SELECTIVE NMR PULSE FOR DOWNHOLE MEASUREMENTS

    公开(公告)号:US20180017700A1

    公开(公告)日:2018-01-18

    申请号:US15305540

    申请日:2015-12-22

    Abstract: Various embodiments include a method for generating a pulse for use in nuclear magnetic resonance (NMR) logging. One such method generates the pulse by adjusting one or more of pulse parameters including a pulse shape, a pulse amplitude, a pulse phase, and/or a pulse frequency. The generated pulse produces a substantially uniform nuclear spin saturation or nuclear spin inversion response from a fluid. A wait time between the pulse transmission and an echo that indicates spin equilibrium has been achieved is substantially equal to a T1 time indicating characteristics of the fluid.

    Azimuthally-selective downhole nuclear magnetic resonance (NMR) tool
    34.
    发明授权
    Azimuthally-selective downhole nuclear magnetic resonance (NMR) tool 有权
    方位角选择性井下核磁共振(NMR)工具

    公开(公告)号:US09377557B2

    公开(公告)日:2016-06-28

    申请号:US14455551

    申请日:2014-08-08

    CPC classification number: G01V3/32 G01N24/081 G01R33/3678 G01R33/3808

    Abstract: In some aspects, a downhole nuclear magnetic resonance (NMR) tool includes a magnet assembly and an antenna assembly. The NMR tool can operate in a wellbore in a subterranean region to obtain NMR data from the subterranean region. The magnet assembly produces a magnetic field in a volume about the wellbore. The antenna assembly produces excitation in the volume and acquires an azimuthally-selective response from the volume based on the excitation. The antenna assembly can include a transversal-dipole antenna and a monopole antenna.

    Abstract translation: 在一些方面,井下核磁共振(NMR)工具包括磁体组件和天线组件。 NMR工具可以在地下区域的井筒中操作,以从地下区域获得NMR数据。 磁体组件在围绕井眼的体积中产生磁场。 天线组件在体积中产生激励,并且基于激励从体积获得方位角选择响应。 天线组件可以包括横向 - 偶极天线和单极天线。

    CARBON SEQUESTRATION MONITORING BY MINERAL REACTION EXTENT MONITORING

    公开(公告)号:US20250003313A1

    公开(公告)日:2025-01-02

    申请号:US18229361

    申请日:2023-08-02

    Abstract: Carbon Capture, Utilization, and Storage (CCUS) is a relatively new technology directed to mitigating climate change by reducing greenhouse gas emissions. Current and new government requirements require proof that carbon dioxide (CO2) is either sequestered in a stable form or safely stored for long periods of time. In instances when the CO2 is sequestered through mineral formation, the need for long-term monitoring can be reduced, as the stability of the sequestered CO2 is inherent based on a chemical change in subterranean rocks. The reactions between CO2 and rock formations are influenced by numerous factors, including temperature, pressure, fluid composition, and the mineralogy of the formation. Furthermore, these reactions occur over large spatial areas and long timescales, making them difficult to monitor directly. Methods and systems of the present disclosure, therefore, may use a combination of laboratory experiments, field monitoring, and modeling to provide convincing evidence of CO2 mineral sequestration.

    Nuclear magnetic resonance (NMR) fluid substitution using machine learning

    公开(公告)号:US11828901B2

    公开(公告)日:2023-11-28

    申请号:US17514654

    申请日:2021-10-29

    CPC classification number: G01V3/32 G01N24/081 G01R33/50 G01V3/38

    Abstract: System and methods for nuclear magnetic resonance (NMR) fluid substitution are provided. NMR logging measurements of a reservoir rock formation are acquired. Fluid zones within the reservoir rock formation are identified based on the acquired measurements. The fluid zones include water zones comprising water-saturated rock and at least one oil zone comprising rock saturated with multiphase fluids. Water zones having petrophysical characteristics matching those of the oil zone(s) within the formation are selected. NMR responses to multiphase fluids resulting from a displacement of water by hydrocarbon in the selected water zones are simulated. A synthetic dataset including NMR T2 distributions of multiphase fluids is generated based on the simulation. The synthetic dataset is used to train a machine learning (ML) model to substitute NMR T2 distributions of multiphase fluids with those of water. The trained ML model is applied to the NMR logging measurements acquired for the oil zone(s).

    Core Data Augmentation Methods For Developing Data Driven Based Petrophysical Interpretation Models

    公开(公告)号:US20230077488A1

    公开(公告)日:2023-03-16

    申请号:US17471345

    申请日:2021-09-10

    Abstract: A method for training a model. The method may include forming a data set from one or more measurements of core samples, selecting one or more parameters from the data set, inputting the one or more parameters into a kernel estimation function, determining a kernel density estimation from the kernel estimation function based at least in part on the one or more parameters, and selecting an input value based at least in part on the kernel density estimation. The method may further include creating a corresponding synthetic target value based at least in part on the input value, augmenting the data set with the corresponding synthetic target value and input value to form a synthetic data set, and training a petrophysical interpretation machine learning model from the data set and the synthetic data set.

    CORRECTION OF DISTORTED GRADIENT DISTRIBUTIONS IN NUCLEAR MAGNETIC RESONANCE LOGGING

    公开(公告)号:US20230068555A1

    公开(公告)日:2023-03-02

    申请号:US17462968

    申请日:2021-08-31

    Abstract: Methods for correcting a gradient distribution in downhole NMR logging are described herein. NMR data is inverted using an effective gradient to obtain an apparent T2 distribution having a first main peak and a distortion caused by a second spurious peak. The first main peak corresponds to the effective gradient. The distortion in the apparent T2 distribution is then corrected by integrating the signal corresponding to the spurious peak into the signal corresponding to the main peak. The corrected apparent T2 distribution and the effective gradient are then used to interpret the NMR data. Thereafter, the interpreted data is used to determine one or more characteristics of the surrounding subsurface rock formation media.

    Borehole Image Gap Filing Using Deep Learning

    公开(公告)号:US20230036713A1

    公开(公告)日:2023-02-02

    申请号:US17392058

    申请日:2021-08-02

    Abstract: System and methods for image gap-filling are provided. An image of a rock formation is obtained from an imaging tool disposed within a borehole. The obtained image is analyzed to identify gaps of missing image data. One or more image masks corresponding to the identified gaps are generated. A machine learning model is trained to produce modeled image data for filling in the missing image data in the identified gaps, based on the generated image mask(s). The image is reconstructed by filling the gaps of missing image data with the modeled image data. The reconstructed image is analyzed to identify geological features of the rock formation.

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