Abstract: A method of predicting hydrocarbon production from one or more artificial lift wells is disclosed. Measurements and/or well test data are obtained from one or more artificial lift wells representing well performance at more than one time period. A statistical model, representing well performance, is generated. Using the statistical model and the measurements and/or well test data, real-time production of the one or more artificial lift wells is predicted and a measure of confidence is generated. Potential optimization opportunities are identified for well performance based on the predicted real-time production of the one or more artificial lift wells. The optimization opportunities are outputted. Hydrocarbon production activities are modified using the optimization opportunities.

Abstract: The systems and methods described herein include training a well performance predictor based on field data corresponding to a hydrocarbon field in which a well is to be drilled; generating a number of candidate well parameter combinations for the well and predicting a performance of the well for each candidate well parameter combination using the trained well performance predictor; and determining an optimized well parameter combination for the well such that the predicted performance of the well is maximized.

Abstract: A method of predicting hydrocarbon production from one or more artificial lift wells is disclosed. Measurements and/or well test data are obtained from one or more artificial lift wells representing well performance at more than one time period. A statistical model, representing well performance, is generated. Using the statistical model and the measurements and/or well test data, real-time production of the one or more artificial lift wells is predicted and a measure of confidence is generated. Potential optimization opportunities are identified for well performance based on the predicted real-time production of the one or more artificial lift wells. The optimization opportunities are outputted. Hydrocarbon production activities are modified using the optimization opportunities.

Abstract: Systems and methods for determining well parameters for optimization of well performance. The method includes training, via a computing system, a well performance predictor based on field data corresponding to a hydrocarbon field in which a well is to be drilled. The method also includes generating, via the computing system, a number of candidate well parameter combinations for the well and predicting, via the computing system, a performance of the well for each candidate well parameter combination using the trained well performance predictor. The method further includes determining, via the computing system, an optimized well parameter combination for the well such that the predicted performance of the well is maximized.

Abstract: Systems and methods for determining well parameters for optimization of well performance. The method includes training, via a computing system, a well performance predictor based on field data corresponding to a hydrocarbon field in which a well is to be drilled. The method also includes generating, via the computing system, a number of candidate well parameter combinations for the well and predicting, via the computing system, a performance of the well for each candidate well parameter combination using the trained well performance predictor. The method further includes determining, via the computing system, an optimized well parameter combination for the well such that the predicted performance of the well is maximized.

Abstract: Method to estimate severity of downhole vibration for a drill tool assembly, including: identifying a dataset comprising selected drill tool assembly parameters; selecting a reference level of downhole vibration amplitude for the drill tool assembly; identifying a surface drilling parameter and calculating a reference surface vibration attribute for the selected reference level of downhole vibration amplitude; determining a surface parameter vibration attribute derived from at least one surface measurement or observation obtained in a drilling operation, the determined surface parameter vibration attribute corresponding to the identified surface drilling parameter; and estimating a downhole vibration severity indicator by evaluating the determined surface parameter vibration attribute with respect to the identified reference surface vibration attribute.

Abstract: Systems and methods for determining well parameters for optimization of well performance. The method includes training, via a computing system, a well performance predictor based on field data corresponding to a hydrocarbon field in which a well is to be drilled. The method also includes generating, via the computing system, a number of candidate well parameter combinations for the well and predicting, via the computing system, a performance of the well for each candidate well parameter combination using the trained well performance predictor. The method further includes determining, via the computing system, an optimized well parameter combination for the well such that the predicted performance of the well is maximized.

Abstract: Method to estimate severity of downhole vibration for a wellbore drill tool assembly, comprising: identifying a dataset comprising selected drill tool assembly parameters; selecting a reference downhole vibration index for the drill tool assembly; identifying a surface parameter and calculating a reference surface vibration attribute for the selected reference downhole vibration index; determining a surface vibration attribute derived from at least one surface measurement or observation obtained in a drilling operation, the determined surface vibration attribute corresponding to the identified surface parameter; and estimating a downhole vibration index by evaluating the determined surface vibration attribute with respect to the identified reference surface vibration attribute.

Abstract: Method to estimate severity of downhole vibration for a drill tool assembly, including: identifying a dataset comprising selected drill tool assembly parameters; selecting a reference level of downhole vibration amplitude for the drill tool assembly; identifying a surface drilling parameter and calculating a reference surface vibration attribute for the selected reference level of downhole vibration amplitude; determining a surface parameter vibration attribute derived from at least one surface measurement or observation obtained in a drilling operation, the determined surface parameter vibration attribute corresponding to the identified surface drilling parameter; and estimating a downhole vibration severity indicator by evaluating the determined surface parameter vibration attribute with respect to the identified reference surface vibration attribute.

Abstract: Method to estimate severity of downhole vibration for a wellbore drill tool assembly, comprising: identifying a dataset comprising selected drill tool assembly parameters; selecting a reference level of downhole vibration index for the drill tool assembly; identifying a surface drilling parameter and calculating a reference surface vibration attribute for the selected reference level of downhole vibration index; determining a surface parameter vibration attribute derived from at least one surface measurement or observation obtained in a drilling operation, the determined surface parameter vibration attribute corresponding to the identified surface drilling parameter; and estimating a downhole vibration index severity indicator by evaluating the determined surface parameter vibration attribute with respect to the identified reference surface vibration attribute.