Reservoir Porosity Prediction Based on BiLSTM-AM Optimized by Improved Pelican Optimization Algorithm

Qiao, Lei; He, Nansi; Cui, You; Zhu, Jichang; Xiao, Kun

doi:10.3390/en17061479

Cited by 1 publication

(3 citation statements)

References 20 publications

(22 reference statements)

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“…(2) Dilated convolution Dilated convolution is a convolution operation using the dilation factor 𝑑 to solve the limited field of perception issue. When the input is 𝑋 = (𝑥 , 𝑥 , … , 𝑥 ) and the filter is 𝐹 = (𝑓 , 𝑓 , … , 𝑓 ), the output 𝑌(𝑇) is expressed as Equation (2).…”

Section: Tcnmentioning

confidence: 99%

“…The number of nodes in the hidden layer is in the range of [10,100], and the optimal value is 50. The number of filters is in the range of [2,60], and the optimal value is 54. The L2 regularization coefficient is in the range of [10 , 10 ], and the optimal value is 0.004.…”

Section: Model Parameter Settingmentioning

confidence: 99%

“…Logging is a technique for determining the characteristics of underground rocks and fluids by means of sensors in boreholes [1][2][3][4]. When the logging instrument fails or the well wall is destroyed, the logging data in some sections will be missing.…”

Section: Introductionmentioning

confidence: 99%

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Well Logging Reconstruction Based on a Temporal Convolutional Network and Bidirectional Gated Recurrent Unit Network with Attention Mechanism Optimized by Improved Sand Cat Swarm Optimization

Wang,

Teng,

Qiao

et al. 2024

Energies

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Geophysical logging plays a very important role in reservoir evaluation. In the actual production process, some logging data are often missing due to well wall collapse and instrument failure. Therefore, this paper proposes a logging reconstruction method based on improved sand cat swarm optimization (ISCSO) and a temporal convolutional network (TCN) and bidirectional gated recurrent unit network with attention mechanism (BiGRU-AM). The ISCSO-TCN-BiGRU-AM can process both past and future states efficiently, thereby extracting valuable deterioration information from logging data. Firstly, the sand cat swarm optimization (SCSO) improved by the variable spiral strategy and sparrow warning mechanism is introduced. Secondly, the ISCSO’s performance is evaluated using the CEC–2022 functions and the Wilcoxon test, and the findings demonstrate that the ISCSO outperforms the rival algorithms. Finally, the logging reconstruction method based on the ISCSO-TCN-BiGRU-AM is obtained. The results are compared with the competing models, including the back propagation neural network (BPNN), GRU, and BiGRU-AM. The results show that the ISCSO-TCN-BiGRU-AM has the best performance, which verifies its high accuracy and feasibility for the missing logging reconstruction.

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Section: Tcnmentioning

confidence: 99%

Section: Model Parameter Settingmentioning

confidence: 99%