Field Applications of Constrained Multiwell Deconvolution

Abstract

Objectives/Scope This paper applies a new constrained multiwell deconvolution algorithm to two field cases: a gas reservoir with two producers, and an oil reservoir with three producers and one injector. Responses given by the constrained multiwell deconvolution are compared with simulations from history-matched reservoir models. Methods, Procedures, Process Permanent downhole pressure gauges are routinely installed in most new wells. The resulting large datasets are usually underexploited, however, because it is near impossible to extract information with conventional techniques in the case of well interferences. Multiwell deconvolution (Levitan, 2006; Cumming et al., 2013) solves this problem by providing constant-rate derivative responses of individual wells as if they were producing alone, and interference effects from any one well to any other. The deconvolved responses have the same durations as the corresponding pressure histories, and show features not visible in any of the test build up or drawdown periods. Results, Observations, Conclusions The published multiwell deconvolution algorithms are extensions of the single-well deconvolution algorithm from von Schroeter et al. (2001, 2004), which has become an essential component of the well test analysis toolbox. The Cumming et al. (2013) multiwell deconvolution algorithm has been successfully applied to a North Sea reservoir by Thornton et al. (2015). Multiwell deconvolution, however, is strongly overdetermined and the algorithm may not converge to the true solutions. It may also yield solutions that can reproduce pressure histories equally well while being non-physical. This is especially true when dealing with real field data that may be affected by noise on pressure and rate data. In order to reduce non-uniqueness and ensure physically feasible results, Cumming et al. (2019) incorporated additional constraints on the shapes of the deconvolved derivatives to discourage non-physical solutions. They also introduced additional knowledge on the reservoir, for instance, if it is a closed system, all deconvolved derivatives should tend towards a common unit slope at some future time. They illustrated the application of their enhanced algorithm with synthetic examples with known solutions involving up to nine wells. In this paper, the constrained multiwell algorithm is successfully applied to both a gas and an oil reservoir. Novel/Additive Information By extracting well and interwell reservoir signatures, multiwell deconvolution allow identification of compartmentalization or unanticipated heterogeneities very early in field life, making it possible to adjust the field development plan and the locations of future wells. In addition, it can accelerate the history-matching process by doing it on constant rate pressure responses rather than on complex production histories. An added advantage is that the comparison between the model derivatives and the actual deconvolved derivatives enables identification of mismatch causes.