Abstract: Systems and methods create a velocity model for well time-depth conversion. In one implementation, a system optimizes a time-depth relationship applied to data points from a single well to estimate coefficients for a velocity function that models the data points. The system optimizes by reducing the influence of outlier data points, for example, by weighting each data point to decrease the influence of those far from the velocity function. The system also reduces the influence of top and bottom horizons of geological layers by applying data driven techniques that estimate the velocity function without undue dependence on the boundary conditions. The system can optimize estimation of a rate of increase in velocity to enable the velocity function to go through a data point on each top horizon. The system may also estimate each base horizon from trends in the data points and adjust the velocity function to go through a data point on each base horizon.

Abstract: Systems and methods create velocity models for a single well or for a set of wells. In one implementation, a system optimizes a time-depth relationship applied to data points from a single well to estimate coefficients for a linear-velocity-in-time function that models the data points. The system optimizes by reducing the influence of outlier data points, for example, by weighting each data point to decrease the influence of those far from the velocity function. The system also reduces the influence of top and bottom horizons of geological layers by applying data driven techniques that estimate the velocity function in a way that reduces dependence on the boundary conditions. The systems and methods can also create velocity models based on data from a set of wells, applying a well weights method to reduce the influence of outlier wells and thereby prevent wells with aberrant data from degrading a correct velocity model.

Abstract: Systems and methods perform correction of velocity cubes for seismic depth modeling. An example system receives a velocity model defined in the time domain for seismic modeling of a subsurface earth volume and receives well depth data associated with the subsurface earth volume. The system updates an average velocity cube associated with the velocity model to correct depth-converted time horizons to accord with known well markers, thereby increasing the accuracy and correctness of the velocity model.

Abstract: Systems and methods create velocity models for a single well or for a set of wells. In one implementation, a system optimizes a time-depth relationship applied to data points from a single well to estimate coefficients for a linear-velocity-in-time function that models the data points. The system optimizes by reducing the influence of outlier data points, for example, by weighting each data point to decrease the influence of those far from the velocity function. The system also reduces the influence of top and bottom horizons of geological layers by applying data driven techniques that estimate the velocity function in a way that reduces dependence on the boundary conditions. The systems and methods can also create velocity models based on data from a set of wells, applying a well weights method to reduce the influence of outlier wells and thereby prevent wells with aberrant data from degrading a correct velocity model.

Abstract: Systems and methods perform correction of velocity cubes for seismic depth modeling. An example system receives a velocity model defined in the time domain for seismic modeling of a subsurface earth volume and receives well depth data associated with the subsurface earth volume. The system updates an average velocity cube associated with the velocity model to correct depth-converted time horizons to accord with known well markers, thereby increasing the accuracy and correctness of the velocity model.

Abstract: Systems and methods create a velocity model for well time-depth conversion. In one implementation, a system optimizes a time-depth relationship applied to data points from a single well to estimate coefficients for a velocity function that models the data points. The system optimizes by reducing the influence of outlier data points, for example, by weighting each data point to decrease the influence of those far from the velocity function. The system also reduces the influence of top and bottom horizons of geological layers by applying data driven techniques that estimate the velocity function without undue dependence on the boundary conditions. The system can optimize estimation of a rate of increase in velocity to enable the velocity function to go through a data point on each top horizon. The system may also estimate each base horizon from trends in the data points and adjust the velocity function to go through a data point on each base horizon.