Knowledge of formation fluid viscosity and its vertical and lateral variations are important for reservoir management and determining field commerciality. Productivity and fluid displacement efficiency are directly related to fluid mobility, which, in turn, is greatly influenced by fluid viscosity. Therefore, viscosity is a critical parameter for estimating the economic value of a hydrocarbon reservoir and also for analyzing compositional gradients and vertical and horizontal reservoir connectivity.

The conventional methods for obtaining formation fluid viscosity are laboratory analysis at surface and pressure/volume/temperature (PVT) correlations. However, deducing viscosity from correlations introduces uncertainties owing to the inherent assumptions. Surface viscosity measurement may be affected by irreversible alteration of the sampled fluid through pressure and temperature changes, as well as related effects of long-term sample storage.

A new downhole sensor for a wireline formation tester tool has been introduced to measure the viscosity of hydrocarbons. The new viscosity sensor uses a vibrating-wire (VW) measurement method with well-established analytical equations for interpretation. Downhole field testing of an experimental prototype has been conducted, with extensive laboratory tests to validate the sensor performance in viscosities ranging from light to heavy oil and at a wide range of well environments. The vibrating wire viscometer sensor meets requirements not only for measurement performance, but also for operations in downhole applications, and possesses the following properties:

In addition to overall results for field tests, field examples of viscosity measurements are presented from a deepwater Gulf of Mexico well. In-situ measurements were performed by flowing noncontaminated reservoir oil using the focused sampling technique. The measurement of bottomhole flowing pressure and temperature, and other fluid properties such as density and gas/oil ratio (GOR), together with viscosity, allowed comprehensive analysis of the integrated dataset to understand the reservoir.