-
公开(公告)号：US20190129053A1
公开(公告)日：2019-05-02
申请号：US16093640
申请日：2017-04-13
Applicant: SCHLUMBERGER TECHNOLOGY CORPORATION
Inventor： Pu Wang , Sandip Bose , Bikash Kumar Sinha , Ting Lei
IPC: G01V1/50
Abstract: Methods are provided for determining properties of an anisotropic formation (including both fast and slow formations) surrounding a borehole. A logging-while-drilling tool is provided that is moveable through the borehole. The logging-while drilling tool has at least one dipole acoustic source spaced from an array of receivers. During movement of the logging-while-drilling tool, the at least one dipole acoustic source is operated to excite a time-varying pressure field in the anisotropic formation surrounding the borehole. The array of receivers is used to measure waveforms arising from the time-varying pressure field in the anisotropic formation surrounding the borehole. The waveforms are processed to determine a parameter value that represents shear directionality of the anisotropic formation surrounding the borehole.
-
公开(公告)号：US20250110250A1
公开(公告)日：2025-04-03
申请号：US18898243
申请日：2024-09-26
Applicant: SCHLUMBERGER TECHNOLOGY CORPORATION
Inventor： Lin Liang , Ting Lei , Yixin Wang
IPC: G01V1/50
Abstract: Aspects described herein provide for methods and apparatus for interpretation of borehole sonic dispersion data using data-driven machine learning based approaches. Training datasets are generated from two possible sources. First, application of machine learning enabled automatic dipole interpretation (MLADI) and/or machine learning enabled automatic quadrupole interpretation (MLAQI) methods on field data processing will naturally create substantial volume of labeled data, i.e., pairing dispersion data with dispersion modes labeled by MLADI and MLAQI. Second, it is also possible to generate large volume of synthetic dispersion data from known model parameters. These two types of labeled data can be used either separately or in combination to train neural network models. These models can map dispersion data to modal dispersion much more efficiently.
-
公开(公告)号：US12055672B2
公开(公告)日：2024-08-06
申请号：US17288622
申请日：2019-10-24
Applicant: SCHLUMBERGER TECHNOLOGY CORPORATION
Inventor： Ting Lei , Sandip Bose , Smaine Zeroug
CPC classification number: G01V1/50 , E21B47/14 , G01V1/46 , G01V2210/1429 , G01V2210/6169 , G01V2210/624 , G01V2210/74
Abstract: A method, computer program product, and computing system for generating high resolution slowness logs. The method computer program product, and computing system includes receiving a plurality of sonic logs from at least one sensor array and generating at least one high-resolution slowness log from the plurality of sonic logs based upon, at least in part, monopole and dipole data from the plurality of sonic logs.
-
公开(公告)号：US10393905B2
公开(公告)日：2019-08-27
申请号：US15238524
申请日：2016-08-16
Applicant: SCHLUMBERGER TECHNOLOGY CORPORATION
Inventor： Bikash K. Sinha , Sandip Bose , Jiaqi Yang , Ting Lei , Tarek M. Habashy , Smaine Zeroug , Ma Luo
Abstract: A method for torsional wave logging in a borehole of a subterranean formation. The method includes obtaining a torsional wave measurement of the borehole, wherein the torsional wave measurement represents characteristics of a torsional wave propagating within a cylindrical layered structure associated with the borehole, wherein the cylindrical layered structure comprises the subterranean formation and a completion of the borehole, analyzing, by a computer processor, the torsional wave measurement to generate a quality measure of the completion, and displaying the quality measure of the completion.
-
公开(公告)号：US09448321B2
公开(公告)日：2016-09-20
申请号：US13734728
申请日：2013-01-04
Applicant: SCHLUMBERGER TECHNOLOGY CORPORATION
Inventor： Bikash K. Sinha , Sandip Bose , Jiaqi Yang , Ting Lei , Tarek M. Habashy , Smaine Zeroug , Ma Luo
CPC classification number: G01V1/50 , E21B47/0005
Abstract: A method for torsional wave logging in a borehole of a subterranean formation. The method includes obtaining a torsional wave measurement of the borehole, wherein the torsional wave measurement represents characteristics of a torsional wave propagating within a cylindrical layered structure associated with the borehole, wherein the cylindrical layered structure comprises the subterranean formation and a completion of the borehole, analyzing, by a computer processor, the torsional wave measurement to generate a quality measure of the completion, and displaying the quality measure of the completion.
Abstract translation: 一种地下地层井眼扭转波测井方法。 该方法包括获得钻孔的扭转波测量，其中扭转波测量表示在与钻孔相关联的圆柱形分层结构内传播的扭转波的特征，其中圆柱形分层结构包括地层并完成钻孔， 通过计算机处理器分析扭转波测量以产生完成的质量测量，并且显示完成的质量测量。
-
Patent Agency Ranking
6.发明授权Systems and methods for identifying sanding in production wells using time-lapse sonic data 有权使用延时声波数据识别生产井中的砂光的系统和方法公开(公告)号：US09217807B2
公开(公告)日：2015-12-22
申请号：US13949202
申请日：2013-07-23
Applicant: Schlumberger Technology Corporation
Inventor： Jiaqi Yang , Bikash K. Sinha , Ting Lei
CPC classification number: G01V1/40 , G01V1/48 , G01V1/50 , G01V2210/612 , G01V2210/6169
Abstract: Systems and methods for identifying sanding in production wells using time-lapse sonic data. Formation anisotropy can be characterized in terms of shear moduli in a vertical wellbore, e.g., vertical shear moduli C44 and C55 in the wellbore axial planes and horizontal shear modulus C66 in the wellbore cross-sectional plane. Changes in formation anisotropy between different times can provide qualitative indicators of the occurrence of sanding in the production well. Before production begins, the horizontal shear modulus C66 is typically less than the vertical shear modulus C44 or C55 or both. At a subsequent time after sanding occurs, the horizontal shear modulus C66 is typically greater than the vertical shear modulus C44 or C55 or both. By comparing the shear moduli of the vertical wellbore at different times, it is possible to identify the occurrence of sanding in the production well using time-lapse sonic data.
Abstract translation: 使用延时声波数据识别生产井中的砂光的系统和方法。 形成各向异性可以用垂直井眼中的剪切模量来表征，例如在井眼轴向平面中的垂直剪切模量C44和C55以及井眼横截面中的水平剪切模量C66。 不同时期地层各向异性的变化可以为生产井中砂磨发生提供定性指标。 在生产开始之前，水平剪切模量C66通常小于垂直剪切模量C44或C55或两者。 在砂光发生后的随后时间，水平剪切模量C66通常大于垂直剪切模量C44或C55或两者。 通过比较不同时间垂直井筒的剪切模量，可以使用延时声波数据来识别生产井中砂磨的发生。
-
公开(公告)号：US20240094423A1
公开(公告)日：2024-03-21
申请号：US18264345
申请日：2022-02-08
Applicant: Schlumberger Technology Corporation
Inventor： Nicholas Norman Bennett , Ting Lei , Erik Wielemaker , Lin Liang , Romain Prioul , John Adam Donald , Olga Podgornova
IPC: G01V1/50
CPC classification number: G01V1/50 , G01V2210/1299 , G01V2210/47 , G01V2210/6169
Abstract: Aspects provide for methods that successfully evaluates multiple compressional and shear arrival events received by a sonic logging tool to evaluate the presence of structures, such as shoulder beds, in downhole environments. In particular, the methods described herein enable automated determination of properties of laminated reservoir formations by, for example, enabling the automated determination of arrival times and slownesses of multiple compressional and shear arrival events received by a sonic logging tool.
-
8.发明申请公开(公告)号：US20220373707A1
公开(公告)日：2022-11-24
申请号：US17754395
申请日：2020-10-02
Applicant: Schlumberger Technology Corporation
Inventor： Ting Lei , Romain Prioul
IPC: G01V1/50
Abstract: A method is presented wherein inversion for formation anisotropic constants is achieved using borehole sonic data.
-
公开(公告)号：US20250012184A1
公开(公告)日：2025-01-09
申请号：US18709883
申请日：2022-11-16
Applicant: Schlumberger Technology Corporation
Inventor： Ting Lei , Daniel Al Choboq , Josselin Kherroubi , Lin Liang , Romain Prioul , Gurami Keretchashvili , Pontus Loviken
Abstract: A general-purpose workflow for automatic borehole sonic data classification to identify data into different physical categories and logging conditions, which are traditionally manually evaluated. The workflow uses machine learning techniques and physical knowledge for data classification, including pre-processing the high-dimensional high-quality dispersion modes extracted using a recently developed physical-driven ML enabled approach.
-
公开(公告)号：US20240418896A1
公开(公告)日：2024-12-19
申请号：US18815460
申请日：2024-08-26
Applicant: Schlumberger Technology Corporation
Abstract: The subject disclosure relates to the interpretation of borehole sonic data using machine learning. In one example of a method in accordance with aspects of the instant disclosure, borehole sonic data is received, and machine learning is used to interpret the borehole sonic data.
-
-
-
-
-
-
-
-
-
Search Results
28
records
(took 0.019 seconds)
