US 11,852,778 B2
Static engine and neural network for a cognitive reservoir system
Azarang Golmohammadizangabad, Los Angeles, CA (US); and Shahram Farhadi Nia, Glendale, CA (US)
Assigned to BEYOND LIMITS, INC., Glendale, CA (US)
Filed by BEYOND LIMITS, INC., Glendale, CA (US)
Filed on Oct. 8, 2021, as Appl. No. 17/497,477.
Application 17/497,477 is a continuation of application No. 16/157,732, filed on Oct. 11, 2018, granted, now 11,143,789, issued on Oct. 12, 2021.
Claims priority of provisional application 62/571,150, filed on Oct. 11, 2017.
Prior Publication US 2022/0026598 A1, Jan. 27, 2022
Int. Cl. G01V 99/00 (2009.01); G06N 3/08 (2023.01); G06N 3/04 (2023.01); G06N 3/088 (2023.01); G06F 30/20 (2020.01); G06N 3/043 (2023.01); G06N 3/045 (2023.01)
CPC G01V 99/005 (2013.01) [G06F 30/20 (2020.01); G06N 3/04 (2013.01); G06N 3/043 (2023.01); G06N 3/045 (2023.01); G06N 3/08 (2013.01); G06N 3/088 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A system for developing a reservoir, the system comprising:
one or more processors; and
at least one non-transitory computer readable medium having stored therein instructions executed by the one or more processors to:
generate, by a neural network, a set of feature vectors defined based on a distance between each observed data point of a set of observed data points in a volume along a well trajectory and a set of randomly generated points in the volume, the set of feature vectors corresponding to well log values captured using one or more measuring tools, the neural network generating a three-dimensional populated log by propagating the well log values across the volume, the neural network quantifying uncertainty by generating a plurality of realizations including the three-dimensional populated log;
generating, by a static modeler in communication with the neural network, a static model of the reservoir by clustering the volume into one or more clusters of rock types based on core values generated from the plurality of realizations;
generating, by a dynamic modeler in communication with the static modeler, a dynamic model of the reservoir using the static model and dynamic data by constructing a reservoir graph representing the clusters as graph vertices of the static model; and
generating, by a reasoner in communication with the dynamic modeler, a ranking of sub-volumes of the reservoir for drilling based on the dynamic model.