Schlumberger
Scaling Data Analytics in Oil & Gas: Overcoming Schlumberger's Big Data Integration Challenges with Cloud-Based Solutions
1926 - Texas, USA
Energy and Utilities
10,001+ employees
Overview
Schlumberger (SLB), the world’s largest offshore drilling company, was facing significant challenges in processing and analyzing the immense volumes of data generated by its operations. These challenges stemmed from the integration of diverse data sources, the ingestion of rapidly increasing data volumes, and the management of complex datasets. To address these issues and harness the full potential of their data, SLB partnered with Folio3 to deploy a robust Big Data Analytics platform, designed to transform their operations through real-time, actionable insights.
The Challenge: Managing and Analyzing Massive Data Volumes
SLB needed a solution that could manage and integrate these massive data volumes while ensuring real-time analytics and insights.
As SLB’s operations expanded, so did the volume and complexity of their data. Traditional data processing methods became inadequate, leading to several critical challenges:
Scalability Issues: Existing systems were unable to scale with the growing data volumes, resulting in processing bottlenecks and delays.
Data Integration: SLB’s data came from multiple sources, including legacy systems, each with different formats and structures, making integration a significant challenge.
Data Consistency: Variations in data formatting across platforms caused inconsistencies, hampering the accuracy of analytics.
Operational Inefficiencies: The lack of a streamlined continuous integration and deployment (CI/CD) process led to prolonged downtimes and operational inefficiencies.
The Solution: Cloud-Based Advanced Data Processing & Analytics
Folio3 conducted a thorough assessment of SLB’s data infrastructure and identified the need for a scalable, cloud-based advanced data analytics system. The solution was implemented using Microsoft Azure, leveraging its powerful tools to meet SLB’s specific needs.
Technologies Involved In This Case
Microsoft Azure
Azure Data Factory
SAP
SQL Server
Google Analytics
Pandas
Azure Data Lake Storage (ADLS) (Cold & Hot Tiers)
Azure Databricks
Azure Analysis Services
Power BI
Results & Achievements
Seamless Data Integration
Successfully integrated data from multiple sources, ensuring consistency and reliability.
Enhanced Data Consistency
Achieved high data consistency through automated standardization processes.
Real-Time Analytics
Enabled real-time analytics, allowing for quick, data-driven decisions.
Reduced Report Processing Times
Optimized report generation, reducing times from minutes to milliseconds.
Daily Data Snapshots
Maintained daily snapshots for robust auditing and historical analysis.
Advanced AI/ML Capabilities
Applied sophisticated AI/ML models for predictive insights.
Customizable Dashboards
Empowered users with customizable, real-time dashboards.
Scalable, Future-Proof Architecture
Developed a platform that seamlessly integrates with any data ingestion system, ensuring future-proofing and operational efficiency.
Operational Efficiency with CI/CD
Implemented CI/CD pipelines, reducing deployment times and downtime.
Significant Cost Savings
Achieved substantial cost savings through optimized infrastructure design, reducing operational expenses while scaling analytical capabilities.
