US 11,989,648 B2
Machine learning based approach to detect well analogue
Mandar Shrikant Kulkarni, Pune (IN); Hiren Maniar, Houston, TX (US); and Aria Abubakar, Houston, TX (US)
Assigned to SCHLUMBERGER TECHNOLOGY CORPORATION, Sugar Land, TX (US)
Filed by Schlumberger Technology Corporation, Sugar Land, TX (US)
Filed on Sep. 11, 2020, as Appl. No. 16/948,281.
Claims priority of application No. 201921038601 (IN), filed on Sep. 24, 2019.
Prior Publication US 2021/0089892 A1, Mar. 25, 2021
Int. Cl. G06N 3/08 (2023.01); G06F 16/245 (2019.01); G06F 16/248 (2019.01)
CPC G06N 3/08 (2013.01) [G06F 16/245 (2019.01); G06F 16/248 (2019.01)] 17 Claims
OG exemplary drawing
 
1. A method comprising:
collecting a training log from a plurality of well logs;
creating a log window of a plurality of log windows from the training log;
creating a positive window from the log window;
creating a negative window from the training log; and
training a siamese neural network (SNN) that includes a first self attention neural network (ANN) and a duplicate self attention neural network with the log window, the positive window, and the negative window, to recognize a similarity between the log window and the positive window and to differentiate against the negative window, wherein training the siamese neural network comprises generating the first self attention neural network output of the first self attention neural network using a plurality of convolutional neural networks.