Analysis of Gas Compositional Data for EOR Process Monitoring
Authors: Peck Douglas G. 
Published: December 2007

An approach to analyze gas compositional data taken during a miscible gas injection project has been developed to identify the onset and relative efficiency of EOR response. The particular application posed a new challenge because immiscible gas had been injected prior to miscible gas injection. Other approaches to analyze gas composition data either rely on an equation of state characterization or do not provide reliable information for this particular mechanism. In some cases, erroneous conclusions could be drawn about EOR response. Through insight into the EOR mechanism, the produced gas composition is split into three constituents: miscible injectant, lean gas, and enriched lean gas. The lean gas constituent represents the immiscible gas and the lean gas bank propagating ahead of the miscible region, and the enriched lean gas constituent represents the light intermediates associated with the developed oil bank. A novel feature of this approach is that the properties of these three constituents are derived directly from field data - not from any simulations - by appropriate selection of compositional indicators. Thus, there is no reliance on predicted data from equations of state.

The estimation of relative amounts of the three constituents is accomplished by analyzing changes in two gas phase compositional indicators, while honoring material balance constraints. The new technique identifies the onset of EOR response independent of changes in oil rate, which cannot be used reliably as a single indicator of EOR response when other operational factors can affect oil rate.

The approach has already been applied to field data from a pilot project in the Kuparuk River Field in Alaska, U.S.A. A few field examples of the application of the approach will be provided.

Introduction

In the Kuparuk River Field, a miscible gas injection project was started in 1988 and was expanded significantly in 1996. Produced fluid monitoring is one component of the complete surveillance program which has one primary objective - to determine the process status by determining relative pattern performance. Once the relative pattern efficiency is known, then the process can be optimized. For example, injectant should be allocated to the most efficient patterns, that is, patterns with low amounts of returned injectant. Also, an estimate of the amount of incremental oil due to the EOR process is needed to determine relative pattern efficiency. This is of particular interest early on in an enriched gas injection project, when the actual and predicted performances are being compared. This information can then be incorporated into reservoir engineering studies to develop more robust optimization tools (reservoir simulation models), which can be used to optimize oil production rate and reserves.

Because other non-EOR related processes are present, such as changing zone pressures, changing gas lift rates, and infill drilling, to name a few, not every increase in oil rate can be attributed to the EOR process, and other surveillance data, in addition to production rate data, must be used to determine the onset of EOR response and its efficiency. The EOR process is compositional in nature; consequently, EOR response should be associated with changes in produced fluid compositions. Hence, compositional analysis of produced fluids should be helpful in identifying the onset of EOR response. Also, the vapor phase typically has the highest mobility in the reservoir, so it travels through the reservoir faster than other fluids. If changes are identified soon after enriched gas injection into a pattern begins, then corrective actions could be taken earlier. This is the reason for the focus of the compositional surveillance program in the Kuparuk River Field on the vapor phase.

Compositional analysis techniques are usually focused on calculating the amount of returned injectant, which will be referred to as returned miscible injectant (RMI). Two major reasons for RMI estimation exist. One is to estimate relative pattern efficiency, which has been the basis for MI allocation in other EOR projects. The other reason is to estimate the amount of injectant available to be reinjected. These estimates are quite dependent on the surface facility configuration. P. 289^