Apparatus and method for fracking optimization

1. An apparatus for fracking optimization, wherein the apparatus comprises:

at least a sensing device, the sensing device configured to detect a reservoir datum;

at least a processor communicatively connected to the at least a sensing device; and

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

receive the reservoir datum from the at least a sensing device, wherein the reservoir datum comprises at least a ground motion during fracking;

convert the at least a ground motion into a voltage measurement, wherein the voltage measurement is a difference in electric potential between two points;

generate production training data as a function of a fracking simulation model, wherein the fracking simulation model is configured to calculate a fracture propagation using a linear elastic fracture mechanism, wherein the linear elastic fracture mechanism comprises a criteria comprising an evaluation of a plurality of reservoir datums, wherein the evaluation of a plurality of reservoir datums comprises:

a maximum tensile stress criterion;

a minimum strain energy density criterion;

a maximum principal strain criterion; and

a maximum strain criterion;

train a fracking optimization machine-learning model using the production training data;

determine an optimal production parameter as a function of the fracking optimization machine-learning model, wherein the optimal production parameter input comprises the reservoir datum and the output comprises the optimal production parameter; and

generate an optimal production plan as a function of the optimal production parameter.

2. The apparatus of claim 1 , wherein the at least a sensing device comprises a seismic sensor.

3. The apparatus of claim 1 , wherein the reservoir datum comprises a pre-fracking reservoir datum and a post-fracking reservoir datum.

4. The apparatus of claim 1 , wherein the reservoir datum comprises a fluid parameter.

5. The apparatus of claim 1 , wherein the reservoir datum comprises a downhole condition.

6. The apparatus of claim 1 , wherein generating the production training data comprises: converting the reservoir datum and the optimal production parameter into a cleansed data format using a data conversion module, wherein the cleansed data format comprises a cleansed reservoir datum and a cleansed optimal production parameter.

7. The apparatus of claim 1 , wherein determining the optimal production parameter comprises determining the optimal production parameter using fuzzy logic.

8. The apparatus of claim 1 , wherein generating the optimal production plan comprises generating a production plan classifier; and classifying the optimal production parameter to an optimal production plan using the production plan classifier.

9. The apparatus of claim 1 , wherein the optimal production plan comprises an optimal fracking stimulation process comprising at least an optimal fracking stimulation step.

10. A method for fracking optimization, wherein the method comprises:

at least a sensing device, the sensing device configured to detect a reservoir datum;

at least a processor communicatively connected to the at least a sensing device; and

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

receive the reservoir datum from the at least a sensing device, wherein the reservoir datum comprises at least a ground motion during fracking;

convert the at least a ground motion into a voltage measurement, wherein the voltage measurement is a difference in electric potential between two points;

generate production training data as a function of a fracking simulation model, wherein the fracking simulation model is configured to calculate a fracture propagation using a linear elastic fracture mechanism, wherein the linear elastic fracture mechanism comprises a criteria comprising an evaluation of a plurality of reservoir datums, wherein the evaluation of a plurality of reservoir datums comprises:

a maximum tensile stress criterion;

a minimum strain energy density criterion;

a maximum principal strain criterion; and

a maximum strain criterion;

train a fracking optimization machine-learning model using the production training data;

determine an optimal production parameter as a function of the fracking optimization machine-learning model, wherein the optimal production parameter input comprises the reservoir datum and the output comprises the optimal production parameter; and

generate an optimal production plan as a function of the optimal production parameter.

11. The method of claim 10 , wherein the at least a sensing device comprises a seismic sensor.

12. The method of claim 10 , wherein the reservoir datum comprises a pre-fracking reservoir datum and a post-fracking reservoir datum.

13. The method of claim 10 , wherein the reservoir datum comprises a fluid parameter.

14. The method of claim 10 , wherein the reservoir datum comprises a downhole condition.

15. The method of claim 10 , wherein generating the production training data comprises: converting the reservoir datum and the optimal production parameter into a cleansed data

format using a data conversion module, wherein the cleansed data format comprises a cleansed reservoir datum and a cleansed optimal production parameter.

16. The method of claim 10 , wherein determining the optimal production parameter comprises determining the optimal production parameter using fuzzy logic.

17. The method of claim 10 , wherein generating the optimal production plan comprises: generating a production plan classifier; and classifying the optimal production parameter to an optimal production plan using the production plan classifier.

18. The method of claim 10 , wherein the optimal production plan comprises an optimal fracking stimulation process comprising at least an optimal fracking stimulation step.