OIL & GAS

Petabytes of seismic data. Growing every survey. Most of it never touched again.

RNDA encodes waveforms at acquisition and discards the originals. Storage costs crater. Proprietary survey data can't be stolen if it doesn't exist.

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The Problem

Seismic surveys generate petabytes of waveform data. Storage, retrieval, and reprocessing are the dominant infrastructure costs. Every query requires decompressing raw data before processing.

How RNDA Solves It

Proven on 10,000 real geological waveforms

Real IRIS/EarthScope waveform data across multiple geological stations. Encoded, queried, and validated at production scale.

Waveform similarity search at 30ms

Submit a raw seismic waveform — RNDA finds the most geologically similar historical records in milliseconds. Discrimination gap 0.97 — same geological station clusters at 0.4–1.0, different stations near zero.

Binary waveform queries

The waveform IS the query. No text description needed. Submit new survey data and find similar historical formations from 40 years of archives. Raw data permanently discarded after encoding.

Survey data without survey data storage

Encode at acquisition, discard raw waveforms before transmission. Only signatures reach the archive. Storage costs crater.

How RNDA Applies

01

Storage Elimination

Seismic waveform data compressed 14.3x eliminates expensive on-prem tape archives and cuts cloud storage bills for multi-basin 3D surveys. A 500 TB seismic library costs $9,650/year post-RNDA versus $138,000/year uncompressed — a 93% reduction.

02

Privacy Protection

Proprietary subsurface survey data and reservoir models are encoded in a form that cannot be reverse-engineered. Competitors who compromise storage infrastructure get 256-byte signatures, not exploration targets or acquisition parameters.

03

Compliance Management

MMS/BSEE data retention mandates for offshore operators require multi-decade preservation of seismic records. RNDA enables 40-year seismic archives without exponential storage budget growth — compliance cost scales with signature count, not data volume.

04

Intelligent Retrieval

Submit any seismic waveform and find the most geologically similar historical formations in 30ms. Proven on 10,000 real IRIS/EarthScope waveforms — discrimination gap 0.97. Same geological station clusters at 0.4–1.0, different stations near zero. Time-to-insight from days to minutes.

05

Collaborative Intelligence

Joint-venture partners and service companies share compressed seismic representations across organizational boundaries without exposing raw proprietary acquisition parameters. Only signatures cross the boundary — the subsurface intelligence travels without the data.

Storage Impact

Industry stat: Oil & gas seismic archives range from a few TB to 2 petabytes per company; a single land survey generates 2 TB per 8-hour shift (StoneFly / Rigzone)

500 TB × 20% × $276/TB ÷ 14.3x compression (mid-size operator)

500 TB seismic library saves ~$128K/year — 93% storage reduction on real IRIS/EarthScope waveform data

Proof of Concept Results

Real data. Measured numbers. No synthetic results.

14.3x
COMPRESSION
10,000
RECORDS TESTED
30.5ms
QUERY LATENCY
0.97 discrimination gap
SIMILARITY RANGE

Source: Real IRIS/EarthScope waveform data

What Becomes Possible

"A new seismic survey is acquired in an unexplored basin. The raw waveform data is encoded and permanently discarded at acquisition. In 30ms, RNDA queries 10,000 historical waveform signatures and returns the most geologically similar surveys — along with their known outcomes. The original survey data never exists in the system."

Ready to see it on your data?

Every number on this page came from a real POC. Yours will be built the same way — against your actual data type, measured compression, real query latency.

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RNDA — Reconstruction-Native Data Architecture