Development of Deep Transformer-Based Models for Long-Term Prediction of Transient Production of Oil Wells

TL;DR

This paper introduces transformer-based neural networks for long-term prediction of transient oil well production, demonstrating superior performance over RNNs and enabling multi-well modeling for improved oilfield management.

Contribution

It develops a novel transformer-based modeling approach for transient oil well production prediction, including transfer learning and multi-well generalization techniques.

Findings

Transformer outperforms LSTM/GRU in pressure forecasting

Transfer learning improves model accuracy for new wells

Multi-well models capture well interference effects

Abstract

We propose a novel approach to data-driven modeling of a transient production of oil wells. We apply the transformer-based neural networks trained on the multivariate time series composed of various parameters of oil wells measured during their exploitation. By tuning the machine learning models for a single well (ignoring the effect of neighboring wells) on the open-source field datasets, we demonstrate that transformer outperforms recurrent neural networks with LSTM/GRU cells in the forecasting of the bottomhole pressure dynamics. We apply the transfer learning procedure to the transformer-based surrogate model, which includes the initial training on the dataset from a certain well and additional tuning of the model's weights on the dataset from a target well. Transfer learning approach helps to improve the prediction capability of the model. Next, we generalize the single-well model based on the transformer architecture for multiple wells to simulate complex transient oilfield-level patterns. In other words, we create the global model which deals with the dataset, comprised of the production history from multiple wells, and allows for capturing the well interference resulting in more accurate prediction of the bottomhole pressure or flow rate evolutions for each well under consideration. The developed instruments for a single-well and oilfield-scale modelling can be used to optimize the production process by selecting the operating regime and submersible equipment to increase the hydrocarbon recovery. In addition, the models can be helpful to perform well-testing avoiding costly shut-in operations.