In this episode of The Novi AI Roundup, we go beyond traditional metrics like “feet between wells” or “wells per section” to explore a more physical, multi-dimensional way to teach spacing to machine learning models. Drawing from the URTeC 2025 paper “Describing Well Spacing: Dimensionality at Work”, we dive into how techniques like Voronoi tessellations, nonlinear transformations, and causal modeling can better describe the reservoir neighborhood, leading to more realistic forecasts and clearer insight into spacing degradation. From lateral to vertical interference, this episode rethinks what it means to teach physics to a machine.This podcast episode is based on the technical paper “Describing Well Spacing: Dimensionality at Work”, authored by Kiran Sathaye, Dillon Niederhut, and Alexander Cui. The paper was presented at URTeC 2025. Download the full paper here.