Abstract: Provided are embodiments that include identifying candidate well parameters for a hydrocarbon well that include a location, a well production rate and candidate horizontal wellbore lateral lengths for the well, conducting simulations of hydrocarbon wells located at the location and having lateral lengths corresponding to the candidate horizontal wellbore lateral lengths and operating at the well production rate to determine a gas breakthrough productivity indexes (GBPIs) for the simulated wells, determining (based on the GBPIs) a relationship of GBPI to horizontal wellbore lateral length, determining (based on the relationship) an optimized horizontal wellbore lateral length for the production rate, and developing the well based on the optimized horizontal wellbore lateral length.

Abstract: A method for training a predictive reservoir simulation in which high-confidence reservoir sample data is used to identify misallocated historical production data used in the simulation. A neural network algorithm is trained with high-confidence reservoir historical production data. High-confidence reservoir sample data is obtained by at least one sensor at a reservoir location over a time interval, after which the reservoir historical production data is parametrically varied over the time interval to determine a time-indexed discrepancy between the reservoir historical production data and the high-confidence reservoir sample data over the time interval. The time-indexed discrepancy and a defined threshold discrepancy are then used as inputs to a machine learning process to further train the neural network algorithm to identify reservoir historical production data whose discrepancy exceeds the threshold discrepancy and thereby constitutes misallocated historical production data.

Abstract: Provided are embodiments that include identifying candidate well parameters for a hydrocarbon well that include a location, a well production rate and candidate horizontal wellbore lateral lengths for the well, conducting simulations of hydrocarbon wells located at the location and having lateral lengths corresponding to the candidate horizontal wellbore lateral lengths and operating at the well production rate to determine a gas breakthrough productivity indexes (GBPIs) for the simulated wells, determining (based on the GBPIs) a relationship of GBPI to horizontal wellbore lateral length, determining (based on the relationship) an optimized horizontal wellbore lateral length for the production rate, and developing the well based on the optimized horizontal wellbore lateral length.

Abstract: A method for training a predictive reservoir simulation in which high-confidence reservoir sample data is used to identify misallocated historical production data used in the simulation. A neural network algorithm is trained with high-confidence reservoir historical production data. High-confidence reservoir sample data is obtained by at least one sensor at a reservoir location over a time interval, after which the reservoir historical production data is parametrically varied over the time interval to determine a time-indexed discrepancy between the reservoir historical production data and the high-confidence reservoir sample data over the time interval. The time-indexed discrepancy and a defined threshold discrepancy are then used as inputs to a machine learning process to further train the neural network algorithm to identify reservoir historical production data whose discrepancy exceeds the threshold discrepancy and thereby constitutes misallocated historical production data.