Reservoir fluid property estimation using mud-gas data

A method is disclosed for generating a machine learning model to predict a reservoir fluid property, such as gas-oil ratio or density, based on standard mud-gas and petrophysical data. It has been found that this model predicts these reservoir fluid properties with an accuracy that is close to that...

Ausführliche Beschreibung

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Bibliographische Detailangaben
Hauptverfasser:	Gulnar Yerkinkyzy, Tao Yang, Knut Uleberg, Ibnu Hafidz Arief, Margarete Maria Kopal
Format:	Patent
Sprache:	eng
Schlagworte:	DETECTING MASSES OR OBJECTS EARTH DRILLING EARTH DRILLING, e.g. DEEP DRILLING FIXED CONSTRUCTIONS GEOPHYSICS GRAVITATIONAL MEASUREMENTS INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIRCHEMICAL OR PHYSICAL PROPERTIES MEASURING MINING OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR ASLURRY OF MINERALS FROM WELLS PHYSICS TESTING
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Beschreibung
Zusammenfassung:	A method is disclosed for generating a machine learning model to predict a reservoir fluid property, such as gas-oil ratio or density, based on standard mud-gas and petrophysical data. It has been found that this model predicts these reservoir fluid properties with an accuracy that is close to that which can be achieved using advanced mud-gas data. This is advantageous, as than standard mud-gas data and petrophysical data is much more readily available than advanced mud-gas data.