Patent ID: 11898419
Assignee: SCHLUMBERGER TECHNOLOGY CORPORATION
Field: Civil engineering (Other fields)
Classification: CPC E  G | IPC E  G

Claim 0:
1. A method, comprising:
receiving first data;
building a first model of a well based at least partially upon the first data;
receiving second data;
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;
combining the first model and the second model to produce a combined model;
calibrating the combined model to produce a calibrated model, wherein calibrating the combined model comprises:
receiving measured data;
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;

updating the calibrated model to produce an updated model;
building a machine-learning (ML) model based at least partially upon real-time data and the updated model via an adaptive learning process comprising weighing one or more model insights and adding one or more failure patterns unrecognizable based on the simulated results, wherein the one or more failure patterns are associated with an electronic submersible pump (ESP) at the well, a gas-oil ratio or a water-to-oil ratio of the fluid flowing out of the well, a well valve, well tubing, well casing, a well pumping module, or any combination thereof; and
generating, via the ML model, a proactive solution in response to predicting one or more potential problems based on one or more data patterns recognized in the real-time data and the one or more failure patterns, wherein the data patterns comprise at least ESP motor temperature, intake pressure, outlet pressure, and motor current amperage (amps), wherein the ML model is configured to provide a probability or confidence of the one or more potential problems or the one or more model insights, and wherein the proactive solution comprises a remedial plan to solve the one or more potential problems.