Application of Time-Lapse Shut-In Pressures for Mapping Reservoir Heterogeneity in Brown Fields

In a homogeneous non-faulted reservoir where oil production has been ongoing long enough for a pseudo steady-state pressure regime to exist, the time-lapse shut-in pressures for all wells are expected to be similar in magnitude and trend. This gives a cluster of similar lines on a plot of shut-in bottom hole pressure over time. By contrast, heterogeneous non-faulted reservoirs can exhibit variations in these trends. Traditionally, mapping a reservoir's heterogeneity requires the use of seismic or facies data. This study aims to investigate whether the differences in pressure trends observed in heterogeneous reservoirs can be used to map reservoir heterogeneities.

Numerical experiments are performed using hypothetical models to study the impact that stepwise and continuous permeability variations, the permeability ratio, duration of shut-in, and flowrate all have on the time-lapse pressure trends of wells located within regions of similar and different permeability magnitudes. The time-lapse shut-in bottom-hole pressures for all wells are plotted on the same axis to assess cluster differentiation. Closed polygons are drawn around the wells within each differentiated cluster.

The simulation results indicate that pressure cluster differentiation typically implies heterogeneity, whereas time-lapse pressure clustering does not necessarily imply homogeneity. Therefore, in a reservoir where time-lapse pressure cluster differentiation is observed, mapping the spatial locations of wells within each pressure cluster would result in a reservoir heterogeneity map comparable to what is obtained traditionally from facies and seismic maps as part of geological modeling. A key contribution of this study is the development of an alternative spatial heterogeneity map, based on dynamic data (pressure), that can be used as a trend map for guiding 3D model property distributions.

The application of the alternative spatial heterogeneity map for 3D geo-model property distributions ensures that important geo-model connectivity patterns are represented, facilitating subsequent history-matching efforts.