This tutorial will help you install and use the PipelineML GeoPackager.

PipelineML GeoPackager is a QGIS plugin that reads a PipelineML file and translates its contents into a GeoPackage. This allows QGIS to natively consume and store PipelineML data for GIS analysis, and demonstrates the ability for PipelineML to be implemented in a fully open architecture. It is free, open-source software (FOSS), distributed under the GNU General Public License.

PipelineML GeoPackager is implemented as a processing plugin with an algorithm provider, so it can be run within the components of the QGIS processing framework (such as the graphical modeler or the batch processing interface) and thus integrated into more complex workflows. It requires QGIS version 3.0 (or higher).


The easiest way to install PipelineML GeoPackager is directly from the Plugin Manager dialog (within the QGIS application). For detailed instructions on how to do this, see QGIS Training Manual > Installing New Plugins. Alternatively, it can be downloaded in zipped format from the QGIS plugin repository and then installed using the Install from ZIP tab on the Plugin Manager dialog.

When the plugin is loaded, the provider PipelineML should appear in the Processing Toolbox, with an algorithm called PipelineML to GeoPackage. (The algorithm ID for Python scripting is pml:pml2gpkg.)


To execute the algorithm, double-click PipelineML to GeoPackage in the toolbox. The following dialog should appear.

The browse buttons can be used to help populate the parameters, which are explained in the table below.

Source PipelineML fileINPUTFull path to the PipelineML file (e.g., C:\Data\PML-1.0-Official-Sample-Files-1\PML_Valid_Medium01.xml).
Destination GeoPackageOUTPUTFull path to the GeoPackage file destination (e.g., C:\Data\PML-1.0-Official-Sample-Files-1\PML_Valid_Medium01.gpkg), or empty to save to a temporary file.

Once the parameters are populated, click Run. The dialog should switch to the Log tab, which indicates progress as the algorithm executes.

When the algorithm is finished, the resulting layers in the GeoPackage are added to the current project in QGIS.


PipelineML GeoPackager is written in Python using the GDAL (Geospatial Data Abstraction Library) API that ships with QGIS. Source code is hosted on GitHub, where you can report bugs, request enhancements, or otherwise become involved.


  1. Hello,
    I´ve been reading about PipelineML and found it very interesting. But from that reading, I still do not grasp how a PipelineML file is created in order to open/convert it with the PipelineML GeoPackager plugin. I would very much appreciate if you could provide some info as to this matter. Literature, videos, tutorials, etc.

    • Ricardo,

      That is a good question. In order to generate a PipelineML file, someone needs to create an export routine from a database where your oil and gas pipeline asset data is stored. This might include one of the following oil and gas asset database models (or others):

      In the oil and gas industry, several key database models play a crucial role in managing assets:

      1. PPDM (Petroleum Data Model):

      The PPDM model is a data management framework that supports the entire spectrum of upstream oil and gas activities. It focuses on ensuring high-quality data governance and management practices. PPDM can integrate with GIS systems for spatial data management, enhancing the ability to visualize and analyze data related to wells, reservoirs, and production facilities.
      Industry standard: Developed and maintained by the PPDM Association, it’s a widely adopted data model offering a standardized framework for managing various types of oil and gas data. It provides a common language and structure for different software systems to exchange information, improving data consistency and interoperability across the industry.
      Focus: PPDM covers a broad range of data entities, including wells, facilities, pipelines, seismic surveys, and land ownership. (http://downloads.esri.com/support/datamodels/petroleum/petrodatamodels.pdf)

      2. Industry-specific data models:

      Tailored solutions: Several software vendors and service providers in the oil and gas sector have developed their own data models catered to specific asset management needs. These models might focus on particular aspects like wellbore data, production optimization, or reservoir management.
      Focus: These models address specific needs within a particular segment of the industry or a specific software application.

      3. Esri Data Model for Petroleum:

      Esri’s offering: Esri, a leading provider of Geographic Information Systems (GIS) software, offers a data model specifically designed for the oil and gas industry. This model leverages Esri’s geospatial expertise and integrates well with their ArcGIS platform.
      Focus: The Esri data model focuses on representing and managing spatial data related to oil and gas assets, including well locations, pipelines, facilities, and seismic surveys. It can be used for various purposes, such as exploration planning, environmental impact assessments, and asset management. (http://downloads.esri.com/support/datamodels/petroleum/petrodatamodels.pdf)
      While PODS may not be prevalent compared to the options listed above, it’s important to understand its purpose:

      4. PODS (Petroleum Object Data Standard):

      PODS is designed to efficiently manage pipeline data throughout its lifecycle. It is a database model that integrates with GIS platforms, such as ESRI’s ArcGIS, to provide a detailed representation of pipeline systems. This allows companies to manage their assets more effectively, from planning and construction to operation and maintenance.
      Limited adoption: Primarily focused on managing well completion data, PODS didn’t achieve widespread adoption in the industry. However, it played a role in the development of PPDM and contributed to establishing standardized data management practices in the early stages.

      5. PCDM (Pipeline Component Data Model)

      This is an emerging open-source data model designed to be modular and flexible. This is a model specifically designed to be highly compatible with PipelineML. It is in the process of being prepared for release as an open-source data model.

      It’s crucial to remember that the specific database models used by an oil and gas company depend on their individual needs, existing infrastructure, and software choices. The combination of industry standards, Esri’s offering, and potentially customized models, allows companies to manage their diverse assets effectively.

      Whichever data model you are using to store your oil and gas asset information, you (or your vendor) would need to write an export routine that converts your internal codes and values into those that match the PipelineML standard. This might be as simple as writing a SQL query that outputs such an XML-based file. Thanks for the feedback. I will work on creating some additional resources. Let me know if you have any more questions.

      [The summary of database models above is a composite generated from the Google Gemini and ChatGPT AI engines.]

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