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Machine Learning Petroleum Engineer ( R-00069437-OTHLOC-PL-2D1827 )

Technology

Machine Learning Developer

Yearly

No

Morgantown, West Virginia, United States

Description

Job Description:

Looking for an opportunity to make an impact?

The Leidos Research Support Team supporting the National Energy Technology Laboratory (NETL) is seeking a Machine Learning Petroleum Engineer to join our Team in Albany, OR, Morgantown, WV, or Pittsburgh, PA.  This opportunity will allow side by side execution of research with world-class scientists and engineers using state of the art equipment to contribute to new areas of basic and applied research.   

The employee will participate in studies relating to model development, workflow, and artificial intelligence/machine learning (AI/ML) algorithm design related to geospatial energy and oil & gas industry data. This work will involve a multidisciplinary team participation alongside other data scientists, engineers, geologists, and computer scientists to help develop next-generation tools aligned to the priorities of the DOE Fossil Energy and Carbon Management mission space.  

Location: Pittsburgh, PA, Morgantown, WV or Albany, OR

If this sounds like the kind of environment where you can thrive, keep reading!

Primary Responsibilities Include:

  • Apply artificial intelligence/machine learning (AI/ML) methods to generate models and tools relating to subsurface geologic natural and engineered systems.
  • Design a variety of supervised and unsupervised AI/ML algorithms after assessing the most effective methods that incorporate big, disparate, multivariate data.
  • Correctly and efficiently interpret AI/ML algorithm results.
  • Reviewing engineering and production data (i.e., wellbore, wireline, etc.), typically from Offshore wells, to generate models.
  • Collaborate with multidisciplinary group of science researchers to accomplish project tasks.
  • Build relationships with internal and external clients.
  • Create and deliver oral and poster presentations of results.
  • Effectively communicate scientific results through peer-review journal manuscripts and technical reports.

Required Qualifications:

  • Master’s degree in petroleum engineering, reservoir engineering, wellbore engineering, machine learning, data science, math, computational geology, geophysics, geology, physics or similar field .
  • Proficiency in code development using Python for advanced analytics, including neural networks, long-short term memory (LSTM), clustering algorithms, natural language processing (NLP) and other Machine Learning approaches with Python libraries including scikit learn, tensor flow, and others to develop AI/ML algorithms and models.
  • Experience developing code in an Integrated Development Environment (IDE), documenting code, and committing code to a repository, such as GitHub, BitBucket, or similar.
  • Ability to examine & synthesize engineering data related to the oil & gas industry and geological properties.  Experience is necessary in petroleum systems including, but not limited to: reservoir, mud logs, logging while drilling, and wireline data .
  • Understanding of and experience in subsurface topics .

Preferred Qualifications:

  • Advanced degree and/or certifications petroleum engineering, reservoir engineering, wellbore engineering, machine learning, data science, math, computational geology, geophysics, geology, physics or similar field.
  • Background in applications leveraging advanced analytics for energy and environmental systems .
  • Familiarity with offshore geological and subsurface data and their interpretation software (Petra, Petrel, etc.).
  • Familiarity with image processing algorithms, including LabelIMG and You Only Look Once (YOLO).
  • Experience in data labeling approaches to refine and build on current classification capabilities and support AI/ML big data analysis.
  • Evidence of substantive participation in subsurface geologic scientific community.

Pay Range: