Planning your Drilling Spacing Unit in any shale reservoir requires an understanding of stress. Tight spacing, Parent/Child issues, uncertain economics, lean teams, and the need to drill to value top the list of questions our clients bring to the Petro.ai Drilling Spacing Unit Design Service (DSUDS) for actionable decisions. Foundation technologies like Frac Fingerprint, a geomechanical understanding of stress, and Viscoplastic Stress Relaxation (VSR) Theory, conceptualizing stress in changing lithography, deliver a high level of consistent accuracy in modeling any shale reservoir.
“No one really comes to us saying, ‘we need VSR or Frac Fingerprint,’” Dr. Brendon Hall, VP of Geoscience at Petro.ai smiles and explains, “But those technologies are the foundation of every single project we do. There’s no project or client that we’ve worked on that doesn’t use the aspects of VSR and Frac Fingerprint. It’s fit for purpose. Frac Fingerprint is an elegant feature to tie all the factors together and predict production, or as we call it, drainage.
“When we do these predictions, we take all the public data that’s available for a client’s wells. Traditionally companies take this data and build a machine learning model, a regression model, a multivariate model and use that to predict the observed production.
“What Dr. Troy Ruths (CEO of Petro.ai) and Dr. Mark Zoback (Professor of Geoscience at Stanford University) observed is that what’s really going to drive production is drainage area. Drainage area is a concept that focuses on a region around the well that is stimulated by the completion activities, by the fracing activities. That stimulation connects a fracture network together, to drain hydrocarbons from a specific area around a well.
“With that information, Frac Fingerprint and all the underlying data ties together into one model that reveals how to proceed with every aspect of filling out your Drilling Spacing Unit (DSU). That includes the all-important well spacing.
“Historically, most companies use many different variables to describe spacing like distance to nearest neighbor, vertical distance to nearest neighbor, percentage overlap. These kinds of estimations all measure spacing in various ways. They become a matrix of measurements that have to be blended together to analyze.
“What Petro.ai does is, we step back and think physically about what’s going on. We have a horizontal well, the rock has been fractured around it and you’re going to drain those hydrocarbons. We model the reservoir according to common sense, physical reasoning, and data indicators which estimate how the rock was stimulated. Once we have an estimate of this drainage area, then we can account for spacing effects by dividing the area up between children or carving out the parent effect.
“Every use case that we have, everything that we’ve done revolves around the Frac Fingerprint and VSR. This is our secret sauce—coming up with a very efficient and effective way to estimate these properties for all the wells across the entire AOI (area of interest).
- One use case we see repeatedly is spacing analysis. Petro.ai has a feature now that varies continuously as we move the wells closer or further away. We simulate this when we compute it and can vary the spacing very easily and look at how that affects the results. We’re able to do that by providing iterative tradeoffs and determining an optimal pad design.
- Another major use case is tying together a number of different data types into a geomechanical model that helps our clients understand their reservoir.
- In addition to the geomechanical model, Petro.ai provides a model of minimum horizontal stress that takes into account some physical factors that the status quo approach does not. This status quo method uses the Eaton equations. These equations have a number of simplifying assumptions that might not be accurate in all cases especially if there’s a lot of clay rich material in the rock formations which is often the case in the shales we’re looking at.
- Another type of use case is if you want to use a process that will determine the VSR effect when you’re trying to figure out the stress profile which means bringing in a lot of different data types that can be amalgamated to come up with a stress profile.
- A use case example in certain basins is when there’s a lot of risk because you have clay rich material and stress relaxation could be a factor. Petro.ai’s VSR methodology accounts for that. VSR applies to the carbonate issue that many clients have. Carbonates are not clay rich. This is one of the paradoxes. Typically, operators think that they’re a high stress zone, Zoback would say that they’re a low stress zone. VSR helps to determine the stress through these layers.
“In every use case we have encountered, the specific Frac Fingerprint that is developed for the reservoir, that drainage area isn’t something you can download from IHS. It’s not reported as part of these wells.
