1020 Jones A Million wells, using procedural data generation based on reduced physics to train deep
Channel: Force Seminars
Date: 2019-10-04

2019 Force Symposium machine learning and subsurface data
Presentations https://www.npd.no/en/force/archive/hackathon-2019/


Million wells, using procedural data generation based on reduced physics to train deep neural network architectures for subsurface development projects in California, USA
By Nathaniel Jones
Traditional machine learning approaches to forecasting rates and recovery volumes in conventional
oil fields struggle due to few (generally less than 1000) examples of past completions in a given field's
producing zone(s) and the heavily skewed distribution of outcomes (e.g. production rates and
reserves) within those available real datasets. This generally results in machine learning models
performing best at forecasting in the range of low performing (p80 and greater) completions and
generally showing very poor predictive performance at identifying the 'best' (pl0) or even above
average performing completions in conventional datasets. Non-linear regression fitting models (ANN,
random forest, gradient boosted trees, etc.) invariably overfit and exhibit poor performance at
predicting future completions. Additionally, variable importance metrics show the models are not
identifying relationships between the outcomes and the input features beyond the move obvious
first order type relationships that are generally well understood by asset development professionals.
Machine learning models capable of handling large numbers of input features and subtle
relationships in other areas of research are generally trained on very large datasets, often many
millions of examples with a statistically balanced sample of the variance in the inputs/outputs for the
dataset. Due to the tremendous cost of gathering new completion examples, it is not feasible to
create very large datasets from a single field's data. Many researchers in other industries have
overcome the challenge of very expensive data by training models on abundant datasets that are
related to their data and then transferring the weights from the model to the smaller, more limited
dataset and conditioning the last layer(s) to their dataset. This talk will review the results from
process of training a deep neural network model on a million (synthetic) example dataset of
completions generated using simple darcy flow physics and show how this approach can reduce
overfitting and improve the predictive performance and utility of machine learning models aimed at
prediction of outcomes of new completions.