000			02119cam a2200289zu 4500
001			88965572
003			FRCYB88965572
005			20250429183910.0
006			m o d
007			cr un
008			250429s2020 fr | o|||||0|0|||eng d
020			_a9780128209578
035			_aFRCYB88965572
040			_aFR-PaCSA _ben _c _erda
100	1		_aSun, Shuyu
245	0	1	_aReservoir Simulations _bMachine Learning and Modeling _c['Sun, Shuyu', 'Zhang, Tao']
264		1	_bGulf Professional Publishing _c2020
300			_a p.
336			_btxt _2rdacontent
337			_bc _2rdamdedia
338			_bc _2rdacarrier
650		0	_a
700	0		_aSun, Shuyu
700	0		_aZhang, Tao
856	4	0	_2Cyberlibris _uhttps://international.scholarvox.com/netsen/book/88965572 _qtext/html _a
520			_aReservoir Simulation: Machine Learning and Modeling helps the engineer step into the current and most popular advances in reservoir simulation, learning from current experiments and speeding up potential collaboration opportunities in research and technology. This reference explains common terminology, concepts, and equations through multiple figures and rigorous derivations, better preparing the engineer for the next step forward in a modeling project and avoid repeating existing progress. Well-designed exercises, case studies and numerical examples give the engineer a faster start on advancing their own cases. Both computational methods and engineering cases are explained, bridging the opportunities between computational science and petroleum engineering. This book delivers a critical reference for today's petroleum and reservoir engineer to optimize more complex developments. - Understand commonly used and recent progress on definitions, models, and solution methods used in reservoir simulation - World leading modeling and algorithms to study flow and transport behaviors in reservoirs, as well as the application of machine learning - Gain practical knowledge with hand-on trainings on modeling and simulation through well designed case studies and numerical examples.
999			_c1330402 _d1330402