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Reservoir characterization using artificial neural network; Neural network wo mochiita choryuso tokusei kaiseki

Abstract

Neural network is used for the prediction of porosity and permeability using logging data as reservoir characteristics, and the validity of this method is verified. For the prediction of reservoir characteristics by the use of seismic survey data, composite seismic survey records obtained by density logging and acoustic logging are used to experiment the prediction of porosity and permeability continuous along lines of wells. A 3-output back propagation network is used for analysis. There is a possibility that this technique when optimized will improve on prediction accuracy. Furthermore, in the case of characteristics mapping, 3-dimensional seismic data is applied to a carbonate rock reservoir for predicting spatial porosity and permeability. This technique facilitates the comprehensive analysis of core data, well data, and seismic survey data, enabling the derivation of a high-precision spatial distribution of reservoir characteristics. Efforts will continue for further improvement on prediction accuracy. 6 refs., 7 figs., 3 tabs.
Authors:
Shimada, N; Kozawa, T; [1]  Nishikawa, N; Tani, A [2] 
  1. Japan National Oil Corp., Tokyo (Japan)
  2. Fuji Research Institute Corp., Tokyo (Japan)
Publication Date:
May 27, 1997
Product Type:
Conference
Report Number:
CONF-9705167-
Reference Number:
SCA: 990200; 020200; 440700; 440400; PA: NEDO-97:912232; EDB-97:123675; SN: 97001846524
Resource Relation:
Conference: 96. SEGJ conference, Butsuri tansa gakkai dai 96 kai (1997 nendo shunki) gakujutsu koenkai, Tokyo (Japan), 27-29 May 1997; Other Information: PBD: 27 May 1997; Related Information: Is Part Of Proceeding of the 96th (spring, fiscal 1997) SEGJ Conference; PB: 502 p.; Butsuri tansa gakkai dai 96 kai (1997 nendo shunki) gakujutsu koenkai koen ronbunshu
Subject:
99 MATHEMATICS, COMPUTERS, INFORMATION SCIENCE, MANAGEMENT, LAW, MISCELLANEOUS; 02 PETROLEUM; 44 INSTRUMENTATION, INCLUDING NUCLEAR AND PARTICLE DETECTORS; RESERVOIR ROCK; PETROLEUM DEPOSITS; SYSTEMS ANALYSIS; WELL LOGGING; NEURAL NETWORKS; PERFORMANCE; POROSITY; PERMEABILITY; OIL SATURATION; SEISMIC SURVEYS; GAMMA-GAMMA LOGGING; SONIC LOGGING; FORECASTING; ACCURACY; CARBONATE ROCKS; SPATIAL DISTRIBUTION; DRILL CORES
OSTI ID:
522633
Research Organizations:
Society of Exploration Geophysicists of Japan, Tokyo (Japan)
Country of Origin:
Japan
Language:
Japanese
Other Identifying Numbers:
Other: ON: DE97770262; TRN: 97:912232
Availability:
Available from The Society of Exploration Geophysicists of Japan, 2-18, Nakamagome 2-chome, Ota-ku, Tokyo, Japan; OSTI as DE97770262
Submitting Site:
NEDO
Size:
pp. 145-148
Announcement Date:

Citation Formats

Shimada, N, Kozawa, T, Nishikawa, N, and Tani, A. Reservoir characterization using artificial neural network; Neural network wo mochiita choryuso tokusei kaiseki. Japan: N. p., 1997. Web.
Shimada, N, Kozawa, T, Nishikawa, N, & Tani, A. Reservoir characterization using artificial neural network; Neural network wo mochiita choryuso tokusei kaiseki. Japan.
Shimada, N, Kozawa, T, Nishikawa, N, and Tani, A. 1997. "Reservoir characterization using artificial neural network; Neural network wo mochiita choryuso tokusei kaiseki." Japan.
@misc{etde_522633,
title = {Reservoir characterization using artificial neural network; Neural network wo mochiita choryuso tokusei kaiseki}
author = {Shimada, N, Kozawa, T, Nishikawa, N, and Tani, A}
abstractNote = {Neural network is used for the prediction of porosity and permeability using logging data as reservoir characteristics, and the validity of this method is verified. For the prediction of reservoir characteristics by the use of seismic survey data, composite seismic survey records obtained by density logging and acoustic logging are used to experiment the prediction of porosity and permeability continuous along lines of wells. A 3-output back propagation network is used for analysis. There is a possibility that this technique when optimized will improve on prediction accuracy. Furthermore, in the case of characteristics mapping, 3-dimensional seismic data is applied to a carbonate rock reservoir for predicting spatial porosity and permeability. This technique facilitates the comprehensive analysis of core data, well data, and seismic survey data, enabling the derivation of a high-precision spatial distribution of reservoir characteristics. Efforts will continue for further improvement on prediction accuracy. 6 refs., 7 figs., 3 tabs.}
place = {Japan}
year = {1997}
month = {May}
}