公开(公告)号：US20240102380A1

公开(公告)日：2024-03-28

申请号：US18516294

申请日：2023-11-21

Applicant: Schlumberger Technology Corporation

Inventor： Emilien Dupont ,  Sergey Doronichev ,  Velizar Vesselinov ,  Valerian Guillot ,  Carlos Boneti ,  Jose Celaya Galvan

IPC: E21B47/04 ,  E21B41/00 ,  G01V1/50

CPC classification number: E21B47/04 ,  E21B41/00 ,  G01V1/50 ,  G01V2200/16 ,  G01V2210/6169

Abstract: A system can include a processor; memory operatively coupled to the processor; and processor-executable instructions stored in the memory to instruct the system to: receive a marker on a well log for a well in a geographic region; and iteratively propagate the marker automatically to a plurality of well logs for other wells in the geographic region.

公开(公告)号：US11828167B2

公开(公告)日：2023-11-28

申请号：US17048411

申请日：2019-04-18

Applicant: Schlumberger Technology Corporation

Inventor： Emilien Dupont ,  Sergey Doronichev ,  Velizar Vesselinov ,  Valerian Guillot ,  Carlos Boneti ,  Jose Celaya Galvan

IPC: E21B47/04 ,  G01V1/50 ,  E21B41/00

CPC classification number: E21B47/04 ,  E21B41/00 ,  G01V1/50 ,  G01V2200/16 ,  G01V2210/6169

Abstract: A system can include a processor; memory operatively coupled to the processor; and processor-executable instructions stored in the memory to instruct the system to: receive a marker on a well log for a well in a geographic region; and iteratively propagate the marker automatically to a plurality of well logs for other wells in the geographic region.

公开(公告)号：US20230195952A1

公开(公告)日：2023-06-22

申请号：US17999291

申请日：2021-05-20

Applicant: Schlumberger Technology Corporation

Inventor： Valerian Guillot ,  Florian Karpfinger ,  Ali Ozbek ,  Riadh Boualleg ,  Ling Li

IPC: G06F30/13 ,  E21B44/00 ,  E21B7/04 ,  E21B41/00 ,  E21B49/00

CPC classification number: G06F30/13 ,  E21B7/04 ,  E21B41/00 ,  E21B44/00 ,  E21B49/00 ,  E21B2200/20

Abstract: A method to design well trajectories includes determining dogleg severity as a function of inclination, and a corresponding rate of penetration performance of the tool by a hybrid model including physical modelling and machine learning correction. The method includes solving for optimal steering parameters to predict a dogleg severity as close as possible to a desired dogleg severity at a given inclination of the trajectory, which is repeated for dogleg severity and inclination combinations of interest. The rate of penetration for feasible points is also determined and a rate of penetration (or time-to-target) map can be produced. Potential trajectories are then evaluated relative to the map to estimate drilling time-to-target, and an optimal trajectory can be selected that has a lowest time-to-target while also being feasible for the tool and optionally avoiding risks or downhole obstacles.

公开(公告)号：US20220397027A1

公开(公告)日：2022-12-15

申请号：US17661943

申请日：2022-05-04

Applicant: SCHLUMBERGER TECHNOLOGY CORPORATION

Inventor： Valerian Guillot ,  Fatma Mahfoudh ,  Josselin Kherroubi ,  Anderson Schmidt

IPC: E21B44/00 ,  E21B47/07 ,  E21B45/00

Abstract: Planning a wellbore includes determining drillability values from surface drilling parameters for an offset wellbore. The drillability values are used to prepare a protein code sequence of protein codes assigned to a range of drillability values. The protein code sequence from the offset wellbore is used to develop a protein code sequence for a planned wellbore. A machine learning model analyzes the offset surface drilling parameters and protein code sequence, and provides target surface drilling parameters for the planned wellbore.

公开(公告)号：US11520075B2

公开(公告)日：2022-12-06

申请号：US16439624

申请日：2019-06-12

Applicant: SCHLUMBERGER TECHNOLOGY CORPORATION

Inventor： Shashi Menon ,  Aria Abubakar ,  Vikas Jain ,  David Furse Allen ,  John Rasmus ,  John Paul Horkowitz ,  Valerian Guillot ,  Florent D'Halluin ,  Ridvan Akkurt ,  Sylvain Wlodarczyk

IPC: G01V11/00 ,  G06Q10/10 ,  G01V99/00

Abstract: Methods and systems for augmented geological service characterization are described. An embodiment of a method includes generating a geological service characterization process in response to one or more geological service objectives and a geological service experience information set. Such a method may also include augmenting the geological service characterization process by machine learning in response to a training information set. Additionally, the method may include generating an augmented geological service characterization process in response to the determination information.