Streamline-Based Production Data Integration With Gravity and Changing Field Conditions Academic Article uri icon

abstract

  • Recently, streamline-based flow simulation models have offered significant potential in integrating dynamic data into high-resolution reservoir models. A unique feature of the streamline-based production data integration has been the concept of a travel-time match that is analogous to seismic tomography, allowing the use of efficient and proven techniques from geophysics. In this paper, we propose a generalized travel-time inversion method for production data integration that is particularly well-suited for large-scale field applications with gravity and changing conditions. Instead of matching the production data directly, we minimize a travel-time shift derived by maximizing a cross-correlation between the observed and computed production response at each well. There are several advantages of our proposed method. First, it is general and extremely computationally efficient. The travel-time sensitivities can be computed analytically with a single forward streamline simulation that can be much faster than a conventional reservoir simulator. Second, it is robust and the minimization is relatively insensitive to the choice of the initial model. Finally, it is field-proven because we utilize established techniques from geophysical inverse theory. We demonstrate the power and utility of our proposed method using synthetic and field examples. The synthetic examples include a large-scale 3D example with a quarter-million grid cells involving infill drilling and pattern conversions. The field example is from the Goldsmith San Andres Unit (GSAU) in West Texas and includes multiple patterns with 11 injectors and 31 producers. Starting with a reservoir model based on well-log and seismic data, we integrate water-cut history for 20 years in less than 2 hours on a PC.

altmetric score

  • 3

author list (cited authors)

  • He, Z., Yoon, S., & Datta-Gupta, A.

citation count

  • 54

publication date

  • December 2002