Predicting well performance from unconventional reservoirs with the improved machine learning method for a small training data set by incorporating a simple physics constrain

A non-transitory computer readable medium storing instructions executable by a computer processor, the instructions comprising functionality for, comprising:

• obtaining a training data set for training a machine learning (ML) model, wherein the ML model generates predicted well production data based on geological, completion, and petrophysical data of interest, wherein the training data set comprises historical well production data and corresponding geological, completion, and petrophysical data;
• selecting an artificial neural network model structure, the model structure including a number of layers and a number of nodes of each layer;
• generating, using an ML algorithm applied to the training data set, a plurality of individually trained ML models, wherein each individually trained ML model is generated based on one of a plurality sets of initial model parameters and selecting the plurality of individually trained ML models based on loss values of the training data set;
• calculating a model performance of each trained model by evaluating a difference between a model prediction and a well performance data,
• determining, based on the model performance, an order of individually trained ML models based on both the loss value of the training data set and the loss values of a validation data set and selecting, based on the order, a plurality of top-ranked individually trained ML models;
• constraining the plurality of top-ranked individually trained ML models using one or multiple known physical rules and selecting a subset of the top-ranked individually trained ML models that are based on a sensitivity analysis including a perforated well length rule;
• generating, using the geological, completion, and petrophysical data of interest as input to the constrained models, a plurality of individual predicted well production data;
• and
• generating, based on the plurality of individual predicted well production data, a final predicted well production data.

A system comprising:

• a tight reservoir;
• a data repository storing a training data set for training a machine learning (ML) model, wherein the training data set comprises historical well production data and corresponding geological, completion, and petrophysical data;
• and
• an analysis and modeling engine comprising functionality for:
• obtaining the training data set for training ML model, wherein the ML model generates predicted well production data based on geological, completion, and petrophysical data of interest, wherein the training data set comprises the historical well production data and corresponding geological, completion, and petrophysical data;
• selecting an artificial neural network model structure, the model structure including a number of layers and a number of nodes of each layer;
• generating, using an ML algorithm applied to the training data set, a plurality of individually trained ML models, wherein each individually trained ML model is generated based on one of a plurality sets of initial model parameters and selecting the plurality of individually trained ML models based on loss values of the training data set;
• calculating a model performance of each trained model by evaluating a difference between a model prediction and a well performance data,
• determining, based on the model performance, an order of individually trained ML models based on both the loss value of the training data set and the loss values of a validation data set and selecting, based on the order, a plurality of top-ranked individually trained ML models;
• constraining the plurality of top-ranked individually trained ML models using one or multiple known physical rules and selecting a subset of the top-ranked individually trained ML models that are based on a sensitivity analysis including a perforated well length rule;
• generating, using the geological, completion, and petrophysical data of interest as input to the constrained models, a plurality of individual predicted well production data;
• and
• generating, based on the plurality of individual predicted well production data, a final predicted well production data.