Production from shale gas reservoirs in the USA has become an important component in the increase of natural gas supply. The Haynesville shale, in particular, is a major contributor in gas supply due mainly to its relatively higher initial deliverability compared to other gas shale plays.

One of the critical questions in developing a play efficiently and economically is the well spacing. There are several approaches to addressing this question. The paper looks at one approach, namely the process behind building a calibrated, history matched multi well reservoir model. The model is run in prediction mode with different sensitivities to answer the well spacing issue. The model honors the initial static and dynamic conditions, is capable of running in a reasonable time and, most importantly, has been useful to management in the decision making process.

In this field case, a half section in the state of Louisiana has been drilled and completed with 4 horizontal multistage producing wells and 2 vertical microseismic monitoring wells, 1 of which was subsequently converted to a downhole pressure monitoring well. During the entire hydraulic fracturing operation, downhole microseismic data were simultaneously recorded in both observation wells. The pressure data from the monitor well acquired during production was entered into the reservoir model as another history matching variable. The microseismic data were used to calculate the fracture parameters and as a limiting constraint in the process. This dual porosity model is a practical example application of the methodology previously described in SPE paper 132180 by Du et al. (2010).

The sections in this paper describe a method for building a reservoir model that honors the static boundary conditions. The model was built in two parts according to the Dual Porosity nature of it: a conventional geological model representing the initial porosity and permeability of the rock matrix, and a second part that models the fracture network generated by the stimulation operations and the pre-existing natural fractures. The paper then explains how this model was tuned to enable a production and pressure history match. There is also a section devoted to the generation and utilization of a critical correlation between pressure depletion and a combined fracture half-length times the square root of permeability (Xf.√k) parameter which greatly reduced the uncertainty caused by the non-uniqueness of the match. Finally, some general conclusions regarding the results are presented.

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