An example of the application of neural networks of a simple architecture to unfocused well electrometry probes
DOI:
https://doi.org/10.32347/2411-4049.2024.3.177-182Keywords:
geophysical exploration of wells, resistivity, oil and gas wells, Shoulder effect, inverse problem, vertical resolutionAbstract
An effective method of finding stable solutions of inverse problems of electric and induction logging along the well is proposed, which allows avoiding the influence of the resistance values of the neighboring formations on the determination of the geoelectrical parameters of the object under study. A highly efficient method was proposed for solving such an unstable inverse problem. This method is based on the application of a neural network with inverse error propagation of a simple architecture. Namely three-layer. The mathematical statement of the problem is given, both the topology of the neural network and all its parameters are described in detail. In the course of the numerical experiment, they were selected as optimal. The process of building a base for training a neural network is described in detail. Namely, how each of the examples of the learning base is built by solving a direct problem. With this cut parameter, the training for each example is chosen arbitrarily, which guarantees a comprehensive range for training the neural network. The number of examples in the training base is one hundred thousand examples. As the activation function, the sigmoid is chosen due to the fact that it is differentiable everywhere. The results of testing the written program are given. The learning rate was estimated to obtain the required small error. It is shown that this approach is stably convergent. For testing, the parameters of the layers of the cut, which are inherent to the geophysical parameters of the cuts of the Dnipro-Donetsk depression, were chosen. A complex of lateral logging sounding was chosen as the electrical logging equipment. Four-probe low-frequency induction logging equipment was chosen as induction logging equipment. Examples for induction and electrical logging are given separately. The obtained results are analyzed in detail. Ways of further improvement of the obtained neural network and its use for other problems of geophysics are given.
References
Myrontsov, M.L. (2019). Electrometry of oil and gas wells. Kyiv: "EUSTON" LLC [in Ukrainian].
Yehurnova, M.G., Zaykovsky, M.Ya., Zavorotko, Y.M., Tsyokha, O.G., Knishman, O.Sh., Mulyar, P.M., Demyanenko, I.I. (2005). Oil and gas prospects of Ukraine. Oil and gas potential and features of lithogeophysical structure of Lower Carboniferous and Devonian deposits of the Dnieper-Donets depression. Kyiv: Naukova Dumka [in Ukrainian].
Jun Wang, Junxing Cao, Jiachun You, Ming Cheng1 and Peng Zhou. (2021). A method for well log data generation based on a spatio-temporal neural network. Journal of Geophysics and Engineering, 18, 700–711. https://doi.org/10.1093/jge/gxab046
Lei Wu, Zhenzhen Dong, Weirong Li, Cheng Jing and Bochao Qu. (2021). Well-Logging Prediction Based on Hybrid Neural Network Model. Energies, 14. https://doi.org/10.3390/en14248583
Anderson, B.I. (2001). Modeling and inversion methods for the interpretation of resistivity logging tool response. Delft: DUP Science.
Rumelhart, D.E., Hinton, G.E., Williams, R.J. (1986). Learning Internal Representations by Error Propagation. Parallel Distributed Processing, 1, 318–362.
Myrontsov, M.L., Dovgyi, S.O., Trofymchuk, O.M., Lebid, O.G., Okhariev, V.O. (2022). Development and testing of tools for modeling R&D works in geophysical instrument-making for oil and gas well electrometry. Science and Innovation, 18(3), 28–36. https://doi.org/10.15407/scine18.03.028
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 M.L. Myrontsov

This work is licensed under a Creative Commons Attribution 4.0 International License.
The journal «Environmental safety and natural resources» works under Creative Commons Attribution 4.0 International (CC BY 4.0).
The licensing policy is compatible with the overwhelming majority of open access and archiving policies.