US 12,331,629 B2
Well planning system
Lucian Johnston, Sugar Land, TX (US); and Michael Dietrick Sturm, Houston, TX (US)
Assigned to Schlumberger Technology Corporation, Sugar Land, TX (US)
Filed by Schlumberger Technology Corporation, Sugar Land, TX (US)
Filed on Sep. 22, 2023, as Appl. No. 18/472,391.
Application 18/472,391 is a continuation of application No. 17/812,183, filed on Jul. 13, 2022, granted, now 11,802,471.
Application 17/812,183 is a continuation of application No. 16/646,177, granted, now 11,391,143, issued on Jul. 19, 2022, previously published as PCT/US2018/050314, filed on Sep. 11, 2018.
Claims priority of provisional application 62/557,115, filed on Sep. 11, 2017.
Prior Publication US 2024/0011385 A1, Jan. 11, 2024
Int. Cl. E21B 44/00 (2006.01); E21B 7/04 (2006.01); G06Q 50/02 (2012.01)
CPC E21B 44/00 (2013.01) [E21B 7/04 (2013.01); G06Q 50/02 (2013.01)] 18 Claims
OG exemplary drawing
 
1. A method comprising:
receiving a digital well plan;
issuing drilling instructions for drilling a well based at least in part on the digital well plan;
comparing acquired information associated with drilling of the well with well plan information of the digital well plan to determine if there is at least one deviation from the digital well plan;
determining that there is the at least one deviation;
performing a search of a database upon the determining that there is the at least one deviation, wherein the search generates results that comprise at least one outcome that is classified as being a positive outcome or a negative outcome;
analyzing the at least one deviation to determine at least one factor of the digital well plan as being at least in part an underlying cause of the at least one deviation; and
training a neural network as a machine learning model based on the results to electronically adjust the digital well plan to increase a likelihood of at least one positive outcome.