After six years of building machine learning models for unconventional development, we have learned a HUGE amount about what it takes to build analytics-ready datasets. It’s not easy: messy tables, multiple sources, industry-specific challenges, and complicated transformations to get the data right.
We have put all of our experience and (and custom data-processing algorithms) in your hands with Data Engine. Anyone working in Data Science of Machine Learning will attest that building the input dataset is the most time-consuming, error-prone part of the dataset. Data Engine makes it quick, easy, painless, and repeatable to build analytics-ready datasets.
Latest Resources
[URTeC 2022] The Diminishing Returns of Lateral Length Across Different Basins (ID 3723784)
The Diminishing Returns of Lateral Length Across Different Basins Talk Details:: Tuesday, June 21st at 2:15 PM | Room 371 Theme 7: Applications in Reserves …
[URTeC 2022] Accelerating field optimization for Shell in the Neuquén Basin using Novi Labs machine learning
Accelerating field optimization for Shell in the Neuquén Basin using Novi Labs machine learning AUTHORS:: DP. Zannitto2, C. Kosa1 (1. Novi Labs 2. Shell …
[URTeC 2022] Understanding the Spacing, Completions, and Geological Influences on Decline Rates and B Values (ID 3723711)
Understanding the Spacing, Completions, and Geological Influences on Decline Rates and B Values Talk Details:: Wednesday, July 28th at 11:15 AM | Room 371 Theme …
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