APPARATUS AND METHOD FOR GENERATING A RESERVOIR MODEL
In an aspect, an apparatus for generating a reservoir model is disclosed. The apparatus includes at least a processor and memory communicatively connected to the at least a processor. The memory instructs the processor to receive a condition data associated with a target well. The memory instructs the processor to generate a plurality of reservoir conditions associated with the target well as a function of the condition data. The memory instructs the processor to identify a plurality of flagged data as a function of the plurality of reservoir conditions. The memory instructs the processor to predict reservoir geometry associated with the target well as a function of the plurality of flagged data.
1 . An apparatus for generating a reservoir model, 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 condition data associated with a target well;
generate a plurality of reservoir conditions associated with the target well as a function of the condition data;
identify a plurality of flagged data as a function of the plurality of reservoir conditions, wherein identifying the plurality of flagged data comprises:
receiving flag training data comprising a plurality of expected reservoir conditions correlated to examples of flagged data;
receiving past flagged data outputs of a machine learning model correlated to an accuracy score;
iteratively updating the flag training data based on a past flagged data classifier outputs;
training a flag data classifier using the updated flag training data; and
outputting, by the flag data classifier, the plurality of flagged data;
determine a reservoir geometry associated with the target well as a function of the plurality of flagged data; and
display the reservoir geometry, wherein displaying the reservoir geometry comprises generating a three-dimensional representation of the target well, wherein the reservoir geometry comprises isosurfaces that represent at least a reservoir property, and wherein the isosurfaces highlight regions of interest to users.
2 . The apparatus of claim 1 , wherein the memory further instructs the at least a processor to generate a pressure report as a function of the plurality of flagged data.
3 . The apparatus of claim 1 , wherein the memory further instructs the at least a processor to generate verification data as a function of the plurality of reservoir conditions and a plurality of measured reservoir conditions.
4 . The apparatus of claim 1 , wherein determining the reservoir geometry further comprises:
training a reservoir machine-learning model using reservoir training data, wherein the reservoir training data comprises a plurality of flagged data as inputs correlated to reservoir geometry as outputs; and
determining the reservoir geometry as a function of the flagged data using the reservoir machine-learning model.
5 . The apparatus of claim 1 , wherein determining the reservoir geometry comprises determining the reservoir geometry using a reservoir model.
6 . The apparatus of claim 1 , wherein the memory further instructs the at least a processor to determine fracture network conditions as a function of the plurality of flagged data and one or more fracture network conditions of a secondary well.
7 . The apparatus of claim 6 , wherein the memory further instructs the at least a processor to predict secondary well conditions as a function of the one or more fracture network conditions of the secondary well.
8 . The apparatus of claim 6 , wherein the memory further instructs the at least a processor to generate a fracture network management recommendation as a function of the one or more fracture network conditions of the secondary well.
9 . The apparatus of claim 1 , wherein the memory further instructs the at least a processor to generate a reservoir management recommendation as a function of the reservoir geometry.
10 . (canceled)
11 . A method for generating a reservoir model, wherein the method comprises:
receiving, using at least a processor, condition data associated with a target well;
generating, using the at least a processor, a plurality of reservoir conditions associated with the target well as a function of the condition data;
identifying, using the at least a processor, a plurality of flagged data as a function of the plurality of reservoir conditions, wherein identifying the plurality of flagged data comprises:
receiving flag training data comprising a plurality of expected reservoir conditions correlated to examples of flagged data;
receiving past flagged data outputs of a machine learning model correlated to an accuracy score;
iteratively updating the flag training data based on a past flagged data classifier outputs;
training a flag data classifier using the updated flag training data; and
outputting, by the flag data classifier, the plurality of flagged data;
determining, using the at least a processor, a reservoir geometry associated with the target well as a function of the plurality of flagged data; and
displaying, using the at least a processor, the reservoir geometry, wherein displaying the reservoir geometry comprises generating a three-dimensional representation of the target well, wherein the reservoir geometry comprises isosurfaces that represent at least a reservoir property, and wherein the isosurfaces highlight regions of interest to users.
12 . The method of claim 11 , wherein the method further comprises generating, using the at least a processor, a pressure report as a function of the plurality of flagged data.
13 . The method of claim 11 , wherein the method further comprises generating, using the at least a processor, verification data as a function of the plurality of reservoir conditions and a plurality of measured reservoir conditions.
14 . The method of claim 11 , wherein determining the reservoir geometry further comprises:
training a reservoir machine learning model using reservoir training data, wherein the reservoir training data comprises a plurality of flagged data as inputs correlated to examples of reservoir geometry as outputs; and
determining the reservoir geometry as a function of the flagged data using the reservoir machine learning model.
15 . The method of claim 11 , wherein determining the reservoir geometry comprises determining the reservoir geometry using a reservoir model.
16 . The method of claim 11 , wherein the method further comprises determining, using the at least a processor, fracture network conditions as a function of the plurality of flagged data and one or more fracture network conditions of a parent well.
17 . The method of claim 16 , wherein the method further comprises predicting, using the at least a processor, secondary well conditions as a function of the fracture network conditions of the secondary well.
18 . The method of claim 16 , wherein the method further comprises generating, using the at least a processor, a fracture network management recommendation as a function of the fracture network conditions of the secondary well.
19 . The method of claim 11 , wherein the method further comprises generating, using the at least a processor, a reservoir management recommendation as a function of the reservoir geometry.
20 . (canceled)