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Keywords: neural network
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Proceedings Papers

Publisher: Society of Petroleum Engineers (SPE)
Paper presented at the SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition, October 10–12, 2023
Paper Number: SPE-215253-MS
... speed. In general, there are three main ways to implement the surrogate model: partial differential equation solving based on neural network, difference equation solving using neural network, and data-driven surrogate model. Reservoir simulation technology based on partial differential equation...
Proceedings Papers

Publisher: Society of Petroleum Engineers (SPE)
Paper presented at the SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition, October 10–12, 2023
Paper Number: SPE-215306-MS
... capabilities in the reservoir simulator in the same way backpropagation methods are used in training neural networks. Once calibrated, CGNet is employed for well-control optimization. Validation with the fine-scale model shows that CGNet closely matches the optimized net-present value (NPV). Numerical examples...
Proceedings Papers

Publisher: Society of Petroleum Engineers (SPE)
Paper presented at the SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition, October 10–12, 2023
Paper Number: SPE-215341-MS
... of using single-phase liquid type curves. In the inverse part, an automatic interpretation model of gas/water RTA is established based on one-dimensional convolutional neural network (CNN). CNN-based proxy model is built through training large synthetic data set from forward model. Automatic inversion...
Proceedings Papers

Publisher: Society of Petroleum Engineers (SPE)
Paper presented at the SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition, October 10–12, 2023
Paper Number: SPE-215427-MS
... and completion design scheme. Meanwhile, real time intelligent prediction models with machine learning methods, such as Artificial neural network, Convolutional neural network, K nearest neighbor, etc, are established to predict and avoid pipe stuck, lost circulation, gas kick events. Moreover, a continuous...
Proceedings Papers

Publisher: Society of Petroleum Engineers (SPE)
Paper presented at the SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition, October 10–12, 2023
Paper Number: SPE-215415-MS
.... Long Short Term Memory is a form of the recurrent neural network and in the RNN output from the last step is fed as input in the current step. LSTM was designed by Hochreiter and Schmidhuber and tackled the problem of long-term dependencies of RNN where the RNN may not be able to predict words stored...
Proceedings Papers

Paper presented at the SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition, October 12–14, 2021
Paper Number: SPE-205683-MS
... UC with a highest correlation coefficient 0.402 and a lowest error RMSE 1.15. This method has been successfully used in offshore oil field in sand control optimization. log analysis sand control reservoir characterization structural geology neural network accuracy training set size...
Proceedings Papers

Paper presented at the SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition, October 12–14, 2021
Paper Number: SPE-205677-MS
... prior to the pipe sticking in some cases (thereby partly confirming our hypothesis) and were sensitive to large variations in the drilling parameters. machine learning deep learning neural network artificial intelligence time series data upstream oil & gas normal drilling operation...
Proceedings Papers

Paper presented at the SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition, October 12–14, 2021
Paper Number: SPE-205687-MS
... diameter representing the current value and the polar angle representing the time. The current-time curves of massive oil wells are then plotted in images with fixed resolution and divided into nine different groups to correspond to nine frequent working status of screw pump. A convolutional neural network...
Proceedings Papers

Paper presented at the SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition, October 12–14, 2021
Paper Number: SPE-205710-MS
.... This study proposes a new drilling loss prevention idea to evaluate fractured lost circulation risk using seismic and wellbore data by a novel neural network. The approach works in two steps. First, the fracture anisotropy of a lost circulation sample curve is computed and interpreted using well logs. Second...
Proceedings Papers

Paper presented at the SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition, October 12–14, 2021
Paper Number: SPE-205689-MS
... science applying in petroleum engineering and provides a clear methodology and specific suggestions on how to improve the success rate of R&D projects which apply data science to solve problems in petroleum engineering. artificial intelligence data-driven method knowledge neural network data...
Proceedings Papers

Paper presented at the SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition, October 12–14, 2021
Paper Number: SPE-205782-MS
... prediction methods integrating in a single step dimensionality reduction, extraction of input data structure pattern and prediction of formation volume factor B o . The SOM neural network method applies an unsupervised training algorithm combined with back propagation neural network BPNN to subdivide...
Proceedings Papers

Paper presented at the SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition, October 12–14, 2021
Paper Number: SPE-205736-MS
..., business performance, and production safety. fuzzy logic machine learning neural network machinelearningmastery prediction technology conference artificial lift system gas injection upstream oil & gas exhibition algorithm machine learning algorithm artificial intelligence ml model...
Proceedings Papers

Paper presented at the SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition, October 12–14, 2021
Paper Number: SPE-205772-MS
... optimization production logging sensor data security correlation neural network ir 4 drilling operation automation production enhancement deep learning platform internet of things industry 4 algorithm iot petroleum industry application disruptive technology The value of deploying...
Proceedings Papers

Paper presented at the SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition, October 12–14, 2021
Paper Number: SPE-205745-MS
... parameter method of well and layer selection and its stimulation evaluation model. Combined with artificial neural network and BP algorithm, the index weights of strata with different reservoir physical properties are calculated to analyze the final evaluation value of fracturing effect. On the basis...
Proceedings Papers

Paper presented at the SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition, October 12–14, 2021
Paper Number: SPE-205720-MS
... with high accuracy. The most widely used machine learning approaches are: Artificial Neural Network (ANN), Support Vector Machines and Adaptive Neuro-Fuzzy Inference Systems. In this study, these approaches are introduced by providing their capability and limitations. After that, the study focuses on using...
Proceedings Papers

Paper presented at the SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition, October 12–14, 2021
Paper Number: SPE-205556-MS
... simulator. The proxy can be used for reservoir management tasks like history matching, uncertainty quantification, and field development optimization. A deep-learning based methodology for accurate proxy-flow modeling is presented which combines U-Net (a variant of convolutional neural network) to predict...
Proceedings Papers

Paper presented at the SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition, October 12–14, 2021
Paper Number: SPE-205571-MS
... Abstract The objective of the study is to build a robust Recurrent Neural Network system using Long-Short-Term-Memory (LSTM) to predict future vibrations during drilling operations. This provides a reliable solution to the complex problem of modeling several forms of vibrations encountered...
Proceedings Papers

Paper presented at the SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition, October 12–14, 2021
Paper Number: SPE-205627-MS
... variational autoencoder (biLSTM-VAE) to project raw drilling data into a latent space in which the real-time bit-wear can be estimated. The proposed deep neural network was trained in an unsupervised manner, and the bit-wear estimation is demonstrated as an end-to-end process. machine learning deep...
Proceedings Papers

Paper presented at the SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition, October 12–14, 2021
Paper Number: SPE-205638-MS
... accuracy. A number of scenarios showing a comparison of different predictive models were studied, and the results demonstrated that adding drilling data and/or feature engineering into the model could improve the accuracy of the models. machine learning neural network learner artificial...

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