Interpretation of Interwell Connectivity Tests in a Waterflood System

Abstract

This study is an extension of a novel technique to determine interwell connectivity in a reservoir based on fluctuations of bottom hole pressure of both injectors and producers in a waterflood system. The technique uses a constrained multivariate linear regression analysis to obtain information about permeability trends, channel and barriers. Some of the advantages of this new technique are simplified one-step calculation of interwell connectivity coefficients, small number of data points and flexible testing plan. However, the previous study did not provide either in-depth understanding or any relationship between the interwell connectivity coefficients and other reservoir parameters. This paper presents a mathematical model for bottom hole pressure responses of injectors and producers in a waterflood system. The model is based on available solution for fully penetrating vertical wells in a closed rectangular reservoir. It is then used to calculate interwell relative permeability, average reservoir pressure change and total reservoir pore volume using data from interwell connectivity test described in previous study. Reservoir compartmentalization can be inferred from the results. Cases of producers as signal wells, injectors as response wells and shut-in wells as response wells are also presented. Summary of results for these cases are provided. Reservoir behaviors and effect of skin factors are also discussed in this study. Some of the conclusions drawn from this study are:The mathematical model works well with interwell connectivity coefficients to quantify reservoir parameters;The procedure provides in-depth understanding of the multi-well system with water injection in the presence of heterogeneity;Injectors and producers have the same effect in terms of calculating interwell connectivity and thus, their roles can be interchanged. This study provides flexibility and understanding to the method of inferring interwell connectivity from bottom-hole pressure fluctuations. Interwell connectivity tests allow us to quantify accurately various reservoir properties in order to optimize reservoir performance. Different synthetic reservoir models were analyzed including: homogeneous, anisotropic reservoirs, reservoirs with high permeability channel, partially sealing fault and sealing fault. The results are presented in details in the paper. A step-bystep procedure, charts, tables, and derivations are included in the paper. Introduction Previous study carried out by Dinh and Tiab (2007) has introduced a new technique to infer interwell connectivity from bottom-hole pressure fluctuations in a waterflood system. The technique was proven to yield good results based on numerical simulation models of various cases of heterogeneity. In this study, an analytical model for multi-well system with water injection was derived for the technique. The model is based on available solution for a fully penetrating vertical well in a closed rectangular multi-well system and uses the principle of superposition in space. Based on analytical analysis, a new technique to analyze data of interwell connectivity test was developed. This technique utilizes the least squares regression method to calculate the average pressure change. Thus, reservoir pore volume, average reservoir pressure and total average porosity can be estimated from available input data. The results were verified using a commercial black oil numerical simulator.

Characterization of fractured reservoirs using tracer and flow‐rate data

“…[8] Well-to-well flow-rate models have been discussed regularly in the oil and gas literature over the past decade [Albertoni and Lake, 2002;Yousef et al, 2005;Dinh, 2009;Lee, 2010;Juliusson, 2012]. Most of these models are only applicable in reservoirs with low compressibility when production wells run at constant bottomhole pressure conditions (the Interwell Transmissibility Model [Juliusson, 2012] is an exception).…”

“…Thus, the IWCs are particularly useful for mapping highly conductive flow paths between wells. Methods to infer the IWC have been developed by Dinh [2009], using only bottomhole pressure data, and by Juliusson [2012], using both bottomhole pressure and flow-rate data. In contrast, the methods developed by Albertoni and Lake [2002], Yousef et al [2005], and Lee [2010] depend only on flow-rate data.…”

Fracture Characterization in Enhanced Geothermal Systems by Wellbore and Reservoir Analysis

“…Interwell connectivity (IWC) models have been discussed by several researchers over the past decade (Albertoni and Lake, 2002;Dinh, 2009;Juliusson, 2012;Lee, 2010;Yousef et al, 2005). These models are applicable in reservoirs with low compressibility when production wells run at constant bottomhole pressure conditions (the Interwell Transmissibility Model (Juliusson, 2012) is an exception).…”

“…The IWC's are particularly useful for mapping highly conductive flow paths between wells. The method outlined by Dinh (2009) relies on bottomhole pressure data, the Interwell Transmissibility method presented in the Quarterly Report from Spring 2012 requires both bottomhole pressure and flow-rate data, but the methods developed by Albertoni and Lake (2002), Yousef et al (2005), and Lee (2010) depend only on flow-rate data. A recent flow-rate based method, referred to as the M-ARX method Lee (2010), was used to compute IWC in this work.…”

Joint Interpretation of Interwell Connectivity by Integrating 4D Seismic with Injection and Production Fluctuations

“…The weight coefficients in the linear model are used to evaluate the communication between producer and injectors. Instead of using rate, Tiab and Dinh (2007, 2009 used bottomhole pressure (BHP) responses of injectors and producers to determine the interwell connectivity coefficient in the MLR model in water flooding system. The most recently used method is called Capacitance-Resistance Model (CRM) (or Capacitance Model (CM) in some references) developed by Yousef et al (2006), which integrates both flow rate and BHP in a nonlinear signalprocessing model to provide a more robust interpretation result of the interwell connectivity.…”