Unified Data Ecosystem for Enhanced Decision-Making for an O&G company
Unified Data Ecosystem for Enhanced Decision-Making for an O&G company
About the client
A Middle-east based Upstream Oil & Gas company that was facing delays in resource allocation and production optimization on account on lack of holistic insights. Also, they were incurring significant cost in equipment failure and outages.
Overcoming Operational Challenges with Data-Driven Solutions
The client collected vast amounts of data from different upstream operations, including exploration, drilling, and production. However, this data was siloed in multiple systems, leading to inefficiencies in accessing and analyzing it.
Key data sources and isolated systems included:
The lack of integration across these systems made it nearly impossible to generate holistic insights, delaying decisions on resource allocation, predictive maintenance, and production optimization.
- Exploration Data: Geological and geophysical data stored in legacy databases like Petrel .
- Drilling Operations Data: IoT sensor data from rigs, managed through proprietary SCADA systems.
- Production Data: Well output data housed in an ERP system (SAP ECC) .
- Asset Management Data: Maintenance logs in a CMMS (Computerized Maintenance Management System, e.g., Maximo ).
- Finance and Logistics Data: Financial data from SAP HANA and supply chain details stored in separate spreadsheets.
The lack of integration across these systems made it nearly impossible to generate holistic insights, delaying decisions on resource allocation, predictive maintenance, and production optimization.
1. Data Integration Layer
Technology Used: Apache NiFi for data ingestion and ETL (Extract, Transform, Load) pipelines.
Process:
- Connected to each isolated system to extract data in its native format.
- Transformed the data using pre-defined schemas to standardize formats (e.g., converting SCADA sensor data and geological data to a unified schema).
- Loaded the processed data into a centralized storage layer.
2. Centralized Data Storage and Processing
Technology Used: AWS S3 for raw data storage and Snowflake for structured data warehousing.
Process:
- Data ingested from multiple sources was first stored in AWS S3 for scalability and cost efficiency.
- Snowflake was used as a data warehouse to provide fast, query-ready access to structured data.
- Historical and real-time data were cataloged using AWS Glue, making it accessible across departments.
3. Data Analytics and Visualization
Technology Used: Microsoft Power BI and Tableau for dashboards, Python with pandas and scikit-learn for advanced analytics and machine learning.
Process:
- Created dynamic dashboards that integrated data streams to provide real-time insights into key KPIs such as drilling efficiency, production rates, and asset health.
- Built ML models to predict equipment failure based on historical maintenance and IoT sensor data, enabling proactive maintenance scheduling.
4. Real-Time Data Stream Processing
Technology Used: Apache Kafka for event streaming.
Process:
- Enabled real-time data ingestion from SCADA systems and IoT devices into the unified platform, ensuring up-to-the-minute updates for critical operations.
5. Security and Access Management
Technology Used: AWS IAM (Identity and Access Management) for secure access control, ensuring that only authorized users could access sensitive operational data.
Process:
- Set up role-based access controls for teams like exploration, drilling, and production.
- Ensured compliance with industry standards like GDPR and ISO 27001 for data security.
Enhanced Decision-Making:
- Unified dashboards provided leadership with a comprehensive view of operations, reducing decision-making time by 30%.
- Improved ability to identify underperforming wells and redirect resources accordingly.
Operational Efficiency:
- Real-time monitoring and predictive maintenance reduced equipment downtime by 25%.
- Automated data workflows eliminated manual reporting, saving teams 15+ hours per week.
Cost Savings:
- Optimized production workflows saved the company approximately $1.2 million annually in operational expenses.
Scalability for Future Growth:
- The modular architecture allowed the company to integrate new data sources seamlessly, preparing it for future expansion in other regions.