Iterative updating of reservoir models constrained to dynamic data

T. Kashib, S. Srinivasan

Research output: Contribution to journalArticlepeer-review


Geostatistical algorithms are being widely used to integrate different data such as seismic amplitude, well logs and core measurements into reservoir models. However, approaches to integrate dynamic/production data efficiently into these models are largely tacking. Production data differs from other types of static data (such as porosity, permeability, amplitude, etc.) primarily because they are non-linearly related to the connectivity characteristics of the reservoir. In this paper, we develop a gradual deformation methodology to integrate two-phase production data in order to give rise to a suite of reservoir models that are conditioned to static data, as well as dynamic data. We utilize the Sequential Indicator Simulation algorithm within a non-stationary Markov Chain to iteratively update the realizations till a history match is obtained. The methodology is tested on a synthetic 2D and 3D reservoir.

Original languageEnglish (US)
Pages (from-to)23-32
Number of pages10
JournalJournal of Canadian Petroleum Technology
Issue number11
StatePublished - Nov 2007

All Science Journal Classification (ASJC) codes

  • General Chemical Engineering
  • Fuel Technology
  • Energy Engineering and Power Technology


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