The Nuclear Magnetic Resonance (NMR) response of gas in gas shale nanopores is different from that of bulk gas, where relaxation is dominated by spin rotation and diffusion is unrestricted. Gas shales are characterized by very low porosity and ultra low permeabilities. Their porosity is dominated by nanometer-scale pores in the organic kerogen that restricts diffusional motion, in addition to having very high surface-to-volume ratios that enhance surface relaxation. At high pressure, the gas exists as an adsorbed phase on the pore surface and as free gas phase in the pore interior. Thus, relaxation and diffusion properties of gas in gas shales are controlled by the combined effects of adsorption, enhanced surface relaxation, restricted diffusion and molecular exchange between the adsorbed and free phases. One of the biggest challenges is the understanding of such effects in order to determine the quantity of free and adsorbed gas from NMR data, and to devise novel techniques to log these unconventional plays. Proper estimation of fluid volumes also requires the knowledge of the hydrogen index for the gas restricted in the gas shale nanopores, which is yet another challenge. The NMR responses of methane gas in Haynesville shale plugs cored from a well in East Texas, USA were studied in laboratory experiments using a 2 MHz NMR spectrometer at elevated pressures up to 5 kpsi. The effects of adsorption, surface relaxation and restricted diffusion have been characterized, and the hydrogen index of the gas has been measured. Mineralogy, elemental analysis and Brunauer-Emmett-Teller (BET) experiments have also been carried out on the same plugs to understand the formation characteristics. In the samples studied, faster relaxation modes (few tens of milliseconds) and slower apparent diffusion coefficients (an order of magnitude less than their bulk values) for the confined gas molecules in comparison to their bulk properties have been observed for the first time with the help of 2D-NMR experiments at high pressure. It has been observed that the relaxation spectra for bound water and the gas in the small pores overlap. Additional information is required to resolve these two fluids. Subsequently, the diffusion dimension is investigated to resolve the various fluids in the nanopores. We formulate new relaxation and diffusion models for the interpretation of the dynamics of gas restricted in gas shale and propose that multi-dimensional NMR logging with pulse sequences optimized for gas shales be further tested in the field, to help quantify the total gas in place.

A new model is proposed for interpreting two-dimensional diffusion-relaxation nuclear magnetic resonance (2D NMR) maps by including the effects of restriction on the diffusion of water in a connected porous medium. The model's theoretical underpinnings are reviewed and a correlation derived between the diffusion and relaxation of restricted water molecules, i.e., the expected 'water line' where the water signal should appear on 2D NMR maps. The main application discussed in this paper is to improve the quantitative separation of different fluids in the formation, leading to better prediction of water and hydrocarbon saturations. In many carbonate formations, the effects of restriction may be quite pronounced making the standard fluid characterization with diffusion-relaxation maps questionable. The restricted-diffusion model is applied to laboratory measurements on carbonate core plugs and to an example log from a Middle East carbonate well where the saturations obtained with the new model are compared against other measures of saturation available in the dataset.

The last decade of nuclear magnetic resonance (NMR) logging has been dominated by T2 measurements and sparse sampling of polarization (differential spectrum) or diffusion (shifted spectrum) effects. Recent literature has expanded on the utility of NMR logging using T1 measurements to provide information on long time constants that are difficult or impossible to derive from T2 measurements using gradient tools. NMR logging modes have typically been designed to focus on a single specific NMR response, either very fast or very slow diffusion, or long T1 times, to identify either oil or gas. However, fluid properties can vary widely and often unpredictably downhole. A small number of NMR measurements that focus on just one NMR parameter may not provide sufficient information to identify and quantify fluid volumes, particularly when several different fluids are present. Achieving accurate fluid properties often requires data acquisition that simultaneously measures T1, T2, and diffusion contrasts. Inversion is most effective when all three NMR properties, T1, T2 and D, are obtained. We review the relative merits of T1, T2, and diffusion-based measurements in the light of petrophysics and instrumentation limitations. The relative merits of forward-model inversion and model-free analysis are discussed with reference to data sets recorded in a broad range of environments. Introduction A trend that has continued throughout the development of NMR fluid typing methods has been the increasing reliance on molecular diffusion (D) to identify and characterize different fluids.1–5 Recognizing the importance of diffusion, Hürlimann and coworkers6 developed diffusion editing (DE) acquisition and 2D NMR (2DNMR) methodology.7 The introduction of DE measurements represents an important advance in NMR logging because the method provides a substantial improvement in diffusion sensitivity relative to the conventional Carr-Purcell-Meiboom-Gill (CPMG) train.8 Also important has been the introduction of 2DNMR analysis. This analysis method invokes a model-independent inversion to generate 2D maps that display multimeasurement NMR data in an easily understandable format. Early examples of 2DNMR used DE data suites with different echo spacings to correlate diffusion rates and transverse relaxation times (T2).5,9 The resulting D- T2 maps provided clear separation of oil and water signals, allowing accurate determination of the fluid saturations and oil properties. Although DE acquisition and 2DNMR inversion processing were developed jointly, it was later demonstrated that the 2DNMR approach could also be extended to sequences comprised entirely of CPMG measurements10 if DE data were not available. As experience with 2DNMR methodology was gathered, it became clear that improvements in both acquisition efficiency and data analysis could be achieved by extending the technique to include longitudinal relaxation (T1). The resulting 3D approach (3DNMR) accounts for all relevant NMR properties, (i.e., T2, T1, and D) in a single comprehensive analysis of multimeasurement NMR data. The remainder of this paper provides a description of multimeasurement NMR analysis. The first section describes in more detail how the various maps (e.g., D-T2, D- T1) are generated and explains the relationships between them. It also discusses the effects of noise and regularization on the resolution and precision of 3DNMR inversion results. Sections two and three outline the application of 3DNMR to fluid typing in wells drilled with water-based mud (WBM) and oil-based mud (OBM) respectively. Field examples are used to illustrate points concerning the processing and interpretation. 3D NMR (3DNMR) Analysis There are several benefits offered by multidimensional NMR acquisition and analysis. First, the contour maps (e.g., D-T2, D-T1, T1- T2), provide a convenient and understandable means of visualizing complex NMR data, allowing identification of different fluids and calibrating their NMR response. From an operational perspective, the multimeasurement approach allows definition of a small set of acquisition sequences that are applicable in a wide range of environments. This approach simplifies NMR job planning, because there is no longer a need to define acquisition parameters for each job.

This paper presents a logical approach to ensure successful nuclear magnetic resonance (NMR) logging for fluid-characterization purposes. Numerous examples are shown in a three-stage process consisting ofplanning,processing and interpretation, andvalidation and integration. First, modeling is performed to assess the NMR contrast, using any available information concerning anticipated fluid types. Knowledge of downhole conditions also allows the estimation of the NMR signal-to-noise ratio (SNR) from which the sensitivity and statistical error can be evaluated. The outcome of the planning process is a suite of NMR pulse sequences that optimizes resolution of the different fluids and minimizes error with the shortest possible acquisition time. Interpreting fluid-characterization NMR logs is relatively straightforward when the fluids have large NMR contrast and obey the standard correlations. However, dissolved gas, wettability variations, internal field gradients, and restricted diffusion can cause deviations from model behavior and have significant effects on NMR relaxation data. These effects need to be recognized and accounted for. To gain insights into the NMR-based fluid evaluation process, we have developed a model-independent technique that gives various sets of D-T1-T2 maps. The maps are analogous to the crossplots commonly used in log interpretation practices and are indispensable in the interpretation of the data. We also explain quantitative interpretation techniques from D-T2 maps. Integrating NMR fluid-characterization results with other information and openhole logs constitutes the final step. As with any technique, results need to be crosschecked with other log data for consistency. The answer gives a water saturation that can be verified with other tools and techniques such as the dielectric log, resistivity logs, and the density magnetic resonance technique method. Introduction Current methods to analyze fluids using suites of NMR measurements employ model-based inversions. Two examples of the forward-modeling approach are the MACNMR1 and Magnetic Resonance Fluid (MRF) characterization methods.2 The MRF technique is based on physical laws that are calibrated empirically to account for the downhole fluid NMR responses. By using realistic fluid models, MRF aims to minimize the number of adjustable parameters to be compatible with the information content of typical NMR log data. Since the model parameters are by design related to the individual fluid volumes and properties, determination of the parameter values (i.e., data-fitting) leads directly to estimates of the fluids petrophysical quantities. Any forward-model approach relies on the validity of the fluid models employed. In "non-ideal" situations where the fluid NMR response deviates from the model behavior (such as internal gradient, oil-wet rocks, restricted diffusion etc.), these techniques may lead to erroneous answers. In some circumstances, "non-ideal" responses may be identified by a poor fit-quality of the echo data, in which case the fluid models can be adjusted by modifying the appropriate model parameters. However, it may not be obvious which element of the fluid model should be modified and what modification is needed to get the desired answer. The maximum entropy principle (MEP) method is a model-independent inversion that provides a simple graphical representation of NMR data for fluid analysis in all environments. The graphical representations (i.e., multi-dimensional distributions) can themselves be used directly for interpretation or, alternatively, they may be used to guide the selection of parameters for model-based processing such as MRF. It is important to recognize that the MEP technique as well as the methods to interpret D-T2 maps are applicable to both CPMG (Carr, Purcell, Meiboom, and Gill) and DE (diffusion editing)3 measurements.

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