Abstract: Techniques are disclosed for performing time-lapse monitor surveys with sparsely sampled monitor data sets (11). An accurate 3D representation (e.g., image) of a target area (e.g., a hydrocarbon bearing subsurface reservoir) is constructed using the sparsely sampled monitor data set (e.g., seismic data set). The sparsely sampled monitor data set may be so limited that it alone is insufficient to generate an accurate 3D representation of the target area, but accuracy is achieved through use of certain external information (14). The external information may include predetermined base survey data from a first time that is used (12) to interpolate data that is not recorded in the sparsely sampled monitor data set to derive a fully sampled monitor data set that can be processed (e.g., using conventional processing techniques) for determining an accurate representation of the target area at a second time.

Abstract: Techniques are disclosed for performing time-lapse monitor surveys with sparsely sampled monitor data sets. An accurate 3D representation (e.g., image) of a target area (e.g., a hydrocarbon bearing subsurface reservoir) is constructed (12) using the sparsely sampled monitor data set (11). The sparsely sampled monitor data set may be so limited that it alone is insufficient to generate an accurate 3D representation of the target area, but accuracy is enabled through use of certain external information (14). The external information may be one or more alternative predicted models (25) that are representative of different predictions regarding how the target area may change over a lapse of time. The alternative models may, for example, reflect differences in permeability of at least a portion of the target area. The sparsely sampled monitor data set may then be processed to determine (23) which of the alternative models is representative of the target area.

Abstract: Techniques are disclosed for performing time-lapse seismic monitor surveys with sparsely sampled monitor data sets. A more accurate 3D representation (e.g., image) of a target area (e.g., a hydrocarbon bearing subsurface reservoir) is constructed using the sparsely sampled monitor data set (11). The sparsely sampled monitor data set may be so limited that it alone is insufficient to generate an accurate 3D representation of the target area, but accuracy is achieved through use of certain external information (14). The external information may include information accurately identifying a shape of the seismic reflector(s) present in the target area. The shape may be predetermined from a fully sampled base survey, and used to enable an accurate 3D representation (12) of the target area to be later generated for a monitor survey using a sparsely sampled monitor data set.

Abstract: A probabilistic method for classifying observed CSEM response for a resistive anomaly to classify the response into multiple geologic categories indicative of hydrocarbon production potential. Each category is assigned a prior probability (301). For each category, conditional joint probability distributions for observed CSEM data in the anomaly region are constructed (303) from rock property probability distributions (302) and a quantitative relationship between rock/fluid properties and the CSEM data (304). Then, the joint probability distributions and prior probabilities for each category (305) are combined with observed data (307) and used in Bayes' Rule (306) to update the prior category probabilities (308). Seismic data may be used to supplement CSEM data in the method.

Abstract: Techniques are disclosed for performing time-lapse monitor surveys with sparsely sampled monitor data sets (11). An accurate 3D representation (e.g., image) of a target area (e.g., a hydrocarbon bearing subsurface reservoir) is constructed using the sparsely sampled monitor data set (e.g., seismic data set). The sparsely sampled monitor data set may be so limited that it alone is insufficient to generate an accurate 3D representation of the target area, but accuracy is achieved through use of certain external information (14). The external information may include predetermined base survey data from a first time that is used (12) to interpolate data that is not recorded in the sparsely sampled monitor data set to derive a fully sampled monitor data set that can be processed (e.g., using conventional processing techniques) for determining an accurate representation of the target area at a second time.

Abstract: Techniques are disclosed for performing time-lapse monitor surveys with sparsely sampled monitor data sets. An accurate 3D representation (e.g., image) of a target area (e.g., a hydrocarbon bearing subsurface reservoir) is constructed (12) using the sparsely sampled monitor data set (11). The sparsely sampled monitor data set may be so limited that it alone is insufficient to generate an accurate 3D representation of the target area, but accuracy is enabled through use of certain external information (14). The external information may be one or more alternative predicted models (25) that are representative of different predictions regarding how the target area may change over a lapse of time. The alternative models may, for example, reflect differences in permeability of at least a portion of the target area. The sparsely sampled monitor data set may then be processed to determine (23) which of the alternative models is representative of the target area.

Abstract: A probabilistic method for classifying observed CSEM response for a resistive anomaly to classify the response into multiple geologic categories indicative of hydrocarbon production potential. Each category is assigned a prior probability (301). For each category, conditional joint probability distributions for observed CSEM data in the anomaly region are constructed (303) from rock property probability distributions (302) and a quantitative relationship between rock/fluid properties and the CSEM data (304). Then, the joint probability distributions and prior probabilities for each category (305) are combined with observed data (307) and used in Bayes' Rule (306) to update the prior category probabilities (308). Seismic data may be used to supplement CSEM data in the method.

Abstract: Techniques are disclosed for performing time-lapse seismic monitor surveys with sparsely sampled monitor data sets. A more accurate 3D representation (e.g., image) of a target area (e.g., a hydrocarbon bearing subsurface reservoir) is constructed using the sparsely sampled monitor data set (11). The sparsely sampled monitor data set may be so limited that it alone is insufficient to generate an accurate 3D representation of the target area, but accuracy is achieved through use of certain external information (14). The external information may include information accurately identifying a shape of the seismic reflector(s) present in the target area. The shape may be predetermined from a fully sampled base survey, and used to enable an accurate 3D representation (12) of the target area to be later generated for a monitor survey using a sparsely sampled monitor data set.

Abstract: Method for determining an expected value for a proposed reconnaissance electromagnetic (or any other type of geophysical) survey using a user-controlled source. The method requires only available geologic and economic information about the survey region. A series of calibration surveys are simulated with an assortment of resistive targets consistent with the known information. The calibration surveys are used to train pattern recognition software to assess the economic potential from anomalous resistivity maps. The calibrated classifier is then used on further simulated surveys of the area to generate probabilities that can be used in Value of Information theory to predict an expected value of a survey of the same design as the simulated surveys. The calibrated classifier technique can also be used to interpret actual CSEM survey results for economic potential.

Abstract: Method for determining an expected value for a proposed reconnaissance electromagnetic (or any other type of geophysical) survey using a user-controlled source. The method requires only available geologic and economic information about the survey region. A series of calibration surveys are simulated with an assortment of resistive targets consistent with the known information. The calibration surveys are used to train pattern recognition software to assess the economic potential from anomalous resistivity maps. The calibrated classifier is then used on further simulated surveys of the area to generate probabilities that can be used in Value of Information theory to predict an expected value of a survey of the same design as the simulated surveys. The calibrated classifier technique can also be used to interpret actual CSEM survey results for economic potential.

Abstract: A method of devising infill strategy in planning a seismic survey, using Monte Carlo methods and value-of-information theory to assess the probable value in well pay-out of a given increase in survey costs to reduce artifact degradation of the quality of the seismic data, where the seismic data will be used to make well-drilling decisions.

Abstract: A method of devising infill strategy in planning a seismic survey, using Monte Carlo methods and value-of-information theory to assess the probable value in well pay-out of a given increase in survey costs to reduce artifact degradation of the quality of the seismic data, where the seismic data will be used to make well-drilling decisions.

Abstract: A method for analyzing a target seismic trace generally comprises selecting other traces in source-receiver space not including traces from the same shot record that contains the target trace, and comparing the selected traces to the target trace. The method generally includes setting a distance value indicative of the number of shot gathers to be considered in conjunction with analyzing a target trace, specifying which traces from a plurality of shot records to compare to the target trace based on the distance value, calculating a first amplitude value associated with the amplitude of the target trace, calculating a second amplitude value associated with the amplitudes of the specified comparison traces, and comparing the first amplitude value to the second amplitude value. The comparison and target traces can be analyzed in various windows of time. The target trace is modified in a suitable manner if it is found to contain noise.

Abstract: A method for determining the reliability of time processed seismic reflection data near a significant velocity differential overhang includes displaying the seismic reflection data as a seismic section showing impedance differential interfaces. The position of an edge of the velocity differential overhang is located and acoustic velocity values are interpreted. A set of curves representing boundaries of reliability for predetermined dip values is determined. The set of curves representing boundaries of reliability may be displayed. The display of the set of curves representing boundaries of reliability may be overlaid on the seismic section representing the seismic reflection data. The dip angle for an interface of interest is approximated. The interface of interest is compared with the curve representing boundaries of reliability for the dip angle for the interface of interest to determine the extent of reliability of the data used to map the end of the interface under the overhang.

Abstract: VSP-based method for tieing surface data shot with different types of seismic sources together. VSP data is acquired using the same types of seismic sources utilized to acquire the surface data. The acquired VSP data is used to produce a correction operator for application to the surface data.