A method for extracting information regarding a drill site, comprising: forming one or more documents having raw data regarding a well site, wherein the one or more documents are daily drilling reports (DDRs), wherein the raw data includes one or more raw comments directed to operational details of the wellsite and/or metadata, such as depth, time, and well name, wherein the one or more raw comments and/or the metadata include unstructured text or numerical data; extracting raw data from the one or more documents regarding the well site to produce extracted raw data; pre-processing the extracted raw data by removing one or more of ambiguity, artifacts, and formatting errors from the one or more raw comments by going through a cleaning and lexical analysis stage to produce pre-processed data, wherein the cleaning and lexical analysis stage iterate through a raw comment and convert the raw comment into clean comment tokens; extracting topics data from the pre-processed data using a natural language processing (NLP) algorithm to produce extracted topics data, wherein symptoms and/or events are associated to each formatted comment in the DDRs, wherein topics represent drilling events, the NLP algorithm including a first NLP model for extracting topics data; extracting measurement data from the pre-processed data using the NLP algorithm to produce extracted measurement data, comprising iterating through input comments and/or searching for unit tokens defined in the pre-processing step , the NLP algorithm including a second NLP model for extracting measurement data; aggregating the extracted topics data and the extracted measurement data to form a set of discrete data points per raw comment to produce aggregated data, wherein the set of discrete data points is a set of calibration points; identifying one or more discrete data points from the aggregated data; and presenting the results of the one or more discrete data points for generating and/or calibrating geomechanical stress models. forming one or more documents having raw data regarding a well site, wherein the one or more documents are daily drilling reports (DDRs), wherein the raw data includes one or more raw comments directed to operational details of the wellsite and/or metadata, such as depth, time, and well name, wherein the one or more raw comments and/or the metadata include unstructured text or numerical data; extracting raw data from the one or more documents regarding the well site to produce extracted raw data; pre-processing the extracted raw data by removing one or more of ambiguity, artifacts, and formatting errors from the one or more raw comments by going through a cleaning and lexical analysis stage to produce pre-processed data, wherein the cleaning and lexical analysis stage iterate through a raw comment and convert the raw comment into clean comment tokens; extracting topics data from the pre-processed data using a natural language processing (NLP) algorithm to produce extracted topics data, wherein symptoms and/or events are associated to each formatted comment in the DDRs, wherein topics represent drilling events, the NLP algorithm including a first NLP model for extracting topics data; extracting measurement data from the pre-processed data using the NLP algorithm to produce extracted measurement data, comprising iterating through input comments and/or searching for unit tokens defined in the pre-processing step , the NLP algorithm including a second NLP model for extracting measurement data; aggregating the extracted topics data and the extracted measurement data to form a set of discrete data points per raw comment to produce aggregated data, wherein the set of discrete data points is a set of calibration points; identifying one or more discrete data points from the aggregated data; and presenting the results of the one or more discrete data points for generating and/or calibrating geomechanical stress models. <EOS>