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Development of an Advanced Web-Based Monitoring and Analytics System for Oil & Gas Operations
  1. case
  2. Development of an Advanced Web-Based Monitoring and Analytics System for Oil & Gas Operations

Development of an Advanced Web-Based Monitoring and Analytics System for Oil & Gas Operations

blackthorn-vision
Energy & natural resources
Manufacturing
Supply Chain

Identified Challenges in Oil & Gas Operational Monitoring

The client faces difficulties in real-time monitoring and analysis of well and equipment performance, leading to blind spots, delayed issues detection, and suboptimal decision-making. Existing systems lack scalability, mobile accessibility, and modern analytics capabilities to handle large data volumes and complex operational parameters, thereby risking equipment failure, downtime, and financial losses.

About the Client

A mid to large-sized energy company specializing in oil and gas production, seeking to enhance operational efficiency through digital transformation.

Goals for the New Monitoring and Analytics Platform

  • Implement a scalable, web-based monitoring system accessible from any device or location with internet connectivity.
  • Ensure high-level data security with deployment options tailored for different organizational needs (cloud and on-premises).
  • Enable real-time data tracking and early error detection across multiple wells and equipment, facilitating proactive maintenance.
  • Provide detailed visualization tools for production management, including real-time and historical analytics on well parameters and operational status.
  • Allow flexible configuration of alarms, notifications, and operational controls based on dynamic production conditions.
  • Facilitate stakeholders’ decision-making with comprehensive, actionable insights derived from large datasets.

Core Functional Capabilities for the Monitoring System

  • Global, device-agnostic access to operational data via secure online portals.
  • Flexible configuration interface for customizing data views, alarms, and user roles according to operational needs.
  • Real-time tracking dashboards displaying production parameters, equipment status, and performance trends.
  • Automated Error Detection (AED) for early identification of potential failures, with configurable alarm rules and severity levels.
  • Historical data analysis tools, featuring charting, reporting, and data export functionalities.
  • Control mechanisms to authorize operational actions such as starting, stopping, or adjusting well processes based on real-time insights.
  • Alarm and event logging for traceability and compliance.

Recommended Technologies and Architectural Approaches

Typescript
HTML5
SaaS deployment models
Angular with NGRX for frontend development
Highcharts for data visualization
WebSockets for real-time data streaming
Aggrid for data grids
Java Spring Boot for backend services
REST API with Swagger documentation
PostgreSQL, Cassandra, Redis for data storage
Kafka, RabbitMQ for messaging and data pipelines
Docker, Kubernetes, Helm for containerization and orchestration
Prometheus for monitoring

Essential External System Integrations

  • Surface processing skids systems for real-time performance data
  • Remote wellhead monitoring sensors
  • Downhole instrumentation data sources
  • Alarm and control system interfaces
  • Existing enterprise data platforms for data aggregation

Critical Non-Functional System Requirements

  • System must support scalability to handle data from hundreds of wells concurrently.
  • Real-time data processing and visualization with minimum latency (preferably under 1-2 seconds).
  • Secure deployment options, with robust encryption and access control protocols.
  • High availability and fault tolerance to minimize downtime.
  • Compliance with industry standards for data security and operational safety.

Projected Business Outcomes and Benefits

The implementation of this advanced monitoring and analytics platform is expected to significantly enhance operational efficiency, minimize blind spots, and improve decision-making processes. It aims to reduce equipment downtime through early fault detection, leading to increased production rates and cost savings. The platform's scalability and real-time insights will support a more agile response to operational issues, contributing to higher profitability and better risk management across oil and gas assets.

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