Contents

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Contents

• Introduction
• Sedimentology concepts
• Fluvial environments
• Deltaic environments
• Coastal environments
• Offshore marine environments

• Sea-level change
• Sequence stratigraphy concepts
• Marine sequence stratigraphy
• Nonmarine sequence stratigraphy
• Basin and reservoir modeling
• Reflection

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Basin and reservoir modeling

• What is a model?

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Basin and reservoir modeling

• What is a model?
• Models are expressions of our ideas how things
  work
• Conceptual models (qualitative models)
• Physical models (experimental models)
• Flume-operated simulations of sedimentologic or
  stratigraphic phenomena at scales ranging from
  bedforms to basins
• Mathematical models (computer models)
• Deterministic models (physically-based or
  process-based) have one set of input parameters
  and therefore yield one unique outcome
• Stochastic models have variable input parameters,
  commonly derived from probability-density
  functions (pdfs), and therefore have multiple
  outcomes as a consequence model runs must be
  repeated many times (realizations) and
  subsequently averaged

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Basin and reservoir modeling

• Forward models simulate sets of processes and
  responses in a system that has specified
  (assumed) initial boundary conditions (e.g., the
  evolution of a sedimentary basin given an initial
  configuration)
• Inverse models use observations as a starting
  point and aim to estimate initial boundary
  conditions and combinations of processes and
  responses that have operated to produce the
  observed conditions (i.e., flip side of forward
  models)
• What is the goal of modeling in sedimentary
  geology?
• Understanding processes and responses in
  sedimentary systems (experimental and
  process-based models)
• Prediction of sedimentary architecture and
  stratigraphy (primarily stochastic models)

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Basin and reservoir modeling

• Architectural models typically simulate specific
  depositional environments (e.g., alluvial
  architecture) different approaches are possible,
  involving different kinds of equations
• Physical
• Empirical
• Probabilistic

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Basin and reservoir modeling

• Stratigraphic models are widely used to simulate
  basin-scale stratal patterns (e.g., sequence
  stratigraphy)
• In geometric models the sediment surface is
  represented by one or more surfaces with
  predetermined geometry
• Many models are based on a diffusion equation
  that relates rates of sediment transport to
  topographic slopes

Slideshow
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Animation 1
Animation 2
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Basin and reservoir modeling

• A classical approach in sedimentologic/stratigraph
  ic modeling has been to start from first
  principles (i.e., basic, small-scale processes of
  sediment transport) and multiply this to the
  desired spatial and temporal scale (upscaling)
• The outcomes of this approach have been very
  disappointing (i.e., upscaling is a very
  complicated procedure)
• There is no law of nature that says that
  complexity complexity greater complexity!

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Basin and reservoir modeling

• Reservoir characterization is the analysis of
  subsurface sediments or sedimentary rocks from
  the perspective of fluid flow through porous
  media, including issues related to resource
  recovery (e.g., groundwater, hydrocarbons)
• The net-to-gross ratio (proportion of permeable
  units) is one of the most basic parameters in
  reservoir studies
• The connectedness between permeable units is
  another important parameter

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Basin and reservoir modeling

• Many reservoir models operate on the scale of
  sedimentary architecture they are mostly
  stochastic
• Object-based models simulate the distribution of
  objects, defined by specified geometries, in 3D
  space simulations are usually constrained by
  well data
• Geostatistical models predict sedimentary facies
  at unvisited sites, based on the quantified
  spatial facies variability derived from well data
  (e.g., sequential indicator simulation)
• Conditioning model output to observations is more
  easily done in stochastic models, but
  process-based models have the advantage that they
  tend to provide sedimentologically more realistic
  output

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Basin and reservoir modeling

• Many reservoir models operate on the scale of
  sedimentary architecture they are mostly
  stochastic
• Object-based models simulate the distribution of
  objects, defined by specified geometries, in 3D
  space simulations are ususally constrained by
  well data
• Geostatistical models predict sedimentary facies
  at unvisited sites, based on the quantified
  spatial facies variability derived from well data
  (e.g., sequential indicator simulation)
• Conditioning model output to observations is more
  easily done in stochastic models, but
  process-based models have the advantage that they
  tend to provide sedimentologically more realistic
  output

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Basin and reservoir modeling

• Many reservoir models operate on the scale of
  sedimentary architecture they are mostly
  stochastic
• Object-based models simulate the distribution of
  objects, defined by specified geometries, in 3D
  space simulations are usually constrained by
  well data
• Geostatistical models predict sedimentary facies
  at unvisited sites, based on the quantified
  spatial facies variability derived from well data
  (e.g., sequential indicator simulation)
• Conditioning model output to observations is more
  easily done in stochastic models, but
  process-based models have the advantage that they
  tend to provide sedimentologically more realistic
  output

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Basin and reservoir modeling

• The challenge for experimental models is to mimic
  real-world conditions as well as possible
  (scaling) this becomes increasingly difficult
  with increasing spatial and temporal scales
  (compare bedforms vs. sedimentary basins)
• Grain size (e.g., how to simulate clays?)
• Grain properties (e.g., how to simulate cohesion
  of sediment grains?)
• Fluid mechanics (e.g., how to keep the Froude
  number reasonable?)
• Experimental models are increasingly used to
  simulate sedimentary architecture and basin-scale
  stratigraphy
• One important outcome of experimental modeling is
  the recognition of non-linear responses

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Animation 1
Animation 2
Animation 3
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Basin and reservoir modeling

• The challenge for experimental models is to mimic
  real-world conditions as well as possible
  (scaling) this becomes increasingly difficult
  with increasing spatial and temporal scales
  (compare bedforms vs. sedimentary basins)
• Grain size (e.g., how to simulate clays?)
• Grain properties (e.g., how to simulate cohesion
  of sediment grains?)
• Fluid mechanics (e.g., how to keep the Froude
  number reasonable?)
• Experimental models are increasingly used to
  simulate sedimentary architecture and basin-scale
  stratigraphy
• One important outcome of experimental modeling is
  the recognition of non-linear responses

Animation