Over the last few years, I’ve spent a lot of time with reservoir engineers and geologists, watching how DSU designs get put together. The process is usually time-consuming and often subjective. Engineers pore over offset wells, looking for trends in spacing, completion design and performance. They’ll sketch a few gun barrels in PowerPoint, apply type curves, and then apply risk factors for spacing or parent offsets. Eventually, those slides get presented internally as the design.
Engineers were doing great work, but the tools made it difficult to test ideas quickly or compare scenarios on equal footing. PetroAI is a reservoir productivity suite that helps engineers and geologists test ideas, quantify tradeoffs, and optimize their sections. In a previous blog I gave an overview of the new inventory editor tool inside PetroAI. Today I’ll be going more into the workflow of designing a pad.
Setting Up the Analysis
The first step is to create a new Project in PetroAI. Inside the project I’ll draw a gun barrel cross section for the area I want to develop. I typically draw sticks on the map and then fine-tune them in the gun barrel view to adjust their spacing and landing position. Once the layout looks right, I save it under a scenario name. Each scenario will get its own unique prediction, and I can filter the view by scenario to keep the workspace clean.
In this project, I built out four scenarios:
- Four wells: three in the BL and one in the B.
- Five wells: three BL and two B.
- Six wells: three BL, two B, and one in the 3BS.
- Seven wells: three BL, two B, and two in the 3BS.
This setup will let us test pad designs that reflect a multi-bench development strategy while accounting for sibling well spacing as well as parent-child interactions coming from the existing PDP. Once the layouts are in place, I define the completion designs and launch predictions.
Reviewing Results
When the build finishes, the first thing I check is the production curves across the scenarios. This gives a quick view of how designs stacked up, what intervals stand out, and where interference effects were showing up. The gun barrel images made those interference patterns intuitive to interpret.
From there, I’ll look at EUR oil per foot trends across the different pad designs. Sometimes the median well performance is the key metric, while other times it makes more sense to sum performance across the section. In this example, the 3BS stood out as a strong target, and adding wells there increased median well performance across the board.
Economics are the next layer. PetroAI allows me to review section-level NPV for each scenario, both as totals and per well. Looking at the summed NPV across scenarios makes the tradeoffs clear. In this case, adding those additional wells increases the section NPV.\
I also ran completion sensitivities. Comparing results across different intensity levels shows how completions impacts both performance and economics. This makes it easier to weigh the cost of higher intensity completions against the potential uplift in recovery.
Why This Workflow Helps
The main benefit of PetroAI is the ability to move quickly and transparently through DSU design. Instead of hand-drawing a few layouts and assigning risk factors by gut feel, I can build multiple scenarios, run predictions, and directly compare production and economics. The tradeoffs become clear, and the process itself is far more repeatable.
For me, the biggest difference is speed of iteration. What I used to see engineers spend days building slides, I can now put together in hours inside PetroAI, with far more rigor behind it.
Stay tuned for our next blog post where we’ll walkthrough a BD deal review using PetroAI.
