2. Introduction to PipelineML
John Tisdale
Enterprise Products
Terry Strahan
MPH

3. What is PipelineML
PipelineML is an initiative to develop an open extensible vendor-neutral international standard to enable the interoperable interchange of pipeline data between parties, disparate systems and software applications without loss of accuracy, density or data resolution; without ambiguity; and without need for conversion between intermediate or proprietary formats while maintaining contextual integrity. This standard is expected to leverage existing OGC standards such as GML, GML-SF, WFS, WMS, etc.

4. What is PipelineML
PipelineML seeks to facilitate exchange of pipeline data between such parties as:
Pipeline Operator Internal Departments (acquisition, construction, field operations, integrity, OneCall, corrosion prevention, ROW, public awareness, HCA, etc.)
Service Providers (surveyors, construction companies, cartographic vendors, etc.)
Regulatory Agencies (DOT/FERC/PHMSA, NPMS, TRRC, etc.)
Emergency Response Agencies

5. What is PipelineML It endeavors to facilitate data exchanges:
Without need to convert data between various formats
Without need for proprietary technologies or standards, although it can be freely utilized in/by proprietary technologies and standards
Example: PDF (Portable Document Framework) as a universal document data exchange format used by many proprietary software applications

6. What is PipelineML Will PipelineML compete with or conflict with PODS?
No, PipelineML is an independent open standard for exchanging pipeline component data
It can be adopted and implemented in any data storage model on the market (such as PODS Spatial, PODS Relational, APDM, UPDM, iSat, etc.)
Whoever wants to use it to enable universal open data exchange can do so
It can become common ground that ties numerous disparate systems together to enable the entire pipeline industry to easily share data

7. What is PipelineML

8. What is PipelineML
Will PipelineML be based on the PODS data storage model?
No, a data interchange standard should service one goal - the interchange of data. The goals of a data storage model are very different and incompatible with the goals of data interchange.
PipelineML needs to support as concise representation of data as possible without introducing ambiguity or losing contextual integrity of data.
However, PODS and PipelineML can work collaboratively to provide a cohesive solution.

9. What is PipelineML
Will PipelineML compete with or conflict with any software applications?
No, PipelineML is an independent open data exchange standard that can be adopted and implemented by any software application on the market whose vendor wishes to enable their clients to easily intake or output pipeline data.
Any software application would simply need to create an import and export feature to provide PipelineML interchange capability.

10. How will PipelineML benefit Industry
Operators
Share data with internal departments and service providers quickly and easily without intermediary, error-prone, costly format conversion.
Service Providers
Exchange data with clients using single data format.
Software Vendors
Provide your clients with enterprise solution integration with your applications quickly and easily.
Regulatory Agencies
Get information from operators quickly and easily using a single standardized format.

11. Interchange Challenges
Three Key Interchange Challenges
Enable Conciseness
Convey meaning with the least amount of information possible.
Avoid Ambiguity
Prevent loss in clarity of meaning due to differences in vernacular, definitions, categorizations, etc. between parties and applications.
Retain Contextual Integrity
Preserve the full contextual meaning of data through the exchange transaction.

12. Pre-PipelineML Data Exchange
Party A
Format = CSV file
Component = Linepipe
Yield Strength = 52,000
Material = Low Carbon Steel
Outside Diameter =
Wall Thickness = 0.250
Grade = X-52
Specification = API-5L
Manufacturer = U.S. Steel
Party B
Format = SDE file
Component = Pipe
Tensile strength = 52000
Material = Steel
Nominal Pipe Size = 12 inch
Wall Thickness = .25 inch
Grade = X52
Spec = API5L
Manufacturer = US Steel
Translation requires complex mapping or human interpretation

13. Addressing the Challenges
Introduction of PipelineML International Code Standard (PICS)
Common global pipeline type code registry
Common definitions for all types of pipeline component types, material types, installation methods, coating types, manufacturers, etc.
Supports international measurement systems and languages
Lookup values on PICS online registry via Web services or download entire registry contents as SQL DDL/DML scripts

14. PipelineML+PICS Data Exchange
Party A
Format = PipelineML
PICS_ID = B34555C5-35DA-4F99-B2C8-2F267F6194CC
Party B
Format = PipelineML
PICS_ID = B34555C5-35DA-4F99-B2C8-2F267F6194CC
PICS
PICS_ID B34555C5-35DA-4F99-B2C8-2F267F6194CC =
Component = Linepipe, Yield Strength = 52000, Material = Low Carbon Steel, Outside Diameter = , Wall Thickness = 0.250, Grade = X-52, Specification = API-5L, Manufacturer = U.S. Steel

15. PipelineML+PICS Data Exchange
Party A
Format = PipelineML
TYPE DATA (PICS)
PICS_ID = B34555C5-35DA-4F99-B2C8-2F267F6194CC
INSTANCE DATA
Install Date = 7/14/1998
Location = 0xE DF6B7A19063E4057EE50229AF557C07E53BDD213063E4000CDB75396F557C0
Party B
Format = PipelineML
TYPE DATA (PICS)
PICS_ID = B34555C5-35DA-4F99-B2C8-2F267F6194CC
INSTANCE DATA
Install Date = 7/14/1998
Location = 0xE DF6B7A19063E4057EE50229AF557C07E53BDD213063E4000CDB75396F557C0
Concise. Unambiguous. Contextual Integrity.

16. PipelineML Components
Universal Type Attributes (from PICS)
Component types - pipe, valves, flanges, meters, tees, sleeves, coatings, etc.
Material types
Anomaly types
Instance Attributes (standardized tags)
Field installation modifications
Installation date and location
Vendors involved in installation, etc.

17. Development Methodology
Leverage existing data exchange standards
Work jointly between PODS and OGC under MOU to combine expertise
Leverage OGC GML, WFS, WMS, etc.
Leverage OGC’s standards maturation and testing procedures
Leverage PODS to vet and mature standard, tags, and PICS code content
Pursue joint PODS/OGC endorsement, support, and integration

18. PipelineML Core Values
Open and extensible
Supports complete interoperability
Non-proprietary standard that can be used in proprietary solutions
Practical and usable
Flexible and extensible
Fit-for-purpose
Concise
Unambiguous
Lossless contextual integrity
Based on existing XML and GML standards

19. Question & Answers Questions
For additional information, please join us for a follow-up session this afternoon