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公开(公告)号：US20240303925A1
公开(公告)日：2024-09-12
申请号：US18663998
申请日：2024-05-14
Applicant: Schlumberger Technology Corporation
Inventor： Lingchen Zhu , Tuanfeng Zhang
Abstract: Neural network systems and related machine learning methods for geological modeling are provided that employ an improved generative adversarial network including a generator neural network and a discriminator neural network. The generator neural network is trained to map a combination of a noise vector and a category code vector as input to a simulated image of geological facies. The discriminator neural network is trained to map at least one image of geological facies provided as input to corresponding probability that the at least one image of geological facies provided as input is a training image of geological facies or a simulated image of geological facies produced by the generator neural network.
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公开(公告)号：US20220082517A1
公开(公告)日：2022-03-17
申请号：US17421406
申请日：2019-01-08
Applicant: Schlumberger Technology Corporation
Inventor： Yi-Qiao Song , Andre Souza , Muthusamy Vembusubramanian , Tuanfeng Zhang , Wenyue Xu
Abstract: A method (and corresponding system) that characterizes a porous rock sample is provided, which involves subjecting the porous rock sample to an applied experimental pressure where a first fluid that saturates the porous rock sample is displaced by a second fluid, and subsequently applying an NMR pulse sequence to the rock sample, detecting resulting NMR signals, and generating and storing NMR data representative of the detected NMR signals. The application of experimental pressure and NMR measurements can be repeated over varying applied experimental pressure to obtain NMR data associated with varying applied experimental pressure values. The NMR data can be processed using inversion to obtain a probability distribution function of capillary pressure values as a function of NMR property values. The probability distribution function of capillary pressure values as a function of NMR property values can be processed to determine at least one parameter indicative of the porous rock sample.
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3.发明申请公开(公告)号：US20190331813A1
公开(公告)日：2019-10-31
申请号：US15966607
申请日：2018-04-30
Applicant: SCHLUMBERGER TECHNOLOGY CORPORATION
Inventor： Tuanfeng Zhang , Richard E. Lewis , Helena Gamero Diaz
Abstract: Methods may include normalizing two or more wellbore logs obtained from the output of two or more wellbore tool surveys of a wellbore in a formation of interest; inputting two or more wellbore logs into a correlation matrix; assigning each of the two or more wellbore logs a positive or negative value based on the impact on a selected wellbore quality; performing a principal component analysis of the two or more wellbore logs to obtain one or more loading vectors; computing weighting factors for each of the two or more wellbore logs from the one or more loading vectors; and generating a quality index by linearly combining the two or more wellbore logs using the computed weighting factors.
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公开(公告)号：US10422924B2
公开(公告)日：2019-09-24
申请号：US14536289
申请日：2014-11-07
Applicant: Schlumberger Technology Corporation
Inventor： Tuanfeng Zhang , Neil F. Hurley , Ridvan Akkurt , David McCormick , Shu Zhang
Abstract: Methods of generating structural models of highly deviated or horizontal wells may be generated from the measurement of true stratigraphic thickness in three dimensions (TST3D). In one aspect, methods may include generating a structural model from one or more deviation surveys of a horizontal well, one or more single channel log measurements, and a three-dimensional reference surface.
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公开(公告)号：US10113411B2
公开(公告)日：2018-10-30
申请号：US14896961
申请日：2014-06-10
Applicant: SCHLUMBERGER TECHNOLOGY CORPORATION
Inventor： Andriy Gelman , Tuanfeng Zhang , Neil F. Hurley
Abstract: The present disclosure introduces methods and apparatus for acquiring a borehole image corresponding to a sidewall surface of a borehole that penetrates a subterranean formation, wherein the subterranean formation comprises structural elements and a varying geophysical characteristic. The borehole image comprises structure corresponding to the structural elements, texture corresponding to the varying geophysical characteristic, and coverage gaps (605) in which the structure and texture are missing. Trends corresponding to the structure are extracted from the borehole image. Missing structure within the gaps (605) is reconstructed based on the extracted trends. Missing texture within the gaps is simulated based on the borehole image and the reconstructed structure. A fullbore image is constructed based on the borehole image, the reconstructed structure within the gaps, and the simulated texture within the gaps.
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Patent Agency Ranking
公开(公告)号：US11989829B2
公开(公告)日：2024-05-21
申请号：US17753617
申请日：2020-09-14
Applicant: Schlumberger Technology Corporation
Inventor： Lingchen Zhu , Tuanfeng Zhang
Abstract: Neural network systems and related machine learning methods for geological modeling are provided that employ an improved generative adversarial network including a generator neural network and a discriminator neural network. The generator neural network is trained to map a combination of a noise vector and a category code vector as input to a simulated image of geological facies. The discriminator neural network is trained to map at least one image of geological facies provided as input to corresponding probability that the at least one image of geological facies provided as input is a training image of geological facies or a simulated image of geological facies produced by the generator neural network.
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公开(公告)号：US20210165937A1
公开(公告)日：2021-06-03
申请号：US16772792
申请日：2018-12-13
Applicant: Schlumberger Technology Corporation
Inventor： William J. Bailey , Emilien Dupont , Lin Liang , Peter G. Tilke , Tuanfeng Zhang , Lingchen Zhu
Abstract: A method, computer program product, and computing system are provided for defining one or more injector completions and one or more producer completions in one or more reservoir models. One or more edges between the one or more injector completions and the one or more producer completions in the one or more reservoir models may be defined. The one or more edges between the one or more injector completions and the one or more producer completions may define a graph network representative of the one or more reservoir models. The one or more reservoir models may be simulated along the one or more edges between the one or more injector completions and the one or more producer completions.
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8.发明申请公开(公告)号：US20150134255A1
公开(公告)日：2015-05-14
申请号：US14536289
申请日：2014-11-07
Applicant: Schlumberger Technology Corporation
Inventor： Tuanfeng Zhang , Neil F. Hurley , Ridvan Akkurt , David McCormick , Shu Zhang
Abstract: Methods of generating structural models of highly deviated or horizontal wells may be generated from the measurement of true stratigraphic thickness in three dimensions (TST3D). In one aspect, methods may include generating a structural model from one or more deviation surveys of a horizontal well, one or more single channel log measurements, and a three-dimensional reference surface.
Abstract translation: 可以从三维（TST3D）的真实地层厚度的测量中产生产生高度偏斜或水平井结构模型的方法。 一方面，方法可以包括从水平井，一个或多个单通道对数测量和三维参考表面的一个或多个偏差调查中产生结构模型。
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公开(公告)号：US11965844B2
公开(公告)日：2024-04-23
申请号：US17421406
申请日：2019-01-08
Applicant: Schlumberger Technology Corporation
Inventor： Yi-Qiao Song , Andre Souza , Muthusamy Vembusubramanian , Tuanfeng Zhang , Wenyue Xu
CPC classification number: G01N24/081 , G01N33/24
Abstract: A method (and corresponding system) that characterizes a porous rock sample is provided, which involves subjecting the porous rock sample to an applied experimental pressure where a first fluid that saturates the porous rock sample is displaced by a second fluid, and subsequently applying an NMR pulse sequence to the rock sample, detecting resulting NMR signals, and generating and storing NMR data representative of the detected NMR signals. The application of experimental pressure and NMR measurements can be repeated over varying applied experimental pressure to obtain NMR data associated with varying applied experimental pressure values. The NMR data can be processed using inversion to obtain a probability distribution function of capillary pressure values as a function of NMR property values. The probability distribution function of capillary pressure values as a function of NMR property values can be processed to determine at least one parameter indicative of the porous rock sample.
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公开(公告)号：US20220335689A1
公开(公告)日：2022-10-20
申请号：US17753617
申请日：2020-09-14
Applicant: Schlumberger Technology Corporation
Inventor： Lingchen Zhu , Tuanfeng Zhang
Abstract: Neural network systems and related machine learning methods for geological modeling are provided that employ an improved generative adversarial network including a generator neural network and a discriminator neural network. The generator neural network is trained to map a combination of a noise vector and a category code vector as input to a simulated image of geological facies. The discriminator neural network is trained to map at least one image of geological facies provided as input to corresponding probability that the at least one image of geological facies provided as input is a training image of geological facies or a simulated image of geological facies produced by the generator neural network.
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