Operators often don’t properly take into account the maturity levels of source rock, underestimating the generative potential for oil in their current assets as well as in buying opportunities.
This can result in over a billion dollars of lost revenue.
This isn’t our estimate. A geochemist from a large independent operator came to us with these numbers. Her company had underestimated a collection of assets they had recently lost in a bidding war. Those assets were currently producing to the tune of hundreds of millions of dollars, wildly outperforming their internal expectations. Had our geochemist’s company properly evaluated the opportunity, taking into account the source rock maturity levels, and true rock type, it would likely have won the bidding war and reaped the benefits. This was only one deal and not an isolated incident.
A quick rundown for the uninitiated: a source rock type indicates whether a resource will be more prone to liquid, gas, or hydrogen. The indicators of source rock type (such as Hydrogen Index) evolve over time. Placing these indicators in context ensures a proper source rock designation which tells us how much potential oil or gas exist in the source rock.
The problem for our geochemist was that the evaluation process involves hours upon hours of fighting with data and plots, trying to get them into the right format to establish this context. With only a handful of geochemistry experts, she also was struggling to guide non-Geochemists through the process with confidence.
She needed a way to help ease the geochemisty expert’s burden and empower the geoscientist. Only then could her team paint the proper picture that would allow decision makers to capitalize on these opportunities.
We built a Spotfire tool that steps through a geochemistry workflow to aid in source rock interpretation, incorporating industry standard plots while allowing users to interact with and evaluate the data.
The plots include TMax vs HI, %TOC vs S2, %TOC vs HI, and a Pseudo Van Krevelen Diagram as well as the incorporation of a well log and other depth and statistical plots:
This tool makes much of the interpretation process dynamic and pain free for the experts while also providing instructions for the geoscientist, allowing both to help operators better understand these resources.
Our tool uses Source Rock Data (well info, depth, and source rock variables like HI, TMax, etc) and Well Log data (optional), so first we must get the data into Spotfire. For the Well Log data, Petro.ai has a handy extension that will automatically convert LAS Files and transform them into a usable Spotfire table.
Once we import the data tables, our tool allows for a hassle-free mapping of variables used in the workflow.
Only the source rock variables you want to evaluate are truly mandatory, though.
Once we map the data, we can examine it using Petro.ai’s well log viewer and see the source rock and well log data side by side.
We can mark the data to select precisely what we want to look at in the analysis moving forward.
Data Quality Control
One of the biggest problems we see with source rock data is undesirable data: irrelevant or even faulty observations. The massive amounts of superfluous information makes getting a handle on where to start one’s analysis difficult.
Two features in our workflow help to easily hide undesirable data. First, an input box allows the user to set a Hydrogen Index threshold. Any data below the threshold will disappear from our plots.
Much source rock data is irrelevant below an HI threshold of 100, or perhaps 80, so this feature allows the users to dismiss those occurrences.
The user can also remove data with more specificity, but still in one swoop, by marking and tagging it Undesirable.
The data will disappear from the plot, and all subsequent plots as soon as the user marks it as such.
We have prepared and quality checked the data, now we can start making our interpretations.
Source Rock Interpretations
Similarly to how we tagged data Undesirable, we can also tag data by Type and Maturity level. We might start by looking at the Hydrogen Index vs Tmax plot. Considering the source rock’s maturity level is vital to properly identifying its type (and potential for oil or gas), and the HI vs Tmax plot is one of the best ways to do so.
Our tool places the data on the Hi vs Tmax plot and allows users to mark and tag observations by type or maturity. Users can also change the colors and/or shape of the markers by a third or fourth variable and even create interactivity between plots.
All of the plots shown in the Rock Evaluation Plot Medley during the introduction (TMax vs HI, %TOC vs S2, %TOC vs HI, Pseudo Van Krevelen) have their own devoted page, and we can iterate through the different plots and refine our interpretations.
Source Rock interpretation plots can take a great deal of experience to master, but fortunately those less familiar can follow some general guidelines to gain insight. Our workflow provides direction for each plot in visual form as well as general instructions.
In addition to the guidance for each plot, the tool provides a resources page for further development and reference.
Once we have interpreted and tagged all our data, we still need to evaluate it for further insight.
First, we can look at the data we deemed undesirable (shown below in red) as a sanity check to make sure we didn’t junk any relevant data…
Then, we can look at our interpretations broken down by well (or other variables) both visually and statistically…
Finally, we can look at the results next to the Petro.ai Well Log visualization and zoom in on certain depths…
Save and Share with Petro.ai
We have iterated through our Geochemistry Source Rock Interpretation workflow, so what now? First, we can export our visuals for presentations to start driving change. Even better, Petro.ai allows us to share our Type and Maturity designations with other users. Now, when one expert goes through the interpretation workflow, the results can be used throughout the organization. Security and permissions ensure only those who should see the results do, but that consideration notwithstanding, everyone can benefit from an expert’s analysis. New employees using the geochemistry workflow can develop their understanding by checking their results against an expert’s interpretations.
Previously, this workflow might have occurred in Excel, being very manual and error prone. Our Source Rock Interpretation Workflow allows a user to set up all of the plots in a matter of seconds, provides a lightning fast data quality check, enables tagging and saving interpretations, and facilitates knowledge sharing throughout the company.
Opportunities are being left on the table when operators underestimate an asset’s future production. Often because experts don’t have the time to properly evaluate and report all of them. Our Source Rock Interpretation Workflow helps those experts unearth the true potential oil in a source rock.
To learn more about this workflow, contact email@example.com.
Jason is a Data Scientist at Petro.ai with a master’s degree in Predictive Analytics and Data Science from Northwestern University. He has experience with a multitude of machine learning techniques such as Random Forest, Neural Nets, and Support Vector Machines. With a previous Master’s in Creative Writing, Jason is a fervent believer in the Oxford comma.