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From operational data to real-time decisions through AI and Data Physics

Field experience and technical knowledge of Oil & Gas combined with advanced models to improve efficiency, safety, and production.

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Our clients

Operational Downtime

Predictive models to anticipate failures and minimize unscheduled maintenance

Production & Efficiency

We improve performance of wells, plants, and critical equipment

Safety & HSE

Automatic detection of unsafe acts and anomalies through real-time video

Modules

Secondary Recovery in Mature Fields

Pattern optimization and injection redistribution

Models that identify connections between wells and effects such as channeling, helping to design efficient development strategies, optimize injection, improve recovery, and reduce operating costs.

  • Increased final recovery
  • Technical-economic optimization of injection
  • Reduced uncertainty in field decisions

Events such as channeling, "bypassed oil," and excessively swept zones significantly affect recovery.

Our machine learning and operations research models use data in combination with the physics governing multiphase flow in the reservoir ("Data Physics"), enabling precise characterization of connectivity between producing and injector wells and identifying critical deviations. With this information, specific action plans can be developed and executed to improve injection efficiency and optimize recovery factor.

  • Increased oil recovery and reduced resource usage
  • Minimization of operating costs and long-term production maximization
  • Greater effectiveness and adaptability to operational needs and changing reservoir conditions
Secondary Recovery

Autonomous and Intelligent Asset Inspection

Drone-in-a-box solutions with RGB, thermal, and LiDAR capture. Automatic detection of leaks, corrosion, hot spots, intrusion, and structural anomalies in pads, ducts, and lines.

  • Better HSE safety indices
  • Reduces and avoids operational risks
  • Significant inspection cost savings

Leaks, flow losses, and operational anomalies in wells, collectors, batteries, and equipment are common in the field

Our computer vision models detect leaks, flow losses, and anomalous behaviors in real time from images. They identify visual or thermal variations — fluid stains, mists, anomalous vapors, temperature differences, missing components — that anticipate failures before they worsen. This allows automatic alerts to the operations team, reduces downtime, and prevents environmental or operational incidents.

  • Optimizes operational efficiency, enabling autonomous inspections
  • Increases visual traceability and safety by identifying unauthorized objects, open fences, and risk conditions
Autonomous and Intelligent Asset Inspection

Predictive Maintenance

Prediction of failures or anomalous conditions and condition-based maintenance

Predictive models for pumps, compressors, generators, and critical equipment. Early anomaly detection, automatic intervention prioritization, and condition-based maintenance.

  • Lower unscheduled shutdown ratio
  • Greater equipment availability
  • Reduced unscheduled maintenance costs

Preventive and reactive maintenance often generates high costs and unscheduled shutdowns that strongly affect operation profitability.

We implement Machine Learning and operations research models that analyze historical and real-time data, evaluating operating conditions and identifying patterns, anticipating failures, and generating early alerts. This allows scheduling preventive interventions and allocating resources in time, avoiding unscheduled shutdowns, and minimizing production impact.

  • Response time optimization and cost reduction associated with unexpected repairs and production losses
  • Greater operational reliability, efficient resource use, and improved equipment lifespan
  • Greater profitability in continuous and safe operations
Predictive Maintenance

Production Engineering

We apply machine learning and artificial vision algorithms to diagnose the extraction status of wells, anticipate artificial lift system failures, and predict flow problems. We model with optimization techniques (operations research) to maximize production and recovery.

  • Higher sustained production
  • Fewer failures and downtime
  • Greater operational stability and predictability

Problem wells, early and/or repetitive failures are often a difficult condition to manage

Our deep learning models work with surface and downhole parameters (sensors) to ensure adequate diagnosis that allows the production engineer to streamline analyses and prioritize follow-up routines.

  • Failure rate reduction and minimization of repetitive interventions
  • Greater operational efficiency through early detection of anomalous conditions
  • Production increase and better resource utilization
Production Engineering

Drilling & Completion

Real-time anticipation for complex operations

Models that identify and predict both undesirable events and optimization opportunities. Artificial vision that recognizes critical activities and detects unsafe acts in real time.

  • Less NPT
  • Better prioritized interventions
  • Greater personnel safety
  • Real-time operational recommendations based on physical models and data

Screen outs, circulation losses, valve or collector obstructions, frac hits, influxes, or blowouts are costly and require complex remediation tasks.

Machine learning models trained with historical data allow real-time monitoring of the behavior of main operations. Using autoencoder networks and other advanced algorithms, we can detect anomalous patterns that predict the occurrence of undesired phenomena during operations. Early alerts allow teams to prevent risks and anticipate failures.

  • Cost and time reduction in drilling and completion
  • Increased safety, efficiency, and profitability of operations
  • Minimization of unforeseen problem impact
  • Safety incident reduction
Drilling & Completion

Processes & Facilities

Automatic deviation detection in gas and crude plants. Hybrid models (physics + ML) to improve treatment efficiency (liquid recovery), avoid quality deviations, and reduce losses.

  • Higher energy efficiency
  • More stable and predictable operation

Operational instability, quality deviations of treated product, and process inefficiencies generate additional costs and unnecessarily high operational burden.

We combine deep learning, physical modeling, and optimization techniques to maximize efficiency in gas, crude, and water plants. We detect hidden patterns, anticipate quality deviations, build digital twins of processes, and recommend optimal operating points to achieve more stable, energy-efficient, and efficient operations.

  • Higher liquid recovery
  • Lower quality deviation in treated product
Processes & Facilities

About Us

Eugenio Leiguarda

Eugenio Leiguarda

Digital Product Engineer

with over 15 years creating and scaling technological solutions for companies in multiple industries

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Diego Leiguarda

Diego Leiguarda

Engineer with over 20 years

of experience in the Oil & Gas industry, led operations, development, and strategic planning at companies like Pan American Energy, YPF, and CGC

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Our DNA: Deep Tech & Data Physics

We don't just analyze patterns; we understand the physics behind operations. Our architecture integrates physical models with state-of-the-art Machine Learning algorithms. This enables executing actions in a fraction of the time, with greater precision and lower cost

Why Vexta

Vertical Integration

We bridge the gap between field sensors and the control dashboard in the office.

Proven Scalability

Solutions designed to grow at the pace of new blocks and drilling pad development.

User Focus

We design workflows that operations teams actually adopt, transforming organizational culture through technology.

Data can take your operations to the next level!

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