Forecasting of oil production driven by reservoir spatial–temporal data based on normalized mutual information and Seq2Seq-LSTM

Abstract

Traditional machine learning methods are difficult to accurately forecast oil production when development measures change. A method of oil reservoir production prediction based on normalized mutual information and a long short-term memory-based sequence-to-sequence model (Seq2Seq-LSTM) was proposed to forecast reservoir production considering the influence of liquid production and well spacing density. First, the marine sandstone reservoirs in the Y basin were taken as the research object to establish the sample database. Then, the feature selection was carried out according to the normalized mutual information, and liquid production, production time, equivalent well spacing density, fluidity and original formation pressure were determined as input features. Finally, a Seq2Seq-LSTM model was established to forecast reservoir production by learning the interaction from multiple samples and multiple sequences, and mining the relationship between oil production and features. The research showed that the model has a high accuracy of production prediction and can forecast the change of production when the liquid production and well spacing density change, which can provide scientific recommendations to help the oilfield develop and adjust efficiently.

Well Production Forecast Post-Liquid Lifting Measures: a Transformer-Based Seq2Seq Method with Attention Mechanism

“…This makes it more suitable for tasks requiring consideration of multistep information. In the context of oil well production prediction, such a Seq2Seq structure can simultaneously train multiple time series samples . This aids not only in learning the correlation of historical time series data but also in gaining a more comprehensive understanding of the impact of various liquid lifting measures on the production data.…”