- journal_title：Geophysics
- Contributor：Paul C. H. Veeken ; Ivan I. Priezzhev ; Leo E. Shmaryan ; Yan I. Shteyn ; Alexander Y. Barkov ; Yuri P. Ampilov
- Publisher：Society of Exploration Geophysicists
- Date：2009-
- Format：text/html
- Language：en
- Identifier：10.1190/1.3223314
- journal_abbrev：Geophysics
- issn：0016-8033
- volume：74
- issue：6
- firstpage：WCD49
- section：INNOVATIVE APPROACHES
Seismic inversion is an important processing step for characterizing reservoirs with properties predicted away from well controls. A new approach to inversion and reservoir modeling is based on a nonlinear multitrace seismic inversion algorithm. Neural-network solutions for the inversion problem exist, whereby a multiattribute analysis increases output reliability. A more robust 3D working method is proposed that uses a simple nonlinear operator. The method is fast, user friendly, and cost effective. Initial input is formed by poststack seismic data and relevant well logs (e.g., acoustic impedance). The latter serve as a training and control point set to calculate optimized weights in the neural-network scheme. The nonlinear output operator trans-forms the seismic data into the desired log-response equivalent. Crucial is finding a real regional minimum for the difference between the computed synthetic and the recorded seismic traces at the target location. Intelligent data decimation eliminates the number of unknown coefficients in the operator computation. Moreover, it speeds up the true 3D minicube processing. Genetic inversion can be applied to properties other than acoustic impedance. However, these attributes should have a meaningful physical relationship to the seismic data, e.g., porosity, density, and saturation. Geologic modeling constitutes the last step of the inversion workflow. The geologic model is populated stochastically with relevant reservoir properties. The capabilities of the genetic inversion were tested on a semicontrolled seismic example and a real case study across the Shtokman gas field, Russia.