WELL CORRELATION USING GLOBAL AND LOCAL MACHINE LEARNING MODELS
A method for correlating well logs includes receiving a well log as input to a first machine learning model that is configured to predict first markers in the well log based at least in part on a global factor of the well log, receiving the well log as input to a second machine learning model that i...
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Format | Patent |
Language | English French |
Published |
03.02.2022
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Abstract | A method for correlating well logs includes receiving a well log as input to a first machine learning model that is configured to predict first markers in the well log based at least in part on a global factor of the well log, receiving the well log as input to a second machine learning model that is configured to predict second markers in the well log based at least in part on local factors of the well log, generating a set of predicted well markers by merging at least some of the first markers and at least some of the second markers, and aligning the well log with respect to one or more other well logs based at least in part on the set of predicted well markers.
Procédé de corrélation de diagraphies de puits, lequel consiste à recevoir une diagraphie de puits comme entrée d'un premier modèle d'apprentissage automatique qui est configuré pour prédire des premiers marqueurs dans la diagraphie de puits sur la base, au moins en partie, d'un facteur global de la diagraphie de puits, à recevoir la diagraphie de puits comme entrée d'un second modèle d'apprentissage automatique qui est configuré pour prédire des seconds marqueurs dans la diagraphie de puits sur la base, au moins en partie, des facteurs locaux de la diagraphie de puits, à générer un ensemble de marqueurs de puits prédits en fusionnant au moins certains des premiers marqueurs et au moins certains des seconds marqueurs, et à aligner la diagraphie de puits par rapport à une ou plusieurs autres diagraphies de puits sur la base, au moins en partie, de l'ensemble de marqueurs de puits prédits. |
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AbstractList | A method for correlating well logs includes receiving a well log as input to a first machine learning model that is configured to predict first markers in the well log based at least in part on a global factor of the well log, receiving the well log as input to a second machine learning model that is configured to predict second markers in the well log based at least in part on local factors of the well log, generating a set of predicted well markers by merging at least some of the first markers and at least some of the second markers, and aligning the well log with respect to one or more other well logs based at least in part on the set of predicted well markers.
Procédé de corrélation de diagraphies de puits, lequel consiste à recevoir une diagraphie de puits comme entrée d'un premier modèle d'apprentissage automatique qui est configuré pour prédire des premiers marqueurs dans la diagraphie de puits sur la base, au moins en partie, d'un facteur global de la diagraphie de puits, à recevoir la diagraphie de puits comme entrée d'un second modèle d'apprentissage automatique qui est configuré pour prédire des seconds marqueurs dans la diagraphie de puits sur la base, au moins en partie, des facteurs locaux de la diagraphie de puits, à générer un ensemble de marqueurs de puits prédits en fusionnant au moins certains des premiers marqueurs et au moins certains des seconds marqueurs, et à aligner la diagraphie de puits par rapport à une ou plusieurs autres diagraphies de puits sur la base, au moins en partie, de l'ensemble de marqueurs de puits prédits. |
Author | MANGSULI, PURNAPRAJNA RAGHAVENDRA ABUBAKAR, ARIA KULKARNI, MANDAR SHRIKANT MANIAR, HIREN |
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DocumentTitleAlternate | CORRELATION DE PUITS A L'AIDE DE MODELES D'APPRENTISSAGE AUTOMATIQUE GLOBAUX ET LOCAUX |
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Snippet | A method for correlating well logs includes receiving a well log as input to a first machine learning model that is configured to predict first markers in the... |
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SubjectTerms | 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 MEASURING MINING OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR ASLURRY OF MINERALS FROM WELLS PHYSICS TESTING |
Title | WELL CORRELATION USING GLOBAL AND LOCAL MACHINE LEARNING MODELS |
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