Production Data QC
Spreadsheet
28 rows x 6 columns
| A | B | C | D | E | F | |
|---|---|---|---|---|---|---|
| 1 | Production Data QC | |||||
| 2 | Settings | |||||
| 3 | Window Size | 5 | points | |||
| 4 | Sigma Threshold | 2 | std dev | |||
| 5 | ||||||
| 6 | Fit Comparison | Raw Data | Cleaned Data | |||
| 7 | Qi (bbl/mo) | 2284.196968 | 3605.161488 | |||
| 8 | Di (1/mo) | 0.04873079498 | 0.1584264226 | |||
| 9 | b | 0.05788834933 | 0.7888223828 | |||
| 10 | R² | 0.2558778555 | 0.9982505356 | |||
| 11 | ||||||
| 12 | Production Data | |||||
| 13 | Month | Raw Rate | Outlier? | Clean Rate | Arps Raw | Arps Clean |
| 14 | 1 | 3000 | False | 3000 | 2175.704099 | 3105.221337 |
| 15 | 2 | 2700 | False | 2700 | 2072.647634 | 2717.045078 |
| 16 | 3 | 500 | True | 2450 | 1974.741047 | 2407.86609 |
| 17 | 4 | 2200 | False | 2200 | 1881.713651 | 2156.429309 |
| 18 | 6 | 1800 | False | 1800 | 1709.287453 | 1773.692785 |
| 19 | 9 | 1400 | False | 1400 | 1481.288486 | 1386.746081 |
| 20 | 12 | 5500 | True | 1175 | 1285.213382 | 1128.573034 |
| 21 | 15 | 950 | False | 950 | 1116.383855 | 945.3422803 |
| 22 | 18 | 800 | False | 800 | 970.8372731 | 809.2716564 |
| 23 | 21 | 700 | False | 700 | 845.2126487 | 704.6568396 |
| 24 | 24 | 600 | False | 600 | 736.6555065 | 621.9906183 |
| 25 | 30 | 480 | False | 480 | 561.3942899 | 500.2360957 |
| 26 | 36 | 400 | False | 400 | 429.6340562 | 415.3982784 |
| 27 | 42 | 340 | False | 340 | 330.142048 | 353.2543637 |
| 28 | 48 | 290 | True | 340 | 254.6954586 | 305.9817152 |
Description
Clean production data before decline curve analysis by detecting and removing outliers using a rolling-window Z-score method. Compare raw vs cleaned data and show the impact on Arps curve fitting — noisy data produces unreliable decline parameters.
Workflow: Flag outliers → interpolate over them → re-fit decline curve → compare parameters and R² before/after cleaning.
Workflow
- Inputs: Monthly production history (time, rate) with outliers
- Step 1: Flag outliers using PO.DCA.Data.OutlierFlag (rolling Z-score)
- Step 2: Clean data by interpolating over flagged points
- Step 3: Fit Arps to both raw and cleaned data, compare R²
- Output: Before/after comparison showing data quality impact on DCA
How to use this blueprint
- In Excel, go to the Petroleum Office ribbon tab and click Blueprint Manager
- Search for Production Data QC
- Click on the blueprint to preview the spreadsheet template
- Click Insert to place it into your worksheet. Modify the input values to match your data.