The Niobrara Shale formation is an attractive and exciting shale oil and gas play that is often compared to the Bakken.
Although the play is in its early stages of development, operators have been quickly leasing land in the core zones, especially in the Weld and Yuma Counties in Colorado, and Cheyenne, Kansas.
The play ranges in thickness from 275 – 400 feet in depth, with three primary carbonate-rich benches that average roughly 10 – 25 feet thick with roughly a 5-10 percent porosity.
Oil and natural gas is trapped between 3,000 – 14,000 feet below the earth’s surface. Operators are extracting the resources from the Niobrara shale formation by drilling vertical and horizontal wells at a depth of roughly 7,000 – 8,000 feet.Although the Niobrara’s geological characteristics can impede effective, economical drilling, the impressive oil and gas production estimates have attracted significant E&P capital investments from some of the top oil and gas operators.
Such impediments include high clay content of the formation, water shortages and the geological transitions from limestone to chalk to calcareous shale to sandstone – each with differing depths and thickness.
For many of these reason, Niobrara shale operators are seeking sections that have a high natural fracture density – which are likely more productive and easier to tap – compared to reservoirs characterized by a lower fracture density – which yield elevated water cuts and reduced output.
The following analytical work-flow of the Niobrara using DI Data and Transform software is designed to help alleviate these hurdles, provide a thorough evaluation and a competitive advantage for DI users.
A Rapid Reconnaissance of the Niobrara Shale Formation:
Let’s assume that an opportunity has arisen that demands a quick evaluation, and we lack time for a full in-house pre-project analysis.
In order to avoid an inherently flawed opportunity evaluation, it’s important to begin an analysis with hyper-clean data.
Using data from DI Analytics, we can easily review the operator statistics table and determine that there’s a significant amount of production in various zones. We’re able to see co-mingled productions, outputs from the A, B, and C benches and the Codell as well.
Matching the production to the correct geological section can sometimes be a challenge, but we can easily segment the information using highly-detailed filters, avoid project build hassles and quickly evaluate an opportunity.
We’re then able to export data and quickly get a CS feed that includes information like the API number, latitude, longitude, well name, operator name – anything that is required for a project analysis.
For the purpose of this analysis, we’re only going to compare Niobrara production from horizontal wells, so we didn’t include any production from the Codell or any co-mingled production.
Thanks to our Drillinginfo geoscientists, who have dedicated roughly two years to correlating events, picking tops and faults, we have access to over 6000 wells and gamma ray logs in the Niobrara Basin.
From here, we’ve created 38 key structured structural, isopach and property maps in high-resolution from the interpretations.
Here, using Transform software, we’ve generated a 3D view of the Wattenburg Field in the Northeast. We can see a structural map of the top of the Niobrara and compare the depths of various regions.
From this view, we can identify how operators are completing their wells simply by zooming in on the populated well-bores. Most look to be multi-pad drilling, deviating out and then hitting multiple zones.
Then, we can flatten out the A Bench top and see the thickness of the A bench. There appears to be some syndepositional tectonic influence in the selected region, resulting in a thicker Niobrara section. This gives us a good look at the structural history of the region’s geology and a quick interpretation.
Cross-section from the SW to the NE. Here we are flattened on the top of the A Bench. We can see the A Bench thickness changes across the transect as well as some probable tectonic influence in the SW.
With our high resolution interpretation and the analytics-ready data focused only on Niobrara horizontal production, we can then use cross-plots to evaluate specific variables.
A quick look at these cross-plots show that thicker B Bench and API gravity between 42 and 52 is optimal. The image shows how we can quickly grid the B Bench thickness while also highlighting points in the optimal API gravity on our basemap.
Here we are looking at the individual property types (thickness of B Bench, oil gravity, porosity, horizontal wellbore length) and their effects on production. In the map view we are showing an isopach of the B Bench and highlighted wells in the wet gas window of oil gravity. Some areas of the thickest B Bench are in this optimal wet gas window.
Next we can build a multi-variate statistical model to predict oil production. We are using a handful of geological attributes as well as horizontal well length. Through this model we are able to predict oil production with a 0.660 confidence level.
The map shown in the image is the oil prediction map for the first 6 month of production. This map has stacked the geological properties by significance and normalized out the true statistical effect of horizontal well length.
By reducing the time needed for interpretation and data conditioning combined with an interpretation package that allows for easy statistical analysis, we quickly created a map that we can use to evaluate opportunities.
This map shows areas of high predicted oil production. This is created using a multi-variate statistical approach where we stack the individual geological properties by significance and we normalize out the true statistical contribution of horizontal well length. Included in the map are some interpreted major faults and some structural contours.
The workflow outlined above was a big hit at SEG 2014. The process can be easily applied to a variety of top shale plays, and expanded upon by adding geophysical and engineering data.
How would you use the features outlined above to improve your project analyses? Let us know in the comments.
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