AUTOMATED FAULT SEGMENT GENERATION
    1.
    发明公开

    公开(公告)号:US20230358910A1

    公开(公告)日:2023-11-09

    申请号:US17738247

    申请日:2022-05-06

    Abstract: The disclosure presents processes to automatically generate one or more set of fault segments from a fault plane pointset. The processes can identify a predominant direction and derive a set of fault segments from the fault plane pointset, where the fault segments are generated by using slices of data from the fault plane pointset that are perpendicular to the predominant direction. For each slice of data, the fault segments can be analyzed with neighboring fault segments to determine if they are overlapping. Fault segments that block or overlap other fault segments can be assigned to a different subset of fault segments from the underlying fault segments. Gaps in the fault plane pointset, and the resulting set of fault segments, can be filled in by merging neighboring fault segments above and below the gap if the neighboring fault segments satisfy a criteria for filling the gap.

    Automated fault segment generation

    公开(公告)号:US12287442B2

    公开(公告)日:2025-04-29

    申请号:US17738247

    申请日:2022-05-06

    Abstract: The disclosure presents processes to automatically generate one or more set of fault segments from a fault plane pointset. The processes can identify a predominant direction and derive a set of fault segments from the fault plane pointset, where the fault segments are generated by using slices of data from the fault plane pointset that are perpendicular to the predominant direction. For each slice of data, the fault segments can be analyzed with neighboring fault segments to determine if they are overlapping. Fault segments that block or overlap other fault segments can be assigned to a different subset of fault segments from the underlying fault segments. Gaps in the fault plane pointset, and the resulting set of fault segments, can be filled in by merging neighboring fault segments above and below the gap if the neighboring fault segments satisfy a criteria for filling the gap.

    Determining fault surfaces from fault attribute volumes

    公开(公告)号:US11965997B2

    公开(公告)日:2024-04-23

    申请号:US17505033

    申请日:2021-10-19

    CPC classification number: G01V1/301 G01V1/306 G01V1/302 G01V2210/65

    Abstract: Hydrocarbon exploration and extraction can be facilitated by determining fault surfaces from fault attribute volumes. For example, a system described herein can receive a fault attribute volume for faults in a subterranean formation determined using seismic data. The fault attribute volume may include multiple traces with trace locations. The system can determine a set of fault samples for each trace location. Each fault sample can include fault attributes such as a depth value, an amplitude value, and a vertical thickness value. The system can determine additional fault attributes such as a dip value and an azimuth value for each fault sample of each trace location. The system can determine fault surfaces for the faults using the fault samples and fault attributes. The system can then output the fault surfaces for use in a hydrocarbon extraction operation.

    FREQUENCY-DEPENDENT MACHINE LEARNING MODEL IN SEISMIC INTERPRETATION

    公开(公告)号:US20230288594A1

    公开(公告)日:2023-09-14

    申请号:US17825914

    申请日:2022-05-26

    CPC classification number: G01V1/345 G01V1/282 G01V1/301 G06N20/20 G01V2210/642

    Abstract: Frequency-dependent machine-learning (ML) models can be used to interpret seismic data. A system can apply spectral decomposition to pre-processed training data to generate frequency-dependent training data of two or more frequencies. The system can train two or more ML models using the frequency-dependent training data. Subsequent to training the two or more ML models, the system can apply the two or more ML models to seismic data to generate two or more subterranean feature probability maps. The system can perform an analysis of aleatoric uncertainty on the two or more subterranean feature probability maps to create an uncertainty map for aleatoric uncertainty. Additionally, the system can generate a filtered subterranean feature probability map based on the uncertainty map for aleatoric uncertainty.

    DETERMINING FAULT SURFACES FROM FAULT ATTRIBUTE VOLUMES

    公开(公告)号:US20230117096A1

    公开(公告)日:2023-04-20

    申请号:US17505033

    申请日:2021-10-19

    Abstract: Hydrocarbon exploration and extraction can be facilitated by determining fault surfaces from fault attribute volumes. For example, a system described herein can receive a fault attribute volume for faults in a subterranean formation determined using seismic data. The fault attribute volume may include multiple traces with trace locations. The system can determine a set of fault samples for each trace location. Each fault sample can include fault attributes such as a depth value, an amplitude value, and a vertical thickness value. The system can determine additional fault attributes such as a dip value and an azimuth value for each fault sample of each trace location. The system can determine fault surfaces for the faults using the fault samples and fault attributes. The system can then output the fault surfaces for use in a hydrocarbon extraction operation.

Patent Agency Ranking