$Optimization of discrete fracture network (DFN) using streamlines and machine learning$

Optimization of discrete fracture network (DFN) using streamlines and machine learning

A methodology is provided to optimize the dynamic connectivity of a discrete fracture network (DFN) model of a subsurface reservoir against observed reservoir production measures using streamlines and machine learning. Adjustment of discrete fracture network properties of the reservoir is made local...

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Bibliographische Detailangaben
Hauptverfasser:	Awan, Anwar Rahim, Amari, Mustafa, Camargo, Otto E. Meza, Al-Garni, Marei
Format:	Patent
Sprache:	eng
Schlagworte:	CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING EARTH DRILLING EARTH DRILLING, e.g. DEEP DRILLING FIXED CONSTRUCTIONS MINING OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR ASLURRY OF MINERALS FROM WELLS PHYSICS
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Beschreibung
Zusammenfassung:	A methodology is provided to optimize the dynamic connectivity of a discrete fracture network (DFN) model of a subsurface reservoir against observed reservoir production measures using streamlines and machine learning. Adjustment of discrete fracture network properties of the reservoir is made locally and minimizes computer processing time spent in history matching. An iterative workflow identifies history match issues between measured and predicted or simulated water cut of reservoir produced fluids. Streamline analysis quantifies injector-producer communication and identifies reservoir grid block bundles that dominate dynamic response. A genetic algorithm updates discrete fracture network properties of the reservoir model to improve dynamic history match response.