Intelligent Monitoring and Optimization Control Platform for ESP Wells
End-to-end management across the full operating lifecycle of ESP wells, covering both in-depth single-well analysis and multi-well block management.
JuraESP is an intelligent monitoring and optimization control platform for ESP wells throughout their full operating lifecycle. It connects wellsite equipment, edge computing, central platforms, and specialized applications to support both in-depth single-well analysis and multi-well management. The platform enables centralized monitoring, intelligent diagnosis, health early warning, and constraint-based parameter optimization and control. It also captures operational knowledge and applies AI to support knowledge retrieval, explanation, and report generation.
Production, downhole, electrical, equipment, pump inspection, and daily report data are scattered across different sources.
Pressure, temperature, current, and liquid-rate indicators are strongly coupled, so single-threshold alarms cannot accurately reflect actual operating conditions.
Reservoir pressure and fluid supply capability change dynamically, causing design and operating conditions to gradually drift away from the current optimum state.
Fixed thresholds often trigger alarms only after problems occur and are insufficient for identifying gradual deterioration and potential failures.
Adjusting frequency and choke settings requires balancing production rate, pump intake pressure, pump efficiency, motor load, and equipment safety at the same time.
Experience in diagnosis, tuning, field handling, and pump inspection is scattered and difficult to consolidate into rules, cases, and standards.
Enable real-time monitoring of key well metrics, including production, bottomhole pressure, pump efficiency, current, and liquid production rate.
Provide macroscopic oversight and intelligent optimization of ESP operations through zoned visualization, status charts, and efficiency trend tracking.
Adopt a large-screen interface to centrally display the overall operation status of all ESP wells across the entire oilfield and the real-time operating conditions of key wells.
Use PCA dimensionality reduction, the system processes 13 parameters from well production operations and visualizes the well’s current operating status and historical production performance in a 3D format.
Utilize deep learning on key parameters, it facilitates single-well health assessment, full-field ESP lifetime prediction, and macroscopic health visualization, delivering precise data for optimized maintenance and risk mitigation.
Perform multidimensional statistics and analysis of core ESP indicators, with full traceability across key technical domains: design, operations, maintenance, and QC.
Match ESP capability to current production requirements while preserving adaptability to future reservoir and operating condition changes.
Specialized models handle deterministic engineering calculations, while the intelligent assistant understands user intent, orchestrates tools, retrieves knowledge, explains results, and generates reports; engineer confirmation and feedback are continuously accumulated as knowledge assets.
Use a nine-zone inflow–outflow relationship chart to identify the current operating condition, historical trajectory, and optimization direction.
Connect equipment, data, analysis, and operations to achieve seamless coordination across the value chain.
Combine mechanistic models, expert rules, and data-driven methods to improve diagnosis and optimization quality.
Coordinate reservoir inflow, lift capacity, and surface adjustments to maximize production and efficiency.
Multi-level constraints and protection mechanisms ensure reliable, compliant, and safe operation under different conditions.
From planning, design, operation, and evaluation to continuous improvement, deliver sustained value.
Open architecture and flexible deployment support multiple asset types and large-scale rollout.
Designed for onshore and offshore oilfields operating large numbers of ESP wells, JuraESP integrates real-time production data, ESP operating parameters, and historical maintenance records with AI algorithms, machine learning, and digital twin technology to deliver end-to-end intelligent analysis for individual wells. The platform provides a complete workflow covering real-time monitoring, anomaly detection, condition diagnosis, and treatment tracking, enabling proactive well management and faster operational decision-making.
Challenges
Solution
Designed for oilfields operating large numbers of ESP wells, JuraESP leverages real-time production data, well history, digital twin technology, and AI-powered reinforcement learning to establish a closed-loop workflow for single well operation optimization. Covering target well identification, condition diagnosis, solution simulation, parameter optimization, and performance evaluation, the platform automatically identifies underperforming wells, recommends optimal operating parameters, and validates optimization strategies through digital twin simulation. This enables oilfields to increase production, reduce energy consumption, and extend equipment life without additional hardware investment.
Challenges
Solution
Designed for new ESP well commissioning and pump replacement during major workovers, JuraESP integrates reservoir productivity analysis, wellbore multiphase flow calculations, equipment performance models, and intelligent selection algorithms into a unified design workflow encompassing production forecasting, wellbore analysis, equipment selection, and design verification. The system automatically optimizes ESP system design, ensuring precise alignment between reservoir inflow capacity and equipment operating parameters while delivering high-production, energy-efficient, and highly reliable design solutions for new wells.
Challenges
Solution
Condition-based maintenance
Intelligent well selection
Dynamic parameter control
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