The determination of well and reservoir parameters is paramount during exploration, appraisal of new reservoirs, and equally important during the development and production phases of a field. The interpretation of pressure transient-test data is one of the tools to obtain such parameters under dynamic conditions. Often this data is substantially influenced by non-reservoir factors such as gauge drift, adjacent noise due to natural or operational reasons, insufficient gauge resolution and dominant tidal effects. Any of these non-reservoir factors can significantly lead to a misleading interpretation of the formation. Rigorous vigilance against such occurrences is particularly important in designing deep transient well tests. The paper quantifies these effects.

Obtaining strong, unequivocal reservoir responses in the pressure data are imperative in extracting the reservoir and well characteristics. Individual and convoluted effects of noise, drift, resolution, periodic tides have been looked into quantitatively to demonstrate the situations when the reservoir signal is too weak to make any meaningful characterization. Reservoir models have been utilized to develop quantitative criteria to describe the dominance and subsidence of the effects of noise, resolution, drift and periodic tides under different operating conditions. These criteria should guide test design so that the subsequent test can produce meaningful data.

Depending on the amount of disruption caused in the measurements, there are situations when the test objective may not be achieved at all. Failure to create dominant reservoir responses results from insufficient signal-to-noise ratio due to the rate of production and the pressure drawdown. It is a function of formation, fluid properties or mechanical environment. A minimum rate of production for creating the required magnitude of signal-to-noise ratio must be achieved to interpret correctly the reservoir response. The paper provides guidelines to determine the minimum rate and drawdown needed to obtain the presence of deep heterogeneities or boundaries with a reasonable level of certainty. If a test is run with a rate lower than the minimum value, for example, the data will be biased by other hardware or natural factors, unrelated to the reservoir response. Examples are also presented with artifacts of non-reservoir effects to show how misleading characteristics of the reservoir and the well can be deduced with such distorted data.

This study establishes cause-effect relationships due to certain non-reservoir factors so that engineers can select their hardware, choose the methods and timings to mitigate the associated undesirable effects. Such a practical guide to select the most suitable transient test will rightfully fill in its place in the literature. The methodology applies equally to wireline testing operations, deep transient testing, drill-stem testing and production testing.

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