Study on prediction and development of petroleum reservoir parameters based on seismic multi-parameters

Pingping Xuan

doi:10.1117/12.2692212

24 October 2023 Study on prediction and development of petroleum reservoir parameters based on seismic multi-parameters

Pingping Xuan

Proceedings Volume 12804, Second International Conference on Sustainable Technology and Management (ICSTM 2023); 1280412 (2023) https://doi.org/10.1117/12.2692212
Event: 2nd International Conference on Sustainable Technology and Management (ICSTM2023), 2023, Dongguan, China

Abstract

In this paper, the prediction and development of petroleum reservoir parameters based on seismic multi-parameters are proposed. In order to improve the accuracy of reservoir prediction, this paper uses improved SA(simulated annealing) algorithm neural network to predict reservoir. On this basis, this project proposes a neural network based on SA algorithm, which has the advantages of good global convergence and high operational efficiency, and integrates it with SA algorithm. The weights of the network are adjusted by SA algorithm instead of the traditional local gradient descent method, so as to overcome the shortcoming that ANN (artificial neural network) is easy to fall into local minima, thus improving the operational efficiency and stability of ANN. The experimental results show that the void ratio obtained by this method is in good agreement with the real void ratio, and it has a good learning effect. The absolute error of predicted thickness is less than 1m, and the relative error is less than 10%. The prediction results show that this method improves the stability of the prediction results, and further shows that the prediction and development of petroleum reservoir parameters have important practical significance.

Citation Download Citation

Pingping Xuan "Study on prediction and development of petroleum reservoir parameters based on seismic multi-parameters", Proc. SPIE 12804, Second International Conference on Sustainable Technology and Management (ICSTM 2023), 1280412 (24 October 2023); https://doi.org/10.1117/12.2692212

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