Diagnosing CO2 Flood Performance Using Actual Performance Data
Authors: Masoner L.O. , Abidi H.R. , Hild G.P. 
Published: December 2007

Abstract
Successfully operating a CO2 flood requires the ability to make informed decisions regarding WAG ratio, half-cycle-slug size, ultimate-CO2-slug size, and conformance improvement opportunities (conformance refers to vertical and areal sweep efficiency). The decision quality and confidence improves significantly when analyzing actual performance data on an injector centered pattern basis. Injector centered patterns provide a framework for building the smallest injection cost center possible. A pattern is built by allocating a percent of the total production from each of the impacted offsetting producers and 100% of the injection. This framework enables diagnosing a pattern's financial and technical performance relative to CO2 and waterflood performance. Manipulating the pattern data and generating performance measures utilizes off-the-shelf tools (Production AnalystTM and Microsoft Excel) and takes only a few minutes once the calculations are programmed. Key performance measures calculated from actual performance data yield insights on allocation factor quality, total and incremental cash flow, total and incremental operating expense to oil production ratio, waterflood and CO2 reserves estimates, optimum water-alternating-gas (WAG) and half- cycle-slug size, gross and net CO2 utilization, ultimate-CO2-slug size (or when to go to chase waterflood), contacted pore volume size, pattern sweep efficiency, and others. This paper describes how to optimize early through mature pattern management decisions in a CO2 flood using these key performance measures. These measures are based on efforts to improve injection management of the 260+ WAG patterns in the Rangely Weber Sand Unit (RWSU) CO2 flood located in northwest Colorado.

Introduction
Managing the RWSU CO2 WAG injection project requires making decisions regarding the WAG ratio, half-cycle-slug size, and ultimate-CO2-slug size for each WAG injector in the field. The impact of these decisions affects the capital costs of CO2 purchase, water and gas plant loads, fluid handling and lifting operating costs, and ultimate incremental oil recovery. Simulation models provide a tool for examining strategies for these decisions on a regional or fieldwide basis. However, using them to manage these decisions for each, of the WAG injectors in the field poses a daunting task. Not only must the simulation be able to accurately incorporate geological heterogeneities and properly model the CO2 recovery process, but it must be able to address operational impacts such as lift method and problems, injection plugging, workovers and facilities operations. Managing individual injectors utilizing actual performance data has instead eliminated the simulation step by evolving into a production and injection diagnostic tool that provides performance insights and guides decision making. It also consolidates many of the important measures in one location.
The methods and performance measures presented here can be used at scales ranging from a single pattern to the full field. The results can help increase understanding, validate simulations, and improve decision making.
This paper describes the diagnostics tool and discusses the procedures in using the tool to guide decisions.

Description of the Diagnostics Tool
A general description of the tool includes the basic structure and input requirements. The importance of calculating CO2-incremental oil response requires an expanded discussion of the determination of the base waterflood performance. This section closes with detailed discussions on individual performance measures.
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