As a basic form of pattern analysis, the parameters of dune spacing, defect density, crest orientation and crest length are measured from remote images and treated statistically for dunes at White Sands in New Mexico, the Algodones in California, the Agneitir in Mauritania, and the Namib in Namibia. Statistical populations are identified from frequency plots of dune spacing and crest length, field-scale calculations of defect density, and rose diagrams of crest orientation. Single populations characterize simple dune fields (White Sands), whereas multiple populations characterize compound/complex dunes (Algodones, Namib), and complex dune fields (Agneitir). As time increases, dune fields show an increase in dune spacing and crest length, a decrease in defect density, more tightly clustered crest orientation, and a reduction in the variance associated with measurements of these parameters. The results are consistent with models of dune fields as self-organizing complex systems in which a characteristic pattern emerges as a function of constructional time. Because pattern evolution is a function of time, it may be possible to use pattern analysis to augment current methods of age determination. Statistically defined populations can be used in geomorphic backstripping to unstack generations of simple patterns that give rise to complex patterns, and to reconstruct each generation in terms of construction time and palaeo-wind regime.

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Summary This paper presents a practical technique to quantify communication between wells in a reservoir using only production and injection rate data. The technique combines a constrained multivariate linear regression analysis with diffusivity filters to provide information about permeability trends and the presence of transmissibility barriers. The method was developed and tested using a numerical simulator and then applied to a waterflooded field in Argentina. The simulation results indicate that the connectivity between wells is described by coefficients that only depend on geology and relative position between wells; they are independent of injection/production rates. The results of this work can be used to improve the performance of an existing waterflood by suggesting how well patterns might be changed or managed. They could also be used to model flow in the reservoir.

A revision of the 1989 classic, Enhanced Oil Recovery by Larry Lake, this text, Fundamentals of Enhanced Oil Recovery, retains the original work's emphasis on fractional flow theory and phase behavior to explain enhanced oil recovery (EOR) processes. There is additional coverage on cutting edge (or current) topics, such as low-salinity EOR, steam-assisted gravity drainage, and expanded coverage on thermodynamics and foam EOR. With its frequent reinforcement of two fundamental EOR principles, lowering the mobility ratio and increasing the capillary number, it is an excellent resource for undergraduate classes. Errata (http://go.spe.org/FEORerrata)

Macroscopic dispersion is the mixing, on the scale of several hundreds of grain diameters, at a point in a permeable medium that is free of boundary effects. Megascopic dispersion is the one-dimensional (lD) dispersion derived by averaging across an entire cross section. This work investigates how both dispersions vary with heterogeneity, aspect ratio, diffusion coefficient, and autocorrelation. The theoretical results are compared to existing field and laboratory data and to existing theories for limiting cases.The degree of autocorrelation in the medium determines whether or not megascopic dispersivity (dispersion coefficient divided by velocity) is uniquely defined. Large correlation distances (with respect to the medium dimensions) imply a dispersivity that grows with distance traveled. Small correlation distances imply a dispersivity that is eventually stabilized at some constant value. This value is related to the heterogeneity of the medium. On the field scale, diffusion is insignificant, but on a laboratory scale, it can stabilize the dispersivity even if the medium is correlated. Macroscopic dispersivity is sensitive to diffusion in both the laboratory and field scale. It is smaller than or equal to megascopic dispersivity, also in conformance with experimental data, and comparable to laboratory-measured dispersivity.

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Virtually every sandstone reservoir contains significant amounts of shale. Although the sand/shale sequence in the wells may be known, a reliable reservoir description must define quantitatively the lateral continuity of the shale, the shale distribution in unsampled areas, and the effects of the shales on transport properties. This paper presents statistical techniques by which the lateral continuity and spatial disposition of shales can be estimated. These techniques rely on shale statistics from the wells and outcrop statistics from appropriate depositional environments. The resulting shale description can be conditioned to conform with the observations of the wells. The subsequent discretizing of the reservoir into gridblocks for numerical simulation yields large elemental units that usually contain both sand and shale. The paper concludes by showing how the permeability and paper concludes by showing how the permeability and porosity may be estimated from the disposition of shales porosity may be estimated from the disposition of shales within each block. Introduction Shales either divide reservoirs into separate hydraulic units, or, if the shales are discontinuous, set a tortuous environment in which fluid transport occurs. While continuous correlatable shales are handled with ease in reservoir simulation models, discontinuous shales present a problem. This study is concerned with such present a problem. This study is concerned with such uncorrelatable discontinuous shales in sandstone reservoirs. The primary source of information about shales is, as with virtually all reservoir properties, the wells. An empirical distribution of shale thicknesses can he inferred from an observed sand/shale sequence. Unless the well spacing is extremely dense, nothing is revealed about the shale's lateral dimensions. A secondary source of information is visual studies on shale dimensions in outcrops of ancient sediments. Once the depositional environment is identified by the geologist, outcrop statistics from a depositional environment similar to the one to be simulated are used for the assignment of lateral shale dimensions. Since we are concerned here with uncorrelatable or stochastic shales, the shales are assumed to be randomly distributed in space. This means that the shale dimensions and the coordinates of the shale centers are random, independent statistical events. This paper describes a shale management procedure that handles sampling of the empirical statistics, generates shale coordinates, and maintains control such that conditioning at the wells is achieved. Conditioning means that the fraction of shale and the vertical succession of sand and shale observed in a well are identical to that in a synthetic description. Finally, when a user-defined grid system is superimposed on the resulting description, the procedure calculates effective porosities and permeabilities for each gridblock. Definition Four scales of averaging volume can be recognized for porous media averages: microscopic (the scale of only a porous media averages: microscopic (the scale of only a few pores), macroscopic (the size of conventional core plugs), megascopic (the size of large gridblocks in field plugs), megascopic (the size of large gridblocks in field models), and gigascopic (total formation or regional scale). These concepts are illustrated in Fig. 1a. As discussed by Bear, porous-media transport equations usually are based on the continuum approach. This means that the actual porous medium is replaced by a fictitious continuum. Physical properties and dependent variables are averages over elemental physical volumes constituting the continuum. The suggested distinction among four scales is necessary because measurements on one scale are not necessarily applicable on another scale (Fig. 1b). In particular, measurements on cores, by which intrinsic sand properties are found, cannot be used readily for megascopic blocks, which include gross textural differences such as sand and shales. This discrepancy is particularly noticeable in numerical reservoir simulation particularly noticeable in numerical reservoir simulation where fluid flow equations are formulated on a macroscale but are frequently solved on a megascale. Shales are generally recognized by geologists as fine-grained, indurated sedimentary rocks with finely laminated structure. They are believed to be a lithification product of muddy sediment, of any origin, and the finely laminated texture is related to orientation of micaceous clay mineral constituents. For our purposes, the term "shale" includes shale laminae, shale streaks, and massive shales. Two shale types are distinguished. The definitions are based on whether the dimensions and spatial disposition of the shales are known. Stochastic shales cannot be correlated between wells and appear to be scattered randomly within the sand matrix (Fig. 2a). Deterministic shales are continuous between observation points. There is no uncertainty associated with their existence and lateral continuity (Fig. 2b). Most reservoirs are hybrid with respect to shales in that both deterministic and stochastic shales coexist; however, the occurrence of stochastic shales clearly becomes more prevalent with large well spacings. SPEJ P. 447