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1. (WO2019067282) METHOD FOR COST EFFECTIVE THERMO-DYNAMIC FLUID PROPERTY PREDICTIONS USING MACHINE-LEARNING BASED MODELS
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Pub. No.: WO/2019/067282 International Application No.: PCT/US2018/051684
Publication Date: 04.04.2019 International Filing Date: 19.09.2018
IPC:
G06N 99/00 (2010.01) ,G06F 19/00 (2018.01) ,G06F 17/50 (2006.01)
G PHYSICS
06
COMPUTING; CALCULATING; COUNTING
N
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
99
Subject matter not provided for in other groups of this subclass
G PHYSICS
06
COMPUTING; CALCULATING; COUNTING
F
ELECTRIC DIGITAL DATA PROCESSING
19
Digital computing or data processing equipment or methods, specially adapted for specific applications
G PHYSICS
06
COMPUTING; CALCULATING; COUNTING
F
ELECTRIC DIGITAL DATA PROCESSING
17
Digital computing or data processing equipment or methods, specially adapted for specific functions
50
Computer-aided design
Applicants:
SAUDI ARABIAN OIL COMPANY [SA/SA]; 1 Eastern Avenue Dhahran, 31311, SA
ARAMCO SERVICES COMPANY [US/US]; 9009 West Loop South Houston, Texas 77210-4535, US (AG)
Inventors:
KASHINATH, Abishek; US
Agent:
BRUCE, Carl E.; US
IYER, Sushil; US
COX, Michael E.; US
Priority Data:
62/563,46026.09.2017US
Title (EN) METHOD FOR COST EFFECTIVE THERMO-DYNAMIC FLUID PROPERTY PREDICTIONS USING MACHINE-LEARNING BASED MODELS
(FR) PROCÉDÉ POUR PRÉDICTIONS DE PROPRIÉTÉS THERMODYNAMIQUES DE FLUIDE ÉCONOMIQUES UTILISANT DES MODÈLES BASÉS SUR L'APPRENTISSAGE AUTOMATIQUE
Abstract:
(EN) A method for determining isothermal phase behaviour for reservoir simulation includes generating a training data set using negative flash calculations, training a first machine learning algorithm to identify a supercritical phase and a subcritical phase, training a second machine learning algorithm to identify a number of stable phases in the subcritical phase, and training a third machine learning algorithm to determine a phase split of the subcritical phase that has more than one identified stable phase.
(FR) Selon l'invention, un procédé de détermination de comportement de phase isotherme pour une simulation de réservoir consiste à produire un ensemble de données d'entraînement en utilisant des calculs d'éclair négatif, entraîner un premier algorithme d'apprentissage automatique pour identifier une phase supercritique et une phase sous-critique, entraîner un deuxième algorithme d'apprentissage automatique pour identifier un certain nombre de phases stables dans la phase sous-critique, et entraîner un troisième algorithme d'apprentissage automatique pour déterminer une division de phase de la phase sous-critique qui a plus d'une phase stable identifiée.
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Designated States: AE, AG, AL, AM, AO, AT, AU, AZ, BA, BB, BG, BH, BN, BR, BW, BY, BZ, CA, CH, CL, CN, CO, CR, CU, CZ, DE, DJ, DK, DM, DO, DZ, EC, EE, EG, ES, FI, GB, GD, GE, GH, GM, GT, HN, HR, HU, ID, IL, IN, IR, IS, JO, JP, KE, KG, KH, KN, KP, KR, KW, KZ, LA, LC, LK, LR, LS, LU, LY, MA, MD, ME, MG, MK, MN, MW, MX, MY, MZ, NA, NG, NI, NO, NZ, OM, PA, PE, PG, PH, PL, PT, QA, RO, RS, RU, RW, SA, SC, SD, SE, SG, SK, SL, SM, ST, SV, SY, TH, TJ, TM, TN, TR, TT, TZ, UA, UG, US, UZ, VC, VN, ZA, ZM, ZW
African Regional Intellectual Property Organization (ARIPO) (BW, GH, GM, KE, LR, LS, MW, MZ, NA, RW, SD, SL, ST, SZ, TZ, UG, ZM, ZW)
Eurasian Patent Office (AM, AZ, BY, KG, KZ, RU, TJ, TM)
European Patent Office (EPO) (AL, AT, BE, BG, CH, CY, CZ, DE, DK, EE, ES, FI, FR, GB, GR, HR, HU, IE, IS, IT, LT, LU, LV, MC, MK, MT, NL, NO, PL, PT, RO, RS, SE, SI, SK, SM, TR)
African Intellectual Property Organization (BF, BJ, CF, CG, CI, CM, GA, GN, GQ, GW, KM, ML, MR, NE, SN, TD, TG)
Publication Language: English (EN)
Filing Language: English (EN)