Many have found that the collaboration of data visualization software, Python, and Jupyter Notebook makes it easier to organize data and share and document custom workflows.
With so many oil and gas software applications and so much multidisciplinary data to analyze, it can be cumbersome sharing vital information across disciplines. Many have found a solution in the collaboration of data visualization software, Python, and Jupyter Notebook, which makes it easier to organize data and share and document custom workflows.
Achieving Data Organization
Engineers and analysts know better than anyone that multiple software types are needed for various disciplines. Analysts also know the likelihood of missing vital information during oil and gas campaigns due to software variations that often remain separate.
When discussing temporal data, for example, Python can provide a wider context into many disciplines while enhancing workflows. Data visualization software can be used to combine data from multiple disciplines into a single data hub that’s easier to view. Python can enhance visual renderings on-screen with the assistance of the visual software. Python also helps users make use of existing modules, enhancing workflows in the process. It helps users sift through the many strands of data coming from the field and organizes packaged data that are wired together for better sharing.
Sharing Workflows Across Disciplines
Even the best multi-disciplinary software requires added toolkits to further coalesce workflows and data, and this is where Python comes into play. Using data visualization software and Python, users can run the same procedures on a laptop or desktop while sharing these workflows with other team members. Python can also help users assemble the workflows in a .pdf format. From there, they can send the data and workflows to other parties, and they don’t have to run the code.
Jupyter Notebook—an open-source web application compatible with Python—blurs the line between users and coders. And, it lowers the entry barrier to Python when it comes to workflow creation, allowing users to create workflows in a matter of minutes. Overall, Jupyter Notebook simplifies the process in the following ways:
- writing a script instead of writing code
- providing markdown cells that explain the script
- offering outputs in the browsers instead of external windows, with no need to search for files or photos
Team members can write a package that allows users to download and modify accordingly. Python and data visualization are viable solutions that make data accessible to all types of users without the necessary expertise.
Data visualization and open-source software are viable solutions that make data accessible to all types of users without the necessary expertise. And, both software types allow analysts to tweak a problem in one procedure and allow other procedures to run an allotted course. Since geological landscapes vary based on location, there is no truly one-size-fits-all workflow. The software permits fluid workflows that allow users to make changes based on geological variations, among other factors. This also renders the data easy to visualize while adapting it based on new circumstances, which leads to another benefit: automation.
Obtaining Workflow Automation
The automation of a workflow is especially important due to its ability to cut down the manual script process. Python can help users reduce costs and provide quality workflows that are reliable. This can be applied in situations where analysts assess hundreds of thousands of well locations. If a user applies 14 attributes to thousands of potential well locations, that user can add in the necessary workflows and script it accordingly. Python can script this process 140,000 times or more, avoiding the impossible task of manual processing. Without Python, a team might only run the workflow manually 7,000 times—which could take weeks. With the automated process, it would only take a fraction of the time.
By combining Python, Jupyter Notebook, and data visualization software, users can accurately assess appropriate reservoirs with clear mapping. Also, one can write sophisticated analytical tools based on contour maps on-screen. With Python and command-line modules, users can write advanced analysis without manually repeating the process as new data arrives.
Both types of software eliminate the need to run the procedures multiple times over. If a new seismic survey comes into play, analysts don‘t have to know the exact modeling configurations to integrate the new data into the system. The supporting software maintains a detailed account of the parameters, including who established the guidelines. This is especially useful in light of new data that must be incorporated into existing data.
The Collaboration of Multiple Oil and Gas Software Applications
When used in conjunction with Python and Jupyter Notebook, CoViz 4D provides a Python-level toolkit which supports most CoViz 4D visualization and analysis interactive functions in a batch environment. It also eliminates manual tasks on such procedures as well assessment, easing the burden of interpreting each well, which can absorb weeks of additional time.
When used in conjunction with Python and Jupyter Notebook, CoViz 4D provides a Python-level toolkit which supports most CoViz 4D visualization and analysis interactive functions in a batch environment.
Open-source software enhances the visual acuity of data visualization software. It also makes data more manageable, helping users make on-the-spot decisions that enhance and support oil and gas campaigns. The collaboration of data visualization and open-source software enhances simulations and leads to efficient extraction campaigns, which can help companies reach company goals.
Image courtesy Panchenko Vladimir
