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Common Analytic Workflows for the PUG Community
What comes in the box?
Steve KoppWilly Lynch
Gridding and Contouring
Types of Surfaces
• Top and bottom of formations• Formation characteristics
- Porosity- Permeability
• Elevation• Soil characteristics• Air quality• Water quality
Interpolation Steps
• Understand your data• Experiment with techniques and parameters• Create surfaces• Evaluate your surfaces
Choosing an Interpolator
• Characteristics of phenomena?• Sample spacing
- Oversampled or needs extrapolation?
• Honor the input points?• Barriers or discontinuities?• Specialized needs
- Topo To Raster (hydro applications)
• Suspected spatial patterns, trends, error?
Interpolation algorithms in ArcGIS- Natural Neighbors- Minimum Curvature Spline- Spline with Barriers- Radial Basis Functions- TopoToRaster- Local Polynomial- Global Polynomial- Diffusion Interpolation with Barriers- Kernel Interpolation with Barriers- Inverse Distance Weighted- Kriging- Cokriging- Moving Window Kriging- Geostatistical Simulation
Choosing an interpolation method
• You know nothing about your data…- Use Natural Neighbors. Its is the most conservative, honors
the points. Assumes all highs and lows are sampled, will not create artifacts.
• What does the surface look like…- Use Local Polynomial Interpolation, use the Optimize button
• Your input data is contours…- Use TopoToRaster. It is optimized for contour input. If not
creating a DEM, turn off the drainage enforcement option.
• You want a prediction and error map- Use Kriging in Geostatistical Analyst
• Your surface is not continuous…- Use Spline with Barriers if you know there are faults or other
discontinuities in the surface.
Explore your Data
• Outliers- Incorrect data or the most influential data ?
• Spatial Dependency- If not, why use Geostatistics ?
• Distribution- How close to Gaussian distribution ?
• Stationarity- Data preprocessing if non-stationary
Evaluate the surface
Local Polynomial Interpolation (LPI)
• Prediction• Prediction standard errors – new
• indicate the uncertainty associated with value predicted at each location
• Spatial condition numbers – new• measure of how stable the solution of the
prediction equations is
• Different kernel functions - new• Geoprocessing tool - new
Faulted Gridding and Contouring
Interpolation with Barriers
• Spline with Barriers tool (9.2)- Uses Zoraster algorithm, similar result to ZMap- Straight line barrier exclusion
• Diffusion Interpolation with Barriers (10)• Kernel Interpolation with Barriers (10)
3 Contouring tools
• Contour- If you aren’t sure what to use, use this one
• Contour with Barriers- Supports input of line and polygon barrier features- Includes specific logic for attributing index contours- Slower than the other contouring tools
• Contour List- Primarily used in scripting when you want a specific set
of contours
All create nearly identical geometry
Contour with Barriers
Contour Labeling
Optimal Site Selection
Finding the best place
• Basin and play analysis• Evaluating drilling sites• Analyzing pipeline corridors
- Where to site a new gas station?- Where is economic growth most likely to occur? - Which sites are better for jackalope habitat?
Reality GIS layers Suitability for oil
Model criteria:- High organic source rock- Under heat and pressure- Favorable basin characteristics
Discrete and Continuous Phenomena
• Discrete phenomena- Landuse- Ownership- Geology
• Continuous phenomena- Elevation- Distance- Porosity- Permeability
18
21 10No
Data 1 1 1No
Data 1 2 2
1 1 2 2
Landuse0 = Urban1 = Forest2 = Water
Discrete
70 75 72 65
43 63 57 49
19 25 39 42
11 18 NoData
NoData
PorosityContinuous
The weighted suitability methodology
• There is a fairly standard methodology to follow:
Build a team
Define the model
Define the measures
Run the model
Present the results
Choose an alternative
Feedback
Feedback
Define the model
• This is normally a team activity• Domain experts, decision makers
• Define the problem• Identify likely locations for oil and gas
• Determine how to measure• Need high organic source rock• Need heat and pressure• Need good porosity and permeability, plus a cap rock
• Obtain GIS data
Break big models into sub-models
• Helps clarify relationships, simplifies problems
Source RockSub-model
Input Data(many)
PlaySub-model
Input Data(many)
BasinSub-model
ProspectingModel
Best Oil and GasSites
Input Data(many)
Binary suitability models
• Use for simple problems- Like a query
• Classify layers as good (1) or bad (0)• Combine: [Oil] = [Source]&[Kitchen]&[Reservoir]
• Advantages:- Easy
• Disadvantages:- No “next-best” sites - All layers have same importance- All good values have same importance
Kitchen
Reservoir
Source
10
00
1
0 01
00
Oil
1
1
Weighted suitability models
• Use for complex problems
• Classify layers into suitability 1–9 - Weight and add together:
Oil = ([Source]* 0.5) + ([Kitchen] * 0.3) + ([Reservoir] * 0.2)
• Advantages:- All values have relative importance- All layers have relative importance- Returns suitability on a scale (e.g. 1–9)
• Disadvantages: - Assigning weights requires deeper problem understanding
951Reservoir
Oil
96.6
7.01.8
5.04.2
951
Kitchen
95
1Source
Reclassify - Define a scale of suitability
• Define a scale for suitability- Many possible; typically 1 to 9 (worst to best)- Reclassify layer values into relative suitability- Use the same scale for all layers in the model
Source rock suitability
8765432
9 – Barnett Shale
1 – Granite
Best
Worst
Porosity suitability
8765432
9 >25
1 <5
Best
Worst
Within and between layers
0
3282.5
Distance to existing pipe
9
7
8
65
Pipeline Suitability
Suitability Modeling Steps
• Determine significant layers to the phenomenon being modeled
• Reclassify the values of each layer into a relative scale- Barnett Shale is best, rate it 9- Porosity > .25 is best, rate it a 9
• Weight the importance of each layer
• Add the layers together
• Analyze the results and make a decision
The Weighted Overlay tool
• Weights and combines multiple inputs
• Easy to change see and change all weights of layers and classes in one place
Limitations of a Weighted Overlay Approach
• Results in a surface indicate which sites are more preferred by the phenomenon than others.
• Does not give absolute values (no statistical probability of finding oil there).
• Heavily dependent on the reclassified and weighted values, and therefore the knowledge of the modeler.
Validation
• Ground truth
• User experience
• Alter values and weights
• Perform sensitivity analysis
Fuzzy Overlay
• A site selection technique based on set theory• Similar to Weighted Overlay, plus…
- Reclassification and weighting done with functions- More ways to combine variables (not just Plus)
Great Basin Geothermal Potential
New Zealand Wind Energy Siting
Fuzzy Analysis
• Helpful when you are aware of- Inaccuracies in location- Inaccuracies in classification process
Fuzzy Reclassify
• Predetermined functions are applied to continuous data
• 0 to 1 scale of possibility belonging to the specified set
• Membership functions- FuzzyGaussian – normally distributed midpoint- FuzzyLarge – membership likely for large numbers- FuzzyLinear – increase/decrease linearly- FuzzyMSLarge – very large values likely- FuzzyMSSmall - very small values likely- FuzzyNear- narrow around a midpoint- FuzzySmall – membership likely for small numbers
Fuzzy Reclassify
Fuzzy Overlay
• Meaning of the reclass values possibilities therefore no weighting
• Overlay based on set theory
• Fuzzy analysis- And - minimum value- Or – maximum value- Product – values can be small- Sum – not the algebraic sum- Gamma – sum and product
Pipeline
Pipeline Asset ManagementTypes of Analyses?
• Model the interaction between asset and its operating environment – growth curve prediction
• Optimize Replacement• High Consequence Area or Class Calculation• Pipeline Risk Assessment• Spill or Plume Modeling• Pipeline Routing
Pipeline Asset ManagementWhy Analytical Models?
• Manage Integrity and Prolong Asset Life • Optimize Replacement and Maintenance • Regulatory Compliance• Assess and Mitigate Operating Risk• Ensure Public Safety• Reduce Costs
Pipeline RoutingWhat is the Problem?
• Trying to place an asset from the source to delivery point while:
- Mitigating environmental impact
- Limiting construction costs
- Minimizing operating risk
- Assessing and negotiating land requirements
- Negotiating the stakeholder and regulatory landscape
Houses
Utilities
Topography
Property
Faults
Soils
?
Pipeline RoutingWeighted Overlay Model – Typical Factors
• Availability of data
• Topography- Slope / Curvature
• Land- Land use- Property ownership- Transportation facilities- Animal migration corridors- Traditional hunting and trapping rights
• Environment- Land cover- Environmentally sensitive areas
Pipeline RoutingWeighted Overlay Model – Typical Factors
• Water bodies- Lakes, rivers and wetlands
• Population- Proximity to housing- Large urban centers
• Geology- Surface geology, faults and outcrops
• Soils - Soil classification- Critical factors - acidity, electrical
conductivity, or salinity
• Costs- Total length and distance from roadway- Road, railway, utility and infrastructure crossings
Pipeline Routing Results• Shortest route• Least expensive route
3D Analysis
Analysis with 3-Dimensional Data
• 3D Selection now honored• New analytic capabilities to answer spatial
questions in 3 dimensions- What is close to what?- What is connected to what?- What is on top of (intersects) what?
3D Analysis ToolsFor 3D Points, 3D Lines, and Multipatch geometries
Intersect
Difference
Union
• Union• Intersect• Difference• Near• Inside• Is Closed
Near 3D
Union 3D