In conventional oilfield applications of low-field nuclear magnetic resonance (NMR), data acquisition and analysis are optimal for T2 relaxation in the center of the spectrum, nominally between several milliseconds and several seconds. However, there are numerous applications where the measured magnetization data have short relaxation components, approaching or even below the time resolution of the down-hole and/or laboratory measurement. Examples of these applications include heavy oil, organic shale reservoirs and hydrocarbon and water in small pores. In these applications the relaxation spectra of interest are typically a few milliseconds. Because traditional algorithms used to analyze NMR data to estimate porosity and other petrophysical properties involving short relaxation times can be inaccurate, a new algorithm is proposed to improve the accuracy of these parameters. This algorithm consists of the following steps: First, a T2 distribution is estimated from the measured magnetization data using traditional inverse Laplace transform methods. Second, for a given pulse sequence and a set of acquisition and inversion parameters, a porosity sensitivity curve is computed. Next, a correction factor is derived from this sensitivity curve and applied seamlessly as part of the inversion to obtain a modified T2 distribution. After application of the correction factor, the overall porosity sensitivity is more uniform at short relaxation times.
The performance of the algorithm is illustrated by Monte Carlo simulations and by application on two field examples from unconventional shale reservoirs. Prediction of porosity from NMR measurements is particularly useful in unconventional reservoirs for two reasons. First, NMR measurements provide a direct estimate of effective porosity without requiring detailed knowledge of the complex mineralogy typical of shale formations. Second, the deficit between effective porosity predicted from NMR and total porosity predicted from nuclear logs can be used to obtain accurate estimates of petrophysical quantities such as the kerogen content in shales and the hydrogen index in heavy oil formations. The two field examples are from reservoirs in the Wolfberry trend in the southwestern United States where the Sprayberry formation and Wolfcamp shale are commingled. In the first field example, we find that the deficit between total porosity and effective porosity predicted from NMR T2 distributions using the new algorithm provides a more accurate estimate of the kerogen content. Application of the new algorithm to NMR data in the second field example results in an increase of up to 10% in porosity in zones with T2 less than 10 ms. The total organic content (TOC) prediction from the new algorithm shows an improved correlation with core measurements.
