1887

Abstract

Summary

This article describes the effectiveness of using the simulated annealing method to solve a series of direct gravimetric problems based on gravimetric monitoring data. It is shown that the use of the developed algorithm makes it possible to increase the reliability of the mathematical model and significantly reduce working time when analysing the measured gravitational field over the developed oil and gas fields. The proposed method is developed as one of the subsystems of a special GIS designed for storage, processing, analysis and visualization of gravimetric monitoring data at an oil and gas field. The article describes the operation of the algorithm and demonstrates the results of modeling. For calculations, a sequence of horizontal prismatic bodies was selected, a priori calculation parameters are indicated, and criteria for searching for optimal solutions are developed.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.202050014
2020-09-07
2024-03-29
Loading full text...

Full text loading...

References

  1. Granichin, O.N.
    [2003] Introduction to stochastic optimization and estimation methods. Tutorial. Publishing house of St. Petersburg University, St. Petersburg. (in Russian).
    [Google Scholar]
  2. Nazirova, A., Abdoldina, F., Aymahanov, M., Umirova, G., and Muhamedyev, R.
    [2016] An automated system for gravimetric monitoring of oil and gas deposits. First International Conference, DTGS2016, 585–595. (in Russian).
    [Google Scholar]
  3. Nazirova, A., Abdoldina, F., Dubovenko, Y., and Umirova, G.
    [2019] Application of the simulated annealing method for gravimetric monitoring data analysis of the bowels condition of oil and gas deposits. Bulletin of the Kazakh National Research Technical university after K.I. Satbayev, 3(133), 397–405. (in Russian).
    [Google Scholar]
  4. [2019] Development of GIS subsystems for gravity monitoring data analysis of the subsoil conditions for oil and gas fields. Geoinformatics 2019, 16051.
    [Google Scholar]
  5. Sen, M. and Stoffa, P.
    [2013] Global optimization methods in geophysical inversion. Simulated annealing methods. Cambridge University Press, 81–118.
    [Google Scholar]
  6. Singh, A., and Biswas, A.
    [2015] Application of global particle swarm optimization for inversion of residual gravity anomalies over geological bodies with idealized geometries. Natural Resources Research, 25(3). 297–314.
    [Google Scholar]
  7. Zhao, L.S., Sen, M.K., Stoffa, P.L., and Frohlich, C.
    [1996] Application of very fast simulated annealing to the determination of the crustal structure beneath Tibet. Geophysical prospecting, 125, 355–370.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.202050014
Loading
/content/papers/10.3997/2214-4609.202050014
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error