Abstract: A method for predicting oil flow rates is provided. The method includes accessing historical data from a plurality of databases, accessing historical perforation data and historical reservoir properties data from a simulation model, and determining fluid flow values and rock quality index values associated with perforated intervals of the plurality of wells. The method further includes corresponding the fluid flow values and rock quality values to the well production data, training, using the plurality of input values, a machine learning model for predicting oil flow values at perforated intervals of a plurality of target wells, predicting, using the trained machine learning model, the oil flow values at the perforated intervals of the plurality of target wells, and generating a synthetic production log that includes the predicted oil flow values at the perforated intervals of the plurality of target wells.

Abstract: In one embodiment, a method of processing petroleum extraction data includes receiving petroleum extraction data from one or more data sources, wherein the petroleum extraction data includes structured data and unstructured data, filtering the petroleum extraction data by applying one or more statistical methods to generate filtered petroleum extraction data including transactional data and non-transactional data, and verifying the filtered petroleum extraction data according to one or more attributes. The method further includes calculating one or more parameters from the transactional data at a first frequency and one or more parameters from the non-transactional data at a second frequency, and correlating the one or more parameters from the transactional data and the non-transactional data based on one or more attributes to generate correlated data. The method also includes aggregating and storing the correlated data in a data structure, and performing one or more analytic processes on the aggregated data.

Abstract: A method for predicting oil flow rates is provided. The method includes accessing historical data from a plurality of databases, accessing historical perforation data and historical reservoir properties data from a simulation model, and determining fluid flow values and rock quality index values associated with perforated intervals of the plurality of wells. The method further includes corresponding the fluid flow values and rock quality values to the well production data, training, using the plurality of input values, a machine learning model for predicting oil flow values at perforated intervals of a plurality of target wells, predicting, using the trained machine learning model, the oil flow values at the perforated intervals of the plurality of target wells, and generating a synthetic production log that includes the predicted oil flow values at the perforated intervals of the plurality of target wells.

Abstract: In one embodiment, a method of processing petroleum extraction data includes receiving petroleum extraction data from one or more data sources, wherein the petroleum extraction data includes structured data and unstructured data, filtering the petroleum extraction data by applying one or more statistical methods to generate filtered petroleum extraction data including transactional data and non-transactional data, and verifying the filtered petroleum extraction data according to one or more attributes. The method further includes calculating one or more parameters from the transactional data at a first frequency and one or more parameters from the non-transactional data at a second frequency, and correlating the one or more parameters from the transactional data and the non-transactional data based on one or more attributes to generate correlated data. The method also includes aggregating and storing the correlated data in a data structure, and performing one or more analytic processes on the aggregated data.