Machine Learning Predictions (MLP)

Machine Learning is based on the idea that systems can learn from data, identify patterns, and make decisions with minimal intervention. A key workflow for this feature is the Production Prediction Workflow which reveals optimal engineering parameters to feed into the model. The Production Prediction Workflow document details the steps for creating a production prediction model using the Zones table, and also shows how to set optimal engineering constants and grid data to run the model on the Grids table. For an overview of running the model on a different table, see

Machine learning algorithms can be leveraged to predict production performance, create sweet spot maps, and estimate optimum engineering parameters for your next wells. Computations and templates are created automatically, and the independent variables are selected. The model also provides easy-to-understand error metrics and visualizations that help you determine the best parameters for the model.

In this application of machine learning, the general process is the following:

1. Data table: Determine what data you want to use to build the analytical model. These are the input columns from the selected data table.
2. Attribute to calculate: Identify the output (the column you want to calculate). This column likely has sparse data. The machine learning algorithm will calculate predicted values using your input data.
3. Input attributes: Select the attributes from the selected data table that you want to include in the making of the model. Input attributes should be numeric, not text, although text attributes are accepted.
4. The remaining steps are optional. The application defaults to parameters that have proven to optimize results. However, the output of these options provide interesting results and can help you understand the process and calculations.
5. Select a Prediction Algorithm. The default is Gradient boosting tree which generally gives the best results.
6. (Optional) Adjust the algorithm parameters and run additional processes. The default parameters generally produce close to optimal results.
   • Hyperparameter Tuning
   • Attribute Analysis
   • Transformed Correlation Plots
   • Optimal Training Dataset
7. Select Output parameters. The default parameters

The machine learning algorithm automates the model building process, running through multiple iterations until results are optimized. After the predicted data has been calculated, multiple templates are created for you to analyze the results. The predicted values are written to the data table.

After examining the mean average error (MAE) plot and the Attribute Analysis chart, you can adjust the input attribute selection and possibly the hyperparameters, and run the model again. Iterate on this process until the results are optimized, although the relative gain will likely decrease with each iteration.

You can now Save the model which you can then apply to other data tables. See Run Machine Learning Model for more information on applying the model to other tables.