公开(公告)号：US20230088055A1

公开(公告)日：2023-03-23

申请号：US17933506

申请日：2022-09-20

Applicant: Schlumberger Technology Corporation

Inventor： Peter Tilke ,  Wyame Benslimane ,  Lingchen Zhu ,  Zikri Bayraktar

IPC: G01V1/28 ,  G01V1/30

Abstract: Methods and platforms for allowing efficient identification of 3D stratigraphic models that explain observed log data.

公开(公告)号：US20240299860A1

公开(公告)日：2024-09-12

申请号：US18181201

申请日：2023-03-09

Applicant: Schlumberger Technology Corporation

Inventor： Roman Alexandrovich Skachkov ,  Shahnawaz Hossain Molla ,  Zikri Bayraktar

IPC: B01D3/30 ,  B01D3/26 ,  B01D3/42

CPC classification number: B01D3/30 ,  B01D3/26 ,  B01D3/4211

Abstract: Embodiments presented provide for a multirotational counter rotating reactor. The reactor is configured to accept a fluid stream and separate the fluid stream into high quality liquid and gaseous phases through spinning of the sets of discs as well as through performing a heat transfer to the fluid stream.

公开(公告)号：US20220164594A1

公开(公告)日：2022-05-26

申请号：US17593011

申请日：2020-03-11

Applicant: Schlumberger Technology Corporation

Inventor： Marie LeFranc ,  Zikri Bayraktar ,  Morten Kristensen ,  Philippe Marza ,  Isabelle Le Nir ,  Michael Prange ,  Josselin Kherroubi

IPC: G06K9/62

Abstract: Embodiments of the present disclosure are directed towards systems and methods for automated stratigraphy interpretation from borehole images. Embodiments may include constructing, using at least one processor, a training set of synthetic images corresponding to a borehole, wherein the training set includes one or more of synthetic images, real images, and modified images. Embodiments may further include automatically classifying, using the at least one processor, the training set into one or more individual sedimentary geometries using one or machine learning techniques. Embodiments may also include automatically classifying, using the at least one processor, the training set into one or more priors for depositional environments.

公开(公告)号：US11899157B2

公开(公告)日：2024-02-13

申请号：US17288597

申请日：2019-10-24

Applicant: SCHLUMBERGER TECHNOLOGY CORPORATION

Inventor： Zikri Bayraktar ,  Dzevat Omeragic

IPC: G01V3/24 ,  G01N33/28 ,  G01V3/38

CPC classification number: G01V3/24 ,  G01N33/2823 ,  G01V3/38

Abstract: Methods and systems are provided that predict electromagnetic properties of drilling mud and a formation, which involve a logging tool that measures current injected into a measurement zone adjacent a sensor electrode at multiple frequencies. The measured currents at the multiple frequencies are processed to determine complex impedances for the sensor electrode at the multiple frequencies. The complex impedances are used to generate input data, which is supplied to a system of artificial neural networks (ANNs) that is configured to predict and output electromagnetic properties of the drilling mud and the formation within the measurement zone and possibly tool standoff based on the input data. The system of ANNs can employ a cascaded architecture of multiple ANNs. The electromagnetic properties or tool standoff predicted by the system of ANNs can be used to construct a borehole image over varying azimuth and depth.

公开(公告)号：US11900658B2

公开(公告)日：2024-02-13

申请号：US17593011

申请日：2020-03-11

Applicant: Schlumberger Technology Corporation

Inventor： Marie LeFranc ,  Zikri Bayraktar ,  Morten Kristensen ,  Philippe Marza ,  Isabelle Le Nir ,  Michael Prange ,  Josselin Kherroubi

IPC: G06V10/772 ,  G06F18/2431 ,  G06F18/214 ,  G06F18/2415 ,  G06V10/774 ,  G06V10/82 ,  G06V10/44

CPC classification number: G06V10/772 ,  G06F18/2148 ,  G06F18/2415 ,  G06F18/2431 ,  G06V10/774 ,  G06V10/82 ,  G06V10/454

Abstract: Embodiments of the present disclosure are directed towards systems and methods for automated stratigraphy interpretation from borehole images. Embodiments may include constructing, using at least one processor, a training set of synthetic images corresponding to a borehole, wherein the training set includes one or more of synthetic images, real images, and modified images. Embodiments may further include automatically classifying, using the at least one processor, the training set into one or more individual sedimentary geometries using one or machine learning techniques. Embodiments may also include automatically classifying, using the at least one processor, the training set into one or more priors for depositional environments.

公开(公告)号：US20220341292A1

公开(公告)日：2022-10-27

申请号：US17753598

申请日：2020-09-09

Applicant: Schlumberger Technology Corporation

Inventor： Zikri Bayraktar ,  Hedi Driss ,  Marie Emeline Cecile LeFranc

IPC: E21B41/00 ,  G06F40/279 ,  G01V99/00

Abstract: Aspects of the present disclosure relate to a well analog recommendation system. The well analog recommendation system may generate numerical representations indicative of text-based descriptions within a well report and/or well log associated with a well. Further, the well analog recommendation system may generate a well analog output that may include one or more text-based characterizations associated with one or more additional wells that are determined based on the numerical representation. For example, the well analog recommendation system may compare the numerical representation of the well to one or more numerical representations associated with the one or more additional wells and output the one or more text-based characterizations when the numerical representations are approximately equal or above a threshold.