Pressure communication is commonly observed in fractured horizontal shale wells, particularly at early times when wells are placed on production. In this paper we present a new technique, based on the diffusion exponent from the power‐law model, to quantify connectivity in multistage‐hydraulic‐fractured wells with complex fracture networks. In addition to explaining the theory and analysis techniques, we present examples using measured bottomhole pressure (BHP) from the Permian Basin Wolfcamp Shale that illustrate the utility of this technique to better understand the relationship between completion size, well spacing, and well performance.
Using the concept of anomalous diffusion, Chen and Raghavan (2015) developed a 1D, fractional‐order, transient diffusion equation to model fluid flow in complex geological media. They showed that anomalous diffusion, which can be caused by heterogeneities in the matrix or the fracture system, exhibits a power‐law behavior. In addition to Chen and Raghavan (2015), Acuña (2016) demonstrated that variations in matrix block sizes, fracture conductivity, and drainage shape also exhibit power‐law behavior. While the approach from these two studies is somewhat different, they each demonstrated that a generalized power‐law model is often more appropriate than traditional linear‐ or radial‐flow pressure‐transient analysis techniques for unconventional shale reservoirs. Further, each work shows that the power‐law response can be related to some form of heterogeneity in the drainage volume.
While traditional techniques for estimating well interference have been previously developed and applied in conventional reservoirs, in this paper we focus on quantifying the magnitude of pressure interference (MPI) in unconventional reservoirs, which commonly demonstrate a generalized, power‐law pressure response that is different from radial or linear flow. The examples presented in this paper are for wells from the Permian Basin Wolfcamp Shale. Under the framework of power‐law behavior, our technique involves plotting pressure‐interference‐test (PIT) data in terms of the Chow pressure group (CPG), which enables us to define an indicator of connectivity reflecting temporal and spatial effects. On each test, we derive a diffusion exponent reflective of the MPI. We will show among other things that multiple PITs over time often indicate degrading connectivity between wells.
From PIT analyses in Permian Basin Wolfcamp Shale, we were able to establish a relationship between MPI and well spacing. The first example demonstrates analyses of PITs between wells during the production phase and also shows how connectivity between wells diminishes over time. A second example applies the same analysis techniques to quantify interwell connectivity during the post‐stimulation phase by analyzing a pressure falloff (PFO) after communication with other wells. A third example illustrates an application of desuperposition to remove the effect of a power‐law pressure trend (PT) on interference tests.
Techniques to analyze PITs assuming radial or linear flow have been previously developed; however, Raghavan and Chen (2018) showed that apparent radial or linear flow could exist under anomalous diffusion for heterogeneous reservoirs. In this work, we present a technique for analyzing power‐law PIT data, which is typical of most horizontally fractured shale wells. This model is a unique approach to understanding flow behavior, quantifying well interference, and analyzing and predicting well performance in unconventional reservoirs. Our examples, which are based on high‐quality BHP gauge data, show how this technique could shorten the cycle time for operators to determine the well spacing for a given completion design.