Confidential Technical Briefing

Odin / SkyFrac
Reconstruction & Attack Plan

A technical consolidation of public claims and physics first-principles to define a legally clean independent equivalent, prove it works, and interrogate vendors.

Prepared by Andrew Molt | Target: Engineering Leadership & ARI Development

The Core Thesis

Odin/SkyFrac is not computing exact "true fracture compliance" from surface data alone. Instead, they provide a real-time frac intelligence platform that infers hidden downhole hydraulic state.

What They Likely Do:

Train models on rich historical, downhole, and full-sensor datasets.
Deploy surface/minimum-sensor models during live treatments.
Convert inferred state into a compliance-inspired "C-Factor" (frac-quality/restriction-risk metric).

What We Must Avoid Saying:

• Do not claim they compute "exact true geomechanical compliance."
• Do not state they definitely use PINNs, LSTMs, or XGBoost.
• Do not claim they can uniquely invert true BHTP from surface data alone without uncertainty.

Inference Architecture

graph TD A[Raw Frac Telemetry] --> B(Timestamp Align & Normalize) B --> C{Data Quality Gates} C -->|Pass| D[Treatment State Classifier] D --> E[Residence-Time Correction] E --> F[Hydrostatic & Pipe Friction Model] F --> G[Perf / NWB Tortuosity Model] G --> H[Residual ML Correction] H --> I[Estimated P_wb & P_frac] I --> J[Windowed dP/dV] J --> K((Apparent Restriction Index)) K --> L{Confounder Gates} L -->|Alert| M[Operational Recommendation]

What is C-Factor Really? Probable Signal Flow

1. Input

Raw STP + Rate

2. Wellbore Model

Hydrostatic & Friction

3. Perf Model

Erosion & Tortuosity

4. Hydraulic State

Fracture-Face P_frac

5. Calculation

Windowed dP/dV

6. Normalization

Baseline Tracking

7. Validation

Operational Gates

8. Smoothing

Nonlinear Scaling

9. Final Output

C-Factor

Leadership Takeaway: C-Factor is likely a heavily post-processed, gated, and normalized version of dP/dV—not a direct physical measurement.

The Mathematics of Restriction

1. Fracture-Face Pressure

We must strip out wellbore, perforation, and tortuosity friction to find the pressure actually seen by the fracture.

P_{frac,est} = P_{wb,est} - \Delta P_{perf} - \Delta P_{tort} - \Delta P_{NWB}

Where wellbore pressure accounts for hydrostatic head and pipe friction:

P_{wb,est} = STP + (0.052 \cdot \rho \cdot TVD) - \Delta P_{pipe}

Warning: Field units make \(\Delta P_{perf}\) coefficients brittle. Always validate against dimensionless groups like Reynolds number.

2. Apparent Elastance (Restriction)

Instead of raw instantaneous derivatives, we use causal windowed arrived volume (\(\Delta V_w\)).

E_w = \frac{\Delta P_w}{\Delta V_w}

3. Apparent Restriction Index (ARI)

The final normalized operational metric.

ARI(t) = \frac{E_w(t)}{median(E_w)_{healthy\_baseline}}

The Flaw of Raw Compliance & Gates

The naive industry formula for compliance is \( C = Q / (dP/dt) \). It is operationally dangerous because pipe friction, rate changes, and phase lag pollute the derivative. Furthermore, any real engine must explicitly gate against these physical confounders:

Proppant Duning

Cluster Reallocation

Pressure-Dependent Leakoff

Friction Reducer Drops

Water Hammer / Transients

Sensor Data Quality

Why 20 Minutes? The Four Physical Drivers

1. Residence Time Lag

Surface-proppant arrival delay means restriction is already developing downhole before surface pressure reacts.

2. Fracture Tip Screenout

Proppant bridging at the tip creates backpressure that propagates to wellbore over minutes.

3. Perf Erosion Masking

Perforations erode open, temporarily hiding downhole restriction until erosion stops compensating.

4. Poroelastic Back-Stress

Matrix pressure changes slowly stiffen the fracture face before catastrophic screenout.

Key Insight: Odin’s models likely detect the derivative of the derivative (ARI slope acceleration) to predict these trajectories.

The Missing Link: How Do They Predict 20 Mins Early?

Odin claims to see screenouts up to 20 minutes before surface pressure spikes. A purely physics-based \(dP/dV\) equation cannot do this. To beat them, we must build the Predictive ML Layer.

Hypothesis 1: Time-Series Anomaly Detection

Using sequence models (like LSTMs or Temporal Convolutional Networks) trained on the first derivative of the Apparent Restriction Index (ARI slope). The model learns the subtle trajectory curve that inevitably leads to failure before the human eye can spot the inflection point.

Hypothesis 2: High-Frequency Signatures

Looking at the acoustic/pressure reflection signatures (like subtle water hammer degradation) within the 1Hz or 50Hz data. Changes in the reflection time can indicate near-wellbore choking long before gross pressure rises.

Live Stage Simulation: ARI vs. STP

00:12:45 elapsed

2.0 ARI 1.0 ARI 0.0 ARI

10k PSI 5k PSI 0 PSI

ARI (Restriction)

STP (Surface Pressure)

Acceleration Detected

Warning

Screenout Risk > 90%

Current ARI: 1.27 0.18/min Screenout predicted in 18:30

The Validation Strategy

How we prove to leadership that our engine actually works, without needing to see Odin's code.

Test A: BHTP Reconstruction

Can our physics + residual ML reconstruct true downhole pressure on held-out stages? We must track RMSE and bias specifically during sand ramps and chemical changes.

Test B: ARI vs Raw Surface Pressure

Does our ARI detect restriction earlier and more reliably than a raw STP derivative? We will measure lead time, precision, recall, and false positives per stage.

Test C: Confounder Stress Test

Ensure the model does not confuse an FR loss with rock stiffening, or proppant duning with fracture-face restriction.

Test D: Production Correlation

Does our stage-level ARI score actually add explanatory power to EUR models compared to raw cluster counts and fluid intensity?

The Interrogation Playbook

If we are in a bake-off or meeting with vendors claiming "true compliance," ask these exact questions to reveal their true architecture:

1. "For a new basin, do you need a science well with downhole gauges before your surface-only deployment becomes reliable?"
2. "If Friction Reducer drops mid-stage, how does your model avoid misclassifying the pipe friction spike as reservoir stiffening?"
3. "Is your C-Factor computed from STP, estimated BHTP, or fracture-face pressure? How do you prevent it from blowing up when dP/dt is near zero?"
4. "Does '50 ms response' mean your actual ML model inference time, or just sensor-to-dashboard refresh latency?"

Why Our Strategy Beats "True Compliance" Claims

Competitive Moats

Patent-Proof: A physics-first approach avoids IP infringement of black-box ML.
Explainability: Reason codes build operator trust vs. black-box alerts.
Configurable Gates: Adjust thresholds per basin or operator risk tolerance.

Cost & Scalability

Fewer Science Wells: Leverage industry data vs. requiring expensive downhole gauges.
Lower Compute: Hybrid physics+ML severely reduces edge/cloud inference costs compared to raw neural nets.

Competitive Comparison

Feature	Odin (Inferred)	ARI Engine (Our Approach)
Downhole Dependency	Requires science wells	Surface-first, physics-corrected
Alert Explainability	Black-box "C-Factor"	Reason codes + Gated thresholds
Model Class	Proprietary Neural Network	Hybrid Physics + Residual ML

The Final Verdict

We have not reverse-engineered Odin's proprietary code, but we have reverse-engineered their most likely functional service architecture.

It is a full-sensor-trained, surface-deployed, data-cleansed downhole-state inference system. It should not be framed as "true fracture compliance" from surface data, but rather as apparent hydraulic restriction.

What We Reconstructed

• The train-rich / deploy-surface architecture
• The compliance-inspired "C-Factor" concept
• The hidden-state inference problem
• The correct pressure & friction decomposition
• The major confounders & physical failure modes

What Remains Unknown

• The exact ML model class utilized
• The exact proprietary C-Factor formula
• The specific cloud/edge deployment topology
• The exact trigger thresholds for their alerts

Odin / SkyFrac Reconstruction & Attack Plan