US 12,270,286 B2
Apparatus and method for fracking optimization
David Cook, Lakeway, TX (US)
Filed by David Cook, Lakeway, TX (US)
Filed on Feb. 8, 2024, as Appl. No. 18/436,806.
Application 18/436,806 is a continuation in part of application No. 17/986,375, filed on Nov. 14, 2022, granted, now 11,941,563.
Claims priority of provisional application 63/409,401, filed on Sep. 23, 2022.
Prior Publication US 2024/0183258 A1, Jun. 6, 2024
Int. Cl. E21B 43/26 (2006.01); G06F 30/27 (2020.01); G01V 1/40 (2006.01)
CPC E21B 43/26 (2013.01) [G06F 30/27 (2020.01); E21B 2200/20 (2020.05); E21B 2200/22 (2020.05); G01V 1/40 (2013.01)] 18 Claims
OG exemplary drawing
 
1. An apparatus for fracking optimization, wherein the apparatus comprises:
at least a processor; and
a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to:
receive a plurality of reservoir datums from a full sensor suite, wherein the plurality of reservoir datums comprises:
at least one surface reservoir datum detected using a first sensor of the full sensor suite; and
at least one downhole reservoir datum detected using a second sensor of the full sensor suite;
generate production training data as a function of the plurality of reservoir datums, wherein generating the production training data comprises:
converting the at least one downhole reservoir datum into a cleansed data format using a data conversion module;
identifying a plurality of correlational patterns between the at least one surface reservoir datum and the at least one downhole reservoir datum; and
generating the production training data as a function of the plurality of correlational patterns using a fracking simulation model;
train a fracking optimization machine learning model using the production training data;
receive a surface target reservoir datum from a minimum sensor suite; and
determine an optimal production parameter as a function of the surface target reservoir datum using the trained fracking optimization machine learning model.