Joint interpretation of time-lapse gravity data and production data for a gas reservoir

Abstract

Time-lapse gravity is commonly used to monitor fluid movement and is especially useful when monitoring water encroachment in a gas reservoir. Although time-lapse gravity has considerable resolution, it is beneficial to integrate different types of data to complement the time-lapse gravity interpretation. When monitoring water-influx in a reservoir, an increase in water yield in some wells may indicate increasing saturation values and suggest areas of fluid movement and density contrast change with time. These data can complement the time-lapse gravity analysis, the question to address is how to integrate these data appropriately to enhance the monitoring of the edgewater in a reservoir. The objective of this thesis is to develop a workflow to invert a time-lapse gravity data set and production data in the Sebei Gas field located in Western China and monitor the edgewater encroachment. Three time-lapse gravity surveys were conducted between 2011 and 2013 and were processed to estimate the error associated with the data set. Consequently, production data collected from 286 wells, was evaluated and plotted to determine its feasibility as complementary data for the time-lapse gravity inversion. To jointly interpret these two independent data sets, I converted the sparse production data into a time-lapse reference model that is subsequently incorporated as a guide into a generalized density inversion. To constrain the inversion result to a similar lithologic setting of the reservoir, I imposed spatially varying upper and lower density bound constraints for different depths of the model. Through this approach, I construct a set of density contrast models that are guided by the measured changes in gas/water yield, and whose distribution fits the time-lapse gravity data. The results of this work demonstrate that integrating the production data and setting lithology dependent bound constraints produced an improved definition of density changes. By doing a first order estimation of the porosity in the reservoir, I verified that the porosity estimated using the recovered density contrast of the improved model are more representative of the known porosity of the gas reservoir.