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Data and AI platform: unified data, faster insight, AI at scale

SLB unifies subsurface and operations data and embeds AI across the workflow, so teams move from raw data to decisions without manual reconciliation.

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Challenge

Accessing decision-ready data

Finding, collecting, visualizing, managing, and seamlessly connecting with data can be elusive—making it hard to formulate fast and intelligent insights.

Let us help.

Solution

Get actionable data now

We deliver data-enabled intelligence from trusted sources, so decisions can be made faster and with more confidence. The faster you get the data, the faster you can get the answers you need.

AI enhanced: Our system constantly learns and improves, bringing extraordinary new capabilities to users and workflows for fast and accurate decision making.

Frictionless data ingestion: Structured and unstructured data can flow freely through your domain applications from a single environment.

Simple access to data tools: Our solution connects directly to domain applications, creating a seamless experience for users.

Case Studies

Unifying data and scaling AI

Operators using the SLB data and AI platform have reported faster candidate selection, shorter time-to-insight, and more reliable subsurface decisions. The case studies below are drawn from field deployments on the Lumi™ data and AI platform..

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90% faster well intervention candidate selection

Automated data workflows accelerated well intervention candidate selection by up to 90% for an offshore Indonesia operator, replacing a manual, spreadsheet-driven process with a unified workflow built on trusted well data for faster intervention planning cycles.

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85% to 90% of legacy well data unlocked

Lumi data and AI platform enabled TotalEnergies to unlock 85% to 90% of previously inaccessible legacy well data, applying data conditioning and machine learning workflows to transform decades of disparate formats into high-quality datasets.

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Subsurface decision-making accelerated

AI deployed on a unified data foundation accelerated subsurface decision-making for Petoro by connecting trusted data sources. Technical teams moved from disconnected datasets to a governed, AI-ready environment, so they spent less time preparing data and more time interpreting it.

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4× faster reservoir simulation

Cloud computing and AI enabled Dragon Oil to complete 3,860 CPU hours of simulation in four to five months across six complex reservoirs, equivalent to one and a half years of continuous simulation using the operator’s existing setup, accelerating field development planning across reservoirs.

Benefits

Why SLB Enterprise Data Performance?

Having efficient data collection and a smooth delivery flow improves your productivity and effectiveness, awarding businesses a faster and more intelligent decision-making process.

Reduced risk

Through consistency in operations and a focus on predictability, we can better assess potential dangers.

Productivity

Be smarter, faster, and more efficient with your costs, all while optimizing your infrastructure and innovations.

Increased agility

You can future-proof your organization’s impact at scale with agile reporting functions that prepare you for tomorrow.

Sustainability reporting

Know where your impact is today and how to make the most impact tomorrow by reporting on your company’s footprint.

Capabilities

Making a difference, so you can

Our platform enables businesses to access data in the right context—in the right workflow and in the right location, making collaboration simple. With embedded AI throughout, the solution offers intelligent data ingestion, better quality control, analytics, and visualization. This open-environment platform enables you to easily incorporate what you have with what you need.

Govern data and put into context

Assure data quality and transparency

Remove siloes across domains

Integrate multiple data sources

Ensure user centricity

Ensure openness

Frequently asked questions about the data and AI platform

What does it mean for energy data to be AI-ready?

AI-ready data has been ingested, deduplicated, and validated against a consistent standard before any model touches it. Without that step, AI outputs inherit whatever errors and gaps existed in the source systems. The Lumi data and AI platform applies automated validation and duplicate detection during ingestion specifically so analytics and AI applications start from trusted data rather than raw, unreconciled records.

How do operators eliminate data silos between subsurface and operations teams?

Operators eliminate silos by moving subsurface and operations data into a single governed environment instead of maintaining separate copies in each domain application. The Lumi data and AI platform unifies these datasets and shares them across applications through link-based access rather than file transfer, so a change in one workflow is visible everywhere else.

What's the difference between a data platform and an AI platform for energy companies?

A data platform focuses on ingesting, governing, and storing information so it is consistent and trustworthy. An AI platform builds on that foundation to run models, generate predictions, and automate decisions. The SLB approach combines both: the Lumi data and AI platform handles data unification and governance, while embedded AI and the Tela agentic-AI assistant use that governed data to deliver predictions and recommended actions within the same environment.

How does SLB help scale AI from pilot projects to enterprise-wide deployment?

SLB scales AI through a structured path from data foundation to deployment rather than treating each pilot as a one-off project. The Innovation Factori AI collaboration workspace assesses readiness, builds tailored solutions on governed data from the Lumi platform, and moves validated pilots into enterprise-scale, no-code and low-code deployment across cloud and on-premises environments.

What results can operators expect from unifying their energy data?

Results vary by workflow, but documented outcomes include up to 90% faster well intervention candidate selection, an 80% reduction in time to actionable insights for waterflood optimization (SPE-215944), and up to six times faster property modeling for geomodelers (SPE-211398). The common driver across these results is removing the manual reconciliation step between disconnected data sources.

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