Thermal energy is extracted from geological formations using a heat harvester. In some embodiments, the heat harvester is a once-through, closed loop, underground heat harvester created by directionally drilling through hot rock. The extracted thermal energy can be converted or transformed to other forms of energy.

The present disclosure relates generally to well operations. The present disclosure relates more particularly to a systems and methods for independently and/or simultaneously treating multiple wells from a centralized location using a split flow pumping system configuration. The split flow pumping system configuration may comprise one or more blenders, one or more boost pumps, a pumping system comprising one or more pumps, a component storage system, and a fluid storage system for treatment of two or more wells using two or more treatment compositions. The split flow pumping system configuration may comprise one or more controllers for controlling the one or more blenders, the one or more boost pumps, the pumping system comprising one or more pumps, the component storage system, and the fluid storage system. The system may comprise one or more sensors for collecting data corresponding to the one or more pressures, flow rates, injection rates, compositions, temperatures, and densities of at least one of the first composition and the second composition, wherein the controller controls the one or more pressures, flow rates, injection rates, compositions, temperatures, and densities of at least one of the first composition and the second composition based, at least in part, on the data.

A method for optimizing a drilling roadmap may include identifying an optimal bottom hole assembly (BHA) setup and drilling parameters for a well located in a field. The BHA setup may be based on historical simulation data of the field and drilling roadmap information. The drilling roadmap information may include initial drilling instructions for implementing the drilling roadmap. The method may include implementing and tracking the drilling roadmap at the well. The drilling roadmap may be based on a location of the well on the field and a type of other applications being performed on the field. The method may include obtaining sensor collected data to determine an accuracy of implementation of the drilling roadmap. The accuracy may be determined based on a comparison between tracked drilling parameters and simulated drilling parameters.

A method for optimizing a drilling roadmap may include identifying an optimal bottom hole assembly (BHA) setup and drilling parameters for a well located in a field. The BHA setup may be based on historical simulation data of the field and drilling roadmap information. The drilling roadmap information may include initial drilling instructions for implementing the drilling roadmap. The method may include implementing and tracking the drilling roadmap at the well. The drilling roadmap may be based on a location of the well on the field and a type of other applications being performed on the field. The method may include obtaining sensor collected data to determine an accuracy of implementation of the drilling roadmap. The accuracy may be determined based on a comparison between tracked drilling parameters and simulated drilling parameters.

Systems and methods are provided for monitoring and/or controlling operations of an artificial lift system of a production well, which employ a gateway device disposed at a wellsite corresponding to the well, and at least one remote cloud-computing system operably coupled to the gateway device. The gateway device includes at least one first interface to the artificial lift system, at least one second interface to the at least one cloud-computing system, and a processor configured to execute a first application that acquires data produced by the artificial lift system and communicated to the gateway device via the first interface.

Systems and methods are provided for monitoring and/or controlling operations of an artificial lift system of a production well, which employ a gateway device disposed at a wellsite corresponding to the well, and at least one remote cloud-computing system operably coupled to the gateway device. The gateway device includes at least one first interface to the artificial lift system, at least one second interface to the at least one cloud-computing system, and a processor configured to execute a first application that acquires data produced by the artificial lift system and communicated to the gateway device via the first interface.

A system includes a processor and a memory. The memory includes instructions that are executable by the processor to cause the processor to receive basin data of a target basin including an area of the target basin, a number of exploration wells in the target basin, and a number of discovery wells in the target basin. Additionally, the instructions are executable to cause the processor to provide the basin data as input to a trained machine-learning model to determine a predicted trajectory of exploration efficiency of the target basin. Further, the instructions are executable to cause the processor to, in response to providing the basin data as input to the trained machine-learning model, receive an output from the trained machine-learning model indicating the predicted trajectory of exploration efficiency in the target basin.

The present invention relates to an automatic history matching system for an oil reservoir based on transfer learning, comprising a data reading module, a population reinitializing module, an optimization module, a simulated calculation module, a comparative judgment module and an output module, wherein the data reading module reads an optimized result of an existing oil reservoir, outputs the optimized result to the population reinitializing module, obtains an initial population of a new oil reservoir by calculation and outputs the initial population to the optimization module; the optimized result is outputted to the simulated calculation module to obtain oil reservoir production simulated data, and the oil reservoir production simulated data is outputted to the comparative judgment module; when an error between the simulated data and observed data meets the requirement, the optimized result is outputted to the output module, and the system operation is completed; and if the error does not meet the requirement, optimization will be performed again. The present invention may construct the initial population closer to the optimized result by using experience of adjusting a history matching model in an old example oil reservoir model according to the matching experience of an existing model, and can be integrated with any one evolutionary optimization algorithm, so the system is more suitably applied to an actual engineering problem.

The present invention relates to an automatic history matching system for an oil reservoir based on transfer learning, comprising a data reading module, a population reinitializing module, an optimization module, a simulated calculation module, a comparative judgment module and an output module, wherein the data reading module reads an optimized result of an existing oil reservoir, outputs the optimized result to the population reinitializing module, obtains an initial population of a new oil reservoir by calculation and outputs the initial population to the optimization module; the optimized result is outputted to the simulated calculation module to obtain oil reservoir production simulated data, and the oil reservoir production simulated data is outputted to the comparative judgment module; when an error between the simulated data and observed data meets the requirement, the optimized result is outputted to the output module, and the system operation is completed; and if the error does not meet the requirement, optimization will be performed again. The present invention may construct the initial population closer to the optimized result by using experience of adjusting a history matching model in an old example oil reservoir model according to the matching experience of an existing model, and can be integrated with any one evolutionary optimization algorithm, so the system is more suitably applied to an actual engineering problem.

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.