Common Analytic Workflows for the PUG Community...Common Analytic Workflows for the PUG Community...
Transcript of Common Analytic Workflows for the PUG Community...Common Analytic Workflows for the PUG Community...
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