Collaborative Asset Team Conversations Around Your DSU Design Service Recommendation Reports
Data Science & Analytics

Collaborative Asset Team Conversations Around Your DSU Design Service Recommendation Reports

Rosemary Jackson  •  

Q: I understand Petro.ai’s team presents the DSU Design Service recommendation reports in live meetings, why is that?

A: The Petro.ai DSUDS (Drilling Spacing Unit Design Service) recommendation reports are purposefully delivered in presentations on a weekly or biweekly basis as our technology generates updated subsurface models. Across each basin and across each client, the technical conversations that DSUDS presentations generate within asset teams are among the most highly valued aspects of our process.

When we have our weekly or biweekly check-ins with the client, we generally meet with their asset team,” Charles Connell, COO of Petro.ai explains.“ Even if the reservoir engineer on the client side is our main point of contact, we’ll typically have representatives from other domains on that call. Geology, geophysics, completions will also be there. Management will be there as well, trying to understand economic tradeoffs. This is an opportunity on these check-in calls for us to get input from all the key stakeholders. This ensures that we are 1) aligned with internal objectives and 2) that we are capturing all available knowledge of the asset.

“Oftentimes our clients have spent several years, if not decades, working in a particular geographic area. They have insights that they’ve developed, and we want to make sure that we capture not only the raw data, but their mental models that we can test as well. We can bring together these mental models as well as the data from across these different domains and put them all in Petro.ai to test various hypotheses.

“We’ve seen how geologists might make a map of some petrophysical attribute. But then it’s a map and an RE might look at it and be able to pull some anecdotal evidence or maybe pull out some trends, but it’s not fully integrated into a forecasting workflow.

“Petro.ai allows us to take the interpretations from a geologist, choke strategies from a reservoir engineer, frac designs from a completion engineer and bring those together – in the context of the subsurface - to determine how these different tradeoffs affect well performance.

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“Often, clients begin working with us thinking that they’re one cohesive team. Then, after they work with Petro.ai, they realize just how isolated they actually are from one another in terms of how they think about things, what they prioritize, the data that’s generated. It’s a very different feeling having a PowerPoint where one slide shows a geologist’s view and another slide shows a well decline.

“These separated perspectives can be helpful in vetting a machine learning model that uses both work products as inputs, binding the concepts together and allowing groups to test hypotheses. Petro.ai puts everything into one place and then makes it easy for the entire asset team to feel that their work is both contributing to the accuracy and the efficacy of the model.

“It also allows the various members of the asset team to contextualize their views and weigh the consequences of tradeoffs they make. A geologist might be curious to see why one feature is more important than the other. We can explore that. The reservoir engineer might be more interested in the spacing tradeoffs. We can explore that using the same model.

“The Petro.ai DSUDS report presentations bring the meaning to the models and open up opportunities to discuss critical cross-functional challenges. These asset teams live in a dynamic environment where laterals are being extended, acreage is being added as they plan, and predictions are shifting on multi-bench pads. DSUDS recommendation reports generate the right conversations, link subsurface information, and dive into the interconnected issues of infill well planning.

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