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Open-weight model

well-production-event-causal

by Sankalp Thakur sankalpsthakur/well-production-event-causal

well-production-event-causal is an open-weight model from Sankalp Thakur, released under MIT License. Its published files total 358.3 MB.

Compact causal detectors for production events in real offshore well recordings from Petrobras 3W 2.0, tested on well groups they never saw during training. These are research models with their failures published. None is ready for deployment.

Parameters—
Context—
Weights19.8 MB
Licensemit
AccessOpen weights
Monthly Downloads—

Model Card

By Sankalp Thakur, published under mit, revision ea7b875b5d88.

Compact causal detectors for production events in real offshore well recordings from Petrobras 3W 2.0, tested on well groups they never saw during training. These are research models with their failures published. None is ready for deployment. The validation-selected primary is a 36-parameter logistic model. Two causal TCNs have 4,961 parameters each. Twenty-one of the covered episodes already had an alarm running when they began. In a later 15-method validation comparison, the selected logistic model caught 13 of 24 onsets within 300 s, 11 of them from one recording group. IBM's pretrained TSPulse caught none. - Every trained candidate and reference method, with the full report and…

Read Sankalp Thakur's full model card

Well production-event detectors (Petrobras 3W)

Compact causal detectors for production events in real offshore well recordings from Petrobras 3W 2.0, tested on well groups they never saw during training. These are research models with their failures published. None is ready for deployment.

Results on unseen well groups

The validation-selected primary is a 36-parameter logistic model. Two causal TCNs have 4,961 parameters each.

Measure Result Budget
Episodes covered 24 of 55
Known onsets caught within 300 s 3 of 12
False alarms 42 over 9.124 normal days 1 per day
Normal time spent in alarm 64.392% 1%
Normal time with usable inputs 59.812%

Twenty-one of the covered episodes already had an alarm running when they began. In a later 15-method validation comparison, the selected logistic model caught 13 of 24 onsets within 300 s, 11 of them from one recording group. IBM's pretrained TSPulse caught none.

What's here

  • Every trained candidate and reference method, with the full report and deployment assessment
  • The 15-method validation comparison and the TSPulse causal preflight
  • Streaming replay and timing: median 3.46 and 3.35 ms per packet for the two TCNs on the observed Linux CPU, excluding file decoding and acquisition. This is not a target-device latency.
  • Audit tables in the 3W audit dataset, and a live explorer

Reproduce

From the repository root, at a fixed revision:

uv run --no-project --python 3.12 --with numpy==2.2.6 --with torch==2.8.0 python tools/replay_well_checkpoints.py --artifacts artifacts --receipt replay.json

This checks every artifact hash and reloads all eight learned checkpoints. It does no training.

Limits

  • Scores rank classes. They are not calibrated failure probabilities.
  • Target hardware, site calibration and field acceptance are untested. Eight held-out well groups are kept unopened for a future fresh test.
  • The input checks for malformed, stale or wrong-unit packets are not connected to these classifiers.

License

Authored code and weights are MIT. Data derived from Petrobras 3W keeps CC BY 4.0; see ATTRIBUTION.md. The TSPulse weights and code in the preflight kit keep Apache 2.0.

The full development log, including every recovered failure, is in HISTORY.md.

Identity and Version

Repository
sankalpsthakur/well-production-event-causal
Publisher
Sankalp Thakur
Task
Not stated by the source
Modality
Other
Library
Not stated by the source
Parameters
Not stated by the source
Languages
Not stated by the source
Revision
ea7b875b5d88196d5bc3d50b77f267dad8186133
First published
2026-10-04
Last updated
2026-10-07

Files and Weights

438 files, 358.3 MB in total. The weights are 48 files totalling 19.8 MB in npz.

Weights48 files · 19.8 MB
Configuration279 files · 34.2 MB
Documentation39 files · 177.7 KB
Other71 files · 304.2 MB
Repository1 file · 2.0 KB
Every file
FileTypeSizeSHA-256
artifacts/causal_tcn_seed29.npzWeights22.2 KB ea1756f830cb
artifacts/causal_tcn_seed7.npzWeights22.2 KB 83190a798819
artifacts/checkpoint-reload-inputs.npzWeights39.5 KB 6bde8f111b2d
artifacts/confirmation-traces.npzWeights1.2 MB a9e36eda434a
artifacts/missingness_logistic_C0.1.npzWeights475 B 33ea12ffd937
artifacts/missingness_logistic_C1.0.npzWeights473 B ef0113aa5424
artifacts/missingness_logistic_C10.0.npzWeights474 B 1852c6399a83
artifacts/reference-reload-inputs.npzWeights3.8 MB 1798c750c380
artifacts/signal_logistic_C0.1.npzWeights713 B d9eac1023d4c
artifacts/signal_logistic_C1.0.npzWeights714 B e637778f2382
artifacts/signal_logistic_C10.0.npzWeights714 B f7faa8eed57f
artifacts/training-window-selection.npzWeights108.1 KB b72664cc59af
artifacts/validation-traces.npzWeights3.0 MB 73919eadd166
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foundation-validation/foundation_review/actual-v2-collected/causal_tcn_seed7-actual-replay.npzWeights1.6 KB 195ab1a9af8f
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License and Download

License
mit
Access
Open weights, no gate
Download size
19.8 MB
Download from Sankalp Thakur

Released by Sankalp Thakur through its official repository on Hugging Face. Read the license.

Built From

Memory Requirements

PrecisionWeights in memory
As published19.8 MB

Weights only, from the published parameter count; the key-value cache and runtime add to this.

Questions About well-production-event-causal

Can I use well-production-event-causal commercially?

Yes. well-production-event-causal is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included.