Near-borehole stress analysis is performed from an integrated interpretation of sonic, imaging, and caliper tools to determine the magnitude and orientation of stresses around the borehole. These stresses can affect the wellbore stability because the tectonic stress field is disturbed during the well drilling. This paper presents a case study in which integrated information from borehole acoustic logs, image logs, and caliper tools were used to identify stresses in the near-borehole region of a deepwater presalt formation, offshore Brazil. The objectives were to extract the maximum (SH) and minimum stress (Sh) orientations in the near-borehole region for a particular zone of interest and identify fractures due to tectonic stresses. The Sh orientation was determined by the presence of induced fractures and borehole breakouts (both created during drilling), which were observed in the ultrasonic and resistive imaging log data. The SH orientation can be characterized using cross-dipole sonic anisotropy processing when borehole conditions are favorable. Tectonic fractures were identified using ultrasonic and resistive imaging log data information and the Stoneley reflection method was used to support the conductivity interpretation from these image logs.
The low contrast between formation oil and oil-based mud (OBM) filtrate as well as noise associated in the signal can cause the T2 distribution for the different fluids to resemble each other, making it difficult to identify the formation fluid using nuclear magnetic resonance (NMR) data. One challenges is to quantify the remaining oil volume and estimate the formation fluid proprieties using the diffusion map (T2D) from two-dimensional (2D) inversion. Consequently, it is important to determine new methodologies that can properly enhance the evaluation of such environments. This paper presents an application of blind deconvolution with a maximum likelihood algorithm processing applied to enhance the NMR diffusion map (T2D), helping to identify, quantify, and characterize the remaining oil volume in an invaded zone. The blind deconvolution algorithm is effective even when no information about the noise is known, making it possible to enhance the T2D map, deconvoluting the point-spread function (PSF) from the signal. After the enhancement, a multiple asymmetric Gaussian fit is used to generate a modeled distribution of the formation oil to estimate the remaining formation oil volume and its T2Intrisic logarithmic mean. The methodology using the blind deconvolution over the T2 diffusion map was tested. Promising results provided a formation oil distribution consistent with expected fluid properties measured.
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