Frac Design
The Challenge
Optimizing well location and completion design in unconventional reservoirs is a high-stakes challenge. With dozens of variables at play, from geology and spacing to fluid volume and stage count, operators need fast, accurate tools to forecast performance and guide design choices. Traditional approaches are slow, manual, and often lack the precision required to improve development outcomes.
The Problem
Legacy workflows for frac design rely on trial and error or rigid statistical models that do not account for subsurface physics. These methods limit the ability to predict well performance under new configurations, making it difficult to plan optimal completions or confidently select new well locations. The result is missed opportunities to enhance recovery and reduce costs.
The Solution
Fraceon combines physics-based reservoir modeling with machine learning to create predictive models that fit existing production data and forecast future performance. Operators can input customizable design variables, automatically train and validate models, and receive data-driven recommendations for both new and existing wells. Fraceon improves predictivity, reduces manual effort, and enables faster, more accurate frac design decisions.
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