Integrating Dynamic Data Into High-Resolution Reservoir Models Using Streamline-Based Analytic Sensitivity Coefficients Conference Paper uri icon


  • © Copyright 1998, Society of Petroleum Engineers, Inc. One of the outstanding challenges in reservoir characterization is to build high resolution reservoir models that satisfy static as well as dynamic data. However, integration of dynamic data typically requires the solution of an inverse problem that can be computationally intensive and becomes practically infeasible for fine-scale reservoir models. A critical issue here is computation of sensitivity coefficients, the derivatives of dynamic production history with respect to model parameters such as permeability and porosity. We propose a new analytic technique that has several advantages over existing approaches. First, the method utilizes an extremely efficient three-dimensional multiphase streamline simulator as a forward model. Second, the parameter sensitivities are formulated in terms of one-dimensional integrals of analytic functions along the streamlines. Thus, the computation of sensitivities for all model parameters requires only a single simulation run to construct the velocity field and generate the streamlines. The integration of dynamic data is then performed using a two-step iterative inversion that involves (i) 'lining-up' the breakthrough times at the producing wells and then (ii) matching the production history. Our approach follows from an analogy between streamlines and ray tracing in seismology. The inverse method is analogous to seismic waveform inversion and thus, allows us to utilize efficient methods from geophysical imaging. The feasibility oT our proposed approach for large-scale field applications has been demonstrated by integrating production response directly into three dimensional reservoir models consisting of 31500 grid blocks in less than 3 hours in a Silicon Graphics without any artificial reduction of parameter space, for example, through the use of 'pilot points'. Use of 'pilot points' will allow us to substantially increase the model size without any significant increase in computation time.

author list (cited authors)

  • Vasco, D. W., Yoon, S., & Datta-Gupta, A.

citation count

  • 23

publication date

  • January 1998


  • SPE  Publisher