Direct Hydrocarbon Indicators Analysis Informed by Machine Learning Processes

Various embodiments described herein provide methods of hydrocarbon management and associated systems and/or computer readable media including executable instructions. Such methods (and by extension their associated systems and/or computer readable media for implementing such methods) may include ob...

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Bibliographic Details
Main Authors Steffen, Kurt J, Zhang, Jie, MacDonald, Cody J
Format Patent
LanguageEnglish
Published 01.10.2020
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
DETECTING MASSES OR OBJECTS
EARTH DRILLING
EARTH DRILLING, e.g. DEEP DRILLING
ELECTRIC DIGITAL DATA PROCESSING
FIXED CONSTRUCTIONS
GEOPHYSICS
GRAVITATIONAL MEASUREMENTS
HANDLING RECORD CARRIERS
MEASURING
MINING
OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR ASLURRY OF MINERALS FROM WELLS
PHYSICS
PRESENTATION OF DATA
RECOGNITION OF DATA
RECORD CARRIERS
TESTING
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Summary:Various embodiments described herein provide methods of hydrocarbon management and associated systems and/or computer readable media including executable instructions. Such methods (and by extension their associated systems and/or computer readable media for implementing such methods) may include obtaining geophysical data (e.g., seismic or other geophysical data) from a prospective subsurface formation (that is, a potential formation or other subsurface region of interest for any of various reasons, but in particular due to potential for production of hydrocarbons) and using a trained machine learning (ML) system for direct hydrocarbon indicators (DHI) analysis of the obtained geophysical data. Hydrocarbon management decisions may be guided by the DHI analysis.
Bibliography:Application Number: US202016776314