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Shear waves propagate through rock with different velocities in different directions. This phenomenon is called acoustic anisotropy, and it is caused by the anisotropic nature of the rock's elastic properties. All sedimentary rocks exhibit some degree of acoustic anisotropy related to aligned fractures, layering, or stress imbalance. Until recently, wireline sonic tools were able to measure the anisotropy magnitude and orientation reliably only if the velocity anisotropy was greater than 5%. In this paper, we will discuss the field test results from multiple wells of a new sonic tool that is able to measure anisotropy as low as 1%. We will also comment on how to identify the cause of the anisotropy and its applications. Because we can measure the acoustic anisotropy with this new tool—even if it is very low—and relate it to the earth-stress direction, we are able to provide reservoir engineers with valuable information to optimize the field development and improve well production. The tests were conducted in several Pemex development wells in northern Mexico, mostly in the Burgos basin. The target formations were tight gas sands. The sands have very low permeability and must be stimulated to produce hydrocarbons in commercial quantities. These hydraulically fractured vertical wells have elliptical drainage patterns, and optimum reservoir drainage depends on the correct well placement to avoid creating interference between wells (overlapping drainage areas) or leaving areas untouched (drainage gaps). Since hydraulic fractures open in a plane perpendicular to the minimum stress, determining stress direction is crucial to the placement of new wells. Also, it can help to look for in-field drilling opportunities in brown fields where early drilling strategies did not consider the stress orientation when selecting well locations. In addition, by knowing the stress-field state, it is possible to apply oriented perforating techniques to maximize the results of the fracture treatments. Introduction Sonic logging tools equipped with crossed-dipole transmitters provide the capability to measure shear anisotropy. [1–3] It is well known that any anisotropy present in the formation is important in seismic amplitude variation with offset (AVO) applications and can serve as input to optimal seismic data processing and imaging. Acoustic anisotropy can be characterized either as intrinsic or stress-induced. Intrinsic anisotropy can be related to bedding, shales, or aligned fractures. Stress-induced anisotropy results from an imbalance in the tectonic stresses acting on the rock. Fracture anisotropy is important in determining production and drilling strategies. Stress-induced anisotropy and stress direction are important for geomechanical applications, including oriented perforating for optimized fracturing in both hard and soft formations [4] and placement of wells for maximum production. [5] Crossed-dipole logging with advanced frequency-domain processing provides the unique capability to characterize the mechanical state of the formation around the borehole. Dispersion curves yield information that distinguishes intrinsic from stress-induced anisotropy [6] and describes the radial variation of the shear speeds into the formation. [7]

Inverting borehole sonic data for anisotropic elastic parameters requires data acquired in, at least, two different orientations. In practice this often means combining acquisitions from two offset wells, which is only straightforward if the encountered formations are laterally continuous. If wells do not tie laterally, or if data acquired at different depths in vertically heterogeneous strata need to be combined, then an inversion strategy is required that explicitly takes heterogeneity into account. This short note outlines such a strategy on the basis of borehole sonic and conventional petrophysical data, structural information, and well surveys. A new and sophisticated workflow was developed to estimate TI anisotropic parameters from borehole sonic logs, adopting a smart data clustering approach. Among other things, the output consists of elastic properties as a function of independently acquired data such as shale volume and density. In turn, these expressions constitute a model that is driven by measurements instead of assumptions, that can be established also in the presence of lateral and vertical heterogeneity, and that can be applied in neighboring wells where the amount of sonic data are insufficient for a complete inversion.

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