TY  - JOUR
AV  - none
N2  - Conventional geological modelling methods are not capable to provide precise and comprehensive model of the subsurface structures, when dealing with insufficient data. Knowledge based methods employing rule bases techniques are found vast applications in geoscience studies. These methods are applicable for petroleum reservoir geological modelling and characterizations, specifically for geologically complex structures. In this study, we present a knowledge based seismic acoustic impedance inversion method which employs rule based method for porosity estimation. The back propagation algorithm and the fuzzy neural network are also used in the methodology for parameter optimization and definition of nonlinear relationship between seismic attributes and porosity of the reservoir rock. The methodology initiates by seismic acoustic impedance inversion, followed by conventional porosity estimation. Subsequently, a knowledgebase was designed by investigation on more than 24 published case studies. This knowledgebase was used for definition of rules and optimization number of rules and improve efficiency of the inference engine. The porosity model obtained by conventional method in previous step would be used for primary evaluation of the rules. The extracted rules and optimized number rules then would be used for rule-based porosity estimation. The methodology was applied on a petroleum field containing two heterogeneous reservoir formations. Result of application of the proposed approach was evaluated with core analysis, thin sections and drilling data. Consistency of result obtained by the proposed method with geological data has shown its capability to resolve problem of insufficient data in reservoir geological modelling. © 2020 Elsevier Ltd
N1  - cited By 33
KW  - artificial intelligence; heterogeneous medium; integrated approach; inverse problem; modeling; porosity; reservoir characterization; sedimentology; seismic data
TI  - Integration of knowledge-based seismic inversion and sedimentological investigations for heterogeneous reservoir
ID  - scholars12605
Y1  - 2020///
SN  - 13679120
PB  - Elsevier Ltd
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090595219&doi=10.1016%2fj.jseaes.2020.104541&partnerID=40&md5=a8f615d506fe9cc1473cb18bf5444d3f
A1  - Shahbazi, A.
A1  - Monfared, M.S.
A1  - Thiruchelvam, V.
A1  - Ka Fei, T.
A1  - Babasafari, A.A.
JF  - Journal of Asian Earth Sciences
VL  - 202
ER  -