Downhole measurements of electrokinetic potential are a promising new technology for hydrocarbon reservoir monitoring. Using a 3D finite-element model combining both multiphase flow and electrokinetic components, we investigated the behavior of electrokinetic (streaming) potential during oil production in a range of reservoir environments. We found that streaming-potential signals originate at fluid fronts and at geologic boundaries where fluid saturation changes. As water encroaches on an oil production well, the streaming-potential signal associated with the water front encompasses the well even when the front is up to [Formula: see text] away, so the potential measured at the well starts to change significantly relative to a distant reference electrode. Variations in the geometry of the encroaching water front can becharacterized using an array of electrodes positioned along the well, but a good understanding of the local reservoir geology is required to identify signals caused by the front. The streaming potential measured at a well is maximized in low-permeability reservoirs produced at a high rate and in thick reservoirs with low shale content. However, considerable uncertainties remain, particularly relating to the nature of electrokinetic coupling at high salinity and during multiphase flow. Our results suggest that the streaming potential at low salinity [Formula: see text] is large [Formula: see text] but might become too small to resolve [Formula: see text] at high salinity [Formula: see text], depending on how the available data for the electrokinetic coupling at low salinity are extrapolated into the high-salinity domain. More work remains to determine the behavior of electrokinetic coupling and therefore the utility of this technique at high salinity.
We present results from a new numerical model capable of simulating two‐phase flow in a porous medium and the electrical potentials arising due to electrokinetic phenomena. We suggest that, during water‐flood of an initially oil‐filled reservoir, encroaching water causes changes in the electrokinetic potential at the production well which could be resolved above background electrical noise; indeed, water approaching the well could be detected several 10's to 100's of meters away. The magnitude of the measured potential depends upon the production rate, the coupling between fluid and electrical potentials, and the nature of the front between the displaced oil and the displacing water.
Qualitative and quantitative studies with model compounds have shown that a variety of reactions may occur at 100–300° in molecules containing isocyanate, urea and urethane groups. All or most of these reactions are subject to catalysis so that they may be induced to proceed at lower temperatures. These reactions are quite important as they may affect the production and practical use of polyurethanes and polyureas. By the proper selection of reaction components one may design a polyurethane or polyurea molecule which will give both a reasonable rate of cure to the final state and a degree of temperature stability suitable for many rigorous applications. The choice of reactive groups providing approximately the desired rates of reaction and of suitable catalysts may be used to achieve the necessary curing rate. The initial choice of a catalyst which will have a minimum effect on decomposition reactions, or the removal of the catalyst from the cured polymer will favor polymer stability. A selection of reactants which will minimize those decomposition reactions leading to chain rupture, and which will compensate for what rupture may occur, will promote polymer stability. Simple illustrations of such choices would include eliminating tertiary aliphatic hydroxyl groups from the hydroxyl-bearing component and including some degree of branching commensurate with the degree of elasticity or rigidity desired. Branching should be achieved through the more stable groups, e.g., urethane, urea or trimer, rather than through the less stable allophanate and biuret groups. Many thoroughly tested applications of polyurethanes and mixed polyureaurethanes show that it is readily possible to produce such polymers with excellent thermal stability.
We present an inversion method for 3D electrical imaging in media with an inhomogeneous and anisotropic conductivity distribution. The conductivity distribution is discretized via finite elements and is described by a second-order tensor at each finite element node. The inversion method is formulated as a functional optimization with an error functional containing terms measuring data misfit and model covariance by means of smoothness, anisotropy and deviation from a starting model. Including the model covariance information overcomes the problem of ill-posedness at the expense of limiting the allowed models to the class of models which are compatible with the provided model covariance information. The discretized form of the error functional is minimized by a Levenberg–Marquardt type method using an iterative preconditioned conjugate gradient solver. The use of an iterative solver allows one to bypass the actual computation of the Jacobian or an inverse system matrix. The use of a memory efficient iterative solver together with the implementation on parallel computers allows large-scale inverse problems, comprising several hundred thousand nodes with hundreds of sources and receivers, to be solved. The new method is tested using computer-generated data from two- and three-dimensional synthetic models. For each inversion a choice of penalty parameters, gauging the level of model covariance information imposed, has to be made and the level of regularization required is hard to estimate. We find that running a suite of inversions with varying penalty parameters and subsequent examination of the results (including inspection of residual maps) offers a viable method for choosing appropriate numerical values for the penalty levels. In the applications we found the inversion process to be highly non-linear. Inversion models from intermediate steps of the iterative inversion show structure in places that do not exhibit structure in the true model and only at later iterations do anomalies move to the correct location in the modelling domain. This result indicates that linearized inversions that fail to re-linearize during the inversion process will fail to find meaningful inversion images. The inversion images achieved using the new method recover the important features of the true models, including the approximate magnitudes of the conductivity anomalies and the magnitudes and directions of anisotropy anomalies. The inversion images are generally ‘blurred’, that is sharp edges are smoothed, and the recovered magnitudes of conductivity, anisotropy and anisotropy direction are generally under-estimated.
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