Formatted Title
Nuclear Magnetic Resonance (NMR) Geophysics Data Processing, Data Quality, and Calibration of NMR Data-Derrived Aquifer Parameters
Background/Objectives
Nuclear magnetic resonance (NMR) geophysical tools are now available for near surface and shallow aquifer characterization using technologies first developed for use in the oil and gas industry. NMR logging provides high-resolution quantification of porosity, pore size distribution, and hydraulic conductivity. NMR data can be acquired through non-metallic (PVC) casing using existing well infrastructure eliminating the need for additional invasive borings. NMR data can also be acquired in stable bedrock boreholes, within temporary PVC casing used to stabilize boreholes, or using push tool methods. NMR is a proven technology that provides critical aquifer property data for development of conceptual site models and for remedial system design and optimization; however, data quality objectives need to be considered, and data processing tools understood to generate usable and reliable data that can be calibrated to other aquifer parameter data.
Approach/Activities
NMR detects fluid-filled pore space using magnetic fields that align and then excite polar hydrogen molecules present in water. The response of the hydrogen molecules to an excited magnetic field generates an NMR signal that can be detected by the tool. Primary properties that affect the NMR signal include the hydrogen index, transverse relaxation time (T2), and fluid diffusivity. Since the hydrogen index of water is known, the response can be directly calibrated to total porosity. T2 relaxation time is directly proportional to the pore size distribution, and knowing both porosity and pore size distribution, hydraulic conductivity can be estimated using well-established empirical formulas. Multiple algorithms are available within the processing software to estimate hydraulic conductivity including Ksdr, Ksoe, and Ktc, and each may be more applicable for specific lithologic conditions. The software tools allow for adjusting T2 cutoffs based on lithology (unconsolidated sediments, semi-consolidated material, limestone, or sandstone), and constants in each algorithm used to estimate hydraulic conductivity can be adjusted based on other available aquifer parameter data (e.g., aquifer tests, packer tests, flowmeter testing). Caution should be used when applying calibration factors while processing NMR data to make sure algorithm constants are reasonable.
Results/Lessons Learned
An overview of NMR data processing tools will be provided and examples will be presented that show variability in porosity and hydraulic conductivity estimates based on T2 cutoff assignments and algorithm constant adjustments available in the NMR data processing software. The impact of lithology (in both unconsolidated sediments and bedrock), and the variability and sensitivity of hydraulic conductivity estimates for the different algorithms available will be discussed. Methods used to calibrate NMR hydraulic conductivity data to other data sources will also be provided.