Guided track

Zero to Your First Production Forecast

By day 14 you can take a well's production history and produce a defensible EUR forecast in Python — the single most common analysis in production engineering.

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Days complete
  1. 1

    Day 1 — Get Python running

    ~25 min

    Nothing else matters until the tools work. Everything here runs in your browser, so there is nothing to install.

    menu_book Read Ch 1fitness_centerInstall and Verify
  2. 2

    Day 2 — Engineering maths in code

    ~30 min

    Turn a pressure-gradient calculation you already do by hand into code you can re-run on any well.

    menu_book Read Ch 2fitness_centerFormation Pressure Gradient
  3. 3

    Day 3 — Functions and decisions

    ~30 min

    Wrap a calculation in a function once and it works for every well in the field, not just this one.

    menu_book Read Ch 2fitness_centerProductivity Index
  4. 4

    Day 4 — Organise well data

    ~30 min

    Real fields have dozens of wells. Structure beats a pile of loose variables the moment you have more than one.

    menu_book Read Ch 3fitness_centerWell Header Builder
  5. 5

    Day 5 — NumPy and pandas

    ~35 min

    Every production dataset you will ever touch goes through these two libraries.

    menu_book Read Ch 4fitness_centerProduction Data Loader
  6. 6

    Day 6 — Clean messy production data

    ~35 min

    Field data arrives broken — missing months, negative rates, impossible spikes. Cleaning it is most of the job.

    menu_book Read Ch 4fitness_centerData Cleaning Pipeline
  7. 7

    Day 7 — Plot your first decline

    ~30 min

    The moment the curve appears on screen, the rest of the track stops being abstract.

    menu_book Read Ch 5fitness_centerDecline Rate Comparison
  8. 8

    Day 8 — Compare wells on one chart

    ~30 min

    Reading several wells side by side is what surveillance actually looks like day to day.

    menu_book Read Ch 5fitness_centerMulti-Well Production Overlay
  9. 9

    Day 9 — Load a real production file

    ~30 min

    Go from a CSV somebody emailed you to a clean rate–time series you can trust.

    menu_book Read Ch 6fitness_centerProduction Data Loader
  10. 10

    Day 10 — Arps: exponential vs hyperbolic

    ~35 min

    These are the models behind essentially every production forecast in the industry.

    menu_book Read Ch 9fitness_centerExponential vs Hyperbolic
  11. 11

    Day 11 — The b-factor

    ~30 min

    One number quietly decides your reserves. Get it wrong and the forecast is wrong by millions of barrels.

    menu_book Read Ch 9fitness_centerThe b-Factor
  12. 12

    Day 12 — Abandonment rate and EUR

    ~30 min

    Where you stop the forecast is where the money is. This is the number the business cares about.

    menu_book Read Ch 9fitness_centerAbandonment Rate Sensitivity
  13. 13

    Day 13 — Probabilistic DCA

    ~35 min

    A single number is a guess. A P90–P50–P10 range is an answer you can defend in a meeting.

    menu_book Read Ch 9fitness_centerProbabilistic P10/P50/P90 EUR
  14. 14

    Day 14 — Capstone: forecast a well end to end

    ~40 min

    Put all fourteen days together and produce a forecast you would be happy to put your name on.

    menu_book Read Ch 9fitness_centerModified Hyperbolic - Bounding a b>1 Shale Wellrocket_launch Open the Decline Curve → EUR project

This track deliberately skips well logs and PVT — you don't need either to forecast production. Once you finish, the rest of the book is there for the topics you actually reach for next.