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Client Outcomes

From Data Backlog to Overnight Delivery

Two anonymized examples of how Veritas Seismic has transformed borehole microseismic workflows for operators across North America and the North Sea. Client details withheld by agreement.

All operator names, basin-specific identifiers, and proprietary dataset parameters have been removed or generalized at client request. Outcome metrics are real and verifiable upon engagement under NDA.
Unconventional — North America

A Permian Basin Operator Cuts Catalog Backlog from 3 Weeks to 48 Hours

A mid-size Permian operator was managing a 6-well hydraulic fracture monitoring program with an in-house processing team. Event catalog backlogs were reaching 18–21 days due to analyst bandwidth constraints, forcing completion engineers to make frac decisions on stale data.

Core Challenges
  • 18–21 day event catalog lag creating blind spots in real-time frac optimization
  • In-house analysts spending 70% of time on noise triage, leaving 30% for actual picking
  • Inconsistent pick QA/QC across three analysts with no standardized audit protocol
  • No audit trail deliverable — picks were indefensible in post-job reviews
Our Approach
0148-hour calibration phase: ingested client SEGY archive, aligned to their internal velocity model, established pick style baseline against 200 reference events provided by client geophysicist.
02Deployed full AI pre-conditioning pipeline — reduced analyst triage time by 10× within first week, enabling same-shift event delivery during active frac stages.
03Implemented nightly Tri-Stage Validation cycle: statistical QC pass → cross-channel consistency → named Senior Geophysicist blind re-pick audit on 20% random sample per batch.
04Delivered QA/QC-stamped event catalogs by 06:00 UTC each morning, with full audit trail documentation and per-batch correlation statistics against client reference picks.
Outcome Metrics
18d → 48h
Catalog Lag Reduction
97.2%
Correlation with Client Standards
10×
Analyst Triage Time Saved
58%
Cost Reduction vs Prior Model
// Duration Ongoing program · 6 wells · ~3,200 events cataloged per month
// Format SEGY input · Per-well catalog + QA/QC report · Overnight delivery
Offshore — North Sea

A North Sea Operator Builds a Custom Monitoring Dashboard for Borehole Arrays

An offshore operator running a multi-well borehole array program needed more than just picking services — they needed a real-time event monitoring interface that could be used by operations engineers without geophysics backgrounds. Their existing workflow required a geophysicist on shift 24/7 to interpret outputs, creating an unsustainable staffing model.

Core Challenges
  • Operations engineers could not interpret raw event catalogs without geophysicist present on shift
  • No real-time visualization of event locations relative to wellbore geometry
  • Alarm thresholds for induced seismicity were managed via email, not automated monitoring
  • Data lived in siloed SEGY archives with no queryable interface for post-job analysis
Our Approach
01Deployed standard picking workflow to handle all event cataloging from the borehole array, establishing a clean, validated data stream as input to the monitoring system.
02Built a custom real-time event monitoring dashboard: spatial map view with wellbore overlay, magnitude color-coding, and per-channel SNR readout — designed for operations engineers, not geophysicists.
03Integrated automated alarm thresholds with configurable magnitude triggers and email/SMS notification routing — eliminating the need for an always-on geophysicist for routine monitoring.
04Delivered a queryable archive interface allowing engineers to search, filter, and export historical events by date range, magnitude, or spatial zone without specialist support.
Outcome Metrics
Zero
Always-On Geophysicist Shifts Required
<30s
Event-to-Dashboard Latency
8 wks
Build & Deployment Timeline
96.8%
Pick Correlation with Client Standards
// Duration 8-week build · Ongoing picking + support retainer
// Stack React · WebSockets · PostgreSQL · Python pipeline · AWS

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