Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Jan Limbeck"'
Autor:
Keimpe Nevenzeel, Timothy Park, Kevin Bisdom, Fabian Lanz, Taco den Bezemer, S.M. Bierman, Jan van Elk, Christopher Kelvin Harris, Jan Limbeck, Franz J. Kiraly, Eduardo Barbaro, Stephen Bourne
Publikováno v:
Computational Geosciences. 25:529-551
The Groningen gas field in the Netherlands is experiencing induced seismicity as a result of ongoing depletion. The physical mechanisms that control seismicity have been studied through rock mechanical experiments and combined physical-statistical mo
Autor:
Somnath Mondal, Ashan Garusinghe, Sebastian Ziman, Muhammed Abdul-Hameed, Rakesh Paleja, Matthew Jones, Jan Limbeck, Bryce Bartmann, Jeremy Young, Kent Shanley, Bonner Cardwell, Humphrey Klobodu, Paul Huckabee, Gustavo Ugueto, Christopher Ledet
Publikováno v:
Day 2 Wed, February 02, 2022.
Hydraulic fracturing is a key driver of well productivity and field development planning, in addition to being the most significant portion of capex in shales. Recent breakthroughs in connectivity and digital technologies have enabled the monitoring
Autor:
Jan Limbeck, Alvaro Buoro, Detlef Hohl, Jorge Guevara, Bianca Zadrozny, John Tolle, Ligang Lu
Publikováno v:
SPE Reservoir Evaluation & Engineering. 22:1185-1200
Summary In this paper, we propose a machine–learning methodology using domain–knowledge constraints for well–data integration, prior/expert–knowledge incorporation, and sweet–spot identification. Such methodology enables the analysis of the
Autor:
Bianca Zadrozny, Defletf Hohl, Jorge Guevara, Ligang Lu, Mingqi Wu, Alvaro Buoro, Jan Limbeck, John Tolle
Publikováno v:
Day 2 Tue, September 25, 2018.
We present a new methodology for modeling the cumulative oil and gas production of horizontal wells in a shale play given two types of well completion parameters: lateral length and proppant intensity, we also consider the location of the wells and t
Autor:
Jorge Luis Guevara Diaz, Bianca Zadrozny, Alvaro Buoro, John Tolle, Jan Limbeck, Mingqi Wu, Defletf Hohl
Publikováno v:
HAL
The huge amount of heterogeneous data provided by the petroleum industry brings opportunities and challenges for applying machine learning methodologies. For instance, petrophysical data recorded in well logs, completions datasets and well-production
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::faec81200e8c6ebeabd10947e91f2189
https://hal.archives-ouvertes.fr/hal-02199055
https://hal.archives-ouvertes.fr/hal-02199055