Criteria for Proper Production Decline Models and Algorithm for Decline Curve Parameter Inference
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Copyright 2018, Unconventional Resources Technology Conference (URTeC). Production forecasting of shale wells has proven to be a challenging task. Several empirical models have been proposed for shale well production data analysis; the modified hyperbolic (MH) model is the most popular one utilized by the industry due to its simplicity. However, the parameter minimum decline rate min required in the MH model always depends on an evaluators experience. In addition, in the MH model we assume that the decline during late boundary dominated flow (BDF) is exponential, which is rarely appropriate as pointed out by Fetkovich and by Spivey et al. In this paper, we first present important criteria that have been overlooked in existing empirical models but that are necessary for a robust and accurate production decline model. It turns out that the modified hyperbolic model does not violate these criteria. As a result, on the basis of a two-segment hyperbolic model, we propose an optimization algorithm that can be applied to accurately estimate five important production parameters for a well that is in the BDF regime (1) initial production rate , (2) initial decline rate , (3) initial Arps parameter for early transient flow, (4) final Arps parameter for late BDF, and (5) optimal switching time from early transient flow to late BDF. In addition, we developed a three step diagnostic approach for the analysis of flow regime that a well has gone through. The proposed diagnostic approach is effective in reducing the uncertainty in well flow regime and the estimation of production parameters. The merits of this new algorithm are demonstrated with the application to analysis of real production data in Eagle Ford and Bakken reservoirs. The methods proposed in this paper are the cornerstone to predict well estimated ultimate recovery (EUR). In addition, this work has significant impact on many other related projects such as the construction of type well production profiles, optimal completion design and probabilistic decline curve analysis since they all depend on the five production parameters.
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Proceedings of the 6th Unconventional Resources Technology Conference
