Decline Curve Analysis Using Machine Learning Algorithms: RNN, LSTM, and GRU

Abstract

ABSTRACT The traditional Arp's decline model failed to predict production from many oil and gas reservoirs due to some inherent assumptions like boundary-dominated flow contrary to long transient flow. Fundamentally, this is a time series curve fitting and forecasting problem. Advanced machine learning (ML) algorithms can be used to capture the unusual trend in hydrocarbon production decline. The objective of this study is to develop various ML algorithms such as Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) in forecasting future production performance and estimating ultimate recovery (EUR). Decline curve analysis (DCA) is a straightforward and rapid way to estimate future production simply by suitable curve fitting. However, the traditional Arp's method overestimates the production from many reservoirs, resulting in new empirical methods such as Power Law Exponential Analysis (PLE by Ilk, 2008), Logistic Growth Analysis (LGA by Clark 2011), and Duong Method (DM by Duong 2011). The outcomes of these recent models also depend on the quality of the data and the reservoir flow regimes. The machine learning algorithm is applied to overcome the drawbacks and limitations of the empirical decline curve models. Machine learning algorithms such as RNN, LSTM, and GRU are compared. The first 80% of time-series data is used for training the models and the last 20% is used for testing. The trained models are employed to forecast future rates and to calculate EUR. The value of NSE close to unity suggests good model performance. A normalized Nash-Sutcliffe model efficiency coefficient (NNSE) and Normalized Root Mean Squared Error (NRMSE) are selected for assessing the efficacy of different models. The LSTM models have several unique advantages over typical supervised machine learning algorithms. The models are flexible in handling multiple inputs in time series. The ML models developed in this work can be coupled with an economic model considering the future oil price and operational costs. Machine learning is a research area quickly growing across several industries providing valuable insights. Machine algorithm for time series forecasting in the oil and gas industry has not been comprehensively explored. Results from this work will provide the literature with another application perspective with strong opportunities in production data analysis.