EarthNET

EarthNET is a cloud-native, AI-driven geoscience platform built by Earth Science Analytics (now owned by IMDEX) that accelerates subsurface interpretation workflows for oil and gas, carbon capture and storage, and renewable energy projects.

Reviewed by 7wData

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Publisher review

EarthNET is a cloud-native, AI-driven geoscience platform built by Earth Science Analytics (now owned by IMDEX) that accelerates subsurface interpretation workflows for oil and gas, carbon capture and storage, and renewable energy projects. Launched in 2016 from Stavanger, Norway, the platform integrates machine learning models with physics-based approaches to predict rock and fluid properties, interpret seismic data, and analyze well logs at scale. The suite includes AI Wells (petrophysical prediction), AI Seismic Interpretation (fault and horizon detection), AI Seismic Properties (3D property volume generation), AI Images (computer vision for drill cuttings), a cloud-based Data Lake, visualization tools, and collaboration features.

EarthNET claims >90% reduction in interpretation time (weeks compressed to hours) and >95% accuracy in rock property predictions using quality-controlled training data. The platform operates cloud-agnostic, supporting legacy corporate databases, folder systems, and the OSDU Data Platform. IMDEX acquired an 80.5% stake in July 2025 for A$26 million to integrate EarthNET with existing offerings like Datarock and aiSIRIS, positioning it as a primary growth engine for expanding beyond energy into minerals and mining.

Publicly documented case studies demonstrate deployment in cross-border machine learning projects, dense seismic surveys, and CCUS site characterization, with peer-reviewed publications in industry journals. The platform targets teams of geoscientists, petrophysicists, and geophysicists who currently spend weeks on manual interpretation tasks.

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How it works

  1. AI Wells

    Predicts missing well log curves and rock/fluid properties using machine learning trained on quality-controlled petrophysical data with quantified uncertainty.

  2. AI Seismic Interpretation

    Automatically detects faults, horizons, and geobodies from 3D seismic surveys, compressing interpretation cycles from weeks to hours.

  3. AI Seismic Properties

    Generates 3D reservoir property volume predictions (porosity, saturation, lithology) from seismic and well data using deep learning inversion.

  4. AI Images

    Computer vision tool for automated analysis and digitization of drill cuttings images and other borehole-derived visual data.

  5. Data Lake

    Cloud-based central repository supporting well data, seismic, image data, lab results, and cultural data with metadata indexing and OSDU compatibility.

  6. EarthNET Viewer

    Interactive visualization platform for well bore plots, seismic displays, histograms, 2D/3D maps, and cross-section analysis of subsurface data.

  7. Collaborative Insights Dashboard

    Multi-user project management and analytics interface combining AI recommendations with expert human interpretation for cross-disciplinary teams.

Strengths and trade-offs

Strengths

  • Dramatically cuts interpretation time from weeks to hours on seismic and well log analysis, with quantified >95% prediction accuracy on rock properties.
  • Operates cloud-agnostic (AWS, Azure, on-premise) with OSDU Data Platform compatibility, reducing vendor lock-in for large energy operators.
  • Backed by IMDEX acquisition (July 2025) with integration roadmap into broader orebody knowledge platform for minerals/mining expansion beyond oil & gas.

Trade-offs

  • High model accuracy claims lack independent third-party validation; relies on vendor-reported metrics and case study data without peer-reviewed benchmarking against competing geoscience software.
  • Requires quality-controlled training data upstream; models inherit biases from legacy well-log and seismic datasets which may not represent frontier exploration basins or unconventional plays.
  • Cloud infrastructure costs and GPU compute for real-time 3D property inversion can become substantial for large surveys; pricing opaque and no public per-project or consumption-based rate card available.

Pricing context

EarthNET uses undisclosed enterprise licensing with no public pricing listed. IMDEX (current majority owner) targets A$4 million in revenue and breakeven operating margin for FY2026. Likely deployed as a per-seat annual license or per-project SaaS model targeting oil & gas operators and service companies, but actual pricing tiers, minimums, and consumption-based options remain confidential. Customers historically include oil majors and regional E&P companies via partnership agreements.

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Sources

Reporting on this tool draws on these publicly available sources.

  1. www.earthanalytics.ai — EarthNET overview, core features (AI Wells, AI Seismic Interpretation, AI Images, Data Lake, Viewer), target industries (oil & gas, CCS, renewable energy), deployment model (cloud-native).
  2. www.earthanalytics.ai — Detailed feature descriptions (AI Wells, AI Seismic Interpretation, AI Seismic Properties, AI Images, Data Lake, Viewer, Insights Dashboard), performance claims (>90% reduction in interpretation time, >95% accuracy), workflow acceleration from weeks to hours.
  3. www.imdex.com — IMDEX acquisition of Earth Science Analytics (80.5% stake for A$26 million, July 2025), integration strategy with Datarock/aiSIRIS, revenue forecast (A$4 million FY2026), expansion into minerals/mining, founder retention structure (19.5% put/call after 4 years).
  4. www.earthanalytics.ai — Case studies (Predicting Overlooked Hydrocarbons, Carbon Storage Site Characterization, Utsira OBN Survey Analysis, Cuttings Image Analysis), peer-reviewed publications in First Break and GeoExpro, partnerships with PETRONAS and AWS, industry credibility via Norwegian Petroleum Directorate and Oil and Gas Technology Centre recognition.