# US 12,595,723 B2 · Multi-agent, multi-objective wellbore gas-lift optimization > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,595,723 B2 - Document ID: US12595723B2 - Status: Granted - Kind: B2 - Application number: 17/613,761 - Filed: 2019-07-02 - Published: 2022-07-21 - Granted: 2026-04-07 - Earliest claimed: 2019-07-02 - Assignees: Landmark Graphics Corporation - Applicants: Landmark Graphics Corporation - Inventors: Srinath Madasu; Shashi Dande; Keshava Prasad Rangarajan - Web page: https://patentgrep.com/patent/US12595723B2 ## CPC - E21B 43/122: {Gas lift} - E21B 2200/20: Computer models or simulations, e.g. for reservoirs under production, drill bits - E21B: EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS ## Abstract A system and method for controlling a gas supply to provide gas lift for wellbore(s) using Bayesian optimization. A computing device controls a gas supply to inject gas into wellbore(s). The computing device receives first reservoir data associated with a first subterranean reservoir and simulates production using the first reservoir data, using a model for the first subterranean reservoir. The production simulation provides first production data. The computing device receives second reservoir data associated with a subterranean reservoir and simulates production using the second reservoir data, using a model for the second subterranean reservoir. The production simulation provides second production data. A Bayesian optimization of an objective function of the first and second production data subject to any gas injection constraints can be performed to produce gas-lift parameters. The gas-lift parameters can be applied to the gas supply to control injection of gas into the wellbore(s). ## Claims ### Claim 1 A system comprising: a gas supply arrangement to inject gas into a plurality of wellbores in proximity to production tubing, wherein the plurality of wellbores comprises a plurality of clustered wellbores; and a computing device in communication with the gas supply arrangement, the computing device including a non-transitory memory device comprising instructions that are executable by the computing device to cause the computing device to perform operations comprising: receiving, from a first robot associated with a first wellbore, first reservoir data associated with a first subterranean reservoir to be penetrated by the first wellbore, the first wellbore being associated with a first cluster of wellbores of the plurality of clustered wellbores; simulating production using the first reservoir data associated with the first subterranean reservoir and using a physics-based model, a machine learning model, or a hybrid physics-based machine learning model for the first subterranean reservoir to provide first production data; receiving, from a second robot associated with a second wellbore, second reservoir data associated with a second subterranean reservoir to be penetrated by the second wellbore, the second wellbore being associated with a second cluster of wellbores of the plurality of clustered wellbores; simulating production using the second reservoir data associated with the second subterranean reservoir and using the physics-based model, the machine learning model, or the hybrid physics-based machine learning model for the second subterranean reservoir to provide second production data; performing a Bayesian optimization of an objective function of the first and second production data subject to gas injection constraints and convergence criteria to produce gas-lift parameters, the Bayesian optimization being performed for each of the first cluster of wellbores, the second cluster of wellbores, and across the plurality of clustered wellbores; and applying the gas-lift parameters to the gas supply arrangement by performing a gas-lift control using the first robot or the second robot based on the gas-lift parameters in response to the convergence criteria being met to control an injection of gas into at least one wellbore of the plurality of wellbores. ### Claim 2 (depends on 1) The system of claim 1, further comprising: a production tubing string disposed in the at least one wellbore of the plurality of clustered wellbores; an injection port connected to the production tubing string to inject gas into the production tubing string downhole; and a gas storage device connected to the production tubing string. ### Claim 3 (depends on 1) The system of claim 1, wherein the gas-lift parameters comprise gas injection rate and choke size. ### Claim 4 (depends on 3) The system of claim 3, wherein the gas injection rate is constant or a function of time. ### Claim 5 (depends on 1) The system of claim 1, wherein the convergence criteria comprise a maximum number of iterations and a convergence within a specified tolerance to a maximum production rate and a minimum friction value for the production tubing. ### Claim 6 (depends on 1) The system of claim 1, the operations further comprising: transmitting a signal to the first robot or the second robot associated with at least one of the plurality of clustered wellbores to perform the gas-lift control based on the gas-lift parameters. ### Claim 7 (depends on 6) The system of claim 6, wherein the at least one of the plurality of clustered wellbores is the first wellbore, the system further comprising: the first robot having a first sensor, the first sensor to detect the first reservoir data and receive real-time production data associated with the first wellbore, wherein the first robot transmits the first reservoir data to the computing device; and the second robot having a second sensor, the second sensor to detect the second reservoir data and receive real-time production data associated with the second wellbore, wherein the second robot transmits the second reservoir data to the computing device. ### Claim 8 A method comprising: receiving, from a first robot associated with a first wellbore and by a processing device, first reservoir data associated with a first subterranean reservoir to be penetrated by the first wellbore, the first wellbore being associated with a first cluster of wellbores of a plurality of clustered wellbores; simulating, by the processing device, production using the first reservoir data associated with the first subterranean reservoir and using a physics-based model, a machine learning model, or a hybrid physics-based machine learning model for the first subterranean reservoir to provide first production data; receiving, from a second robot associated with a second wellbore and by the processing device, second reservoir data associated with a second subterranean reservoir to be penetrated by the second wellbore, the second wellbore being associated with a second cluster of wellbores of the plurality of clustered wellbores; simulating, by the processing device, production using the second reservoir data associated with the second subterranean reservoir and using the physics-based model, the machine learning model, or the hybrid physics-based machine learning model for the second subterranean reservoir to provide second production data; performing, by the processing device, a Bayesian optimization of an objective function of the first and second production data subject to gas injection constraints and convergence criteria to produce gas-lift parameters, the Bayesian optimization being performed for each of the first cluster of wellbores, the second cluster of wellbores, and across the plurality of clustered wellbores; and applying, by the processing device, the gas-lift parameters to a gas supply arrangement by performing a gas-lift control using the first robot or the second robot based on the gas-lift parameters in response to the convergence criteria being met to control an injection of gas into the first wellbore or the second wellbore. ### Claim 9 (depends on 8) The method of claim 8, wherein the first wellbore and the second wellbore each include a production tubing string, the method further comprising: injecting gas into the production tubing string downhole; and capturing gas at a gas storage device connected to the production tubing string. ### Claim 10 (depends on 8) The method of claim 8, wherein the gas-lift parameters comprise gas injection rate and choke size. ### Claim 11 (depends on 10) The method of claim 10, wherein the gas injection rate is constant or a function of time. ### Claim 12 (depends on 8) The method of claim 8, wherein the convergence criteria comprise a maximum number of iterations and a convergence within a specified tolerance to a maximum production rate and a minimum friction value for the production tubing. ### Claim 13 (depends on 8) The method of claim 8, further comprising: transmitting a signal to the first robot or the second robot associated with at least one of the plurality of clustered wellbores to perform the gas-lift control based on the gas-lift parameters. ### Claim 14 (depends on 13) The method of claim 13, further comprising: receiving, from the first robot having a first sensor, real-time production data associated with the first wellbore, the real-time production data associated with the first wellbore being the first reservoir data; receiving, from the second robot having a second sensor, real-time production data associated with the second wellbore, the real-time production data associated with the first wellbore being the second reservoir data; transmitting, using the first robot, the first reservoir data to the processing device; and transmitting, using the second robot, the second reservoir data to the processing device. ### Claim 15 A non-transitory computer-readable medium comprising instructions that are executable by a processing device for causing the processing device to perform operations comprising: receiving, from a first robot associated with a first wellbore and by the processing device, first reservoir data associated with a first subterranean reservoir to be penetrated by the first wellbore, the first wellbore being associated with a first cluster of wellbores of a plurality of clustered wellbores; simulating, by the processing device, production using the first reservoir data associated with the first subterranean reservoir and using a physics-based model, a machine learning model, or a hybrid physics-based machine learning model for the first subterranean reservoir to provide first production data; receiving, from a second robot associated with a second wellbore and by the processing device, second reservoir data associated with a second subterranean reservoir to be penetrated by the second wellbore, the second wellbore being associated with a second cluster of wellbores of the plurality of clustered wellbores; simulating production using the second reservoir data associated with the second subterranean reservoir and using the physics-based model, the machine learning model, or the hybrid physics-based machine learning model for the second subterranean reservoir to provide second production data; performing a Bayesian optimization of an objective function of the first and second production data subject to gas injection constraints and convergence criteria to produce gas-lift parameters, the Bayesian optimization being performed for each of the first cluster of wellbores, the second cluster of wellbores, and across the plurality of clustered wellbores; and applying the gas-lift parameters to a gas supply arrangement by performing a gas-lift control using the first robot or the second robot based on the gas-lift parameters in response to the convergence criteria being met to control an injection of gas into the first wellbore or the second wellbore. ### Claim 16 (depends on 15) The non-transitory computer-readable medium of claim 15, wherein the first wellbore and the second wellbore each include production tubing string, the operations further comprising: injecting gas into the production tubing string downhole; and capturing gas at a gas storage device connected to the production tubing string. ### Claim 17 (depends on 15) The non-transitory computer-readable medium of claim 15, wherein the gas-lift parameters comprise gas injection rate and choke size, and wherein the gas injection rate is constant or a function of time. ### Claim 18 (depends on 15) The non-transitory computer-readable medium of claim 15, wherein the convergence criteria comprise a maximum number of iterations and a convergence within a specified tolerance to a maximum production rate and a minimum friction value for the production tubing. ### Claim 19 (depends on 15) The non-transitory computer-readable medium of claim 15, wherein the processing device comprises a robotic operating system, the non-transitory computer-readable medium comprising instructions that are executable by the processing device for causing the processing device to perform operations further comprising: transmitting a signal to the first robot or the second robot associated with at least the first wellbore or the second wellbore to perform the gas-lift control based on the gas-lift parameters. ### Claim 20 (depends on 19) The non-transitory computer-readable medium of claim 19, the operations further comprising: receiving, from the first robot having a first sensor, real-time production data associated with the first wellbore, the real-time production data associated with the first wellbore being the first reservoir data; receiving, from the second robot having a second sensor, real-time production data associated with the second wellbore, the real-time production data associated with the first wellbore being the second reservoir data; transmitting, using the first robot, the first reservoir data to the processing device; and transmitting, using the second robot, the second reservoir data to the processing device.