A New NMR Method of Fluid Characterization in Reservoir Rocks: Experimental Confirmation and Simulation Results
- R. Freedman (Schlumberger) | S. Lo (Schlumberger) | M. Flaum (Rice U.) | G.J. Hirasaki (Rice U.) | A. Matteson (Schlumberger) | A. Sezginer (Sensys Instruments)
- Document ID
- Society of Petroleum Engineers
- SPE Journal
- Publication Date
- December 2001
- Document Type
- Journal Paper
- 452 - 464
- 2001. Society of Petroleum Engineers
- 4.3.3 Aspaltenes, 5.2 Reservoir Fluid Dynamics, 4.3 Flow Assurance, 5.5 Reservoir Simulation, 4.1.2 Separation and Treating, 5.5.2 Core Analysis, 5.2 Fluid Characterization, 5.6.1 Open hole/cased hole log analysis, 6.1.5 Human Resources, Competence and Training, 1.2.3 Rock properties, 5.2.2 Fluid Modeling, Equations of State, 4.3.4 Scale, 5.2.1 Phase Behavior and PVT Measurements, 4.1.5 Processing Equipment, 4.6 Natural Gas, 5.1.5 Geologic Modeling
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This paper introduces a new magnetic resonance fluid (MRF) characterization method. The MRF method is based on two key ingredients—a new microscopic constituent viscosity model (CVM) and a new multifluid relaxation model. The CVM provides a link between nuclear magnetic resonance (NMR) relaxation times and molecular diffusion coefficients in hydrocarbon mixtures such as crude oils. The multifluid relaxation model accounts for the T2 decay of spin-echo signals that arises from intrinsic spin-spin interactions, surface relaxation, and attenuation due to molecular diffusion of fluid molecules in a magnetic field gradient. The MRF method exploits the fact that the molecular diffusion coefficients of brine, oil, and gas molecules typically have values that are well separated from one another. Thus, the diffusion attenuation of a suite of measured NMR signals contains sufficient information to allow differentiation of brine, oil, and gas. The method involves the simultaneous inversion of a suite of spin-echo measurements with the new MRF multifluid relaxation model.
The application of the MRF method to magnetic resonance logging data can provide a detailed formation evaluation. The information provided includes total porosity, bulk volume of irreducible water, brine and hydrocarbon saturation, hydrocarbon-corrected permeability, and oil viscosity.
This paper discusses the theory underlying the CVM and validates the model by testing its predictions on hydrocarbon mixtures including live and dead crude oils. The robustness and accuracy of the multifluid inversion is demonstrated by a Monte Carlo simulation of a model carbonate rock that contains brine, oil, gas, and oil-base mud filtrate (OBMF). The MRF method is applied to suites of spin-echo measurements acquired in the laboratory on partially saturated rocks and shown to provide accurate fluid saturation and oil viscosity estimates.
Since the completion of this work, field test results have shown that the MRF method provides a powerful and unique new formation evaluation tool.
It is well known in the industry that oil-bearing reservoirs can be misinterpreted or even missed altogether by conventional resistivity-based interpretation. One difficulty is the fact that many oil-bearing reservoirs exhibit anomalously low values of resistivity, which results in spuriously high water saturation estimates. Other difficulties in the interpretation of resistivity logs can be traced to fresh formation waters or waters with unknown or variable salinity. Problems also occur in formations with complex lithologies for which use of default parameter values (e.g., m=n=2) in Archie's equation can result in totally erroneous water saturation estimates.
The purpose of this paper is to introduce a new state-of-the-art MRF characterization method. The MRF method overcomes the aforementioned problems inherent in resistivity interpretation. It also provides a wealth of formation evaluation information not obtainable by other well logging or laboratory methods.
The MRF method is based on a new multifluid relaxation model. The method relies on the different sensitivities of spin-echo measurements to the fluids present in rock formations when a suite of measurements is acquired with different pulse parameters. In general, a measurement suite consists of spin-echo sequences acquired with different echo spacings, polarization times, applied magnetic field gradients, and numbers of echoes. Such measurements are sensitive to the viscosities and molecular diffusion coefficients of the fluids and therefore provide the information needed for fluid characterization. The inversion of a measurement suite involves a nonlinear fitting of the full data suite to the MRF multifluid relaxation model.
A key ingredient in the multifluid relaxation model is a new microscopic phenomenological model of relaxation and molecular diffusion in liquid hydrocarbon mixtures. This model is referred to as the CVM. It provides an important link between diffusion-free relaxation times and molecular self-diffusion coefficients in crude oils. This link reduces the number of independent parameters needed to characterize the crude oil NMR response and improves the accuracy and robustness of the inversion.
The inversion of a suite of spin-echo measurements using the MRF multifluid relaxation model could be performed without employing the CVM by assuming that the relaxation time and diffusion coefficients are totally independent parameters. This approach, however, involves solving for more unknowns and requires a quantity and quality of data that is not easily obtainable by a moving logging tool. Moreover, to obtain oil viscosity it would still be necessary, following the inversion, to invoke empirical correlations that relate the relaxation times and diffusion coefficients to viscosity. The CVM simplifies the process by using the empirical correlations at the outset to reduce the number of unknowns and directly obtain robust fluid properties from the inversion.
CVM theory predicts that there exist distributions of relaxation times and molecular diffusion coefficients in liquid hydrocarbon mixtures. CVM further predicts that the two distributions are not independent (i.e., if either is known, the other one can be predicted). Moreover, CVM predicts that the mixture viscosity can be estimated from either the relaxation time distribution or the diffusivity distribution and that the two estimates are theoretically equivalent.
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