Fragmented Operational Data
Well master records, sensor readings, inspections, integrity events, and maintenance data were stored separately, preventing a unified integrity view.
Enterprise Well Integrity Monitoring & Operational Risk Intelligence Platform
Demonstration project
Designed an end-to-end Microsoft Fabric data engineering and analytics solution for onshore and offshore well integrity operations, integrating operational sources, validating engineering data, identifying high-risk wells, and delivering decision-ready safety, compliance, maintenance, and cost intelligence.
Well Integrity ManagementMicrosoft Fabric ETLOperational Risk AnalyticsSensor MonitoringOnshore & Offshore Assets
1.
Scenario Context
Well integrity information was fragmented across operational systems, making it difficult for engineers and management to obtain a trusted, timely view of safety risk, inspection compliance, abnormal operating behaviour, and maintenance exposure.
Manual Data Integration & Quality Risk
Manual preparation increased the likelihood of duplicate records, missing values, inconsistent formats, and unreliable engineering analysis.
High-Volume Sensor Data
Time-series pressure, temperature, and flow readings required scalable incremental processing rather than repeated full reloads.
Limited Risk Visibility
Engineers and leadership lacked centralized near-real-time reporting to identify high-risk wells and respond quickly.
Referential Integrity Requirements
Reliable analytics depended on maintaining correct parent-child relationships between wells and all related operational events.
2.
Solution
A modern Microsoft Fabric ETL architecture was implemented to orchestrate parallel ingestion, staging, transformation, incremental loading, validation, and Power BI refresh.
Five-Source Parallel Extraction
Fabric Data Factory extracted Well Master, Sensor Readings, Inspections, Integrity Events, and Maintenance data simultaneously.
Lakehouse Staging Layer
Each source was landed in a separate staging dataset to isolate raw operational data from analytical tables.
Dataflow Gen2 Transformations
Applied cleansing, deduplication, null handling, format checks, and domain-specific business rules.
Incremental SQL Loading
Stored Procedures appended only new validated records into wells, sensorreadings, inspections, integrityevents, and maintenance tables.
Automated Post-Load Validation
SQL checks verified row counts, foreign keys, and the absence of orphan records before analytics refresh.
Fact Constellation Data Model
Multiple operational fact tables shared the wells dimension while preserving the correct grain for sensors, inspections, incidents, and maintenance.
Six-Page Power BI Suite
Delivered executive overview, risk prioritization, sensor monitoring, inspection compliance, maintenance cost, and asset comparison dashboards.
Automated Semantic Model Refresh
Successful validation triggered Power BI refresh so stakeholders always saw the latest trusted data.
3.
Demonstration Findings
These findings show what the solution reveals within the demonstration scenario.
Operational Sources Unified
Well master, sensor, inspection, integrity-event, and maintenance data were integrated into one governed analytical platform.
Core Analytical Tables Governed
Validated incremental loading maintained trusted wells, sensorreadings, inspections, integrityevents, and maintenance datasets.
Decision-Focused Dashboard Pages
The demonstration provides dedicated views for safety, risk, compliance, maintenance, cost, and asset comparison.
Integrity Visibility Enabled
Automated refresh demonstrates how engineers and leadership can access current validated well performance and risk information.
Proactive Risk Identification
The scenario highlights high-risk wells, abnormal sensor behaviour, repeated failures, and overdue inspections for investigation.
Improved Data Reliability
Deduplication, null handling, format validation, business rules, row counts, and foreign-key checks improved trust in engineering analytics.
Risk-Based Maintenance Decisions
Maintenance history and cost exposure were connected to integrity risk, supporting better prioritization of inspection and intervention spend.
Scalable Data Engineering Foundation
The reusable Microsoft Fabric framework can accommodate future operational sources with minimal architectural change.
Quantification note: The figures describe the demonstration’s data sources, analytical tables and dashboard scope.
4.
Solution Architecture
Well Integrity Monitoring Architecture
Well MasterStatic Asset Records
Sensor SystemsPressure, Temperature, Flow
Inspections & EventsCompliance and Failures
↓↓↓
Fabric Lakehouse StagingFive Raw Datasets
→Dataflow Gen2Clean, Validate, Deduplicate
→SQL ServerIncremental Analytical Tables
↓
Power BI Well Integrity & Operational Risk Suite
Operational sources flow through Microsoft Fabric staging and transformation into validated SQL Server tables and decision-ready Power BI reporting.
5.
ETL Delivery Flow
Extract
Five operational sources in parallel
Stage
Separate Lakehouse staging datasets
Transform
Clean, deduplicate, validate, and apply rules
Load
Stored Procedure incremental append
Validate
Row counts and foreign keys
Refresh
Power BI semantic model and dashboards
Operational Sources → Lakehouse Staging → Dataflow Gen2 → SQL Server Incremental Load → SQL Validation → Power BI Refresh
6.
Technology Stack
Microsoft FabricFabric Data FactoryFabric LakehouseDataflow Gen2SQL ServerStored ProceduresPower BIFact ConstellationIncremental LoadingData Quality ValidationExcelPython
7.
Dashboard Gallery
8.
Project Showcase
Live Power BI Dashboard
9.
GitHub Repository
Explore the Well Integrity Analytics Project
Explore the complete project documentation, Microsoft Fabric ETL architecture, solution design, Power BI dashboards, implementation details, and supporting assets used to build the Enterprise Well Integrity Monitoring & Operational Risk Intelligence Platform.
Explore More Case Studies
Discover more industry transformation and enterprise data analytics projects.