A method includes receiving well log data for a plurality of wells. A flag is generated based at least partially on the well log data. The wells are sorted into groups based at least partially on the well log data, the flag, or both. A model is built for each of the wells based at least partially on the well log data, the flag, and the groups.

The present invention belongs to the field of treatment for data identification and recording carriers, and specifically relates to a method and system for analyzing filling for a karst reservoir based on spectrum decomposition and machine learning, which aims to solve the problems that by adopting the existing petroleum exploration technology, the reservoir with fast lateral change cannot be predicted, and the development characteristics of a carbonate cave type reservoir in a large-scale complex basin cannot be identified. The method comprises: acquiring data of standardized logging curves; obtaining a high-precision 3D seismic amplitude data body by mixed-phase wavelet estimation and maximum posteriori deconvolution and enhancing diffusion filtering. According to the method and the system, the effect of identifying the development characteristics of the carbonate karst cave type reservoir in the large-scale complex basin can be achieved, and the characterization precision is improved.

A global objective function is initialized to an initial value. A particular model simulation process is executed using prepared input data. A mismatch value is computed by using a local function to compare an output of the particular model simulation process to corresponding input data for the particular model simulation process. Model objects associated with the particular model simulation process are sent to another model simulation process. An optimization process is executed to predict new values for input data to reduce the computed mismatch value.

A system, method and program product for implementing a machine learning platform that processes a data map having feature and operational information. A system is disclosed that includes an interpretable machine learning model that generates a function in response to an inputted data map, wherein the data map includes feature data and operational data over a region of interest, and wherein the function relates a set of predictive variables to one or more response variables; an integration/interpolation system that generates the data map from a set of disparate data sources; and an analysis system that evaluates the function to predict outcomes at unique points in the region of interest.

A method of estimating the primary and secondary acoustic velocities, Vp and Vs, of formation surrounding a first wellbore includes obtaining well logging data for a multiplicity of other wellbores to collect, for each other wellbore a plurality of input data sets including at least a Nuclear Magnetic Resonance logging data set, and an element composition scanning data set. The operation further collects, for each other wellbore, at least one output data set including a primary and secondary velocity data set. The method incudes training or establishing at least one regression model using said input and output data sets, obtaining well logging data for said first wellbore to obtain a corresponding plurality of input data sets, and applying the obtained corresponding plurality of input data sets to the trained or established regression model to generate as an output of the regression model, an output data set for the first wellbore including a primary and secondary velocity data set.

A combining mechanism for borehole logging tool data that employs modality merging to combine the output data of various borehole logging tools to provide a combined result and automated interpretation is provided, said mechanism comprising: at least one mechanism for assigning interpretive values to individual processed data types; at least one mechanism for combining the interpretive value data sets; and, at least one mechanism for providing an interpretation. A method of combining borehole logging tool data that employs modality merging to combine the output data of various borehole logging tools to provide a combined result and automated interpretation is also provided, said method comprising: assigning interpretive values to individual processed data types; combining the interpretive value data sets; and, providing an interpretation.

Systems, processes, and computer-readable media for determining lithology porosity of a formation rock from surface drilling parameters using a lithology porosity machine learning model without the use of wireline logging. Lithology porosity at different depths in existing may be determined from the wireline logs. The lithology porosity may be shaly sand, tight sand, porous gas, or porous wet. The lithology porosity machine-learning model may be trained and calibrated using the data from a structured data set having surface drilling parameters from the existing wells and lithology porosity classifications from the wells. The lithology porosity machine learning model may then be used to determine a lithology porosity classification for a new well without the use of wireline logging.

A method of fluid substitution, wherein an initial data set is provided, wherein a substituted data set is provided, wherein a rock physics model is provided, wherein the initial data set includes initial data of a geophysical parameter and initial fluid data, and wherein the substituted data set includes substituted fluid data. The method includes using the model and the initial data set to calculate first calculated data of the geophysical parameter, using the model and the substituted data set to calculate second calculated data of the geophysical parameter, calculating the difference between the first calculated data of the geophysical parameter and the second calculated data of the geophysical parameter, and applying the difference to the initial data of the geophysical parameter to produce substituted data of the geophysical parameter.

A method for includes obtaining a well log comprising a sequence of measurements of a wellbore in a field, and generating change points in the well log based on the sequence of measurements. Each of the change points corresponds to a depth along the wellbore where a probability distribution of the well log changes. The method further includes generating a statistic for each of multiple intervals in the well log, where the intervals are defined by the plurality of change points, categorizing the intervals based on the statistic for each of the intervals to generate categorized intervals, and performing the operation based on the categorized intervals.

A combining mechanism for borehole logging tool data that employs modality merging to combine the output data of various borehole logging tools to provide a combined result and automated interpretation is provided, said mechanism comprising: at least one mechanism for assigning interpretive values to individual processed data types; at least one mechanism for combining the interpretive value data sets; and, at least one mechanism for providing an interpretation. A method of combining borehole logging tool data that employs modality merging to combine the output data of various borehole logging tools to provide a combined result and automated interpretation is also provided, said method comprising: assigning interpretive values to individual processed data types; combining the interpretive value data sets; and, providing an interpretation.