Decompaction of subsurface region using decompaction velocity

    公开(公告)号:US11892580B2

    公开(公告)日:2024-02-06

    申请号:US17148111

    申请日:2021-01-13

    CPC classification number: G01V1/305 G01N33/246 G01V1/306

    Abstract: The thickness of subsurface layers if they had remained as they were prior to compaction may be estimated by restoring the volume of void space lost during compaction. Decompacted depths below seafloor, the depths the layers would be if compaction had not occurred, may be determined for the layers. A surrogate decompaction velocity may then be determined by dividing the decompacted depths by the travel times of acoustic waves that reflect off layers within the subsurface region. The decompaction velocity may be used in post-processing of acoustic data to produce a decompacted digital representation of the subsurface region. The decompacted digital representation may be used to directly interpret the thickness of layers prior to compaction, sedimentation rates over time, fault offsets, and other phenomena distorted by compaction.

    Geological formation permeability prediction system

    公开(公告)号:US11887019B2

    公开(公告)日:2024-01-30

    申请号:US16791571

    申请日:2020-02-14

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

    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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