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