Non-spatial data analysis is just as important as analyzing spatial data during hydrocarbon asset development planning. The combination of non-spatial data and varying streams of spatial data leads to better, faster, and more confident decisions.
That’s why data visualization software
is so crucial, as it combines spatial and non-spatial data into simplified viewing formats. The ability to interpret non-spatial data and link it to the relevant subsurface geologic conditions of a prospect or field is critical to planning the development of the asset. Non-spatial data, such as completion diagrams, core photos, and well logs are most impactful when merged with subsurface data to further illuminate subsurface conditions. Only then will analysts see the entire picture to be able to make the best decisions that maximize production goals.
Obtaining Efficient Data Analysis
Non-spatial data contains pertinent information that could enhance asset operations
, especially when interlinked with geospatial data. Since non-spatial data is usually without a geographic location, such as reports in the form of 1D document files, users need software that can link the data to the 3D representation of a well. Consider production data, for example.
Asset teams need to know exactly where in the well the production is happening. Being able to visualize that production data in a 3D representation allows for teams to obtain a better understanding. There may have been open perforations in several different places that produced oil and that production could’ve been commingled with another well and all a team knows is what was pumped out.
Another point to consider is the amount of time it saves to interlink non-spatial and spatial data. Instead of having to spend a significant amount of time analyzing the producing layers to figure out how to divide up the production amongst the producing layers, 3D visualization allows asset teams to look at adjacent wells that are producing out of one layer to get a sense of what’s going on in another well producing out of another layer. This allows for a better estimate of the oil production.
Data visualization software also helps users by:
Further, this type of software will help users understand the limitations that a field may pose to campaign endeavors. It will also help teams work collaboratively
and overcome some of the toughest challenges that a field may present to engineers. This is enhanced through the binding of non-spatial data and spatial data.
Economic Benefits of Non-Spatial Data Analysis
Since topography is a primary influence in the cost of each well, users need information extracted from non-spatial data analysis that highlights the best well location. For instance, drillers will need to establish operations away from existing infrastructure such as pipelines.
Data visualization software can also connect prime geological locations with surface conditions that may enhance or hamper hydrocarbon extraction goals. In the event of an obstacle, team members have enough leeway to work around challenges and find the best paths that would maximize reservoir exploitation.
For example, if an energy company must negotiate with a seller in a land deal, non-spatial data analysis can provide a greater understanding of the best areas to exploit. Knowing the optimal drilling spots will be an immense asset during the negotiation process, thus allowing energy companies access to the most lucrative spots of a mineral owner’s land. This is especially true when dealing with well positioning, as energy may contend with government boundaries that restrict additional drilling.
How Non-Spatial Data Analysis Aids Regulatory Compliance
Surface information can also be collected and compared to subsurface data to fully assess how operations will conform to regulatory frameworks
. Regulatory guidelines could pertain to tax collections or royalty disbursements. Regardless, data visualization software can help analysts establish suitable boundaries while maintaining stellar records in case state officials need to examine them.
With the merger of spatial and non-spatial data, users can assemble videos and photos of a surface area and compare it to subsurface data to get an operational outline. It also provides analysts with the technology needed to display before-and-after photos that assess how the development would affect the landscape of a particular area and if it falls out of the bounds of state or federal regulations.
To stay ahead of federal or state authorities, engineers are best served via the integration of surface information with subsurface drilling data, such as petrophysical and geological data. This integration helps teammates understand the parameters that everyone must operate under, ensuring that all team members remain on the same page. Any schematics, reports, documents, or photographs would be included in the large geospatial context of the data scheme. This provides stellar visuals in the form of maps, models, or displays that can highlight ownership boundaries. The software can also give clear warning signs if a project veers off course and surpasses administrative confines that could result in fines and stalled operations.
Non-Spatial Data Analysis and Visualization
Through stellar visuals, analysts can better understand surface and subsurface conditions using data visualization software like CoViz 4D
. Looking at a picture file is fine but it doesn’t mean much unless teams can map it and visualize it to see the bigger picture. That’s where CoViz 4D comes into play. It will help analysts achieve the best well-positioning strategies that will foster greater production numbers and provide a safer drilling environment for crew members. With so many non-spatial datasets at play, CoViz 4D provides seamless integration into a cohesive viewing system that all team members can understand.
The addition of non-spatial data will guide teams in finding creative solutions to the toughest surface challenges, ranging from government oversight to mineral owners. With a successful merger of spatial and non-spatial data, operators can streamline workflows efficiently and optimize strategic development. The ability to combine both non-spatial and spatial data into one environment will also help users find the most advantageous zones that will yield the most hydrocarbon extraction opportunities, thus yielding greater profits.
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