Combining Decline-Curve Analysis and Capacitance-Resistance Models To Understand and Predict the Behavior of a Mature Naturally Fractured Carbonate Reservoir Under Gas Injection

摘要

Capacitance-Resistance (CR) models have received renewed interest in the past few years as a fast alternative to reservoir simulation to model and predict complex water or gas floods in mature reservoirs. Using an analogy between reservoirs and electrical systems, CR models represent the interactions between wells through analytical solutions to an equivalent capacitor-resistor circuit. CR models do not require a geologic model and can be built with only production and injection data. When modeling fields with numerous wells and a long history, traditional reservoir simulation workflows are extremely time-consuming. The simplicity of CR models make them extremely attractive to quickly model and predict the behavior of these complex reservoirs. Current CR models are able to represent accurately the behavior of reservoirs under strong water or gas floods, where the injection is the main driving mechanism for production. In such cases, the production rates are strongly correlated to the injection rates and CR model are ideally suited to decipher these interactions. However, most reservoirs start with a period of primary depletion or many are exploited under a weak injection strategy, for which CR models are not ideally suited. Here, we propose to combine decline-curve (DC) analysis with a CR model in order to solve this shortcoming. Using the superposition principle, the contribution of primary depletion to production is represented by DC and the contribution of injection is represented by the CR model. After presenting the formulation and implementation of our DC-CR model, we demonstrate its performance on a deep naturally fractured carbonate reservoir under hydrocarbon gas and nitrogen injection. The reservoir has over 30 years of production history: 23 years of primary depletion and 8 years of gas and nitrogen injection. Using a one-year blind test, we demonstrate that the model is able to accurately predict the reservoir behavior.