Method and system for managing carbon dioxide supplies and supercritical turbines using machine learning

1 . A method, comprising:

performing, using a first injection well, a stimulation operation based on injecting a predetermined amount of carbon dioxide into a subsurface of a geological region of interest,

wherein the predetermined amount of carbon dioxide comprises a gas tracer that is used to detect one or more carbon dioxide leakages from the geological region of interest;

obtaining, by a carbon dioxide manager comprising a computer processor and from a plurality of wells in the geological region of interest, gas tracer data for the geological region of interest,

wherein the gas tracer data is acquired using a plurality of chromatography-based sensors disposed in the plurality of wells;

obtaining, by the carbon dioxide manager and from a plurality of supercritical carbon dioxide power (sCO2) turbines, turbine data regarding the plurality of sCO2 turbines, wherein the turbine data comprises historical electric-power data for the plurality of sCO 2 turbines;

obtaining, by the carbon dioxide manager and from at least one production well among the plurality of wells, acquired production data,

wherein the carbon dioxide manager, the plurality of wells, and the plurality of sCO2 turbines are connected over a carbon dioxide management network;

obtaining, by the carbon dioxide manager and over the carbon dioxide management network, carbon emission data for the plurality of wells;

determining, by the carbon dioxide manager, predicted production data and predicted carbon emission data for a production well among the plurality of wells using a first machine-learning model, the gas tracer data, the turbine data, and the carbon emission data; and

determining, by the carbon dioxide manager, error data based on a mismatch between the predicted production data and the acquired production data;

updating, by the carbon dioxide manager, the first machine-learning model to produce an updated machine-learning model,

wherein the first machine-learning model is updated using the error data and a machine-learning algorithm comprising a backward propagation algorithm;

determining, by the carbon dioxide manager and using the first machine-learning model, predicted carbon dioxide demand data for an sCO 2 turbine among the plurality of sCO 2 turbines for a period of time based on the historical electric-power data,

wherein the first machine-learning model comprises a non-linear autoregressive neural network with an exogenous input (NARX) model, and

wherein the NARX model comprises at least one hidden layer and outputs the predicted production data and the predicted carbon dioxide demand data;

transmitting, by the carbon dioxide manager, one or more commands to a control system based on the predicted production data, the predicted carbon dioxide demand data for the sCO 2 turbine, and the predicted carbon emission data;

distributing, using the control system and in response to the control system receiving the one or more commands, a flow of carbon dioxide between a second injection well and the sCO 2 turbine based on the predicted production data, the predicted carbon dioxide demand data, and the predicted carbon emission data; and

generating electric power using the sCO 2 turbine and a portion of the flow of carbon dioxide that is distributed to the sCO 2 turbine, wherein generating the electric power is performed using a Brayton cycle process,

wherein the one or more commands achieve a predetermined production rate at the production well and a predetermined carbon footprint, and

wherein the predetermined carbon footprint corresponds to carbon emissions that are produced by the production well and the sCO 2 turbine.

2 . The method of claim 1 ,

wherein the NARX model determines a predicted production rate at a predetermined time for the production well.

3 . The method of claim 1 ,

wherein the NARX model determines an amount of carbon dioxide emissions associated with providing electric power to the production well to achieve the predetermined production rate.

4 . The method of claim 1 , further comprising:

obtaining, by the carbon dioxide manager and in response to a user input to a user device, a request to determine carbon dioxide supplies within a carbon dioxide management network.

5 . The method of claim 1 ,

wherein the predicted production data corresponds to an amount of oil, an amount of gas, and an amount of water that are produced by the production well.

6 . The method of claim 1 ,

wherein the predicted carbon emission data corresponds to electric power requirements for the production well that achieve carbon dioxide neutrality with a stimulation operation for the geological region of interest.

7 . The method of claim 1 ,

wherein the sCO 2 turbine operates using carbon dioxide in a fluid state of carbon dioxide (CO 2 ) that is held at or above a predetermined critical temperature and a predetermined critical pressure.

8 . A system, comprising:

a plurality of wells in a geological region of interest, wherein the plurality of wells comprises a production well, a first injection well, and a second injection well;

a plurality of supercritical carbon dioxide (sCO 2 ) turbines;

a first control system coupled to the first injection well;

a second control system coupled to the production well;

a third control system coupled to an sCO 2 turbine among the plurality of sCO 2 turbines;

a fourth control system coupled to the second injection well;

a carbon dioxide management network; and

a carbon dioxide manager comprising a computer processor, wherein the carbon dioxide manager is connected to the first control system, the second control system, the third control system, the fourth control system, and the plurality of wells over the carbon dioxide management network,

wherein the first control system is configured to perform, using the first injection well, a stimulation operation based on injecting a predetermined amount of carbon dioxide into a subsurface of the geological region of interest,

wherein the predetermined amount of carbon dioxide comprises a gas tracer that is used to detect one or more carbon dioxide leakages from the geological region of interest,

wherein the carbon dioxide manager is configured to:

obtain gas tracer data for the geological region of interest from the plurality of wells,

wherein the gas tracer data is acquired using a plurality of chromatography-based sensors disposed in the plurality of wells;

obtain turbine data regarding the plurality of sCO 2 turbines, wherein the turbine data comprises historical electric-power data for the plurality of sCO 2 turbines;

obtain acquired production data from at least one production well among the plurality of wells;

obtain carbon emission data for the plurality of wells;

determine predicted production data and predicted carbon emission data for the production well using a first machine-learning model, the gas tracer data, the turbine data, and the carbon emission data; and

determine error data based on a mismatch between first predicted production data and the acquired production data;

update the first machine-learning model to produce an updated machine-learning model,

wherein the first machine-learning model is updated using the error data and a machine-learning algorithm comprising a backward propagation algorithm;

determine, using the first machine-learning model, predicted carbon dioxide demand data for the sCO 2 turbine for a period of time based on the historical electric-power data,

wherein the first machine-learning model comprises a non-linear autoregressive neural network with an exogenous input (NARX) model, and

wherein the NARX model comprises at least one hidden layer and outputs the predicted production data and the predicted carbon dioxide demand data; and

transmit one or more commands to the third control system based on the predicted production data, the predicted carbon dioxide demand data, and the predicted carbon emission data; and

wherein the third control system is configured to:

distribute, in response to the third control system receiving the one or more commands, a flow of carbon dioxide between the second injection well and the sCO 2 turbine based on the predicted production data, the predicted carbon dioxide demand data, and the predicted carbon emission data, and

generate electric power using the sCO 2 turbine and a portion of the flow of carbon dioxide that is distributed to the sCO 2 turbine, wherein generating the electric power is performed using a Brayton cycle process,

wherein the one or more commands achieve a predetermined production rate at the production well and a predetermined carbon footprint, and

wherein the predetermined carbon footprint corresponds to carbon emissions that are produced by the production well, the second injection well, and the sCO 2 turbine.

9 . The system of claim 8 ,

wherein the NARX model determines a predicted production rate at a predetermined time for the production well.

10 . The system of claim 8 ,

wherein the NARX model determines an amount of carbon dioxide emissions associated with providing electric power to the production well to achieve the predetermined production rate, and

wherein the second injection well performs a carbon dioxide injection based on the amount of carbon dioxide emissions.

11 . The system of claim 8 ,

wherein the sCO 2 turbine operates using carbon dioxide in a fluid state of carbon dioxide (CO 2 ) that is held at or above a predetermined critical temperature and a predetermined critical pressure.