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

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      ACS Omega
      American Chemical Society

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          Abstract

          Gas injection and water injection are common and effective methods to improve oil recovery. To ensure its production effect, it is necessary to simulate the oilfield production process. However, traditional composition simulation runs a large number of calculations and takes a long time. Through the analysis of relevant data, we found that production is affected by many factors and has a strong sequential character. Therefore, this paper proposes a deep learning model for reservoir production prediction based on stacked long short-term memory network (LSTM). It is applied to other well patterns with a short production time and a few samples in the same oilfield block by transfer learning. The model achieves an effective combination with the actual reservoir production process. At the same time, it uses the knowledge learned from the well pattern with sufficient historical data to assist in the establishment of the model of the well pattern with limited data. This can obtain accurate prediction results and save the model training time, thus getting more effective application effects than composition simulation. This paper verifies the effectiveness of the proposed method through the data and multiple different injection combinations of the Tarim oilfield.

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          Most cited references32

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          Recurrent Neural Network Model for Constructive Peptide Design

          We present a generative long short-term memory (LSTM) recurrent neural network (RNN) for combinatorial de novo peptide design. RNN models capture patterns in sequential data and generate new data instances from the learned context. Amino acid sequences represent a suitable input for these machine-learning models. Generative models trained on peptide sequences could therefore facilitate the design of bespoke peptide libraries. We trained RNNs with LSTM units on pattern recognition of helical antimicrobial peptides and used the resulting model for de novo sequence generation. Of these sequences, 82% were predicted to be active antimicrobial peptides compared to 65% of randomly sampled sequences with the same amino acid distribution as the training set. The generated sequences also lie closer to the training data than manually designed amphipathic helices. The results of this study showcase the ability of LSTM RNNs to construct new amino acid sequences within the applicability domain of the model and motivate their prospective application to peptide and protein design without the need for the exhaustive enumeration of sequence libraries.
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            Transfer Learning for Drug Discovery

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              Artificial neural network based production forecasting for a hydrocarbon reservoir under water injection

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                Author and article information

                Journal
                ACS Omega
                ACS Omega
                ao
                acsodf
                ACS Omega
                American Chemical Society
                2470-1343
                07 December 2021
                21 December 2021
                : 6
                : 50
                : 34700-34711
                Affiliations
                [1]College of Computer Science and Technology, China University of Petroleum , Qingdao 266580, China
                Author notes
                Article
                10.1021/acsomega.1c05132
                8697399
                e8931e0d-6e0c-4e5f-afe3-39aa85536030
                © 2021 The Authors. Published by American Chemical Society

                Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works ( https://creativecommons.org/licenses/by-nc-nd/4.0/).

                Funding
                Funded by: Ministry of Education of the People''s Republic of China, doi 10.13039/501100002338;
                Award ID: 20CX05016A
                Funded by: China National Petroleum Corporation, doi 10.13039/501100002886;
                Award ID: ZD2019-183-007
                Categories
                Article
                Custom metadata
                ao1c05132
                ao1c05132

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