Python STOIIP Monte Carlo Calculator

Budget: $10 - $30

I currently rely on an Excel worksheet that runs a manual Monte-Carlo STOIIP calculation. I need the whole workflow migrated to Python so I can launch a single desktop app, enter or paste my inputs, press “Run” and instantly see the same outputs the spreadsheet gives me—plus cleaner visuals and a much faster simulation.

Core functionality • Read the same variables the sheet now uses (area, net thickness, porosity, net-to-gross, initial oil saturation, Boi). • Run a configurable number of Monte-Carlo iterations, reproduce the STOIIP distribution and report P10, P50 and P90. • Plot, with Matplotlib, histograms of each input distribution, a STOIIP histogram with the P10/P50/P90 markers, a “normal fit” curve with the same percentile lines, tornado charts based on normalised sensitivity weights and scatter plots of every input versus STOIIP. • Allow me to save each figure as PNG/PDF and print the numerical summary to a text or CSV file for my report.

Interface expectations I do not have a fixed layout in mind, so feel free to suggest a GUI design that keeps data entry, run control and results tabs clean and intuitive. Tkinter, PyQt or any other desktop framework is fine as long as it stays lightweight and easy to install.

Deliverables

1. Fully commented Python source code.
2. Stand-alone GUI executable (Windows preferred).
3. All generated plots and printed output files from a sample run, appended to a short README.
4. Any helper spreadsheets or notebooks used during development.

Acceptance Results should reconcile with my original Excel model to within reasonable Monte-Carlo variability; I will supply the sheet for cross-checking.

When you respond, please point me to past work that shows you have built scientific, reservoir, or data-driven desktop tools before.