Geophysicist, Seismic Inversion (Senior - Principal - Advisor) Landmark - 211399
- Compensation
- Not disclosed
- Location
- Not listed
- Work setup
- Not specified
- Seniority
- Principal
- Employment
- Part time
- Experience
- Not specified
- Category
- Science
- Posted
Skills
Must have
- Undergraduate degree in Science, Engineering, or a similar technical discipline.
- Minimum of four years of related experience in applied research, technology development, product development, engineering, scientific analysis, or a comparable technical field.
- Demonstrated experience applying scientific or engineering principles to seismic imaging, seismic inversion, quantitative interpretation, reservoir characterization, or a closely related technical pro
- Proficiency in Python and experience developing scientific-computing, data-analysis, machine-learning, or deep-learning workflows.
- Experience designing technical investigations, experiments, model evaluations, or validation studies and interpreting the resulting data.
- Ability to document technical work, manage defined R&D deliverables, and communicate complex concepts and recommendations to multidisciplinary technical stakeholders.
Nice to have
- Master’s degree or PhD in Geophysics, Rock Physics, Petroleum Engineering, Applied Mathematics, Computer Science, or a related technical discipline.
- Experience with full-waveform inversion, pre-stack AVO/AVA inversion, post-stack inversion, quantitative interpretation, rock physics, or seismic reservoir characterization.
- Experience with PyTorch, TensorFlow, scikit-learn, or comparable AI and scientific-computing technologies.
- Experience with uncertainty quantification, probabilistic inversion, geostatistics, sequence stratigraphy, seismic facies classification, or 3D static reservoir modeling.
- Energy-industry R&D experience, cloud or high-performance computing exposure, and a record of technical publications, presentations, patents, or research-to-product delivery.
- Experience with quantum computing, quantum algorithms, or quantum-inspired methods for scientific, optimization, or inverse problems.
Full description
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.
About Landmark
Landmark, a Halliburton business line, provides the industry’s most comprehensive suite of digital solutions for exploration, drilling, and production optimization. Its software and data platforms empower customers to model subsurface assets, manage drilling risk, and accelerate decision-making through cloud, AI, and advanced analytics.
About the Role
We are seeking an R&D geophysicist to develop advanced seismic imaging, inversion, and quantitative-interpretation technologies for reservoir characterization. The role will apply Python, machine learning, deep learning, and mathematical inversion methods to integrate seismic data with well logs, rock physics, petrophysics, sequence stratigraphy, and seismic facies information.
You will develop and validate workflows that transform seismic observations into high-resolution 3D estimates of reservoir properties such as porosity, shale volume, and fluid saturation, including associated uncertainty. Working with geoscientists, reservoir engineers, data scientists, software developers, product stakeholders, and technical customers, you will convert research concepts into tested prototypes and product-ready technical capabilities.
Key Responsibilities
- Develop elastic full-waveform inversion and acoustic or elastic seismic-inversion methods for shot-gather, post-stack, and pre-stack AVO/AVA applications.
- Design AI and deep-learning approaches that improve seismic imaging, inversion, seismic facies classification, and seismic-to-petrophysical property mapping.
- Integrate seismic volumes, well logs, core measurements, rock-physics relationships, and geologic interpretations to construct high-resolution 3D reservoir-property models.
- Develop Python-based research prototypes, algorithms, and reproducible technical workflows using appropriate scientific-computing and machine-learning frameworks.
- Quantify uncertainty in predicted rock properties and fluid saturations using statistical, probabilistic, ensemble, or AI-enabled inverse-problem methods.
- Distinguish and evaluate epistemic and aleatoric uncertainty, analyze model generalization, and identify limitations in training data and model assumptions.
- Validate seismic, reservoir, and machine-learning models using blind-well tests, held-out data, physical consistency checks, and comparison with known subsurface observations.