Reservoir Production Prediction Model Based on a Stacked LSTM Network and Transfer Learning

Dong, Yukun; Zhang, Yu; Liu, Fubin; Liang, Gaolin

doi:10.1021/acsomega.1c05132

Cited by 15 publications

(9 citation statements)

References 22 publications

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“…Its transmission from input state to the hidden state uses the matrix multiplication of the fully connected layer, and the feature state is transferred in the time dimension. A convolutional neural network performs better in dealing with grid data with spatial correlation [21]. ConvLSTM [12] replaces the full connection layer in LSTM units with a convolution layer, which solves the problem that traditional LSTM cannot describe spatial structure to a certain extent.…”

Section: Architecture Of Ctrl-clstmmentioning

confidence: 99%

Prediction of Oil Saturation during Water and Gas Injection Using Controllable Convolutional Long Short-Term Memory

et al. 2022

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Oil saturation is a kind of spatiotemporal sequence that changes dynamically with time, and it is affected not only by the reservoir properties, but also by the injection–production parameters. When predicting oil saturation during water and gas injection, the influence of time, space and injection–production parameters should be considered. Aiming at this issue, a prediction method based on a controllable convolutional long short-term memory network (Ctrl-CLSTM) is proposed in this paper. The Ctrl-CLSTM is an unsupervised learning model whose input is the previous spatiotemporal sequence together with the controllable factors of corresponding moments, and the output is the sequence to be predicted. In this way, future oil saturation can be generated from the historical context. Concretely, the convolution operation is embedded into each unit to describe the interaction between temporal features and spatial structures of oil saturation, thus the Ctrl-CLSTM realizes the unified modeling of the spatiotemporal features of oil saturation. In addition, a novel control gate structure is introduced in each Ctrl-CLSTM unit to take the injection–production parameters as controllable influencing factors and establish the nonlinear relationship between oil saturation and injection–production parameters according to the coordinates of each well location. Therefore, different oil saturation prediction results can be obtained by changing the injection–production parameters. Finally, experiments on real oilfields show that the Ctrl-CLSTM comprehensively considers the influence of artificial controllable factors such as injection–production parameters, accomplishes accurate prediction of oil saturation with a structure similarity of more than 98% and is more time efficient than reservoir numerical simulation.

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Section: Architecture Of Ctrl-clstmmentioning

confidence: 99%

Prediction of Oil Saturation during Water and Gas Injection Using Controllable Convolutional Long Short-Term Memory

et al. 2022

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“…The generation of the steam temperature has a significant delay characteristic. The long short-term memory network (LSTM) has good performance in time-series forecasting, which solves the problems of gradient disappearance, gradient explosion, and a long sequence dependence in the long sequence training process. , Gupta et al used a single layer of LSTM with 32 nodes to predict fouling in air preheaters, which can be predicted 3 months in advance. Tan et al analyzed the effect of different delay time sequences on the model.…”

Section: Introductionmentioning

confidence: 99%

Dense Residual LSTM-Attention Network for Boiler Steam Temperature Prediction with Uncertainty Analysis

et al. 2022

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Flexible operation of large-scale boilers for electricity generation is essential in modern power systems. An accurate prediction of boiler steam temperature is of great importance to the operational efficiency of boiler units to prevent the occurrence of overtemperature. In this study, a dense, residual long short-term memory network (LSTM)-attention model is proposed for steam temperature prediction. In particular, the residual elements in the proposed model have a great advantage in improving the accuracy by adding short skip connections between layers. To provide overall information for the steam temperature prediction, uncertainty analysis based on the proposed model is performed to quantify the uncertainties in steam temperature variations. Our results demonstrate that the proposed method exhibits great performance in steam temperature prediction with a mean absolute error (MAE) of less than 0.6 °C. Compared to algorithms such as support-vector regression (SVR), ridge regression (RIDGE), the recurrent neural network (RNN), the gated recurrent unit (GRU), and LSTM, the prediction accuracy of the proposed model outperforms by 32, 16, 12, 10, and 11% in terms of MAE, respectively. According to our analysis, the dense residual LSTM-attention model is shown to provide an accurate early warning of overtemperature, enabling the development of real-time steam temperature control.

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“…This results in a long training time, low prediction accuracy, and overfitting in the prediction process . The LSTM neural network model in deep learning has high prediction accuracy and can effectively overcome the problems of overfitting in previous methods. , This method is also suitable for predicting the ROP of other oilfields …”

Section: Introductionmentioning

confidence: 99%

“…31 The LSTM neural network model in deep learning has high prediction accuracy and can effectively overcome the problems of overfitting in previous methods. 32,33 This method is also suitable for predicting the ROP of other oilfields. 3 Previous research works also confirmed the efficiency of using the PSO algorithm and LSTM neural networks in solving complex engineering problems.…”

Section: Introductionmentioning

confidence: 99%

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An Advanced Long Short-Term Memory (LSTM) Neural Network Method for Predicting Rate of Penetration (ROP)

Lou

Cheng

et al. 2022

ACS Omega

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Rate of penetration (ROP) is an essential factor in drilling optimization and reducing the drilling cycle. Most of the traditional ROP prediction methods are based on building physical model and single intelligent algorithms, and the efficiency and accuracy of these prediction methods are very low. With the development of artificial intelligence, high-performance algorithms make reliable prediction possible from the data perspective. To improve ROP prediction efficiency and accuracy, this paper presents a method based on particle swarm algorithm for optimization of long short-term memory (LSTM) neural networks. In this paper, we consider the Tuha Shengbei block oilfield as an example. First, the Pearson correlation coefficient is used to measure the correlation between the characteristics and eight parameters are screened out, namely, the depth of the well, gamma, formation density, pore pressure, well diameter, drilling time, displacement, and drilling fluid density. Second, the PSO algorithm is employed to optimize the super-parameters in the construction of the LSTM model to the predict ROP. Third, we assessed model performance using the determination coefficient (R 2 ), root mean square error (RMSE), and mean absolute percentage error (MAPE). The evaluation results show that the optimized LSTM model achieves an R 2 of 0.978 and RMSE and MAPE are 0.287 and 12.862, respectively, hence overperforming the existing methods. The average accuracy of the optimized LSTM model is also improved by 44.2%, indicating that the prediction accuracy of the optimized model is higher. This proposed method can help to drill engineers and decision makers to better plan the drilling operation scheme and reduce the drilling cycle.

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Reservoir Production Prediction Model Based on a Stacked LSTM Network and Transfer Learning

Cited by 15 publications

References 22 publications

Prediction of Oil Saturation during Water and Gas Injection Using Controllable Convolutional Long Short-Term Memory

Prediction of Oil Saturation during Water and Gas Injection Using Controllable Convolutional Long Short-Term Memory

Dense Residual LSTM-Attention Network for Boiler Steam Temperature Prediction with Uncertainty Analysis

An Advanced Long Short-Term Memory (LSTM) Neural Network Method for Predicting Rate of Penetration (ROP)

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