Halliburton is looking to solve the problem of integrating seismic imaging and inversion with petrophysical analysis, sequence stratigraphy, and seismic facies ML classification to build high-resolution 3D reservoir models for the oil and gas industry
Requirements
- Strong proficiency in Python programming and experience with AI/ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn)
- Experience with seismic inversion techniques and seismic interpretation software
- Solid understanding of petrophysical and rock physics principles and methods
- Knowledge of sequence stratigraphy, seismic facies analysis, and their role in reservoir modeling
- Experience in the oil and gas industry, in particular in the field of subsurface uncertainty quantification
- Familiarity with cloud-based computational platforms for running large-scale AI models
- Previous R&D or publication experience related to AI/DL applications in geophysics
Responsibilities
- Seismic Inversion (elastic FWI of shot gathers and acoustic (poststack) and elastic (prestack AVO/AVA based) & Integration with Petrophysical / Rock Physics Analysis:
- Develop and apply advanced seismic inversion techniques to derive rock property models
- Integrate seismic images with well logs and core data to generate 3D geologic static reservoir models using geostatistical and sequence stratigraphy principles
- Design and implement AI and ML algorithms (using Python) to automate and enhance the interpretation of seismic data
- Develop predictive models to estimate rock properties and reservoir parameters with associated uncertainties
- Use AI-driven quantitative interpretation methods to improve seismic-to-petrophysical property mapping and reservoir characterization
- Uncertainty Quantification of Reservoir Characterization
Other
- PhD in Geophysics, Rock Physics, Petroleum Engineering, or a related field
- Minimum of 3-5 years of experience in quantitative interpretation, reservoir modeling, and petrophysics / rock physics
- Strong analytical thinking and problem-solving skills
- Excellent communication and ability to work in a collaborative, multi-disciplinary R&D environment
- Ability to work in Houston, Texas
