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We are seeking a highly skilled and innovative Geophysicist / Rock Physicist with a strong background in GenAI / Deep Learning and Python coding for Quantitative Interpretation. The successful candidate will focus on integrating seismic inversion with petrophysical analysis, sequence stratigraphy, and seismic facies ML classification to build high-resolution 3D reservoir models. This role involves using cutting-edge machine learning and deep learning techniques to estimate uncertainty in the distribution of key rock properties, such as porosity, volume of shale (Vsh), and saturation in oil and gas reservoirs.

Responsibilities and Qualifications:

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.

Requirements:
• Skills acquired through the completion of an undergraduate degree in Science or Engineering.
• 10 years of related experience.
• Completion of a masters or PHD in Science or Engineering is preferred.

Candidates with qualifications exceeding the minimum job requirements will be considered for higher-level positions based on their experience, additional job requirements, and current business needs. Depending on their education, experience, and skill level, candidates may be eligible for a range of job opportunities, including positions ranging from Senior Scientist Technical Advisor, Principal Scientist Technical Advisor and Chief Scientist Technical Advisor.

Halliburton is an Equal Opportunity Employer. Employment decisions are made without regard to race, color, religion, disability, genetic information, pregnancy, citizenship, marital status, sex/gender, sexual preference/ orientation, gender identity, age, veteran status, national origin, or any other status protected by law or regulation.

Location

3000 N Sam Houston Pkwy E,Houston,Texas,77032, United States

Job Details

Requisition Number: 193447
Experience Level: Experienced Hire
Job Family: Engineering/Science/Technology
Product Service Line: Landmark Software & Services
Full Time / Part Time: Full Time

Additional Locations for this position:

Nearest Major Market: Houston
Job Segment: Quantitative Analyst, Data