| CPC E21B 43/123 (2013.01) [E21B 47/114 (2020.05)] | 24 Claims |
|
1. A method comprising:
adjusting input control variables that control at least one gas lift setting or flow control device setting as part of a machine learning process;
receiving sensor data regarding a wellbore from at least one of a distributed fiber optic sensing line positioned along the wellbore and a plurality of subsurface and surface sensors regarding a plurality of gas lift valves when the input control variables are adjusted to control the at least one gas lift setting or the flow control device setting;
identifying a set of signatures from the sensor data received from the at least one of the distributed fiber optic sensing line and the plurality of subsurface and surface sensors;
generating a data driven flow model based on the sensor data being applied to the machine learning process;
generating a production profile based on operation of the data driven flow model, the production profile associated with a plurality of production zones of the wellbore and operation of the plurality of gas lift valves;
identifying, based at least in part on the set of signatures, that a flow associated with an inflow device of a specific zone of the plurality of production zones does not correspond to a target range of the production profile;
adjusting the inflow device of the specific zone to update the flow to correspond to the target range of the production profile based on the input control variables that control the at least one gas lift setting or the flow control device setting;
comparing measurement data sensed by the at least one of the distributed fiber optic sensing and the plurality of subsurface and surface sensors with the set of signatures; and
identifying that a first gas lift valve of the plurality of gas lift valves has malfunctioned based on the comparison of the measurement data with the set of signatures.
|