Uncertainties and Challenges of Modern Well Test Interpretation

Abstract

Abstract Conceptual reservoir models are the basis for any reservoir engineering study and constitute the backbone of pressure transient analysis. The selection of the correct reservoir model is of paramount importance and cannot be overstated since the validity of the well test itself is totally dependent on the applicability of selected reservoir model. Once the proper model has been selected, various computer-aided interpretation techniques and nonlinear estimation methods can be employed to obtain quantitative descriptions of important reservoir parameters and near wellbore conditions. Automated type curve matching, together with non-linear parameter estimation, are two great modern well test analysis tools made possible by recent advances in computer technology. In addition, the use of pressure derivative data has aided in model identification to a degree not experienced before. However, the problem of non-uniqueness in model responses threatens to undermine the accuracy and usefulness of these tools in helping to characterize petroleum reservoirs. In this study, we illustrate the problem of non-unique model response with an example of a simulated pressure buildup test generated for a well near a sealing fault, in an infinitely large reservoir. We show that four different reservoir models can match this model response quite accurately. This makes model selection in field cases quite impossible without requiring additional information. Using field well test data from one slanted-horizontal and a short-radius horizontal well, we show, in one case how the uncertainty in the well model, as well as the value of the effective well length led to multiple matches, resulting in different estimates of reservoir parameters. In the other case, we show that without additional information from a previous test, automatic parameter estimation alone would have led to an erroneous interpretation, and wrong parameter estimates for this short-radius horizontal well. In the field cases presented, we provide steps taken to reduce the uncertainty and non-uniqueness inherent in the process, leading to more accurate estimates of reservoir parameters from the well test data. We conclude that in many cases, integration of additional information from geology, seismic and previous well tests conducted in the same reservoir, is necessary to reduce the non-uniqueness problem and achieve an unequivocal model match.