Apparatus and method for fracking optimization
View Patent ↗An apparatus and method for fracking optimization, wherein the apparatus includes at least a processor, and a memory, wherein the memory containing instructions configuring the at least a processor to receive a reservoir datum from at least a sensing device, generate a production training data include a plurality of reservoir datums as input correlated to a plurality of optimal production parameters as output, 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, and generating an optimal production plan as a function of the optimal production parameter.
1. An apparatus for fracking optimization, wherein the apparatus comprises:
at least a processor; and
a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to:
receive a plurality of reservoir datums from a full sensor suite, wherein the plurality of reservoir datums comprises:
at least one surface reservoir datum detected using a first sensor of the full sensor suite; and
at least one downhole reservoir datum detected using a second sensor of the full sensor suite;
generate production training data as a function of the plurality of reservoir datums, wherein generating the production training data comprises:
converting the at least one downhole reservoir datum into a cleansed data format using a data conversion module;
identifying a plurality of correlational patterns between the at least one surface reservoir datum and the at least one downhole reservoir datum; and
generating the production training data as a function of the plurality of correlational patterns using a fracking simulation model;
train a fracking optimization machine learning model using the production training data;
receive a surface target reservoir datum from a minimum sensor suite; and
determine an optimal production parameter as a function of the surface target reservoir datum using the trained fracking optimization machine learning model.
2. The apparatus of claim 1 , wherein the first sensor comprises a surface sensor and the second sensor comprises a downhole sensor.
3. The apparatus of claim 2 , wherein the minimum sensor suite comprises the surface sensor, and does not include the downhole sensor.
4. The apparatus of claim 1 , wherein the memory contains instructions further configuring the at least a processor to:
receive a reservoir analysis datum; and
generate the production training data as a function of the reservoir analysis datum.
5. The apparatus of claim 1 , wherein the memory contains instructions configuring the at least a processor to generate an optimal production plan as a function of the optimal production parameter.
6. The apparatus of claim 5 , wherein the memory contains instructions configuring the at least a processor to retrain the fracking optimization machine learning model as a function of a production metric.
7. The apparatus of claim 1 , wherein the fracking optimization machine learning model includes a neural network.
8. The apparatus of claim 1 , 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 criterion comprising an evaluation of the plurality of reservoir datums, wherein the evaluation of the 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.
9. The apparatus of claim 1 , wherein the memory contains instructions configuring the at least a processor to generate the production training data before receipt of the surface reservoir datum.
10. A method of fracking optimization, wherein the method comprises:
using at least a processor, receiving a plurality of reservoir datums from a full sensor suite, wherein the plurality of reservoir datums comprises:
at least one surface reservoir datum detected using a first sensor of the full sensor suite; and
at least one downhole reservoir datum detected using a second sensor of the full sensor suite;
using at least a processor, generating production training data as a function of the plurality of reservoir datums, wherein generating the production training data comprises:
converting the at least one downhole reservoir datum into a cleansed data format using a data conversion module;
identifying a plurality of correlational patterns between the at least one surface reservoir datum and the at least one downhole reservoir datum; and
generating the production training data as a function of the plurality of correlational patterns using a fracking simulation model;
using at least a processor, training a fracking optimization machine learning model using the production training data;
using at least a processor, receiving a surface target reservoir datum from a minimum sensor suite; and
using at least a processor, determining an optimal production parameter as a function of the surface target reservoir datum using the trained fracking optimization machine learning model.
11. The method of claim 10 , wherein the first sensor comprises a surface sensor and the second sensor comprises a downhole sensor.
12. The method of claim 11 , wherein the minimum sensor suite comprises the surface sensor, and does not include the downhole sensor.
13. The method of claim 10 , further comprising:
comprising receiving a reservoir analysis datum; and
generating the production training data as a function of the reservoir analysis datum.
14. The method of claim 10 , further comprising generating an optimal production plan as a function of the optimal production parameter.
15. The method of claim 14 , further comprising retraining the fracking optimization machine learning model as a function of a production metric.
16. The method of claim 10 , wherein the fracking optimization machine learning model includes a neural network.
17. The method of claim 10 , 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 criterion comprising an evaluation of the plurality of reservoir datums, wherein the evaluation of the 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.
18. The method of claim 10 , wherein the production training data is generated before receipt of the surface reservoir datum.