Production forecasting and determination of Estimated Ultimate Recovery (EUR) continues to be one of the most important tasks of reservoir engineers. Forecasting exercises, although they may vary among different E&P Companies, is an activity carried out relatively often (quarterly, biannually or annually). Objectives range from complying with government regulations (in the case of Reserves estimation), raising project finance and budgeting allocation within the company, defining "healthy" production targets, downstream activity planning and lookbacks for project outcome evaluation. In the case of Coal Seam Gas (CSG) Reservoirs with hundreds or in some cases thousands of wells, this task usually requires significant allocated time and resources.

It is for this reason that the idea of having an automated tool able to fit the latest production data of a given well, and generate a production profile in a relatively short period of time, is very attractive. Decline Curve Analysis (DCA) continues to be a very popular approach to generate, within a short period of time, a representation of an expected production decline and EUR. However, this technique alone is insufficient to provide adequate confidence over the generated long term production performance for a given well. Many questions still remain unanswered, such as, if the EUR is consistent with the Original Gas in Place (OGIP) connected to the well?, the Recovery Factor (RF) and whether it is consistent with reservoir characteristics.

In this paper, an algorithm developed on the open source programming language Python is able to generate an expected production profile for a given CSG well within minutes and from readily available production and flowing pressure data. In addition, it gives the analyst valuable reservoir information such as OGIP and kh and it ensures that the long term production profile is consistent with reservoir characteristics and reservoir volumetric. Furthermore it allows for a meaningful evaluation and characterization of important behaviors inherent to CSG reservoirs such as Stress Dependent Permeability (SDP), adsorption parameters (PL, VL), interference effects from infield programs, characterization of suitable cleat porosity and multi-layered differential depletion.

The algorithm combines the flowing material balance (FMB) equations and technique adapted to CSG reservoirs derived by C.R. Clarkson et al (2006, 2007) to extract valuable reservoir information such as flow potential kh (when skin is assumed) and OGIP from readily available production and flowing pressure data. This information is then used to inform a simple yet relevant multi-layer heterogeneous CSG reservoir for Recovery Factor (RF) estimation under defined abandonment conditions. This then is used to better inform the expected decline parameters b and Di for DCA estimation providing an additional level of reassurance that the generated production profile and EUR are well supported by the reservoir characteristics and volumetrics.

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