Hr ops · Production

SLB uses Dataiku to reduce well construction tender analysis from 8 hours to 20 minutes

The problem

SLB needed a single data science and AI platform with no- and low-code interfaces to efficiently process domain data at scale. Manual well construction tender analysis took approximately eight hours per well due to unstructured daily drilling report data and unconscious-bias errors. Reservoir pressure analysis took at least one week and lacked any visualization dashboard. The company also faced an annual unplanned employee attrition cost of $80-$200 million with no automated means to identify at-risk employees early.

Workflow diagram · grounded in source
1
Well construction bid received
trigger
“they must correctly size the response to the bid to ensure that the project is profitable while providing competitive prices to customers”
2
Data-driven tender analysis in Dataiku
ai_action
“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”
3
KPI extraction and risk forecasting
ai_action
“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”
4
Auditable 20-minute analysis output
output
“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…”
5
Automated pressure detection
ai_action
“developed a reservoir pressure detection tool that automated the identification and gathering process of stabilized pressure”
6
Pressure results visualization
output
“allows easy visualization of results in Spotfire, and the process to analyze monthly pressure trends well by well is now 76% faster”
7
Employee attrition prediction
ai_action
“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”
8
Talent manager notifications
output
“notify talent managers across the company about at-risk populations so they can effectively take actions as early as possible”
Reported outcome

Dataiku reduced well construction tender analysis to 20 minutes and the approach has been used to assess more than $10 billion in tenders.
Reservoir pressure analysis became 76% faster. The HR predictive model enabled SLB to retain $18-45 million annually in employee value, and the Skills2Career dashboard saved hundreds of thousands in recruitment costs.

Reported metrics
Well tender analysis time (before)approximately eight hours
Well tender analysis time (after)20 minutes
Well construction tenders assessedmore than $10 billion
Reservoir pressure analysis speed improvement76% faster
Show all 10 reported metrics
well tender analysis time (before)approximately eight hours
well tender analysis time (after)20 minutes
well construction tenders assessedmore than $10 billion
reservoir pressure analysis speed improvement76% faster
reservoir pressure analysis time (before)at least one week
annual unplanned employee attrition cost$80-$200 million
employee retention value preserved annually$18-45 million
recruitment drive savingshundreds of thousands of dollars
overall business savingssave millions
training time reductionreducing the time invested in training by months and years
Reported stack
DataikuSpotfirePowerBI
Source
https://www.dataiku.com/stories/detail/dataiku-slb/
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

Dataiku reduced well construction tender analysis to 20 minutes and the approach has been used to assess more than $10 billion in tenders.

What tools did this team use?

Dataiku, Spotfire, PowerBI.

What results were reported?

Well tender analysis time (before): approximately eight hours; Well tender analysis time (after): 20 minutes; Well construction tenders assessed: more than $10 billion; Reservoir pressure analysis speed improvement: 76% faster (source-reported, not independently verified).

How is this hr ops AI workflow structured?

Well construction bid received → Data-driven tender analysis in Dataiku → KPI extraction and risk forecasting → Auditable 20-minute analysis output → Automated pressure detection → Pressure results visualization → Employee attrition prediction → Talent manager notifications.

Related hr ops cases