USE OF THE TECHNOLOGY OF INTERACTIVE CLASSIFICATION OF GEOLOGICAL BODIES FOR GAS DEPOSITS FORECASTING IN EASTERN UKRAINE
DOI:
https://doi.org/10.17721/1728-2713.84.10Keywords:
seismic image, seismic inversion, interactive classification, seismic attributes, geobody, direct indicators of hydrocarbonsAbstract
The possibilities of allocation of geological bodies with certain physical and filtration-capacitive properties on the basis of classification by a set of seismic attributes are considered. This classification is based on the use of computer technology with parallel computing on graphic processors. High-speed parallel computing provides the ability to interactively classify and get results in real-time. At the same time, application of graphic processors allows to realize technologies of interactive classification not only on computing clusters, but also on personal computers. Geological efficiency of technologies of interactive classification is shown. Their application allows to detect geological bodies with certain physical properties on the basis of computer analysis of three-dimensional arrays of seismic data, in particular seismic images and arrays of seismic attributes. It is important to note that computer technologies of the interactive classification of three-dimensional seismic data not only provide a high speed of determination of the spatial position and properties of geological bodies, but also allow to realize the fundamental possibility of such a definition. Traditional approaches to the identification and classification of geological bodies are based on a sequential analysis of sections of three-dimensional seismic data. In this case, the solution of the problem of the identification and classification of geological bodies often encounter the fundamental problems due to the complexity of the visual assessment of three-dimensional geological objects based on the analysis of the sequence of two-dimensional sections of the arrays of seismic data. The authors propose a convenient approach to systematizing methods of interactive classification of geological bodies by one and several seismic attributes. The known technologies of bright spots and AVO are considered as elements of a sequence of methods of interactive classification using different numbers of seismic attributes. According to the results of 3-D seismic survey carried out on the areas of the east of Ukraine, the classification of geological bodies using one and two seismic attributes was performed. Some objects with perspectives concerning presence of hydrocarbon were found and analyzed. The conclusion of classification expediency using several parameters is done. It creates preconditions for realization of more perfect and versatile approaches to the detection of geological bodies with given physical properties.
References
Castagna, J. P., Swan, H. W. (1997). Principles of AVO crossplotting. The Leading Edge, 16, 337–342.
Castagna, J. P., Swan, H. W., Foster, D. J. (1998). Framework for AVO gradient and intercept interpretation. Geophysics, 63, 948–956.
Chopra, S., Castanga, J.P. (2014). AVO. SEG, Investigation in Geophysics Series, 16, 288.
Deutsch, C.V. (1998). Fortran programs for calculating connectivity of three dimensional numerical models and for ranking multiple realizations. Computers & Geosciences, 24, 69.
Forrest, M., Roden, R., Holeywell, R. (2010). Risking seismic amplitude anomaly prospects based on database trends. The Leading Edge, 5, 936-940.
Foster, D.J., Keys, R.G. (1999). Interpreting AVO responses. 69 Annual International Meeting, SEG, Expanded Abstracts, 748–751.
Foster, D.J., Keys, R.G., Reilly, J.M. (1997). Another perspective on AVO crossplotting. The Leading Edge, 16, 1233–1237.
Foster, D.J., Smith, S.W., Dey-Sarkar, S., Swan, H.W. (1993). A closer look at hydrocarbon indicators. 63 Annual International Meeting, SEG, Expanded Abstracts, 731–733.
Hoshen, J., Kopelman, R. (1976). Percolation and cluster distribution. Cluster multiple labeling technique and critical concentration algorithm. Physical Review, 14, 3438-3445.
Roden, R., Chen, C.W. (2017). Interpretation of DHI characteristics with machine learning. First Break, 35, 55-63.
Rudolph, K.W., Goulding, F.J. (2017). Benchmarking exploration predictions and performance using 20+ yr of drilling results: One company's experience. AAPG Bulletin, 101, 161-176.
Rutherford, S.R., Williams, R. H. (1989). Amplitude-versus-offset variations in gas sands. Geophysics, 54, 680–688.
Velve, L.M., Scorstad, A., Vonnet, J. (2018). Recent developments in object modelling opens new era for characterization of fluvial reservoirs. First Break, 36, 85-89.
Verm, R., Hilterman, F. (1995). Lithology color-coded seismic sections: The calibration of AVO crossplotting to 280 AVO rock properties. The Leading Edge, 14, 847–853.
Vyzhva, S.A., Solovyov, I.V., Kruhlyk, V.M, Lisny, G.D. (2018). Prediction of high porosity zones in clay rocks at the Eastern Ukraine. Visnyk of Taras Shevchenko National University of Kyiv. Geology, 80, 28-33. [in Ukrainian]
Published
Issue
Section
License
Copyright (c) 2023 Visnyk of Taras Shevchenko National University of Kyiv. Geology

This work is licensed under a Creative Commons Attribution 4.0 International License.
Read the policy here: https://geology.bulletin.knu.ua/licensing




