PipelineML was designed to overcome the biggest efficiency bottleneck in oil and gas data sharing. Existing data interchange solutions require a person to manually handhold the exchange process. Someone needs to create a seed file to map data between two specific sources. A database administrator must back up a database and someone else must restore the copy and perform transformations on the data. Someone maps columns of data in a spreadsheet and manipulates the data to suit a business unit’s needs. A GIS specialist must import a Shapefile, analyze it, and export data for another application.
There is nothing wrong with any of these approaches to exchanging data as each is well suited to a given set of business needs (and PipelineML will not replace any of them). These are all effective data management tools in the belts of professionals getting work done every day. The opportunity that PipelineML provides is to remove the most onerous and time-consuming aspect of information sharing—translating and mapping data between parties. Because everyone agrees to use a common controlled vocabulary, a person is no longer required to map or transform data between systems. Since all systems know the exact meaning of all terms and values, machines can natively read the information from one system to another. This streamlines the process of sharing information so people can focus on the important task of analyzing the data and making critical decisions about the information.
Hence, PipelineML facilitates machine-readability and machine-to-machine data sharing. This positions the industry to move into a new era of data processing scalability. This capability undergirds PipelineML’s objective to help people move information quickly and easily between disparate platforms, systems, applications, and devices without the need for people to interpret its meaning. There will always be some data that require subject matter expertise for interpretation. However, the majority of pipeline information (anecdotally, perhaps 80%) that needs to change hands is very simple in nature. Most individuals making decisions that keep product safely flowing through pipes and satisfy regulatory requirements simply need answers to basic questions. Where is this asset located? When was it installed? What is the maximum operating pressure? Who was the manufacturer? When was the last pressure test performed? Where are the control valves located on this line? What is the spill volume of this segment? What products are flowing through this line and at what operating pressures? Where is the MTR? What is the average diameter of pipe running through this county? How close does this line come to a high consequence area? What foreign lines utilize this section of a shared right of way? What remediation activities are scheduled for this system? Which integrity engineer is responsible for this section of pipe?
Currently, someone in integrity needs to call someone in the field to determine the current operating pressure of a particular system. This information is already known and its status is up-to-date in an operational software application somewhere. Yet, the integrity department is running one application and the field operations group is using another. Even though both data silos may exist in the same building, they might as well be worlds apart. An integrity engineer’s software cannot communicate with the operations system and even if they could, the vernacular used between these two groups differ significantly. What one group calls “wall thickness,” another calls “NWT.” Many such interchange hurdles exist throughout the various data systems managed by every operator.
PipelineML was engineered to address these lightweight data exchange use cases to support full automation so every application in the enterprise has the ability to send and receive basic information between systems about pipeline assets and the activities being performed on them. PipelineML lays a solid foundation for machines to communicate with other machines, understand the context of inquiries, find answers, and process requests without involving a human being and the entire process can be completed in seconds. Automating the processing and flow of lightweight information exchanges removes the workload off subject matter experts whose time is better spent solving problems machines are not capable of doing. GIS specialists can concentrate on performing real value-add analytics instead of performing menial tasks that can be offloaded to automated processing, such as converting files between formats or projection systems.
Every software application that wants to exchange information using PipelineML must simply conform to explicit naming conventions and a well-defined set of data rules. For example, if two software applications want to exchange information about a pipeline, they must communicate wall thickness as NominalWallThickness and the unit of measure must be clearly articulated as either inches or millimeters (PipelineML was engineered from an international consortium of subject matter experts and supports multiple systems of measurement and languages). This strict vocabulary allows machines to understand the meaning of data without involving a human being.
Besides enabling automation, PipelineML can speed up the process of manually moving data between systems. Someone can export data out of one system and immediately import it into another system with a few clicks (provided that both applications have PipelineML import and export features added). This has the potential to unify operator data submissions to regulatory agencies. If all regulatory agencies standardized on this data interchange standard, any software in the industry could be retrofitted to output data such that the filing process could be done quickly, easily, and in a unified manner (even automatically). This would expedite the process for operators fulfilling reporting requirements for all agencies using a single standard as well as empowering regulatory agencies to utilize machine-readable data imports from all operators using a controlled vocabulary (ready-made scalability). This translates to the saving of large amounts of money on the parts of all parties involved.