Field operations framework

A system, comprising:

• one or more processors;
• a memory operatively coupled to at least one of the one or more processors;
• and
• processor-executable instructions stored in the memory and executable to instruct the system to:
• receive, at a plurality of local edge devices located at respective well sites, real-time operational data from field equipment including artificial lift equipment and a landing pad, at least one of the plurality of local edge devices comprising a mobile gateway included in a drone configured to take off and land to operatively couple the mobile gateway to the artificial lift equipment and the landing pad, each of the respective well sites comprising a wellhead comprising the landing pad and one or more sensors comprising at least one of:
• a temperature sensor and a pressure sensor;
• process, by one or more trained machine-learning models embedded in at least one of the local edge devices, the real-time operational data to determine optimal field operation parameters comprising gas-lift control parameters for the artificial lift equipment;
• automatically control, in a closed-loop manner, the artificial lift equipment at the well sites using the determined optimal field operation parameters, the closed-loop controlling being performed locally by the local edge devices at the well sites to reduce communication latency, the closed-loop controlling comprising, via the mobile gateway:
• generating fluid flow values using information from the one or more sensors;
• and
• issuing one or more control instructions to the artificial lift equipment based on the fluid flow values;
• dynamically generate, based on the automatic controlling, carbon footprint estimates for operation of the well sites, wherein the dynamically generating the carbon footprint estimates comprises receiving travel-related data from a vehicle management system;
• plan, based on the travel-related data, one or more vehicle trips to one or more of the well sites;
• control a vehicle utilized for at least one of the one or more vehicle trips in real-time;
• and
• transmit sensor data acquired by one or more sensors of the vehicle, wherein the dynamically generating the carbon footprint estimates is based on at least a portion of the sensor data.

One or more non-transitory computer-readable storage media comprising processor-executable instructions executable by one or more processors to instruct the one or more processors to:

• receive, at a plurality of local edge devices each comprising at least one of the one or more processors located at respective well sites, real-time operational data from field equipment comprising:
• artificial lift equipment and a landing pad, at least one of the plurality of local edge devices comprising a mobile gateway included in a drone configured to take off and land to operatively couple the mobile gateway to the artificial lift equipment and the landing pad, each of the respective well sites comprising a wellhead comprising the landing pad and one or more sensors comprising at least one of:
• a temperature sensor and a pressure sensor;
• and a variable speed drive (VSD) comprising a machine learning processing component operatively coupled to one or more of:
• sensor or data acquisition components;
• process, by one or more trained machine-learning models embedded in at least one of the local edge devices, the real-time operational data to determine optimal field operation parameters comprising gas-lift control parameters for the artificial lift equipment;
• automatically control, in a closed-loop manner, the artificial lift equipment at the well sites-using the determined optimal field operation parameters, the closed-loop controlling being performed locally by the local edge devices at the well sites to reduce communication latency, the closed-loop controlling comprising, via the mobile gateway:
• generating fluid flow values using information from the one or more sensors;
• issuing one or more control instructions to the artificial lift equipment based on the fluid flow values;
• and
• VSD controlling to control one or more electric motors of a pump or pumps of the field equipment based on information from the one or more of:
• the sensor or the data acquisition components;
• dynamically generate, based on the automatic controlling, carbon footprint estimates for operation of the well sites, wherein the dynamically generating the carbon footprint estimates comprises receiving travel-related data from a vehicle management system;
• and
• plan, based on the travel-related data, one or more vehicle trips to one or more of the well sites.