty in the final reservoir model.Both case studies, the Oukaimeden Sandstone Formation (OSF), Morocco and Wolfville Formation, Canada, are from Late Triassic braided fluvial systems. The OSF dataset has been used to illustrate how geometric information of channel width versus thickness relationships (W:T) are collected using a projection plane technique. The results show W:T variations between 3.49:1 in the Lower Oukaimeden member, 1.54:1 in the Middle Oukaimeden member and 3.75:1 in the Upper Oukaimeden member, demonstrating the observed architectural evolution of the fluvial system. The Wolfville Formation case study shows how DGPS in combination with LiDAR data has been used to more accurately map faults to obtain statistical information on fault orientation (NE-SW) and length (mean ¼ 38.3 m and median ¼ 18.2 m). Another applied analysis technique utilizes a facies classified point-cloud to aid surface correlations between sedimentary logs and construct a log based correlation panel from which estimates of facies frequencies are derived.

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“…We assume that the gas cannot enter these low permeability layers owing to capillary entry pressure effects, and instead the gas flows around the baffles in the more permeable formation. Statistics for the spatial distribution of such baffles, and their correlation scales can be drawn from field analysis of sedimentary deposits [e.g., Enge et al ., ; Nordahl et al ., ; Bryant and Flint , ; Nichols , ]. Typically such baffles may extend of order metres to several tens of metres and they may be separated by vertical distances of tens of cm to several metres.…”

Section: Introductionmentioning

confidence: 99%

Dispersion and dissolution of a buoyancy driven gas plume in a layered permeable rock

Woods

Norris

2016

Water Resources Research

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Using a series of simplified models, we explore the controls on the migration, dispersion and eventual dissolution of a plume of hydrogen gas which may, in principle, rise under buoyancy through a layered permeable rock if released from a Geological Disposal Facility (GDF). We show that the presence of low permeability shale barriers causes the gas to spread laterally as it rises. Averaging over the length scale of the barriers, we use expressions for the Darcy velocity of the gas to describe the dispersion of a tracer and illustrate the effect with a new experiment using a baffled Hele-Shaw cell. While the plume is flowing, a large volume of gas may build up beneath the barriers. If the gas flux subsequently wanes, much of the gas will drain upward through the formation and spread on the upper impermeable boundary of the formation. However, a significant capillary-trapped wake of gas may develop beneath each barrier. Owing to the low solubility of hydrogen in water and assuming relatively slow groundwater flow rates, this trapped hydrogen may require a period of tens to hundreds of thousands of years to dissolve and form a cloud of hydrogen rich water. Although simplified, these models provide a framework to assess the possible travel times and pathways of such a gas plume.

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Quantitative Clastic Reservoir Geological Modelling: Problems and Perspectives

Cited by 23 publications

References 0 publications

Numerical Modelling of Alluvial Deposits: Recent Developments

Numerical Modelling of Alluvial Deposits: Recent Developments

Applications of digital outcrop models: two fluvial case studies from the Triassic Wolfville Fm., Canada and Oukaimeden Sandstone Fm., Morocco

Dispersion and dissolution of a buoyancy driven gas plume in a layered permeable rock

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