Comparison of interwell connectivity predictions using percolation, geometrical, and Monte Carlo models Weiqiang Li a , Jerry L. Jensen b,c, ⁎, Walter B. Ayers a , Stephen M. Hubbard c , M. Reza Heidari b a Department of Petroleum Engineering, Texas A&M University, 3116 TAMU, College Station, TX 77843-3116, United States b Department of Chemical and Petroleum Engineering, University of Calgary, 2500 University Drive NW, Calgary, AB, Canada T2N 1N4 c Department of Geoscience, University of Calgary, 2500 University Drive NW, Calgary, AB, Canada T2N 1N4 abstract article info Article history: Received 3 October 2007 Accepted 7 June 2009 Keywords: Breakthrough time Probability Risk Well placement Square sandbody Reservoir connectivity is often an important consideration for reservoir management. For example, connectivity controls waterflood sweep efficiency and it affects decisions concerning well placement and spacing. The uncertainty of sandbody distributions, however, can make interwell connectivity prediction extremely difficult. Percolation models are a useful tool to simulate sandbody connectivity behavior to estimate interwell connectivity. This study applies a percolation method to estimate interwell connectivity. Using results derived by Andrade, King, and others for fluid travel time between locations in a percolation model, we develop a method to estimate interwell connectivity. Four parameters are needed to use this approach: the net-to-gross ratio p sand , the typical sandbody size, reservoir length and well spacing. To evaluate this new percolation method, the results are compared to results from geometrical models, Monte Carlo, and reservoir simulation. These methods were applied to estimate interwell connectivity for three non-communicating stratigraphic intervals in Monument Butte oil field, Utah. The results suggest that the percolation method can estimate the probability of interwell connectivity reliably for thin intervals for any values of p sand , well spacing, and reservoir length. The geometrical model also performs well, but can only be applied in fields where the well spacing is less than one-half of the sandbody size. The proposed method requires that the reservoir interval for evaluation be sufficiently thin so that 2D percolation results can be applied. For thick intervals or heterogeneous sandbody distributions, the percolation method developed here is not suitable because it assumes thin layers. Future percolation research will be needed to adapt this new method to 3D cases. © 2009 Elsevier B.V. All rights reserved. 1. Introduction Sandstone reservoirs result from long and frequently complex histories of geological evolution. The combined processes of deposi- tion, burial, compaction, diagenesis, and structural deformation make interwell connectivity difficult to predict. Uncertainty about sandbody distribution affects the prediction of hydrocarbon flow in reservoirs and, in particular, interwell connectivity. Since interwell connectivity is important for reservoir manage- ment, a number of methods have been developed and used for its pre- diction. Reservoir simulation is frequently used for connectivity prediction, but it can be quite demanding on time and data analysis. Therefore, alternative methods have been developed for field applica- tion. Since production and injection data are usually measured month- ly, numerous methods have used such data to estimate interwell communication. Qualitative approaches include the use of the Spear- man rank correlation (e.g., Heffer et al.,1997; Soeriawinata and Kelkar, 1999), wavelet analysis (Jansen and Kelkar, 1997), and artificial neural networks (Panda and Chopra, 1998). Albertoni and Lake (2003) devel- oped a quantitative estimator assuming constant porosity, perme- ability, and compressibility. Yousef et al. (2006) modified Albertoni and Lake's (2003) method to allow for variable reservoir and fluid characteristics and to include measurements of bottom hole pres- sure, where available. All of these methods suffer from one or more problems, particularly the sensitivity to changes in flow rates due to effects other than injection rate changes. Workovers and choke size changes, for example, cause changes in production rates which are not related to injection. These changes in production, which are not related to communication within the reservoir, confuse the process of correlating injection and production rates. Thus, while well flow rates are common data which contain information about the degree of communication between wells, that information can be masked by various events. We have taken a percolation-based approach to predict connectivity, depending on geological knowledge of the typical sandbody size in the reservoir. The percolation model is a simple probabilistic model that can be used to simulate connectivity and transport phenomena in geometrically Journal of Petroleum Science and Engineering 68 (2009) 180–186 ⁎ Corresponding author. Department of Chemical and Petroleum Engineering, Uni- versity of Calgary, 2500 University Drive NW, Calgary, AB, Canada T2N 1N4. Tel.: +1 403 210 6324; fax: +1 403 284 4852. E-mail address: jjensen@ucalgary.ca (J.L. Jensen). 0920-4105/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.petrol.2009.06.013 Contents lists available at ScienceDirect Journal of Petroleum Science and Engineering journal homepage: www.elsevier.com/locate/petrol