ZipRecruiter
Reservoir Simulation Research Scientist Machine Learning & Optimization Focus
ZipRecruiter, Houston, Texas, United States, 77246
Job DescriptionJob DescriptionSalary:
About the Role: We are seeking a highly motivated
Reservoir Simulation Research Scientist
to contribute to the next of reservoir modeling technologies. This role focuses on the
research and development (R&D)
of advanced computational methods combining
physics-based reservoir simulation with machine learning, data assimilation, and optimization . You will work on developing novel algorithms, enhancing simulation capabilities, and bridging data-driven and physics-based modeling approaches to support the energy transition and improve reservoir management workflows. Key Responsibilities: Conduct
fundamental and applied research
in reservoir simulation, computational physics, and data-driven methods. Develop and prototype novel algorithms that integrate
machine learning with traditional reservoir simulation workflows , including surrogate modeling, reduced-order modeling, and hybrid physics-ML models. Research and implement advanced
data assimilation techniques , including ensemble-based methods, adjoint-based gradient optimization, and Bayesian inference for history matching and uncertainty quantification. Develop and apply
optimization algorithms
for field development planning, production enhancement, and reservoir control under uncertainty. Collaborate with cross-disciplinary teams including reservoir engineers, geoscientists, data scientists, and software engineers. Publish research outcomes in peer-reviewed journals, patents, and present at industry and academic conferences. Provide technical leadership in framing R&D roadmaps, identifying high-impact research directions, and supporting technology transfer into commercial or operational tools. Contribute to the development of internal software prototypes or production-grade software for reservoir modeling and AI-enabled workflows. Required Qualifications: Ph.D. in
Petroleum Engineering or Reservoir Engineering or a related field
with a focus on numerical simulation, optimization, or machine learning applications. Strong background in
numerical methods for PDEs , linear and nonlinear solvers, and reservoir flow physics. Expertise in
reservoir simulation technologies , including finite difference, finite volume, or finite element methods applied to multiphase subsurface flow. Demonstrated research experience in one or more of the following:
Machine learning
(e.g., surrogate modeling, neural networks, Gaussian processes, physics-informed ML) Data assimilation
(e.g., Ensemble Kalman Filter, Ensemble Smoother, Adjoint-based optimization, Bayesian inference) Optimization
(e.g., field development planning, well control optimization, robust optimization under uncertainty)
Proficiency in
scientific programming
(ideally Python and MATLAB) for algorithm development and prototyping. Proven track record of
peer-reviewed publications, conference presentations, or patents
in relevant technical domains. Qualifications: Experience integrating
physics-based simulation with machine learning frameworks , including Physics-Informed Neural Networks (PINNs) or hybrid models. Knowledge of high-performance computing (HPC), parallel programming, or cloud computing for large-scale simulations. Familiarity with open-source or commercial reservoir simulators (e.g., MRST, Open Porous Media, Eclipse, Intersect, tNavigator, CMG). Experience with probabilistic modeling, uncertainty quantification, and decision-making under uncertainty. Background in related domains such as
CO sequestration, geothermal systems, or unconventional resources modeling
is a plus. Soft Skills: Strong analytical and problem-solving skills with a rigorous scientific approach. Ability to communicate complex technical ideas clearly to both technical and non-technical audiences. Self-driven, collaborative, and passionate about advancing the state of the art in reservoir engineering and computational sciences. Comfortable working in both independent research settings and collaborative, multi-disciplinary environments. Why Join Us? Work on cutting-edge problems at the intersection of
subsurface science, machine learning, optimization and computational physics . Be part of a collaborative R&D team influencing the future of energy, carbon management, and sustainable subsurface technologies. Opportunities to publish, patent, and contribute to open-source software or commercial products. Competitive compensation, research freedom, and professional growth in a dynamic, innovation-driven environment.
remote work #J-18808-Ljbffr
About the Role: We are seeking a highly motivated
Reservoir Simulation Research Scientist
to contribute to the next of reservoir modeling technologies. This role focuses on the
research and development (R&D)
of advanced computational methods combining
physics-based reservoir simulation with machine learning, data assimilation, and optimization . You will work on developing novel algorithms, enhancing simulation capabilities, and bridging data-driven and physics-based modeling approaches to support the energy transition and improve reservoir management workflows. Key Responsibilities: Conduct
fundamental and applied research
in reservoir simulation, computational physics, and data-driven methods. Develop and prototype novel algorithms that integrate
machine learning with traditional reservoir simulation workflows , including surrogate modeling, reduced-order modeling, and hybrid physics-ML models. Research and implement advanced
data assimilation techniques , including ensemble-based methods, adjoint-based gradient optimization, and Bayesian inference for history matching and uncertainty quantification. Develop and apply
optimization algorithms
for field development planning, production enhancement, and reservoir control under uncertainty. Collaborate with cross-disciplinary teams including reservoir engineers, geoscientists, data scientists, and software engineers. Publish research outcomes in peer-reviewed journals, patents, and present at industry and academic conferences. Provide technical leadership in framing R&D roadmaps, identifying high-impact research directions, and supporting technology transfer into commercial or operational tools. Contribute to the development of internal software prototypes or production-grade software for reservoir modeling and AI-enabled workflows. Required Qualifications: Ph.D. in
Petroleum Engineering or Reservoir Engineering or a related field
with a focus on numerical simulation, optimization, or machine learning applications. Strong background in
numerical methods for PDEs , linear and nonlinear solvers, and reservoir flow physics. Expertise in
reservoir simulation technologies , including finite difference, finite volume, or finite element methods applied to multiphase subsurface flow. Demonstrated research experience in one or more of the following:
Machine learning
(e.g., surrogate modeling, neural networks, Gaussian processes, physics-informed ML) Data assimilation
(e.g., Ensemble Kalman Filter, Ensemble Smoother, Adjoint-based optimization, Bayesian inference) Optimization
(e.g., field development planning, well control optimization, robust optimization under uncertainty)
Proficiency in
scientific programming
(ideally Python and MATLAB) for algorithm development and prototyping. Proven track record of
peer-reviewed publications, conference presentations, or patents
in relevant technical domains. Qualifications: Experience integrating
physics-based simulation with machine learning frameworks , including Physics-Informed Neural Networks (PINNs) or hybrid models. Knowledge of high-performance computing (HPC), parallel programming, or cloud computing for large-scale simulations. Familiarity with open-source or commercial reservoir simulators (e.g., MRST, Open Porous Media, Eclipse, Intersect, tNavigator, CMG). Experience with probabilistic modeling, uncertainty quantification, and decision-making under uncertainty. Background in related domains such as
CO sequestration, geothermal systems, or unconventional resources modeling
is a plus. Soft Skills: Strong analytical and problem-solving skills with a rigorous scientific approach. Ability to communicate complex technical ideas clearly to both technical and non-technical audiences. Self-driven, collaborative, and passionate about advancing the state of the art in reservoir engineering and computational sciences. Comfortable working in both independent research settings and collaborative, multi-disciplinary environments. Why Join Us? Work on cutting-edge problems at the intersection of
subsurface science, machine learning, optimization and computational physics . Be part of a collaborative R&D team influencing the future of energy, carbon management, and sustainable subsurface technologies. Opportunities to publish, patent, and contribute to open-source software or commercial products. Competitive compensation, research freedom, and professional growth in a dynamic, innovation-driven environment.
remote work #J-18808-Ljbffr