Chair of Drilling and Completion Engineering (590)
Organisational unit: Chair
271 - 280 out of 365Page size: 10
Research output
- Published
Optimizing Well Integrity Management based on Field Data and International Standards
Benedikt, E. J., 2020Research output: Thesis › Master's Thesis
- Published
Optimum Well Design and Risk Mitigation for Efficient Use of Geothermal Energy in South East Europe Case Study
Juricic, T., 2018Research output: Thesis › Master's Thesis
- Published
Performance investigation of a drag-based hydrokinetic turbine considering the effect of deflector, flow velocity, and blade shape
Maldar, N. R., Yee, N. C., Oguz, E. & Krishna, S., 15 Dec 2022, In: Ocean Engineering. 2022, Vol.266, Part2 15 December, 22 p., 112765.Research output: Contribution to journal › Article › Research › peer-review
- Published
Performance Measurement and Efficiency Improvement for Onshore Drilling Rigs Operated by OMV
Al-Salat, A. Y. Y., 2016Research output: Thesis › Master's Thesis
- Published
Permeability prediction of un-cored intervals using new IMLR method and artificial neural networks: A case study of Bangestan field, Iran
Naeeni, M. N., Zargari, H., Ashena, R., Ashena, R. & Kharrat, R., 2010, Society of Petroleum Engineers - Nigeria Annual International Conference and Exhibition 2010, NAICE. p. 882-890 9 p. (Society of Petroleum Engineers - Nigeria Annual International Conference and Exhibition 2010, NAICE; vol. 2).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
- Published
Possible implementation of automated systems in drilling rig design
Kurz, K., 2013Research output: Thesis › Master's Thesis
- Published
Prediction of Complications and Accidents during Drilling with Application of Machine Learning Model
Seynaroev, M., 2021Research output: Thesis › Master's Thesis
- Published
Prediction of Rheological and Filtration Loss Properties of Nano-Zirconium-Dioxide Drilling Fluids via Machine Learning Techniques for Energy Exploration
Jason, C., Umer Ilyas, S., Ridha, S., Sehar, U., Alsaady, M. & Krishna, S., 2024, Prediction of Rheological and Filtration Loss Properties of Nano-Zirconium-Dioxide Drilling Fluids via Machine Learning Techniques for Energy Exploration. p. 469-477 8 p.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
- Published
Prediction of Weight-on-Bit based on Real-Time Surface Measurements
Paulic, R., 2007Research output: Thesis › Master's Thesis
- Published
Pressure - Equilibrium Curves of Typical Natural Gas Components in a Blend of Imidazolium Based Ionic Liquids for Selective Absorption
Thonhauser, G., 2011.Research output: Contribution to conference › Poster › Research › peer-review
