The identification of a bed's lithology is fundamental to all reservoir characterization because the physical and chemical properties of the rock that holds hydrocarbons and/or water affect the response of every tool used to measure formation properties. Understanding reservoir lithology is the foundation from which all other petrophysical calculations are made. To make accurate petrophysical calculations of porosity, water saturation (Sw), and permeability, the various lithologies of the reservoir interval must be identified and their implications understood. Lithology means "the composition or type of rock such as sandstone or limestone." Lithology focuses on grains, while rock type focuses on pores. The list of rock types contains more than 250 classifications.
To extract fluid types or saturations from seismic, crosswell, or borehole sonic data, we need a procedure to model fluid effects on rock velocity and density. Numerous techniques have been developed. However, Gassmann's equations are by far the most widely used relations to calculate seismic velocity changes because of different fluid saturations in reservoirs. The importance of this grows as seismic data are increasingly used for reservoir monitoring. Gassmann's formulation is straightforward, and the simple input parameters typically can be directly measured from logs or assumed based on rock type.
Combining the information from multiple log types can provide important information, both in terms of identifying possible errors and understanding subsurface formations. The simplest and most common means of combining multiple logs is in a multitrack log display or a crossplot. Both allow the log analyst to visualize the data more effectively than looking at each log individually. Multiple-log interpretation began with, and still revolves around, simple, quick-look visual displays. The familiar third track on a standard log display is the best example.
Locating fractures, recognizing fracture morphology, and identifying fluid-flow properties in the fracture system are important criteria in characterizing reservoirs that produce predominantly from fracture systems. Acoustic techniques can provide insight. Fracture identification and evaluation using conventional resistivity and compressional-wave acoustic logs is difficult, in part because fracture recognition is very dependent on the dip angle of fractures with respect to the borehole. Fractures are physical discontinuities that generate acoustic reflection, refraction, and mode conversion--all of which contribute to a loss of transmitted acoustic energy. In particular, compressional- and shear-wave amplitude and attenuation and Stoneley-wave attenuation are significantly affected by the presence of fractures.
After precipitation, asphaltene can remain as a suspended solid in the oil or deposit onto the rock. Here, the term precipitation corresponds to the formation of a solid phase from thermodynamic equilibrium and deposition means the settling of solid particles onto the rock surface. Deposition will induce alteration of wettability (from water-wet to oil-wet) of the rock and plugging of the formation. These aspects have been known for a long time and are the subject of many recent investigations. This section reviews the investigations and laboratory observations of these aspects.
Before selecting a method of determining permeability in a specific reservoir, one must first be assured that the core measurements are appropriate for reservoir conditions. Sample collection, selection, and preparation are important steps in ensuring that the data set represents the geology at in-situ conditions; some precautions are discussed in Relative permeability and capillary pressure. The correction parameter b is determined by conducting the test at several flowing pressures and extrapolating to infinite pressure. Alternatively, one can use an empirical correlation established by Jones to estimate b. The correlation, with R2 of 0.90, is based on measurements on 384 samples (mostly sandstones) with permeabilities ranging from 0.01 to 2500 md.
Many approaches to estimating permeability exist. Recognizing the importance of rock type, various petrophysical (grain size, surface area, and pore size) models have been developed. This page explores techniques for applying well logs and other data to the problem of predicting permeability [k or log(k)] in uncored wells. If the rock formation of interest has a fairly uniform grain composition and a common diagenetic history, then log(k)-Φ patterns are simple, straightforward statistical prediction techniques can be used, and reservoir zonation is not required. However, if a field encompasses several lithologies, perhaps with varying diagenetic imprints resulting from varying mineral composition and fluid flow histories, then the log(k)-Φ patterns are scattered, and reservoir zonation is required before predictive techniques can be applied.
Estimating permeability has been approached using a variety of models considering different rock characteristics. Two ideas inherent in Kozeny-Carman are important for later developments: the dependence of k on a power of porosity and on the inverse square of surface area. The various forms of Eq. 1 have been used as a starting point for predicting permeability from well log data by assuming that residual water saturation is proportional to specific surface area, Σ. Specific surface as ratio of pore surface area to rock volume: ....................(1b) Granberry and Keelan published a set of curves relating permeability, porosity, and "critical water" saturation (Sciw) for Gulf Coast Tertiary sands that frequently are poorly consolidated. Because Sciw is taken from the capillary pressure curve, it is a function of the size of interconnected pores. Figure 1 cannot be used to estimate permeability from porosity and water saturation as determined from well logs because it reflects only the critical water saturation.
Rock type influence on permeability discusses how permeability can be significantly affected by rock type, grain size, and extent of compaction or cementation. This page discusses several models that have been developed for estimating permeability based on grain size. Using experimental procedures that were later adopted by Beard and Weyl, Krumbein and Monk measured permeability in sandpacks of constant 40% porosity for specified size and sorting ranges. Although the Krumbein and Monk equation is based on sandpacks of 40% porosity and does not include porosity as a parameter, Beard and Weyl showed that Eq. 1 fits their own data fairly well even though porosity of the Beard and Weyl samples ranges from 23% to 43%. In fact, because of difficulties in obtaining homogeneous sandpacks, Beard and Weyl chose to use computed k values from Eq. 1 rather than their measured data in tabulating values for fine and very fine samples with poor or very poor sorting.