US/UK joint Engineering, Mathematics, Physics, or Earth Sciences Internship (6 months) - Development of Stochastic Framework for 1D Mechanical Earth Models
Abingdon, United Kingdom / Cambridge, USA (possibity to work remotely)
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Schlumberger-Doll Research in Cambridge, MA, USA and Schlumberger Abingdon Technology Center in Abingdon, UK are seeking a highly motivated intern to assist with cutting edge research into automatic construction of borehole geomechanics models, also known as 1D Mechanical Earth Models (MEMs). A 1D MEM is comprised of attributes required to predict the mechanical failure of rocks i.e. the pore pressure, rock properties, and the in situ stress field. Traditional methods for constructing MEMs are tedious, time-consuming, and often impractical on a multi-well scale. The objective of this project is to test and improve a new theoretical framework for stochastic inversion of 1D MEMs. The framework builds on recently published work [Rodriguez, et. al 2020 ].
The intern, located in the UK, will collaborate with research scientists in the USA and the UK to apply this theory to field data. Such data would include core data, well logs, and stress measurements.
The hosts of this internship are from Schlumberger-Doll Research, which is the original flagship research laboratory, located in Cambridge, Massachusetts, USA, and the Schlumberger Abingdon Technology Center focusing on commercial software development, located in Abingdon, UK.
Essential Responsibilities and Duties:
The intern will have the opportunity to acquire theoretical and practical knowledge of geomechanics, computer programming, and probability theory. He or she would be expected to develop software using the Python language, and to apply this software to real field data.
The goal of the internship would be to assess the validity, robustness, and practicality of the inversion methodology, based on its performance in relation to a variety of field data sets. The intern would compare results with those obtained by traditional methods (human versus machine comparisons); tune input parameters to improve the efficiency and accuracy of stochastic inversion; identify deficiencies in the theory and assist with necessary alterations; and prepare internal and external publications.
The main output of this work would be efficient python code that would help drive cloud-based automation techniques.
Schlumberger is an equal employment opportunity employer. Qualified applicants are considered without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, or other characteristics protected by law.