Intelligent seismic inversion workflow for high-resolution reservoir characterization

Artun F. E., Mohaghegh S.

COMPUTERS & GEOSCIENCES, vol.37, no.2, pp.143-157, 2011 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 37 Issue: 2
  • Publication Date: 2011
  • Doi Number: 10.1016/j.cageo.2010.05.007
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.143-157
  • Keywords: Seismic inversion, Neural networks, Reservoir characterization, Buffalo Valley Field, ARTIFICIAL NEURAL-NETWORKS, WIRELINE LOGS, PREDICTION, REGRESSION
  • Istanbul Technical University Affiliated: Yes


Developing a geological model is the first and a very important step during the reservoir simulation and modeling process. The geological model usually represents our best interpretation of the reservoir characteristics that extends beyond the well where we have actual measurements (logs, core, well test, etc.). The only real measurement with a large areal extent that geoscientists have access to is seismic data. Therefore, using seismic data to populate the high-resolution geological model is becoming increasingly popular. Using reservoir characteristics at the wellbore as the control point helps geoscientists in measuring the goodness of the correlation they create between seismic data and well logs. This paper presents a unique approach in accomplishing this task. The uniqueness of this approach is based on the fact that (a) it reduces the complexity of the model building process by dividing a very complex problem into two slightly less complex problems (surface seismic to VSP and VSP to log-i.e., divide and conquer) and (b) it effectively employs a synthetic set of formations representing the actual sequence of geological layers in the field in order to build a model and learn from it, and then, apply the lessons learned to the model building process for the actual reservoir. The results show that this strategy proves to be successful. (C) 2010 Elsevier Ltd. All rights reserved.