The ability to predict in real-time subsurface formation properties during the drilling process has been many times documented as being critical to high performance drilling activities. Lowering the costs of geothermal wells could benefit of real time formation properties prediction. Machine learning and AI are generally successful in formation properties prediction if relevant data for training purposes exists. A combination of real time measurements and machine learning could reduce the number of physical experiments while the real time obtained data will improve the accuracy of machine learning prediction. This paper is showing the development of a testing protocol using NMR that could provide relevant data for machine learning future applications. Nuclear Magnetic Resonance (NMR) technology can be used for the measurement of petrophysical properties of reservoirs using drill cuttings. NMR provides a non-destructive and efficient method for analyzing the properties of rock formations, which can aid in reservoir characterization, well placement, and production optimization. Traditionally, petrophysical analysis of reservoirs has relied on core samples, which can be costly and time-consuming to acquire. On the other hand, cuttings can be utilized for petrophysical analysis, which are small rock fragments generated during drilling operations and are readily available. Hence, the utilization of NMR technology with cuttings presents a valuable opportunity to gather crucial data without the need of core analysis. In this paper, we present results on porosity measurements on artificially generated cuttings from sandstone and limestone blocks in the range of 0.5 – 2mm. NMR porosity using T1 and T2 relaxation times of the cuttings as well as their core, were measured and compared in varying conditions- 100% saturated and dry. The results show that as the cutting size increases, the porosity value becomes closer to the cores. Among the samples considered in this work, the results obtained for sandstone, followed the trend more closely. Furthermore, in order to simulate well site conditions, cuttings corresponding to mesh 10 and 35 dimensions (0.5 - 2 mm) were analyzed in different states: excess solution, no excess solution, 24 – 96 hours dry. An inverse relationship was observed between porosity and drying time, and as anticipated, the larger cuttings gave results more closely related to the cores. Overall, the results demonstrate the potential of the application of NMR technology in measuring petrophysical properties of rocks using drill cuttings in real-time. This can be made possible by utilizing the result trends and developing predictive models that consider cutting size, drying time, and other relevant factors. The ability to rapidly assess porosity with NMR technology using cuttings offers a cost-effective and time-efficient alternative to traditional core analysis methods. Ultimately, harnessing NMR technology with drill cuttings has the potential to revolutionize how we gather essential data for reservoir management, leading to more informed decision-making in the energy industry.

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