Optimized long short-term memory (LSTM) network for performance prediction in unconventional reservoirs

Qiu, Kaixuan; Jia, Li; Chen, Da

doi:10.1016/j.egyr.2022.11.130

Cited by 14 publications

(3 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recurrent neural networks can handle time series problems, but with the continuous input of time series, the traditional recurrent neural networks are prone to gradient disappearance or gradient explosion due to the abnormal computation of gradients, which leads to the degradation of model accuracy [18]. To solve the impact caused by gradient disappearance or gradient explosion of recurrent neural networks, the LSTM model is generated by introducing three gating units and memory units based on the improvement of traditional recurrent neural networks [19]. The control of the information replaces the information retained in the memory unit through the three gating units; the principle of the LSTM algorithm is shown in Figure 2.…”

Section: Long and Short-term Memory Neural Networkmentioning

confidence: 99%

Prediction Model of Ammonia Nitrogen Concentration in Aquaculture Based on Improved AdaBoost and LSTM

Wang,

Xu,

et al. 2024

Mathematics

View full text Add to dashboard Cite

The concentration of ammonia nitrogen is significant for intensive aquaculture, and if the concentration of ammonia nitrogen is too high, it will seriously affect the survival state of aquaculture. Therefore, prediction and control of the ammonia nitrogen concentration in advance is essential. This paper proposed a combined model based on X Adaptive Boosting (XAdaBoost) and the Long Short-Term Memory neural network (LSTM) to predict ammonia nitrogen concentration in mariculture. Firstly, the weight assignment strategy was improved, and the number of correction iterations was introduced to retard the shortcomings of data error accumulation caused by the AdaBoost basic algorithm. Then, the XAdaBoost algorithm generated and combined several LSTM su-models to predict the ammonia nitrogen concentration. Finally, there were two experiments conducted to verify the effectiveness of the proposed prediction model. In the ammonia nitrogen concentration prediction experiment, compared with the LSTM and other comparison models, the RMSE of the XAdaBoost–LSTM model was reduced by about 0.89–2.82%, the MAE was reduced by about 0.72–2.47%, and the MAPE was reduced by about 8.69–18.39%. In the model stability experiment, the RMSE, MAE, and MAPE of the XAdaBoost–LSTM model decreased by about 1–1.5%, 0.7–1.7%, and 7–14%. From these two experiments, the evaluation indexes of the XAdaBoost–LSTM model were superior to the comparison models, which proves that the model has good prediction accuracy and stability and lays a foundation for monitoring and regulating the change of ammonia nitrogen concentration in the future.

show abstract

Section: Long and Short-term Memory Neural Networkmentioning

confidence: 99%

Prediction Model of Ammonia Nitrogen Concentration in Aquaculture Based on Improved AdaBoost and LSTM

Wang,

Xu,

et al. 2024

Mathematics

View full text Add to dashboard Cite

show abstract

“…Certainly, numerical simulation methods have good flexibility and can also deal with various complicated reservoir seepage problems. However, there will be a time-consuming and computationally inefficient process when they are adopted to compute the complex fracture geometry with a large number of grids [13,14]. Generally speaking, the analytical model offers relative simplicity and it can cover the fundamental flow mechanism with simple solutions.…”

Section: Introductionmentioning

confidence: 99%

A New Approach for Production Prediction in Onshore and Offshore Tight Oil Reservoir

Qiu,

Fan,

Chen

et al. 2023

JMSE

Self Cite

View full text Add to dashboard Cite

Rapid technological advances have accelerated offshore and onshore tight oil extraction to meet growing energy demand. Reliable tools to carry out production prediction are essential for development of unconventional reservoirs. The existed tri-linear analytical solutions are verified to be versatile enough to capture fundamental flow mechanisms and make accurate production predictions. However, these solutions are obtained in Laplace space with the Laplace transform and numerical inversion, which may lead to uncertainty in the solution. In this paper, a general analytical solution is derived in real-time space through integral transform and average pressure substitution. Namely, the partial differential equations describing subsurface fluid flow are firstly triple-integrated and then the obtained volume average pressure are replaced with the rate-dependent expressions. Furthermore, the ordinary differential equations related to oil rate are solved analytically in real-time space. To validate our model, this derived solution is verified against two numerical models constructed with two typical physical configurations. The great match indicates the accuracy and applicability of the analytical solution. According to the developed workflow, two field cases including offshore and onshore tight oilfield data are selected for history matching and production prediction. This new approach not only makes the obtained solution more simplified, but also helps field engineers diagnose flow patterns more quickly to better optimize production schemes.

show abstract

“…However, RF is relatively slow and memory intensive when dealing with large amounts of data, and its interpretability is limited. LSTM is specialized in handling time series data and is useful for predicting current odor patterns by remembering previous information [6]. LSTM performs well in modeling time series data and is suitable for handling odor data with temporal dependencies.…”

Section: Introductionmentioning

confidence: 99%

A Study on the Performance Evaluation of the Convolutional Neural Network–Transformer Hybrid Model for Positional Analysis

Lee

2023

Applied Sciences

View full text Add to dashboard Cite

In this study, we identified the different causes of odor problems and their associated discomfort. We also recognized the significance of public health and environmental concerns. To address odor issues, it is vital to conduct precise analysis and comprehend the root causes. We suggested a hybrid model of a Convolutional Neural Network (CNN) and Transformer called the CNN–Transformer to tackle this challenge and assessed its effectiveness. We utilized a dataset containing 120,000 samples of odor to compare the performance of CNN+LSTM, CNN, LSTM, and ELM models. The experimental results show that the CNN+LSTM hybrid model has an accuracy of 89.00%, precision of 89.41%, recall of 91.04%, F1-score of 90.22%, and RMSE of 0.28, with a large prediction error. The CNN+Transformer hybrid model had an accuracy of 96.21%, precision and recall of 94.53% and 94.16%, F1-score of 94.35%, and RMSE of 0.27, showing a low prediction error. The CNN model had an accuracy of 87.19%, precision and recall of 89.41% and 91.04%, F1-score of 90.22%, and RMSE of 0.23, showing a low prediction error. The LSTM model had an accuracy of 95.00%, precision and recall of 92.55% and 94.17%, F1-score of 92.33%, and RMSE of 0.03, indicating a very low prediction error. The ELM model performed poorly with an accuracy of 85.50%, precision and recall of 85.26% and 85.19%, respectively, and F1-score and RMSE of 85.19% and 0.31, respectively. This study confirms the suitability of the CNN–Transformer hybrid model for odor analysis and highlights its excellent predictive performance. The employment of this model is expected to be advantageous in addressing odor problems and mitigating associated public health and environmental concerns.

show abstract

Optimized long short-term memory (LSTM) network for performance prediction in unconventional reservoirs

Cited by 14 publications

References 32 publications

Prediction Model of Ammonia Nitrogen Concentration in Aquaculture Based on Improved AdaBoost and LSTM

Prediction Model of Ammonia Nitrogen Concentration in Aquaculture Based on Improved AdaBoost and LSTM

A New Approach for Production Prediction in Onshore and Offshore Tight Oil Reservoir

A Study on the Performance Evaluation of the Convolutional Neural Network–Transformer Hybrid Model for Positional Analysis

Contact Info

Product

Resources

About