Apparatus and method for multi-stage fracking

1. An apparatus for multi-stage fracking, wherein the apparatus comprises:

a pump comprising one or more valves, wherein the pump is configured to pump a fracking fluid into a rock region comprising a plurality discreate fracture networks on a pump configuration;

a computing device communicatively connected to the pump, wherein the computing device comprises:

at least a processor; and

a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to:

receive a first set of reservoir data from a first set sensing devices located on a surface of a target well;

determine an optimal fracking stimulation parameter as a function of the first set of reservoir data using a fracking optimization machine-learning model, wherein determining the optimal fracking stimulation parameter further comprises:

generating a fracking simulation model as a function of a second set of reservoir data; and

adjusting a pump horsepower as a function of the fracking simulation model;

identify a fracking stage as a function of the optimal fracking stimulation parameter;

adjust the pump configuration of the pump as a function of the fracking stage, wherein the one or more valves are actuated to control a release of the fracking fluid as a function of the pump configuration;

receive the second set of reservoir data using a second set of sensing devices based on the actuation of the one or more valves, wherein the second set of reservoir data is associated with an effect of actuation of the one or more valves on the rock region; and

update the optimal fracking stimulation parameter as a function of the second set of reservoir data.

2. The apparatus of claim 1 , wherein determining the optimal fracking stimulation parameter comprises:

iteratively training a fracking optimization machine-learning model using fracking training data, wherein the fracking training data comprises the first set of reservoir data and the second set of reservoir data correlated to examples of optimal fracking stimulation parameters; and

determining the optimal fracking stimulation parameter using a trained fracking optimization machine-learning model.

3. The apparatus of claim 1 , wherein the memory further instructs the processor to convert the reservoir data into a cleansed data format using a data conversion module.

4. The apparatus of claim 1 , wherein the first set of reservoir data comprises a downhole condition associated with the rock region.

5. The apparatus of claim 1 , wherein the second set of reservoir data comprises a fluid parameter associated with the rock region.

6. The apparatus of claim 1 , wherein:

the second set of sensing devices further comprises at least a geophone located at the surface of the target well; and

the second set of reservoir data comprises a ground motion during fracking.

7. The apparatus of claim 1 , wherein the first set sensing devices comprises a string of fiber optic sensors disposed along a well casing.

8. The apparatus of claim 1 , wherein pumping the fracking fluid comprises:

creating a discrete fracture network in the rock region; and

isolating a previous rock region, wherein isolating the previous rock region comprises using mechanical isolation.

9. The apparatus of claim 8 , wherein identifying the fracking stage comprises identifying the fracking stage as a function of the usage of mechanical isolation.

10. A method for multi-stage fracking, wherein the method comprises:

pumping, using a pump comprising one or more valves, a fracking fluid into a rock region comprising a plurality discreate fracture networks on a pump configuration;

receiving, using at least a processor, a first set of reservoir data from a first set sensing devices located on the surface of a target well;

determining, using the at least a processor, an optimal fracking stimulation parameter as a function of the first set of reservoir data using a fracking optimization machine-learning model, wherein determining the optimal fracking stimulation parameter further comprises:

generating a fracking simulation model as a function of a second set of reservoir data; and

adjusting a pump horsepower as a function of the fracking simulation model;

identifying, using the at least a processor, a fracking stage as a function of the optimal fracking stimulation parameter;

adjusting, using the at least a processor, the pump configuration of the pump as a function of the fracking stage, wherein the one or more valves are actuated to control a release of the fracking fluid as a function of the pump configuration;

receiving, using at least a processor, the second set of reservoir data using a second set of sensing devices based on the actuation of the one or more valves, wherein the second set of reservoir data is associated with effect of actuation of the one or more valves on the rock region; and

updating, using at least a processor, the optimal fracking stimulation parameter as a function of the second set of reservoir data.

11. The method of claim 10 , wherein determining the optimal fracking stimulation parameter comprises:

iteratively training a fracking optimization machine-learning model using fracking training data, wherein the fracking training data comprises the first set of reservoir data and the second set of reservoir data correlated to examples of optimal fracking stimulation parameters; and

determining the optimal fracking stimulation parameter using a trained fracking optimization machine-learning model.

12. The method of claim 10 , wherein the method further comprises converting, using the at least a processor, the reservoir data into a cleansed data format using a data conversion module.

13. The method of claim 10 , wherein the first set of reservoir data comprises a downhole condition associated with the rock region.

14. The method of claim 10 , wherein the second set of reservoir data comprises a fluid parameter associated with the rock region.

15. The method of claim 11 , wherein:

the second set of sensing devices further comprises at least a geophone located at the surface of the target well; and

the second set of reservoir data comprises ground motion during fracking.

16. The method of claim 10 , wherein the first set sensing devices comprises a string of fiber optic sensors disposed along a well casing.

17. The method of claim 10 , wherein pumping the fracking fluid comprises:

creating a discrete fracture network in the rock region; and

isolating a previous rock region, wherein isolating the previous rock region comprises using mechanical isolation.

18. The method of claim 17 , wherein identifying the fracking stage comprises identifying the fracking stage as a function of the usage of mechanical isolation.