Schlumberger
Overcoming Schlumberger's Big Data Integration Challenges with Cloud-Based Digital Oilfield 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, including oilfield data integration, oilfield IoT data ingestion, and comprehensive data management in oil and gas industry for 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. This platform was designed to transform their operations through real-time, actionable insights, marking a significant step towards advanced digital oilfield solutions.
The Challenge: Managing and Analyzing Massive Data Volumes
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 to achieve real-time analytics in oil and gas industry:
Scalability Issues: Existing systems were unable to scale with the growing data volumes, causing processing bottlenecks and delays, and hindering their ability to achieve real-time analytics in oil and gas industry.
Data Integration Complexities: SLB’s operational data came from multiple sources, including disparate legacy systems of oil and gas, each with different formats and structures, hence requiring effective oilfield data integration solutions.
Data Inconsistency: Variations in data formatting across platforms caused inconsistencies, hampering the accuracy and reliability of analytics.
Operational Inefficiencies: The lack of a streamlined continuous integration and deployment (CI/CD) process led to prolonged downtimes and operational inefficiencies, making it difficult to reduce unplanned downtime and optimize operations.
The Solution: Cloud-Based Advanced Data Processing, Analytics & Digital Oilfield Solutions
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 for cloud-based digital oilfield solutions.
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 achieved robust oilfield data integration solutions from multiple sources, ensuring consistency and reliability across operations.
Enhanced Data Consistency
Achieved high data consistency through automated standardization processes, improving the quality of data management in oil and gas industry.
Real-Time Analytics
Enabled true real-time analytics in oil and gas industry, allowing for quick, data-driven decisions that impact operations instantly.
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, supporting regulatory compliance with powerful analytical reporting.
Advanced AI/ML Capabilities
Applied sophisticated AI/ML models for predictive insights, further advancing the digital oilfield solutions.
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 for cloud solutions for oil and gas.
Operational Efficiency with CI/CD
Implemented CI/CD pipelines, reducing deployment times and downtime, helping SLB reduce unplanned downtime. This also displayed effective legacy system modernization of oil and gas strategies.
Significant Cost Savings
Achieved substantial cost savings through optimized infrastructure design, reducing operational expenses while scaling analytical capabilities.
