Internship - Leverage Machine Learning to increase tender win-rates ! | Schlumberger

Job Details

Internship - Leverage Machine Learning to increase tender win-rates !

Montpellier - France


SLB is the world’s leading energy services company, with around 85,000 employees representing 170 nationalities, in 120 countries. Working globally– sometimes in remote and challenging locations – we invent, design, engineer, manufacture, apply, and maintain state-of-the-art technology to help customers find and produce energy.


Montpellier Technology Center (MpTC) is located in the South of France; we develop leading-edge Wellbore & Structural Geology Modelling Software.

In connection with SLB digital centers in Norway, the UK, the US, China and India, our innovation activities span from high precision wellbore logs to high volume seismic data, from automated real-time systems to large scale reservoir simulation.

Our strength comes from our passion for innovation and our multicultural population.

MpTC welcomes about 100 employees from more than 23 nationalities – a dynamic and energetic team working in a highly collaborative and innovative environment. We continuously offer opportunities to progress and develop, through training programs and early career exposure to our worldwide digital organization in Europe, the US and Asia.



Today the tender win-rate is becoming one of the most crucial SLB targets in a fierce competitive environment. Our organization has no luxury to spend weeks on the manual offset well analysis (OWA) during a bidding process.  As the rig rental represents 50% of the well cost on average, Well Construction Services (WCS) teams’ goal is to precisely estimate the drill time of a well campaign to maximize win rates of profitable bids.

You will need to build a data-driven model to predict the drilling operations and duration for any new tender.

Working closely with international WCS teams (UAE, Mexico…), you will apply the latest technologies in machine learning to predict the drilling activities that fits the Client Invitation to Tender scope of work. You will also oversee the development of a recommender system of offset wells to enrich the operational sequence model that you will have built.

As part of an agile cross functional environment, you will integrate your solution into a web application and collect WCS users’ feedback, before eventually deploying the product to production into DrillPlan solution.



This is a unique and fulfilling opportunity to work with international teams and deliver a solution that will help increase Schlumberger’s market share.

You’ll be exposed to the challenges that drilling experts face to predict well constriction time and operations.



  • Strong problem-solving skills and technical judgment
  • Excellent English communication skills
  • Passionate in Machine Learning, programming
  • Python 3



Penultimate or final year

Internship - Leverage Machine Learning to increase tender win-rates !
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