Smart Pad Layout Optimizer

A web app that uses real-world drone imagery to create smart pad layouts for hydraulic fracturing operations. It optimizes equipment placement while maintaining safety zones and operational efficiency.

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Inspiration

In oil & gas completions, pad layouts are usually designed manually using static diagrams. This can lead to inefficiencies, unsafe spacing, and re-work in the field. We wanted to use drone imagery + AI + interactive tools to make pad planning faster, safer, and smarter.

What it does

  • Upload drone or site imagery and scale it using TIFF world files / manual input.
  • Drag-and-drop library of equipment (frac pumps, blenders, sand storage, datavan, wireline, etc.).
  • Automatically shows clearances, collisions, and exclusion zones.
  • AI Optimize button: analyzes the site image and generates a suggested optimal layout following operational principles (e.g., iron line for pumps, clear sight for datavan).
  • Export layouts as CSV or PDF with coordinate and equipment index.

How we built it

  • Frontend: Single-page app built in HTML, TailwindCSS, and vanilla JS (planned React/Angular).
  • Canvas rendering: custom functions for scaling, zooming, rotation, collision detection, and layout grouping.
  • AI integration: calls Gemini API to analyze drone imagery + constraints, returns JSON layout suggestions.
  • Export tools: jsPDF + jsPDF-autotable for PDF, CSV export via in-browser string generation.
  • Data inputs: real drone scans, equipment spec sheets, GPS world files.

Challenges we ran into

  • Translating between pixels and real-world coordinates was tricky.
  • Handling rotated bounding boxes and collision detection required custom math.
  • Integrating AI while keeping results safe and logical.

Accomplishments that we're proud of

  • Fully working prototype with drag-and-drop equipment placement.
  • AI-driven layout generation that respects real-world constraints.
  • Smooth UX (zoom, rotate, lock/unlock groups, info panels).
  • Exportable, shareable layouts for field operations.

What we learned

  • How to bridge geospatial concepts with front-end canvas rendering.
  • The importance of human-centered design in industrial tools.
  • That AI suggestions must be combined with engineer input — not replace it.

What's next for Smart Pad Layout Optimizer

  • Add 3D visualization of equipment and piping.
  • Better AI optimization using reinforcement learning or genetic algorithms.
  • Integrate with actual field GPS trackers for planned vs. actual comparison.

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