Integrating Field Production History in Stochastic Reservoir Characterization Conference Paper uri icon

abstract

  • Summary This paper focuses on integrating field production history into reservoir characterization through stochastic inverse modeling. A key element of our approach is a three-dimensional streamline simulator which is orders of magnitude faster than traditional numerical simulators and thus, allows for rapid inversion of multiphase production data. Equiprobable permeability fields, conditioned to field production history, are then generated using simulated annealing. We also explore the spatial resolution associated with estimates of reservoir permeability variations derived using field production history. Based on techniques from geophysical inverse theory, we address such issues as data sensitivity, spatial resolution, averaging kernels and uncertainties associated with our estimates of reservoir permeability. The proposed inversion technique has been applied to synthetic as well as field cases. The synthetic example involves a sensitivity analysis of multiphase production history in heterogeneous five-spot and nine-spot patterns. The field example consists of production history from a five-spot pattern in the North Robertson Unit, a low permeability carbonate reservoir in West Texas. Water-cut history at the producers are used to estimate permeability variations in a two-layer (matrix-fracture) model of the reservoir. All computations were performed on a 125 MHz pentium with an average run time of about 4 wall-clock hours, indicating the feasibility of our approach. Introduction Historical field production performance is perhaps the single-most important dynamic data because of its wide prevalence. Since one of the primary objectives during reservoir characterization is to build a conceptual reservoir model to predict future field production, it is imperative that stochastic reservoir models adequately reproduce all existing flow and transport data including multiphase production history. However, traditional reservoir characterization techniques do not account for field history data and thus tend to overestimate uncertainty in performance predictions. Furthermore, geostatistical methods cannot adequately address critical issues such as data sensitivity and scale, spatial resolution of estimates and the relative worth of different data types, especially when dynamic data are involved. Characterizing heterogeneous permeable media using flow and transport data typically requires the solution of an inverse problem. Previous work in incorporating dynamic data into reservoir characterization has been mostly limited to pressure transient tests1, multiwell pressure interference tests and tracer tests. To a large degree multiphase production data have not been directly incorporated into stochastic reservoir characterization. Common practice has been to attempt to adjust reservoir properties through history matching or trial and error. Such history matching can be time consuming and is often carried out in a rather ad hoc manner. Alternatively, production history data may be utilized directly to condition geostatistical simulations of reservoir properties such as permeability. Incorporating production data will be critical for identifying channels or barriers to flow which are known to have a significant impact on the sweep efficiency and hence, ultimate oil recovery. In this paper we present a methodology for direct utilization of production data during characterization of a reservoir. Our approach is based on a three-dimensional, multiphase streamline simulator which is orders of magnitude faster than conventional numerical simulators. We use the streamline model to invert field production history, for example water-cut as a function of time, using a combinatorial optimization technique, the simulated annealing algorithm. Although simulated annealing has been used previously for stochastic reservoir modeling, to our knowledge this is the first attempt to rigorously integrate commonly available production data such as water-cut history into reservoir characterization.

author list (cited authors)

  • Vasco, D. W., & Datta-Gupta, A.

citation count

  • 22

publication date

  • September 1997