SYSTEM AND METHOD FOR PREDICTING OIL PRODUCTION FROM GAS EQUIVALENT PRODUCTION STREAM USING MACHINE LEARNING
A system and method for predicting oil production is disclosed herein. A computing system receives production data for a plurality of wells in a plurality of geographic regions. The computing system identifies a subset of wells from the plurality of wells. The subset of wells includes wells that have a threshold amount of production data. The computing system trains a machine learning model using the subset of wells. The computing system applies the trained learning model to a target well to predict oil production from the target well.
1 . A method for predicting oil production, the method comprising:
receiving, by a computing system, production data for a plurality of wells in a plurality of geographic regions;
identifying, by the computing system, a subset of wells from the plurality of wells, wherein the subset of wells comprises wells that have a threshold amount of production data;
training, by the computing system, a machine learning model using the subset of wells; and
applying, by the computing system, the trained machine learning model to a target well to predict oil production from the target well.
2 . The method of claim 1 , wherein training, by the computing system, the machine learning model using the subset of wells comprises:
training a plurality of machine learning models using the subset of wells, wherein each of the plurality of machine learning models has a different underlying machine learning algorithm.
3 . The method of claim 2 , further comprising:
selecting, from the plurality of machine learning models, the trained machine learning model based on the trained machine learning model achieving a highest level of accuracy.
4 . The method of claim 1 , wherein identifying the subset of wells comprises: parsing, via an automated script, the production data to identify a subset of wells that have the threshold amount of production data.
5 . The method of claim 1 , wherein identifying the subset of wells comprises:
parsing, via an automated script, the production data to identify a subset of wells for which an estimated ultimate recovery is known.
6 . The method of claim 1 , further comprising:
generating, by the computing system, a training data set for training the machine learning model, the training data set comprising the subset of wells.
7 . The method of claim 5 , wherein generating, by the computing system, the training data set comprises:
clustering the plurality of wells using a clustering algorithm based on distance; and
imputing missing values in the production data of the subset of wells based on wells co-located with the wells in the subset of wells.
8 . A system for predicting oil production, the system comprising:
a processor; and
a memory having programming instructions stored thereon, which, when executed by the processor, causes the system to perform operations comprising:
receiving production data for a plurality of wells in a plurality of geographic regions;
identifying a subset of wells from the plurality of wells, wherein the subset of wells comprises wells that have a threshold amount of production data;
training a machine learning model using the subset of wells; and
applying the trained machine learning model to a target well to predict oil production from the target well.
9 . The system of claim 8 , wherein training the machine learning model using the subset of wells comprises:
training a plurality of machine learning models using the subset of wells, wherein each of the plurality of machine learning models has a different underlying machine learning algorithm.
10 . The system of claim 9 , further comprising:
selecting, from the plurality of machine learning models, the trained machine learning model based on the trained machine learning model achieving a highest level of accuracy.
11 . The system of claim 8 , wherein identifying the subset of wells comprises: parsing, via an automated script, the production data to identify a subset of wells that have the threshold amount of production data.
12 . The system of claim 8 , wherein identifying the subset of wells comprises:
parsing, via an automated script, the production data to identify a subset of wells for which an estimated ultimate recovery is known.
13 . The system of claim 8 , further comprising:
generating a training data set for training the machine learning model, the training data set comprising the subset of wells.
14 . The system of claim 13 , wherein generating the training data set comprises:
clustering the plurality of wells using a clustering algorithm based on distance; and
imputing missing values in the production data of the subset of wells based on wells co-located with the wells in the subset of wells.
15 . A non-transitory computer readable medium comprising instructions, which, when executed by a processor, cause a computing system to perform operations comprising:
receiving, by the computing system, production data for a plurality of wells in a plurality of geographic regions;
identifying, by the computing system, a subset of wells from the plurality of wells, wherein the subset of wells comprises wells that have a threshold amount of production data;
training, by the computing system, a machine learning model using the subset of wells; and
applying, by the computing system, the trained machine learning model to a target well to predict oil production from the target well.
16 . The non-transitory computer readable medium of claim 15 , wherein training, by the computing system, the machine learning model using the subset of wells comprises:
training a plurality of machine learning models using the subset of wells, wherein each of the plurality of machine learning models has a different underlying machine learning algorithm; and
selecting, from the plurality of machine learning models, the trained machine learning model based on the trained machine learning model achieving a highest level of accuracy.
17 . The non-transitory computer readable medium of claim 15 , wherein identifying the subset of wells comprises: parsing, via an automated script, the production data to identify a subset of wells that have the threshold amount of production data.
18 . The non-transitory computer readable medium of claim 15 , wherein identifying the subset of wells comprises:
parsing, via an automated script, the production data to identify a subset of wells for which an estimated ultimate recovery is known.
19 . The non-transitory computer readable medium of claim 15 , further comprising:
generating, by the computing system, a training data set for training the machine learning model, the training data set comprising the subset of wells.
20 . The non-transitory computer readable medium of claim 19 , wherein generating, by the computing system, the training data set comprises:
clustering the plurality of wells using a clustering algorithm based on distance; and
imputing missing values in the production data of the subset of wells based on wells co-located with the wells in the subset of wells.