ML Quantitative Interpretation

Halliburton•Houston, TX

47d

About The Position

We are looking for the right people - people who want to innovate, achieve, grow and lead. We attract and retain the best talent by investing in our employees and empowering them to develop themselves and their careers. Experience the challenges, rewards and opportunity of working for one of the world's largest providers of products and services to the global energy industry.

Requirements

Skills acquired through the completion of an undergraduate degree in Science or Engineering.
10 years of related experience.

Nice To Haves

Completion of a masters or PHD in Science or Engineering is preferred.

Responsibilities

Seismic Inversion (acoustic (poststack) and elastic (prestack AVO/AVA based) & Integration with Petrophysical / Rock Physics Analysis:
Perform rock physics analysis to model Elastic Impedance and Extended Elastic Impedance parameters in relation with reservoir properties such as porosity, Vsh, and saturation.
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.
AI / ML Implementation:
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
Analyze and quantify the uncertainty of the 3D distribution of rock properties and fluid saturations using combination of AI models and mathematical statistics in the solving the inverse problem.
Validate reservoir and ML models against known well data (blind tests) and adjust for consistency in the context of sequence stratigraphy and seismic facies. Analyze generalization gap and quantify epistemic and aleatoric uncertainty.
Collaboration and Innovation:
Collaborate with multi-disciplinary teams including geologists, reservoir engineers, and data scientists to refine models and enhance predictions.
Stay up to date with the latest advancements in AI, DL, and geophysics to innovate new techniques and improve workflows
Contribute to R&D publications, presentations, and patents related to rock physics, seismic inversion, AI, and quantitative reservoir characterization.