US 12,353,807 B2
Automated reservoir model prediction using ML/AI intergrating seismic, well log and production data
Sridharan Vallabhaneni, Bangalore (IN); Samiran Roy, Bengaluru (IN); Soumi Chaki, West Bengal (IN); Bhaskar Jogi Venkata Mandapaka, Bangalore (IN); Rajeev Pakalapati, Bangalore (IN); Shreshth Srivastav, Noida (IN); and Satyam Priyadarshy, Herndon, VA (US)
Assigned to Landmark Graphics Corporation, Houston, TX (US)
Filed by Landmark Graphics Corporation, Houston, TX (US)
Filed on Sep. 9, 2020, as Appl. No. 17/016,075.
Prior Publication US 2022/0075915 A1, Mar. 10, 2022
Int. Cl. G06F 30/27 (2020.01); G01V 1/28 (2006.01); G01V 1/30 (2006.01); G06F 113/08 (2020.01)
CPC G06F 30/27 (2020.01) [G01V 1/282 (2013.01); G01V 1/302 (2013.01); G01V 2210/614 (2013.01); G06F 2113/08 (2020.01)] 15 Claims
OG exemplary drawing
 
1. A method comprising:
training a first machine learning model using a first neural network to generate one or more integrated enhanced logs based, at least in part, on an integrated data set, wherein the integrated data set includes 2D, 3D, and 4D seismic attributes derived from a phase, a frequency, and an amplitude of seismic signal data, the 2D, 3D, and 4D seismic attributes matched to well log data by bringing at least some of the well log data and the seismic signal data used to generate the 2D, 3D, and 4D seismic attributes to a same sampling rate, the well log data including core data, production data, and drilling data;
generating the one or more integrated enhanced logs from the first machine learning model;
grouping the one or more integrated enhanced logs into an ensemble of integrated enhanced logs to form a static reservoir 3D model of a subterranean reservoir;
inputting additional data to the first machine learning model to produce one or more updated integrated enhanced logs;
grouping the one or more updated integrated enhanced logs into an ensemble of updated integrated enhanced logs to form an updated 3D model; and
training a second machine learning model using a second neural network to generate a dynamic reservoir 3D model, wherein the updated 3D model and a set of dynamic modeling data are used as input for training the second machine learning model, wherein the dynamic modeling data includes data used to predict flow properties of the subterranean reservoir.