An Intelligent Systems Approach to Reservoir Characterization

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Today, the major challenge in reservoir characterization is integrating data coming from different sources in varying scales, in order to obtain an accurate and high-resolution reservoir model. The role of seismic data in this integration is often limited to providing a structural model for the reservoir. Its relatively low resolution usually limits its further use. However, its areal coverage and availability suggest that it has the potential of providing valuable data for more detailed reservoir characterization studies through the process of seismic inversion. In this paper, a novel intelligent seismic inversion methodology is presented to achieve a desirable correlation between ... continued below

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Mohaghegh, Shahab D.; Toro, Jaime; Wilson, Thomas H.; Artun, Emre; Sanchez, Alejandro & Pyakurel, Sandeep August 1, 2005.

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Today, the major challenge in reservoir characterization is integrating data coming from different sources in varying scales, in order to obtain an accurate and high-resolution reservoir model. The role of seismic data in this integration is often limited to providing a structural model for the reservoir. Its relatively low resolution usually limits its further use. However, its areal coverage and availability suggest that it has the potential of providing valuable data for more detailed reservoir characterization studies through the process of seismic inversion. In this paper, a novel intelligent seismic inversion methodology is presented to achieve a desirable correlation between relatively low-frequency seismic signals, and the much higher frequency wireline-log data. Vertical seismic profile (VSP) is used as an intermediate step between the well logs and the surface seismic. A synthetic seismic model is developed by using real data and seismic interpretation. In the example presented here, the model represents the Atoka and Morrow formations, and the overlying Pennsylvanian sequence of the Buffalo Valley Field in New Mexico. Generalized regression neural network (GRNN) is used to build two independent correlation models between; (1) Surface seismic and VSP, (2) VSP and well logs. After generating virtual VSP's from the surface seismic, well logs are predicted by using the correlation between VSP and well logs. The values of the density log, which is a surrogate for reservoir porosity, are predicted for each seismic trace through the seismic line with a classification approach having a correlation coefficient of 0.81. The same methodology is then applied to real data taken from the Buffalo Valley Field, to predict inter-well gamma ray and neutron porosity logs through the seismic line of interest. The same procedure can be applied to a complete 3D seismic block to obtain 3D distributions of reservoir properties with less uncertainty than the geostatistical estimation methods. The intelligent seismic inversion method should help to increase the success of drilling new wells during field development.

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  • Report No.: none
  • Grant Number: FC26-03NT41629
  • DOI: 10.2172/878495 | External Link
  • Office of Scientific & Technical Information Report Number: 878495
  • Archival Resource Key: ark:/67531/metadc878871

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  • August 1, 2005

Added to The UNT Digital Library

  • Sept. 21, 2016, 2:29 a.m.

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  • Dec. 5, 2016, 9:26 p.m.

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Mohaghegh, Shahab D.; Toro, Jaime; Wilson, Thomas H.; Artun, Emre; Sanchez, Alejandro & Pyakurel, Sandeep. An Intelligent Systems Approach to Reservoir Characterization, report, August 1, 2005; United States. (digital.library.unt.edu/ark:/67531/metadc878871/: accessed December 13, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.