Operating wellbore equipment using a data driven physics-based model

Aspects of the present disclosure relate to receiving data associated with a subterranean reservoir to be penetrated by a wellbore and training a neural network with both the data and a physics-based first principles model. The neural network is then used to make predictions regarding the properties...

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Bibliographic Details
Main Authors Rangarajan, Keshava Prasad, Madasu, Srinath
Format Patent
LanguageEnglish
Published 16.08.2022
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
CONTROL OR REGULATING SYSTEMS IN GENERAL
CONTROLLING
COUNTING
EARTH DRILLING
EARTH DRILLING, e.g. DEEP DRILLING
FIXED CONSTRUCTIONS
FUNCTIONAL ELEMENTS OF SUCH SYSTEMS
MINING
MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS ORELEMENTS
OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR ASLURRY OF MINERALS FROM WELLS
PHYSICS
REGULATING
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Summary:Aspects of the present disclosure relate to receiving data associated with a subterranean reservoir to be penetrated by a wellbore and training a neural network with both the data and a physics-based first principles model. The neural network is then used to make predictions regarding the properties of the subterranean reservoir, and these predictions are in turn used to determine one or more controllable parameters for equipment associated with a wellbore. The controllable parameters can then be used to control equipment for formation, stimulation, or production relative to the wellbore.
Bibliography:Application Number: US201716754233