Application of Machine Learning for Oil Production Forecasting
Application of Machine Learning for Oil Production Forecasting


Our main objective here is to develop a new methodology to make robust and accurate oil rate prediction based on limited initial production data. We will show that the resulting model is useful for production forecasting, business planning and decision making in response to the fast pace development for unconventional resource plays.

A machine learning approach based on LSTM (Long Short Term Memory) is used to tackle the production forecasting problem. In time series prediction, one main difficulty is how to stabilize the solution, as the error can easily accumulate over time. Besides, modifying the objective function that aims for long term accuracy or incorporating physics-based modeling, one effective way to make the algorithm more robust is through feature engineering. By leveraging historical data from other wells, the prediction has been improved significantly. We also build another model in the accumulated curve domain, and ensemble multiple models to reduce the variance.

Forecasting is highly challenging in many domains with complex multivariate correlation structures and nonlinear dynamics. We have utilized existing data and built two prediction models, one from the decline curve domain, the other from the accumulated curve. Based on the observation, the first model is slightly over-predicted, and the second one moderately under-predicted, and through integrating these two models, the final result is more promising. We have conducted hindcasting for more than 300 wells, and the mean difference between the predicted and actual accumulated production of the first 2 year is less than 0.2%, with the variance less than 5%.


Cheng Zhan is a Senior Data Scientist at Anadarko Petroleum, where he works on field development optimization and long-term production forecasting. He focuses on building machine learning algorithms to create strategic and financial impact for the company. Prior to his current role, he worked as a Geophysicist at TGS and CGG, utilizing seismic data and inversion methods to help operators make better decisions in exploration. He holds a PhD in Mathematics from University of Houston, and a B.S. in Mathematics from Sun Yat-sen University.

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