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.
The accurate determination of fluid properties and contamination while sampling with a wireline pump-out formation tester is essential to achieve the primary objective of obtaining representative reservoir fluid samples with minimum rig time. Despite advancements in fluid identification sensors, sampling in mixed phases, especially immiscible fluids, still poses a great challenge. It often happens that apparent erratic sensor responses are attributed to sensor noise, but careful study reveals that the sensors are actually showing the true nature of the multi-phase fluid flow. However, if this multi-phase behavior is not considered, it can be difficult to determine fluid type and contamination. This work addresses the development of a new numerical and analytical investigation that makes it possible to not only understand the cleaning behavior of formation fluids but also quantitatively determine fluid qualities in real time. This newly developed technique highlights the variables that play an important role in guiding the clean-up process and, at the same time, provides the temporal characteristics of the contamination level versus both time and fluid volume. Furthermore, uncertainties in the pumping time required to achieve the desired level of contamination are also calculated with this method. Field examples from Gulf of Mexico, South America and North Sea are provided to demonstrate the efficiency of this technique in oil-based mud and water-based mud contamination examples for both hydrocarbon fluid and formation water samples, with comparisons to PVT laboratory measurements. An error analysis is performed for each example, and results are presented in this research.The new analysis technique is applied to a high-resolution fluid density sensor that monitors the change of resonance frequency of a vibrating tube-carrying fluid sample. The same interpretation method is also applied to a capacitance sensor and a resistivity sensor to further confirm the results derived from the density sensor.
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