The business and technology partnership between Dataiku and SLB brings leading solutions in AI and energy together for customers. The partnership delivers Dataiku’s end-to-end data science platform as an integral component of the Delfi™ digital platform providing data doers with the tools they need to build AI models themselves, together with a comprehensive data ecosystem that stretches across the entire energy life cycle. Explore our AI Solutions
The democratization of AI is a vision both companies share, a vision that has driven our desire to deliver solutions into the hands of geoscientists and engineers—enabling them to create, deploy, and manage AI-driven solutions. Fusing our products has enabled a new breed of digital experts and accelerated existing domain workflows. We’ve enabled energy companies of all sizes to harness enterprise AI, through a highly scalable, powerful, and collaborative environment.
The Dataiku and SLB partnership is laid bare in this detailed and insightful interview. Dataiku Customer Service Director, Jeff Cavey meets SLB’s Global Artificial Intelligence and Innovation Factori Manager, Patricia Cejas Manceron to discuss how the two companies work closely together to develop the most cutting-edge AI solutions for energy innovation and deploy them for SLB customers.
SLB and Dataiku offer low-code, no-code data science together with a large library of tailored solutions that provides great domain starting points for various challenges. And our accelerator packs represent best-in-class plugins to connect up to common industry systems.
Our INNOVATION FACTORI is designed to solve energy industry challenges with the help of design thinking and a deep understanding of AI, all supported from a network of innovation centers and experts across the globe, tailored for your challenges and organization.
Full lifecycle ML, LLMOps, and analytics that geoscientists and engineers can leverage to build, manage, and deploy AI enhanced digital solutions that accelerate energy workflows.
With the Dataiku LLM mesh, you can build Gen AI applications, address compliance, cost management, and technology dependencies. Large language models (LLM) can be integrated effectively, scalable, and securely.
AI helps domain professionals perform everyday tasks faster, better and at a much bigger scale. But what does that actually look like and is it really easy for anyone to build AI models? Trygve Randen finds out in a 1-2-1 tutorial with Florian Douetteau, Dataiku’s CEO.
The energy industry is facing a dynamic business environment with significant economic pressures over the last few years; tighter margins and fewer resources are driving a need for greater efficiency and performance.
Our top ten lessons learned from the execution of dozens of AI projects within our own organization and dozens more through industry collaboration projects.
To manage a high-performance supply chain, it is critical to understand exactly when products will be delivered and what the lead time for their delivery is.
Leveraging AI to support managers with Insights for improving employee performance appraisal.
Our tailored solutions form a suite of proven, reusable, and customizable digital technologies that can accelerate business growth and time to value for customers.
Underpinned by our network of INNOVATION FACTORI centers, our tailored solutions use cutting-edge AI and machine learning techniques to deliver mature, deployable solutions to suit use cases across the energy industry.
Ready-to-use code, plugins, and projects to help you start building workflows fast.
Today there are 25 individual components across the following categories:
Cross-domain
These projects aim to enhance knowledge sharing across domains, optimize data discovery, and enable data mining and analytics on unstructured data. Examples include LAS plugin, DLIS plugin, Techlog 2 Dataiku, isolation forest well log outlier detection, Dataiku to PowerBI, well log imputing for OSDU Data, Operations Data Foundation connector, Spotfire connector, fact extraction, and well log visualization dashboard.
Subsurface
These projects aim to enhance petroleum system's elements characterization and optimize data discovery. Examples include facies classification from well logs, ML assisted petrophysical interpretation, marker prediction, Petrel connector, and ProSource connector.
Production
These projects aim to reduce operational spend, increase production from existing wells by reducing well downtime, emissions, improve operating efficiency, and reduce spend and consumable rate. Examples include choke flow rate, sand management using Pipesim steady-state multiphase flow simulator integration, anomaly detection for ESP, decline curve analysis, Avocet connectivity, gas lock prediction, and well rate estimation.
Drilling
These projects aim to provide accurate offset analysis, implement lessons learned, optimize data discovery, reduce drilling time by providing optimum parameters. Examples include drilling NLP insights project, WITSML parser, and ROP prediction.
Digital transformation is as much about cultural transformation as digital technologies. We believe that in an organization, AI should not be limited to the data scientist population alone. The business value of AI can be fully unlocked only when we democratize its use.
Through our upskilling programs and partnership with Dataiku, over the last 2 years, we have had more than 2,200 employees gain at least one Dataiku certification, and more than 6,300 certifications overall. We now have certified domain experts with data science expertise in every geography we operate.
Interview with INNOVATION FACTORI on how digital transformation with artificial intelligence (AI), machine learning (ML), and automation can revolutionize and bring efficiency, productivity, and sustainability to the energy industry.
