Abstract: A method to classify one or more seismic surfaces or surface patches based on measurements from seismic data, including: obtaining, by a computer, a training set including a plurality of previously obtained and labeled seismic surfaces or surface patches and one or more training seismic attributes measured or calculated at, above, and/or below the seismic surfaces; obtaining, by the computer, one or more unclassified seismic surfaces or surface patches and one or more seismic attributes measured or calculated at, above, and/or below the unclassified seismic surfaces; learning, by the computer, a classification model from the previously obtained and labeled seismic surfaces or surface patches and the one or more training seismic attributes; and classifying, by the computer, the unclassified seismic surfaces or surface patches based on a comparison between the classification model and the unclassified seismic surfaces or surface patches.

Abstract: A method to classify one or more seismic surfaces or surface patches based on measurements from seismic data, including: obtaining, by a computer, a training set including a plurality of previously obtained and labeled seismic surfaces or surface patches and one or more training seismic attributes measured or calculated at, above, and/or below the seismic surfaces; obtaining, by the computer, one or more unclassified seismic surfaces or surface patches and one or more seismic attributes measured or calculated at, above, and/or below the unclassified seismic surfaces; learning, by the computer, a classification model from the previously obtained and labeled seismic surfaces or surface patches and the one or more training seismic attributes; and classifying, by the computer, the unclassified seismic surfaces or surface patches based on a comparison between the classification model and the unclassified seismic surfaces or surface patches.

Abstract: A method for identifying a plurality of features of interest in a seismic image includes ranking each feature of interest. The method also includes modeling a relationship between the rank of each feature of interest and a user rating of the feature of interest. The method further includes updating the ranking of the plurality of features of interest, including (1) receiving a user rating for one feature of interest that has not been previously rated by a user; (2) updating the model of the relationship between the rank of each feature of interest and the user rating of the feature of interest based on the user rating; (3) applying the model to the ranking of the plurality of features of interest; and (4) repeating steps (1)-(3) until a termination criterion is met.

Abstract: A method to classify one or more seismic surfaces or surface patches based on measurements from seismic data, including: obtaining, by a computer, a training set including a plurality of previously obtained and labeled seismic surfaces or surface patches and one or more training seismic attributes measured or calculated at, above, and/or below the seismic surfaces; obtaining, by the computer, one or more unclassified seismic surfaces or surface patches and one or more seismic attributes measured or calculated at, above, and/or below the unclassified seismic surfaces; learning, by the computer, a classification model from the previously obtained and labeled seismic surfaces or surface patches and the one or more training seismic attributes; and classifying, by the computer, the unclassified seismic surfaces or surface patches based on a comparison between the classification model and the unclassified seismic surfaces or surface patches.

Abstract: A method includes retrieving a seismic data set, receiving training data that includes one or more seed points of an identified geobody, determining a geobody trajectory of the identified geobody, based on the one or more seed points of the identified geobody, displaying the geobody trajectory, receiving inputs expanding the geobody trajectory, shrinking the geobody trajectory, confirming the geobody trajectory, or a combination thereof, training a classification algorithm using the geobody trajectory, running the classification algorithm on the seismic data set, receiving an output of one or more sets of voxels from the classification algorithm, skeletonizing the one or more sets of voxels to present the one or more sets of voxels as a set of possible geobody trajectories, and retraining the classification algorithm based on feedback received from a reviewer.

Abstract: A workflow is presented that facilitates defining geocontextual information as a set of rules for multiple seismic attributes. Modeling algorithms may be employed that facilitate analysis of multiple seismic attributes to find candidate regions that are most likely to satisfy the set of rules. These candidates may then be sorted based on how well they represent the geocontextual information.

Abstract: A method includes retrieving a seismic data set, receiving training data that includes one or more seed points of an identified geobody, determining a geobody trajectory of the identified geobody, based on the one or more seed points of the identified geobody, displaying the geobody trajectory, receiving inputs expanding the geobody trajectory, shrinking the geobody trajectory, confirming the geobody trajectory, or a combination thereof, training a classification algorithm using the geobody trajectory, running the classification algorithm on the seismic data set, receiving an output of one or more sets of voxels from the classification algorithm, skeletonizing the one or more sets of voxels to present the one or more sets of voxels as a set of possible geobody trajectories, and retraining the classification algorithm based on feedback received from a reviewer.

Abstract: A method for identifying a plurality of features of interest in a seismic image includes ranking each feature of interest. The method also includes modeling a relationship between the rank of each feature of interest and a user rating of the feature of interest. The method further includes updating the ranking of the plurality of features of interest, including (1) receiving a user rating for one feature of interest that has not been previously rated by a user; (2) updating the model of the relationship between the rank of each feature of interest and the user rating of the feature of interest based on the user rating; (3) applying the model to the ranking of the plurality of features of interest; and (4) repeating steps (1)-(3) until a termination criterion is met.

Abstract: A method to classify one or more seismic surfaces or surface patches based on measurements from seismic data, including: obtaining, by a computer, a training set including a plurality of previously obtained and labeled seismic surfaces or surface patches and one or more training seismic attributes measured or calculated at, above, and/or below the seismic surfaces; obtaining, by the computer, one or more unclassified seismic surfaces or surface patches and one or more seismic attributes measured or calculated at, above, and/or below the unclassified seismic surfaces; learning, by the computer, a classification model from the previously obtained and labeled seismic surfaces or surface patches and the one or more training seismic attributes; and classifying, by the computer, the unclassified seismic surfaces or surface patches based on a comparison between the classification model and the unclassified seismic surfaces or surface patches.

Abstract: Systems and methods which provide electromagnetic subsurface mapping to derive information with respect to subsurface features whose sizes are near to or below the resolution of electromagnetic data characterizing the subsurface are shown. Embodiments operate to identify a region of interest (203) in a resistivity image generated (202) using electromagnetic data (201). One or more scenarios may be identified for the areas of interest, wherein the various scenarios comprise representations of features whose sizes are near to or below the resolution of the electromagnetic data (204). According to embodiments, the scenarios are evaluated (205), such as using forward or inverse modeling, to determine each scenarios' fit to the available data and further to determine their geologic reasonableness (206). Resulting scenarios may be utilized in a number of ways, such as to be substituted in a resistivity image for a corresponding region of anomalous resistivity for enhancing the resistivity image (207).

Abstract: A workflow is presented that facilitates defining geocontextual information as a set of rules for multiple seismic attributes. Modeling algorithms may be employed that facilitate analysis of multiple seismic attributes to find candidate regions that are most likely to satisfy the set of rules. These candidates may then be sorted based on how well they represent the geocontextual information.

Abstract: Method for generating a three-dimensional resistivity data volume for a subsurface region from an initial resistivity model and measured electromagnetic field data from an electromagnetic survey of the region, where the initial resistivity model is preferably obtained by performing multiple ID inversions of the measured data [100]. The resulting resistivity depth profiles are then registered at proper 3D positions [102]. The 3D electromagnetic response is simulated [106] assuming the resistivity structure is given by the initial resistivity model. The measured electromagnetic field data volume is scaled by the simulated results [108] and the ratios are registered at proper 3D positions [110] producing a ratio data volume [112]. A 3D resistivity volume is then generated by multiplying the initial resistivity volume by the ratio data volume (or some function of it), location-by location [114]. A related method emphasizes deeper resistive anomalies over masking effects of shallow anomalies.

Abstract: Systems and methods which provide electromagnetic subsurface mapping to derive information with respect to subsurface features whose sizes are near to or below the resolution of electromagnetic data characterizing the subsurface are shown. Embodiments operate to identify a region of interest (203) in a resistivity image generated (202) using electromagnetic data (201). One or more scenarios may be identified for the areas of interest, wherein the various scenarios comprise representations of features whose sizes are near to or below the resolution of the electromagnetic data (204). According to embodiments, the scenarios are evaluated (205), such as using forward or inverse modeling, to determine each scenarios' fit to the available data and further to determine their geologic reasonableness (206). Resulting scenarios may be utilized in a number of ways, such as to be substituted in a resistivity image for a corresponding region of anomalous resistivity for enhancing the resistivity image (207).

Abstract: Method for conducting an efficient and interpretable controlled-source electromagnetic reconnaissance survey for buried hydrocarbons. While a part of the survey area is being set up for measurement and data are being acquired, data from a nearby part of the survey area, surveyed just previously, are being rapidly processed and analyzed. If the analysis shows resistive anomalies of interest in a portion of a survey area, a fine-grid survey is quickly designed for that portion, and that survey is conducted next before moving source and receivers to a more distant part of the survey area.

Abstract: Method for generating a three-dimensional resistivity data volume for a subsurface region from an initial resistivity model and measured electromagnetic field data from an electromagnetic survey of the region, where the initial resistivity model is preferably obtained by performing multiple ID inversions of the measured data [100]. The resulting resistivity depth profiles are then registered at proper 3D positions [102]. The 3D electromagnetic response is simulated [106] assuming the resistivity structure is given by the initial resistivity model. The measured electromagnetic field data volume is scaled by the simulated results [108] and the ratios are registered at proper 3D positions [110] producing a ratio data volume [112]. A 3D resistivity volume is then generated by multiplying the initial resistivity volume by the ratio data volume (or some function of it), location-by location [114]. A related method emphasizes deeper resistive anomalies over masking effects of shallow anomalies.

Abstract: Described herein are methods of evaluating reservoirs. At least one of the methods includes providing a three dimensional reservoir framework having a plurality of cells; assigning one or more constant reservoir property values to some or all of the cells to provide a first three dimensional reservoir model; updating the first three dimensional reservoir model by populating some or all of the cells with one or more variable reservoir property values to provide a second three dimensional reservoir model; and updating the second three dimensional reservoir model by populating some or all of the cells with one or more reservoir property values derived from seismic data to provide a third three dimensional reservoir model. Other methods are also described.

Abstract: Method for conducting an efficient and interpretable controlled-source electromagnetic reconnaissance survey for buried hydrocarbons. While a part of the survey area is being set up for measurement and data are being acquired, data from a nearby part of the survey area, surveyed just previously, are being rapidly processed and analyzed. If the analysis shows resistive anomalies of interest in a portion of a survey area, a fine-grid survey is quickly designed for that portion, and that survey is conducted next before moving source and receivers to a more distant part of the survey area.

Abstract: Method for conducting an efficient and interpretable controlled-source electromagnetic reconnaissance survey for buried hydrocarbons. While a part of the survey area is being set up for measurement and data are being acquired, data from a nearby part of the survey area, surveyed just previously, are being rapidly processed and analyzed (110). If the analysis shows resistive anomalies of interest in a portion of a survey area, a fine-grid survey is quickly designed for that portion, and that survey is conducted next before moving source and receivers to a more distant part of the survey area.

Abstract: Method for conducting an efficient and interpretable controlled-source electromagnetic reconnaissance survey for buried hydrocarbons. While a part of the survey area is being set up for measurement and data are being acquired, data from a nearby part of the survey area, surveyed just previously, are being rapidly processed and analyzed (110). If the analysis shows resistive anomalies of interest in a portion of a survey area, a fine-grid survey is quickly designed for that portion, and that survey is conducted next before moving source and receivers to a more distant part of the survey area.

Abstract: Described herein are methods of evaluating reservoirs. At least one of the methods includes providing a three dimensional reservoir framework having a plurality of cells; assigning one or more constant reservoir property values to some or all of the cells to provide a first three dimensional reservoir model; updating the first three dimensional reservoir model by populating some or all of the cells with one or more variable reservoir property values to provide a second three dimensional reservoir model; and updating the second three dimensional reservoir model by populating some or all of the cells with one or more reservoir property values derived from seismic data to provide a third three dimensional reservoir model. Other methods are also described.