A computer-implemented method, comprising the steps of: (a) receiving first data, wherein the first data comprises well completion data, well survey data, fluid data, or a combination thereof; (b) building a first model of a well based at least partially upon the first data; (c) receiving second data, wherein the second data comprises a shapefile in a geospatial vector that spatially describes vector features including points, lines, and polygons, representing a wellsite that includes the well; (d) building a second model comprising a network of flowlines based at least partially upon the second data, wherein at least one of the flowlines is connected to the well; (e) combining the first model and the second model to produce a combined model, the combining using geographic latitude and longitude coordinates to ensure alignment of the models; (f) calibrating the combined model to produce a calibrated model representing a production system at the wellsite, wherein calibrating the combined model comprises: receiving measured data from sensors embedded in equipment at the well or positioned about the wellsite; running a simulation of the combined model to produce simulated results; and adjusting a calibration parameter to cause the simulated results to match the measured data, wherein the calibration parameter comprises a productivity index of a fluid flowing out of the well; (g) updating the calibrated model to produce an updated model; (h) receiving real-time data; and (i) building a machine-learning model based at least partially upon the real-time data and the updated model; (h) detecting (2606, 2608, 2610) one or more problems using the ML model; (i) ranking the detected problems using the ML model; (j) determining an adjustment to an operating parameter of an oilfield production to address the highest ranked problem using the ML model; and. (k) outputting the determined adjustment to an operator for implementation in the oilfield production system. (a) receiving first data, wherein the first data comprises well completion data, well survey data, fluid data, or a combination thereof; (b) building a first model of a well based at least partially upon the first data; (c) receiving second data, wherein the second data comprises a shapefile in a geospatial vector that spatially describes vector features including points, lines, and polygons, representing a wellsite that includes the well; (d) building a second model comprising a network of flowlines based at least partially upon the second data, wherein at least one of the flowlines is connected to the well; (e) combining the first model and the second model to produce a combined model, the combining using geographic latitude and longitude coordinates to ensure alignment of the models; (f) calibrating the combined model to produce a calibrated model representing a production system at the wellsite, wherein calibrating the combined model comprises: receiving measured data from sensors embedded in equipment at the well or positioned about the wellsite; running a simulation of the combined model to produce simulated results; and adjusting a calibration parameter to cause the simulated results to match the measured data, wherein the calibration parameter comprises a productivity index of a fluid flowing out of the well; receiving measured data from sensors embedded in equipment at the well or positioned about the wellsite; running a simulation of the combined model to produce simulated results; and adjusting a calibration parameter to cause the simulated results to match the measured data, wherein the calibration parameter comprises a productivity index of a fluid flowing out of the well; (g) updating the calibrated model to produce an updated model; (h) receiving real-time data; and (i) building a machine-learning model based at least partially upon the real-time data and the updated model; (h) detecting (2606, 2608, 2610) one or more problems using the ML model; (i) ranking the detected problems using the ML model; (j) determining an adjustment to an operating parameter of an oilfield production to address the highest ranked problem using the ML model; and. (k) outputting the determined adjustment to an operator for implementation in the oilfield production system.