Fast Prototyping Informs the Way Forward
In the oilfield today it’s about reducing cost, increasing expert efficiency, and getting value from every bit of data that has already been paid for.
A common issue for oil & gas industry experts is spending too much time on activities that neither add value nor make use of their skills. These are most often associated with data, in particular geoscience, but can also be due to infrastructure, legacy softwares and limiting collaboration tools. Innovation is stifled.
Enthought scientists collaborate with clients using an extensive set of Python-based analysis tools, many open-source, to quickly pursue concepts, and explore and iterate new workflows. Business value is delivered early.
Fast prototyping software technology enables experts to:
- Apply visualization tools to understand data relationships
- Craft and apply custom data analysis techniques to diverse data sets
- Prototype user interfaces to streamline workflows
- Prepare and test for in-house deployment for final applications
Brendon Hall | 0m48s | Visualizing Cross-disciplinary Data in CanopyGeo
Deep Geoscience Expertise + Business Challenges + Scientific Software Craftsmen = Possibilities
Our highly technical clients have deep expertise in geoscience, oil & gas, and all aspects of their business. Enthought brings the potential of a new generation of digital technologies to their business, in particular AI/Machine Learning techniques.
Enthought experts combine strength in fundamental science, oil & gas domain knowledge, and scientific software development. Collaborations with client experts develop new possibilities in expert efficiency, new workflows, and innovation.
Common themes for tools and techniques are:
- Data Wrangling and Consolidation: Consistent, accessible, shared, machine readable, and located to enable efficient computations (e.g. cloud).
- Visualization and Image Analysis: Efficiently accessed and integrated data to generate insights to inform and prioritize deeper analysis.
- Modeling and Simulation: Computational models to replace or guide physical experiments while generating significantly more data to enable AI/Machine Learning.
- Predictive Tools (AI and Machine Learning): Computational models remove drudgery from the work of experts and increase the quality and consistency of results.
CanopyGeo Environment Gallery
Scroll to see examples of how the CanopyGeo environment enables Enthought scientific software craftsmen to quickly prototype and test ideas, iterating with clients to create customized software development roadmaps.
CanopyGeo environment provides novel capabilities to co-visualize data spanning multiple geoscience disciplines, significantly reducing the limitations of domain-specific softwares to accelerate innovative analysis and research method prototyping.
This screen shows a composite set of seismic data, horizons, and well paths in a single display
Extensible and Flexible
The CanopyGeo environment enables Enthought experts to add new data types, editors, menu entries, and menus to build custom algorithms, automate cross-domain workflows, and explore multi-domain visualization tools. Entought scientists contribute reader-writers, data objects, editors, 3D elements for the 3D viewer, menu actions, dockable side panels, and more through specialized add-ons.
The flexible I/O and data architecture and standard readers mean new data file formats and CanopyGeo data objects can be added and accessible to its various tools. With Python scripts and the object model, the platform can be extended to handle additional data file formats.
Enthought experts experiment with new workflows through display and edit of well
The CanopyGeo environment provides Enthought experts with access to the scientific Python ecosystem for custom data processing, interactive analyses, and advanced plotting and visualization.
CanopyGeo includes an IPython interactive command line console for inspecting and processing data objects. This enables dragging a SEG-Y data object into the console, printing its header, editing its data and running advanced analysis or data processing scripts on it.
The CanopyGeo advanced editor enables writing scripts to automate analysis pipelines and new workflows. For example, a script can access a data object loaded in the data panel, analyze its content, create new data objects based on this analysis, and open a visualization tool to display the result. With the editor, scripts are dragged to open. Experts can, find context sensitive help, use auto-completion and right-click to run the script in the IPython console.
Data can be visualized in 2D using a map view, or along a traverse (inline, crossline, or user-defined)