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