Abstract: Methods and systems for predicting hydrocarbon production and production uncertainty are disclosed.

Abstract: Systems and methods for estimating reservoir productivity as a function of position in a subsurface volume of interest are disclosed. Exemplary implementations may: obtain subsurface data and well data corresponding to a subsurface volume of interest; obtain a parameter model; use the subsurface data and the well data to generate multiple production parameter maps; apply the parameter model to the multiple production parameter maps to generate refined production parameter values; generate multiple refined production parameter graphs; display the multiple refined production parameter graphs; generate one or more user input options; receive a defined well design and the one or more user input options selected by a user to generate limited production parameter values; generate a representation of estimated reservoir productivity as a function of position in the subsurface volume of interest using the defined well design and visual effects; and display the representation.

Abstract: A method is described for data analytics using highly-correlated features which includes receiving a training dataset representative of a subsurface volume of interest; identifying at least two highly-correlated features in the training dataset; calculating a trend of the at least two highly-correlated features; calculating a residual of at least one of the highly-correlated features and the trend; and using data analytic methods on features in the training dataset that include one or more of these trend and residual combinations to predict a response variable. The method may be executed by a computer system.

Abstract: A method is described for data analytics including receiving a dataset representative of a subsurface volume of interest; identifying at least two features in the dataset; performing optimization methods to fit the at least two features to a response variable; calculating partial dependency functions of the at least two features; calculating a simplicity of each of the partial dependency functions; calculating an importance of each of the at least two features; selecting at least one highly ranked feature based on a combination of the simplicity and the importance; and performing optimization methods to fit the at least one highly ranked feature to a response variable. The method may be executed by a computer system.

Abstract: A method is described for data analytics including receiving a training dataset representative of a subsurface volume of interest with co-located measured explanatory features and a response feature; generating an ensemble of models using an ensemble of decision tree regressions; generating a surrogate model by fitting response surfaces of the ensemble of models with a power law combination of each of the explanatory features, and products and ratios of each pair of the explanatory features; receiving a second dataset of explanatory features from locations away from the co-located measured explanatory features, wherein the second dataset of explanatory features are a same type as the co-located measured explanatory features; and generating, using the surrogate model a smooth prediction of the response feature based on the second dataset of explanatory features. The method may be executed by a computer system.

Abstract: A method is described for data analytics including receiving a set of M variables representative of a subsurface volume of interest, derived from one or more of co-located well-log data, seismic data, and production data; performing a global optimum branch-and-bound algorithm to find a collection of N variables from the set of M variables that achieve the best fit in multiple regression; adding random variables to the collection of N variables; a slightly non-linear optimization until a statistically significant percentage of the random variables are eliminated; and performing, a highly non-linear optimization to select a final set of features. The method may be executed by a computer system.

Abstract: Systems and methods are disclosed for generating a set of facies realizations. A computer-implemented method may use a computer system that includes a physical computer processor and data storage. The computer-implemented method may include: obtaining a geobody index, obtaining facies probability vectors for the multiple geobodies, assigning facies to the multiple geobodies based on the facies probability vectors, obtaining a target facies proportion for the subsurface volume of interest, reassigning a first geobody having a first facies based on a first facies probability vector of the first geobody, and reassigning remaining ones of the multiple geobodies with different facies.

Abstract: Systems and methods for estimating a likelihood of an object element in a given position in a subsurface volume of interest are disclosed. Exemplary implementations may: obtain target subsurface data from the subsurface volume of interest; obtain an object element set corresponding to the subsurface volume of interest; generate correlation values as a function of position in the subsurface volume of interest by applying the object filters to the target subsurface data; and generate object element likelihood values by applying the object templates to positions in the subsurface volume of interest corresponding to the correlation values.

Abstract: A method is described for automated post-geosteering including receiving a pilot well log and a lateral well log with an initial lateral well path; performing automated post-geosteering to generate a corrected well path image; and displaying the corrected well path image on a graphical display. The method may be executed by a computer system.

Abstract: Methods and systems for predicting hydrocarbon production and production uncertainty are disclosed.

Abstract: Systems and methods are disclosed for generating a set of facies realizations. A computer-implemented method may use a computer system that includes a physical computer processor and data storage. The computer-implemented method may include: obtaining a geobody index, obtaining facies probability vectors for the multiple geobodies, assigning facies to the multiple geobodies based on the facies probability vectors, obtaining a target facies proportion for the subsurface volume of interest, reassigning a first geobody having a first facies based on a first facies probability vector of the first geobody, and reassigning remaining ones of the multiple geobodies with different facies.

Abstract: A method is described for generating a reservoir property model based on the quality of a seismic inversion product. The method may include receiving a seismic inversion product volume, a seismic attribute volume, and well data from wells drilled in a subsurface volume of interest; identifying collocated cells in the seismic volumes which correspond to the well data; creating attribute vectors from the seismic volumes in each of the collocated cells and a range of neighboring cells; calculating a seismic inversion error magnitude property at the collocated cells; training a data analytics method to predict the observed seismic inversion error magnitude property; verifying that the data analytics method accurately predicts the seismic inversion error magnitude using cross-validation; generating an inversion quality volume; and generating the reservoir property model based on the inversion quality volume. The method may be executed by a computer system.

Abstract: A method is described for generating a reservoir property model based on the quality of a seismic inversion product. The method may include receiving a seismic inversion product volume, a seismic attribute volume, and well data from wells drilled in a subsurface volume of interest; identifying collocated cells in the seismic volumes which correspond to the well data; creating attribute vectors from the seismic volumes in each of the collocated cells and a range of neighboring cells; calculating a seismic inversion error magnitude property at the collocated cells; training a data analytics method to predict the observed seismic inversion error magnitude property; verifying that the data analytics method accurately predicts the seismic inversion error magnitude using cross-validation; generating an inversion quality volume; and generating the reservoir property model based on the inversion quality volume. The method may be executed by a computer system.

Abstract: A computer implemented method for identifying reservoir facies in a subsurface region includes obtaining a set of seismic data points of both petrophysical and geophysical parameters relating to the subsurface region, identifying one or more correlated clusters of petrophysical parameters, generating, from the one or more correlated clusters of petrophysical parameters, one or more corresponding multi-dimensional clusters of seismic data points, storing, in a facies database, a multi-dimensional cluster center point for at least one multi-dimensional clusters, and recursively splitting the multi-dimensional clusters into distinct sub-clusters of seismic data points corresponding to facies types.

Abstract: Systems and methods for estimating a likelihood of an object element in a given position in a subsurface volume of interest are disclosed. Exemplary implementations may: obtain target subsurface data from the subsurface volume of interest; obtain an object element set corresponding to the subsurface volume of interest; generate correlation values as a function of position in the subsurface volume of interest by applying the object filters to the target subsurface data; and generate object element likelihood values by applying the object templates to positions in the subsurface volume of interest corresponding to the correlation values.

Abstract: A method is described for automated post-geosteering including receiving a pilot well log and a lateral well log with an initial lateral well path; performing automated post-geosteering to generate a corrected well path image; and displaying the corrected well path image on a graphical display. The method may be executed by a computer system.

Abstract: Systems and methods for estimating reservoir productivity as a function of position in a subsurface volume of interest are disclosed. Exemplary implementations may: obtain subsurface data and well data corresponding to a subsurface volume of interest; obtain a parameter model; use the subsurface data and the well data to generate multiple production parameter maps; apply the parameter model to the multiple production parameter maps to generate refined production parameter values; generate multiple refined production parameter graphs; display the multiple refined production parameter graphs; generate one or more user input options; receive a defined well design and the one or more user input options selected by a user to generate limited production parameter values; generate a representation of estimated reservoir productivity as a function of position in the subsurface volume of interest using the defined well design and visual effects; and display the representation.

Abstract: A computer implemented method for identifying reservoir facies in a subsurface region includes obtaining a set of seismic data points of both petrophysical and geophysical parameters relating to the subsurface region, identifying one or more correlated clusters of petrophysical parameters, generating, from the one or more correlated clusters of petrophysical parameters, one or more corresponding multi-dimensional clusters of seismic data points, storing, in a facies database, a multi-dimensional cluster center point for at least one multi-dimensional clusters, and recursively splitting the multi-dimensional clusters into distinct sub-clusters of seismic data points corresponding to facies types.

Abstract: A system and method, implemented on a computer, for determining the uncertainty of soft-data debiasing of property distributions of spatially correlated reservoir data are provided. The method includes inputting, into the computer, soft-data at a plurality of spatial locations on a grid and hard-data at a plurality of location along well paths that intersect the grid. Each location of the hard-data is collocated with soft-data values of one or more types.

Abstract: A method of automatically interpreting well log data indicative of physical attributes of a portion of a subterranean formation which include some portion of samples with known facies classification to be used as training data, dividing the training data into two subsets, a calibration set and a cross-validation set, using an automated supervised learning facies identification method to determine a preliminary identification of facies in the subterranean formation based on the calibration set, calculating a confusion matrix for the supervised learning facies identification method by comparing predicted and observed facies for the cross-validation set, calculating a facies transition matrix characterizing changes between contiguous facies, and using the preliminary identification, the facies transition matrix, and the confusion matrix, iteratively calculating updated facies identifications.