Optimization of an information system module for solving a direct gravimetry problem using a genetic algorithm

Authors

DOI:

https://doi.org/10.15587/1729-4061.2022.253976

Keywords:

direct gravimetry problem, genetic algorithm, gravimetric monitoring, global optimization methods

Abstract

Optimal approaches to solving many problems are required in many areas. One of these areas is the determination of the occurrence of gravity anomalies in oil and gas fields. In this paper is proposed a new approach for determining the source of gravity anomalies in an oil and gas fields by estimating the gravity parameters associated with simple-shaped bodies such as a homogeneous sphere, a horizontal prism, and a vertical step. The approach was implemented in the computational module of the GeoM information system for optimizing the solution of a series of direct gravimetry problems using a genetic algorithm (GA). Approach is based on solving the direct gravimetry problem to minimize the discrepancy of gravity variations by the genetic algorithm. The method allows to select values simultaneously for several parameters of the studied environment. The task is realized through successive approximations based on a given initial approximation of the medium.

The paper indicates the initial calculation parameters and criteria for finding optimal solutions for models of the geological environment. The calculations were carried out for such models of the environment as a homogeneous sphere, a horizontal prism and a vertical ledge. For calculations, the results of gravimetric monitoring at one of the Kazakh oil and gas fields were used. The paper demonstrates the operation of the algorithm and presents the results of modeling for three available field profiles. The obtained results of the system showed an acceptable accuracy of the algorithm up to 10-11. The genetic algorithm made it possible to significantly increase the reliability of the model and reduce the working time for analyzing the measured gravitational field

Supporting Agency

  • This work is a continuation of the project study "Development of a geographic information system for solving the problem of gravimetric monitoring of the state of the subsoil of oil and gas regions of Kazakhstan based on high-performance computing in a limited amount of experimental data" No. AP05135158 (grant funding for scientific projects of the Ministry of Education and Science of the Republic of Kazakhstan for 2018-2020). The authors also express their special gratitude to the Scientific-Productional Centre "Geoken" for providing gravimetric monitoring data, carried out over 7 cycles of observations, for scientific research.

Author Biographies

Assem Nazirova, Almaty University of Power Engineering and Telecommunications named after Gumarbek Daukeyev; Satbayev University

Master, Doctoral Student

Department of Information Technology Engineering

Department of Software Engineering

Maksat Kalimoldayev, National Academy of Sciences of the Republic of Kazakhstan

Doctor of Physical and Mathematical Sciences, Professor, Vice-President-Chief Scientific Secretary

Institute of Information and Computer Technologies

Farida Abdoldina, Almaty Management University

PhD, Associate Professor, Director

Office of Academic Excellence and Methodology

Yurii Dubovenko, National Academy of Sciences of Ukraine

PhD, Associate Professor, Senior Researcher

Institute of Geophysics NAS of Ukraine

References

  1. Obornev, E. A., Obornev, I. E., Rodionov, E. A., Shimelevich, M. I. (2020). Application of Neural Networks in Nonlinear Inverse Problems of Geophysics. Computational Mathematics and Mathematical Physics, 60 (6), 1025–1036. doi: https://doi.org/10.1134/s096554252006007x
  2. Abdelrahman, E. M., Sharafeldin, S. M. (1995). A least‐squares minimization approach to depth determination from numerical horizontal gravity gradients. GEOPHYSICS, 60 (4), 1259–1260. doi: https://doi.org/10.1190/1.1443857
  3. Shlyahovskii, V. A. (1984). Izucheniye neftegazoperspektivnyh struktur s pomowiyu dialogovoi sistemy interpretacii gravitacionnyh anomalii. Kyiv.
  4. Holland, J. H. (1975). Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control, and artificial intelligence. The University of Michigan Press, 96.
  5. Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley, 412.
  6. Abdoldina, F. N., Nazirova, A. B., Dubovenko, Y. I., Umirova, G. K. (2021). Solution of the gravity exploration direct problem by the simulated annealing method for data interpretation of gravity monitoring of the subsoil conditions. Series of Geology and Technical Sciences, 445 (1), 13–21. doi: https://doi.org/10.32014/2021.2518-170x.2
  7. Tabassum, M. (2014). A genetic algorithm analysis towards optimization solutions. International Journal of Digital Information and Wireless Communications, 4 (1), 124–142. doi: https://doi.org/10.17781/p001091
  8. Hamdia, K. M., Zhuang, X., Rabczuk, T. (2020). An efficient optimization approach for designing machine learning models based on genetic algorithm. Neural Computing and Applications, 33 (6), 1923–1933. doi: https://doi.org/10.1007/s00521-020-05035-x
  9. Abu Taleb, A. (2021). Sink mobility model for wireless sensor networks using genetic algorithm. Journal of Theoretical and Applied Information Technology, 99, 540–551. Available at: http://www.jatit.org/volumes/Vol99No2/24Vol99No2.pdf
  10. Girgis, M. R., Mahmoud, T. M., Abdullatif, B. A., Rabie, A. M. (2014). Solving the Wireless Mesh Network Design Problem using Genetic Algorithm and Simulated Annealing Optimization Methods. International Journal of Computer Applications, 96 (11), 1–10. doi: https://doi.org/10.5120/16835-6680
  11. Butko, T., Prokhorov, V., Chekhunov, D. (2017). Devising a method for the automated calculation of train formation plan by employing genetic algorithms. Eastern-European Journal of Enterprise Technologies, 1 (3 (85)), 55–61. doi: https://doi.org/10.15587/1729-4061.2017.93276
  12. Baghlani, A., Sattari, M., Makiabadi, M. H. (2014). Application of genetic programming in shape optimization of concrete gravity dams by metaheuristics. Cogent Engineering, 1 (1), 982348. doi: https://doi.org/10.1080/23311916.2014.982348
  13. Jabri, A., El Barkany, A., El Khalfi, A. (2017). Multipass Turning Operation Process Optimization Using Hybrid Genetic Simulated Annealing Algorithm. Modelling and Simulation in Engineering, 2017, 1–10. doi: https://doi.org/10.1155/2017/1940635
  14. Asfahani, J., Tlas, M. (2011). Fair Function Minimization for Direct Interpretation of Residual Gravity Anomaly Profiles Due to Spheres and Cylinders. Pure and Applied Geophysics, 169 (1-2), 157–165. doi: https://doi.org/10.1007/s00024-011-0319-x
  15. Blokh, Y. I. (2009). Interpetaciya gravitacionnyh i magnitnykh anomalii. Moscow, 231. Available at: http://sigma3d.com/pdf/books/blokh-interp.pdf
  16. Nazirova, A., Abdoldina, F., Dubovenko, Y., Umirova, G. (2019). Development of GIS subsystems for gravity monitoring data analysis of the subsoil conditions for oil and gas fields. 18th International Conference on Geoinformatics - Theoretical and Applied Aspects. doi: https://doi.org/10.3997/2214-4609.201902099
  17. Nazirova, A., Abdoldina, F., Aymahanov, M., Umirova, G., Muhamedyev, R. (2016). An Automated System for Gravimetric Monitoring of Oil and Gas Deposits. Digital Transformation and Global Society, 585–595. doi: https://doi.org/10.1007/978-3-319-49700-6_58
  18. Hassanat, A., Prasath, V., Abbadi, M., Abu-Qdari, S., Faris, H. (2018). An Improved Genetic Algorithm with a New Initialization Mechanism Based on Regression Techniques. Information, 9 (7), 167. doi: https://doi.org/10.3390/info9070167
  19. Yang, M.-D., Yang, Y.-F., Su, T.-C., Huang, K.-S. (2014). An Efficient Fitness Function in Genetic Algorithm Classifier for Landuse Recognition on Satellite Images. The Scientific World Journal, 2014, 1–12. doi: https://doi.org/10.1155/2014/264512
  20. D’Angelo, G., Palmieri, F. (2021). GGA: A modified genetic algorithm with gradient-based local search for solving constrained optimization problems. Information Sciences, 547, 136–162. doi: https://doi.org/10.1016/j.ins.2020.08.040
  21. Verma, A., Mittal, N. (2014). Congestion Controlled WSN using Genetic Algorithm with different Source and Sink Mobility Scenarios. International Journal of Computer Applications, 101 (13), 8–15. doi: https://doi.org/10.5120/17746-8819
  22. Lambora, A., Gupta, K., Chopra, K. (2019). Genetic Algorithm- A Literature Review. 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon). doi: https://doi.org/10.1109/comitcon.2019.8862255
  23. Abdoldina, F. N., Nazirova, A. B., Dubovenko, Y. I., Umirova, G. K., Jamalov, D. K., Sliamhan, K. D. (2020). Geoinformacionnaya Sistema “GеоМ” dlya obrabotki dannyh gravimetricheskogo monitoringa. Svidetel’stvo o vnesenii v gosudarstvennyi reestrprav na ob’ekty, ohranyaemye avtorskim pravom No.13336 ot “19” noyabrya 2020 g.

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Published

2022-04-30

How to Cite

Nazirova, A., Kalimoldayev, M., Abdoldina, F., & Dubovenko, Y. (2022). Optimization of an information system module for solving a direct gravimetry problem using a genetic algorithm . Eastern-European Journal of Enterprise Technologies, 2(9 (116), 21–34. https://doi.org/10.15587/1729-4061.2022.253976

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Section

Information and controlling system