US 12,340,294 B2
Dynamic well construction model
Sarah Louise Rawlinson, Stonehouse (GB); Avirup Bhattacharyya, Stonehouse (GB); George Emlyn Jones, Stonehouse (GB); Andrew Whitmore, Stonehouse (GB); Maja Ignova, Stonehouse (GB); and Ian Farmer, Stonehouse (GB)
Assigned to Schlumberger Technology Corp. / SHTC, Sugar Land, TX (US)
Filed by Schlumberger Technology Corporation, Sugar Land, TX (US)
Filed on Dec. 10, 2020, as Appl. No. 17/247,399.
Claims priority of provisional application 62/947,025, filed on Dec. 12, 2019.
Prior Publication US 2021/0180439 A1, Jun. 17, 2021
Int. Cl. G06N 20/20 (2019.01); E21B 43/25 (2006.01); E21B 43/30 (2006.01); E21B 47/04 (2012.01); G01V 1/52 (2006.01); G06F 9/54 (2006.01); G06F 18/214 (2023.01); G06N 20/00 (2019.01)
CPC G06N 20/20 (2019.01) [E21B 43/25 (2013.01); E21B 43/30 (2013.01); E21B 47/04 (2013.01); G01V 1/52 (2013.01); G06F 9/541 (2013.01); G06F 18/214 (2023.01); G06N 20/00 (2019.01); E21B 2200/20 (2020.05)] 14 Claims
OG exemplary drawing
 
1. A method comprising:
receiving first inputs, from a user via a user interface of a computing device or from a user device via a network interface of the computing device, wherein the first inputs specify at least a geographic location of a proposed well;
displaying input fields on the user device or on a graphical user interface (GUI) of the user interface, wherein the input fields are associated with a machine learning model, of a group of machine learning models associated with a plurality of geographic locations, and wherein the machine learning model is associated with the specified geographic location of the proposed well;
receiving second inputs to the input fields, from the user via the user interface or via the network interface of the computing device, wherein the second inputs specify characteristics of the proposed well, wherein the second inputs include at least a rate of penetration input, a depth input, and a planned drilling distance input, and wherein the second inputs are received as part of an application programming interface call;
transmitting, by the computing device to the user device or to the GUI of the user interface, an indicator with feedback for construction of the proposed well using the second inputs and a machine learning model trained using data from actual wells, wherein the indicator indicates a percentage chance that the construction of the proposed well, based on the specified characteristics of the proposed well, will meet at least one run objective for the proposed well, wherein the indicator further indicates at least one of the second inputs that is identified as causing a reduction of the percentage chance, and wherein transmitting the indicator occurs automatically as a response to the application programming interface call;
receiving revised inputs, in response to the indicator, from the user via the user interface or from the user device via the network interface of the computing device, wherein the revised inputs specify at least one adjusted characteristic of the proposed well, and wherein the revised inputs include the at least one of the second inputs identified as causing the reduction of the percentage chance; and
constructing the proposed well in accordance with the adjusted characteristics.