SLB: Putting Data & AI to Work for Energy

SLB partners with Dataiku to drive improvements and save millions of dollars through the use of data and AI across the business. Use cases include interpretation and processing of well logs, fault interpretations, drilling time reduction, HR optimization, and much more.


faster to analyze legacy data when sizing well construction tenders.


more efficient in conducting reservoir pressure analysis.

$18-45 million

retained from total unplanned employee attrition costs.


SLB is on a journey to reshape the future of energy, and data is a huge part of that vision.

I strongly believe that AI and machine learning can transform our industry and really reshape the future of energy by integrating our domain and digital capabilities with very, very powerful platforms like Dataiku.

— Rakesh Jaggi, President of Digital & Integration at SLB

SLB needed a single data science and AI platform to access the SLB domain data through no- and low-code code interfaces, where prior work is easily discoverable, and the technology is close to the systems where the insights and models will be deployed to. They of course also needed to empower data scientists and technical experts to be more efficient and effective.

Billion dollar decisions are now data driven, while keeping our well engineering experts in control of the outcome.

SLB & Dataiku Featured Stories

See how SLB is making data and AI part of the day-to-day for people across the organization with Dataiku. From detailed use cases to visionary interviews with company leaders, learn more about how SLB and Dataiku are working together.

SLB: Sizing Billion USD Well Construction Tenders Using Web Application & Machine Learning Models

SLB took a data-driven approach to assess over $10 billion worth of well construction tenders. Manually classifying a well previously took approximately 8 hours; the same can now be achieved within 20 minutes using a web application developed in Dataiku. According to SLB, "Billion-dollar decisions are now data-driven while keeping our well engineering experts in control of the outcome."


SLB: Automatic Reservoir Pressure Analysis

Reservoir pressure is essential data for different analysis such as proposals of drilling new wells, workover operations, and more. However, this analysis could take at least one week to get completed, and also there is no general visualization dashboard that allow the engineer to have an overview of the data that is being analyzed, which limits the efficiency of this process. Here's how SLB solved this challenge with Dataiku.


SLB: Efficient Data Workflows for Unlocking Geothermal Opportunities

Using Dataiku, SLB has build sophisticated workflows that automate the reading of LAS log files and extraction of bottom hole temperature accurately for over 20,000 wells, including multiple files per well with various flow scenarios. It has streamlined the process of classifying data by service providers, standardizing filed and well names, and temperature values/depth.


SLB: An Innovative Approach to Employee Digital Competency Mapping for Career Development

SLB's D&I (Digital & Integration) HR team launched digital upskill programs focusing on measurable digital skills with multiple learning partnerships to address this issue. They collected considerable data through these learning programs and related campaigns, but needed a reliable way to track that data for effective talent management — here's how they did it with Dataiku.


SLB: Predictive Maintenance to Improve Reliability of Drilling Services

This case study focuses on breaking of the rotary steerable system hinge pin. SLB used Dataiku to reduce the likelihood of failure by a factor of 5x, thus incrementally improving the reliability of the directional drilling services provided to SLB's customers.


SLB People Analytics: Optimizing the End-to-End Talent Lifecycle With Dataiku

SLB's People Analytics team uses Dataiku to better equip its talent management teams globally (reducing the time invested in training by months and years) and improve talent retention (saving millions of dollars annually).

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SLB: Using Dataiku to Democratize AI Within the Organization

Dataiku, and its close integration with the DELFI E&P cognitive environment, has been a key driver in democratizing the use of data science within SLB. Non data scientists contribute to 40% of projects, and models and insights are used throughout 70 countries.


The SLB AI Cloud Platform

Dataiku’s end-to-end data science platform integrated with the DELFI environment from Schlumberger, a suite of solutions and products for the energy industry, enables operators of all types to stimulate the creativity of their engineers bringing concepts to life and at scale.

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SLB & Dataiku Use Cases

The partnership with Dataiku has enabled SLB to drive improvements and save millions on use cases across the business, including interpretation and processing of well logs, fault interpretations, drilling time reduction, HR optimization, and much more. Here is just a small taste of how SLB makes data and AI part of day-to-day decision making for everyone across the organization, whether a technical expert, data expert, or somewhere in between.

From 8 Hours to 20 Minutes to Size Well Construction Tenders

One of the main integrated well construction services that SLB offers to operators is the delivery of “lump sum turnkey” wells, where SLB delivers wells to its customers at a fixed cost. Because SLB carries an increased proportion of well delivery risk for this service, they must correctly size the response to the bid to ensure that the project is profitable while providing competitive prices to customers.

Determining the cost of a well starts with the manual classification of operations, followed by the extraction of key performance indicators before eventually building an operational sequence and forecasting the risks associated with each well of an invitation to tender.

The key challenges are:

  1. Data is often stored in unstructured reports (daily drilling reports, or DDRs).
  2. The period to respond to a tender is extremely short, so quick turnaround time is of the essence.

Due to these challenges of scalability and speed — not to mention questions of accuracy, as human errors are incorporated by the unconscious bias of the well engineer — classifying a well previously took an SLB engineer approximately eight hours.

SLB developed a data-driven approach with Dataiku that has so far been used to assess more than $10 billion worth of well construction tenders and allows engineers to do the same analysis in just 20 minutes. In addition, the updated process allows for a structured, auditable, and data-driven approach to predicting the time it will take to drill the wells, as defined in the tender’s scope of work.

76% More Efficient Reservoir Pressure Analysis

Reservoir pressure is essential data for different analysis — for example, proposals for drilling new wells, workover operations, reservoir and production engineering analysis, and more.

When events beyond the control of SLB occur that result in the suspension of oil production operations — these events include well shutdowns as well as production flowline ruptures — occur, SLB does a well-by-well analysis to identify start and end of shutoff events and stabilized pressures to get reservoir pressure data.

This analysis used to take at least one week to complete, and also there was also no general visualization dashboard that allowed the engineer to have a general overview of the data being analyzed, which further limited the efficiency of this process.

Using an efficient Dataiku workflow with a combination of datasets, recipes, and programming, the team developed a reservoir pressure detection tool that automated the identification and gathering process of stabilized pressure. The tool also allows easy visualization of results in Spotfire, and the process to analyze monthly pressure trends well by well is now 76% faster.

Optimizing Human Resources Processes With Dataiku

SLB’s use of Dataiku doesn’t just stop with its core business units — it also extends to its supporting business functions, including human resources (HR).

For example, SLB’s People Analytics team uses Dataiku to better equip its talent management teams globally, reducing the time invested in training by months and years and improving talent retention (saving millions of dollars annually).

What does that mean concretely? Well, just like any modern company, SLB is focused on improving employee retention. Using Dataiku, they have built data pipelines from troves of data (i.e., across salary information, vacation data, performance and career stagnation information) to notify talent managers across the company about at-risk populations so they can effectively take actions as early as possible. They also provide the talent managers with insights on how they can improve the environment for their employees, such as salary, skill, or schedule changes.

Each year, the cost of unplanned employee attrition costs SLB $80-$200 million but, with the predictive model in Dataiku, the company has been able to retain between $18-45 million of that total thanks to the work the People Analytics team has done to maintain the high-value employees identified.

In addition, SLB’s D&I (Digital & Integration) HR team used Dataiku plus PowerBI to develop their Skills2Career Dashboard, which transformed the talent acquisition model from external hiring to internal upskilling, saving hundreds of thousands of dollars in recruitment drives and external hiring.

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