Vexta
Vexta provides an AI-driven operational intelligence platform for oil and gas operators, combining physics-based models with machine learning to predict equipment failures, optimize reservoir recovery, and detect safety anomalies in real time. The platform covers the full production lifecycle, from predictive maintenance and drilling recommendations to autonomous drone-based asset inspection using RGB, thermal, and LiDAR sensors.
- Artificial Intelligence
- Energy
- Data & Analytics
- Internet of Things
- Industrial Automation
- Drones & Uncrewed Systems
- Software Only
- Hardware
Funding
Founders
Founder details are not available yet.
Product
Problem
Oil and gas operators face costly unplanned downtime, inefficient production, and safety risks due to reactive maintenance practices, limited visibility into subsurface reservoir behavior, and manual inspection processes that miss early signs of equipment failure or unsafe conditions.
Solution
Vexta provides an AI-driven operational intelligence platform that integrates physics-based models with machine learning to deliver real-time decision support for oil and gas operations. The company addresses challenges across the production lifecycle: predicting equipment failures before they occur, optimizing water injection patterns in mature fields to maximize hydrocarbon recovery, detecting anomalies in real-time via computer vision on video feeds, and providing drilling recommendations to prevent costly non-productive time. Its hybrid "Data Physics" architecture combines domain knowledge of multiphase flow and reservoir mechanics with deep learning algorithms, enabling more accurate predictions than purely data-driven approaches. Vexta also deploys drone-in-a-box solutions with RGB, thermal, and LiDAR sensors for autonomous asset inspection, automatically detecting leaks, corrosion, hot spots, and intrusions.
Target Audience
Primary customers are oil and gas operators, including production engineers, drilling teams, plant managers, and HSE personnel at upstream and midstream companies operating mature fields, unconventional assets, and processing facilities across Latin America and North America.
Features
- Predictive maintenance models for pumps, compressors, and generators that analyze historical and real-time sensor data to anticipate failures and prioritize interventions based on condition
- Reservoir management for secondary recovery using machine learning and operations research to characterize inter-well connectivity and identify issues like channeling, bypassed oil, and over-swept zones
- Computer vision algorithms that detect fluid leaks, flow losses, thermal anomalies, missing components, unauthorized objects, and unsafe acts in real-time from video feeds
- Drilling and completion models using autoencoder networks and other deep learning techniques to predict events like screen outs, circulation losses, frac hits, and influxes before they occur
- Production engineering diagnostics combining surface and downhole sensor data with deep learning to assess well health, anticipate artificial lift failures, and predict flow problems
- Hybrid process models for gas and crude plants that combine physics with ML to build digital twins, detect quality deviations, and recommend optimal operating setpoints
- Drone-in-a-box inspection systems with RGB, thermal, and LiDAR capture for automated detection of fugitive emissions, corrosion, and structural anomalies in pads, pipelines, and facilities