US 12,449,562 B2
Determining a landing zone in a subterranean formation
Jordan Alexander, Austin, TX (US)
Assigned to Enverus, Inc., Austin, TX (US)
Appl. No. 17/442,449
Filed by Enverus, Inc., Austin, TX (US)
PCT Filed Mar. 24, 2020, PCT No. PCT/US2020/024393
§ 371(c)(1), (2) Date Sep. 23, 2021,
PCT Pub. No. WO2020/198194, PCT Pub. Date Oct. 1, 2020.
Claims priority of provisional application 62/824,121, filed on Mar. 26, 2019.
Prior Publication US 2022/0155483 A1, May 19, 2022
Int. Cl. G01V 20/00 (2024.01); E21B 7/00 (2006.01); E21B 41/00 (2006.01); E21B 49/00 (2006.01); G06F 30/27 (2020.01); G06N 20/20 (2019.01); G06N 5/01 (2023.01)
CPC G01V 20/00 (2024.01) [E21B 7/00 (2013.01); E21B 41/00 (2013.01); E21B 49/00 (2013.01); G06F 30/27 (2020.01); G06N 20/20 (2019.01); E21B 2200/20 (2020.05); E21B 2200/22 (2020.05); G06N 5/01 (2023.01)] 30 Claims
OG exemplary drawing
 
1. A computer-implemented method for generating a geological model, comprising:
identifying, with one or more hardware processors, a first plurality of wells drilled into a reservoir basin from a terranean surface, with each of the first plurality of wells associated with one of a plurality of landing zones formed under the terranean surface in the reservoir basin, each of the landing zones comprising a discrete geological layer, where each of the first plurality of wells is associated with the one landing zone based on a horizontal portion of the each well being formed within the one landing zone based on a known, digital trajectory of the each well;
identifying, with one or more hardware processors, a plurality of well data for each of a second plurality of wells drilled into the reservoir basin from the terranean surface;
comparing, with the one or more hardware processors and a machine learning process, the plurality of well data for each well of the second plurality of wells with a reservoir basin database that associates the well data with one of the plurality of landing zones;
correlating, with the one or more hardware processors and the machine learning process, each of the second plurality of wells with one landing zone of the plurality of landing zones based on the comparison, the machine learning process trained to derive a particular landing zone of the each of the second plurality of wells based at least in part on the plurality of well data for the each of the second plurality of wells;
generating, with the one or more hardware processors, a geological model of the reservoir basin based on the correlated wells of the second plurality of wells and the first plurality of wells;
identifying, at a server computing system that stores the generated geological model, a request from a client computing system that comprises an identification of one or more drilled wells in the reservoir basin;
determining, with the server computing system and based on the generated geological model, a particular landing zone for each of the identified one or more drilled wells; and
preparing, with the server computing system, a graphic that describes the determined particular landing zones for display at the client computing system.