Acoustoelastic Estimation of the In Situ Stresses from Sonic Logs for a Carbonate Reservoir

Abstract

Abstract Knowledge of the magnitude of in situ stresses is crucial to a broad range of applications, particularly drilling, well completion, and hydraulic fracturing design. Stress field characterization is attained by the determination of magnitude and direction of the three principal stresses: the vertical or overburden stress (Sv), the minimum horizontal principal stress (Shmin), and the maximum horizontal principal stress (SHmax). While numerous direct borehole measurements, such as density logs and leak-off tests, provide reliable assessments of Sv and Shmin, estimating local SHmax remains a challenge. This paper presents a workflow that provides with an approach for estimation and constraining of the maximum horizontal principal stress in a carbonate reservoir from borehole sonic logs, by comparison to the isotropic stress state. Our study was restricted to cases with limited achievable data, namely, conventional logs, which is expected to be the circumstances for most practical applications. The results were compared to published regional stress reports, with the goal of validating and understanding the variations of stresses at the field scale. Those findings were in agreement with the estimations obtained from other acoustoelastic methods and the region fault regime in terms of the relative obtained magnitudes of the stresses. In addition to an increasing trend in the calculated stress magnitudes with depth, we observe local variations of stresses in this oilfield, that we suspected to be caused partially by the non-uniform distribution of production and injection activities. The novelty of our proposed workflow is the ability to give borehole estimates of the maximum horizontal principal stress magnitude for a carbonate field located in a regionally compressive zone, without the need for availability of costly measurements, and by taking advantage of techniques to fill in the gaps of limited data, thus, allowing for integrative use of multiple data types that are commonly available.