METHODS AND DEVICES FOR DYNAMIC PORE NETWORK MODELING OF TWO-PHASE FLOW IN MIXED-WET POROUS MEDIA

    公开(公告)号:US20240069577A1

    公开(公告)日:2024-02-29

    申请号:US18239493

    申请日:2023-08-29

    CPC classification number: G05D7/0617 G01N15/08

    Abstract: A method and system for predicting dynamic two-phase fluid flow in a mixed-wet porous medium by one or more central processing units (CPUs), comprising determining a set of possible movements of main terminal menisci (MTMs) within a pore network model (PNM) of a porous media sample having a set of pore elements, generating pressure fields for each of the set of movements of MTMs, based on at least an inlet capillary pressure or a set of flow injection boundary conditions, based on the pressure fields, determining a set of local capillary pressures and a set of arc meniscus (AM) locations, generating a set of fluid displacements potentials based on at least the set of local capillary pressures and a set of threshold capillary pressures, and determining a highest positive fluid displacement potential from the set of fluid displacements.

    Method for determining the pore size distribution in a reservoir

    公开(公告)号:US11852576B2

    公开(公告)日:2023-12-26

    申请号:US17649473

    申请日:2022-01-31

    Abstract: A method for determining the pore size distribution in a reservoir, including the steps: drilling a core sample out of the reservoir, determining a porosity distribution along the core sample, obtaining T2-distributions at different saturation levels of the core sample with formation brine, performing time domain subtraction on the T2-distributions to obtain T2-distributions at all saturation levels, determining the pore throat size distribution along the core sample, determining first porosities from the T2-distributions that correspond to second porosities of the pore throat size distribution for each saturation level, determining T2-distributions at the first porosities from the T2-distributions, determining pore throat sizes at the second porosities from the pore throat size distributions, plotting the pore throat sizes as function of the relaxation times T2 to obtain the surface relaxation, and determining the pore size distribution of the reservoir.

    Filter life indicator media and holder

    公开(公告)号:US11679385B2

    公开(公告)日:2023-06-20

    申请号:US17124375

    申请日:2020-12-16

    Applicant: ENTEGRIS, INC.

    Abstract: A media sample holder includes a base and a plurality of retention assemblies including retaining tabs and opposing flexible release lever arms, configured to allow attachment of the base to an attachment adapter. The media sample holder can attach a media sample on or near a filter. The media sample holder held in the media sample holder can have a different removal efficiency curve than a removal efficiency curve of the filter. The media sample can be placed at or near the filter for a period of time, then tested to determine the status and/or life of the filter based on the relationship between the remaining life, exposure, or removal efficiency of the filter and the exposure or removal efficiency of the tested media sample.

    GEOLOGICAL FORMATION PERMEABILITY PREDICTION SYSTEM

    公开(公告)号:US20230186126A1

    公开(公告)日:2023-06-15

    申请号:US16791571

    申请日:2020-02-14

    CPC classification number: G06N7/01 G06N3/086 G01N15/08 E21B49/00 G01N33/246

    Abstract: Systems, methods, and apparatuses are provided for permeability prediction. The method acquires data associated with one or more geological formations, calculates, using processing circuitry and a trained Hidden Markov model, log-likelihood values to group the data into a plurality of clusters, and trains an artificial neural network for each of the plurality of clusters when the mode of operation is training mode. Further, the method acquires one or more formation properties corresponding to a geological formation, determines using the trained Hidden Markov model, a log-likelihood score associated with the one or more formation properties, identifies a cluster associated with the one or more formation properties as a function of the log-likelihood score, and predicts a permeability based at least in part on the one or more formation properties and a trained artificial neural network associated with the identified cluster when the mode of operation is forecasting mode.

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