ABSTRACT A robust set of modal composition data (238 samples) for Eocene to Pliocene sandstone from the Cook Inlet forearc basin of southern Alaska reveals strong temporal trends in composition, particularly in the abundance of volcanic lithic grains. Field and petrographic point-count data from the northwestern side of the basin indicate that the middle Eocene West Foreland Formation was strongly influenced by nearby volcanic activity. The middle Eocene to lower Miocene Hemlock Conglomerate and Oligocene to middle Miocene Tyonek Formation have a more mature quartzose composition with limited volcanic input. The middle to upper Miocene Beluga Formation includes abundant argillaceous sedimentary lithic grains and records an upward increase in volcanogenic material. The up-section increase in volcanic detritus continues into the upper Miocene to Pliocene Sterling Formation. These first-order observations are interpreted to primarily reflect the waxing and waning of nearby arc magmatism. Available U-Pb detrital zircon geochronologic data indicate a dramatic reduction in zircon abundance during the early Eocene, and again during the Oligocene to Miocene, suggesting the arc was nearly dormant during these intervals. The reduced arc flux may record events such as subduction of slab windows or material that resisted subduction. The earlier hiatus in volcanism began ca. 56 Ma and coincided with a widely accepted model of ridge subduction beneath south-central Alaska. The later hiatus (ca. 25–8 Ma) coincided with insertion of the leading edge of the Yakutat terrane beneath the North American continental margin, resulting in an Oligocene to Miocene episode of flat-slab subduction that extended farther to the southwest than the modern seismically imaged flat-slab region. The younger tectonic event coincided with development of some of the best petroleum reservoirs in Cook Inlet.

Abstract Predictions of reservoir rock quality and distribution are commonly one ofthe major uncertainties in wildcat drilling. The need for improved prediction of reservoir quality has been documented by Rose (1987) and Sluijk and Parker (1984). Rose notes, in one of the few reported examples of assessment of wildcat failures, that incorrect prediction of commercial reservoir rock was the main reason for 40% ofthe dry holes analyzed. Interestingly, the geologists involved in reservoir quality assessment correctly perceived this factor as the prime geologic uncertainty 79% of the time. Comparison of predrill predictions with postdrill results by Shell (Sluijk and Parker) indicated that, in general, reservoir parameters were seriously overestimated, whereas hydrocarbon charge and retention were reasonably well predicted. In addition to the reduction of exploration risk, there are other potential applications for this course. Examples include basin analysis, where improved porosity and permeability estimation will allow for more detailed assessment of large scale fluid flow patterns, and in hydrocarbon production, where predrill predictions may assist in the recognition of impaired productivity related to formation damage. Although the concepts, techniques, and examples presented in this course relate almost entirely to clastic rocks, there is no reason the approach advocated here cannot be successfully applied to carbonates. Geochemical models of the various physical and chemaical mechanisms of diagenesis are only rarely linked quantitatively to levels of porosity and permeability. Consequently, attempts to use such models to predict reservoir parameters do not yet have the ability to provide meaningful quantitative estimates in other than

Abstract Porosity and permeability are the most important attributes of reservoir rock. They determine the amount of fluid a rock can contain and the rate at which that fluid can be produced (Dickey, 1986). Porosity is defined as the "property possessed by a rock of containing interstices, without regard to size, shape, or interconnection of openings. It is expressed as the percentage of the total (bulk) volume occupied by the interstices" (API, 1941). Permeability is a measure of the ability of a rock to transmit fluids. A detailed discussion of porosity and permeability and the measurement of these rock properties is the subject of Chapter 11. Accurate porosity values are critical to predrill evaluation of resources in potential reservoirs (potentially recoverable hydrocarbons in undiscovered fields are called resources, not reserves). Once a discovery is made, reservoir rock properties (porosity/permeability) are used to establish pore volume, hydrocarbon pore volume, recoverable reserves, production rates, well spacings, fluid injection rates, etc. The need for accurate estimates of rock properties extends to the whole "life cycle" (discovery, appraisal, planning, development, and management) of a reservoir (Table 1-1). Porosity and permeability data and estimates provide essential input to mathematical reservoir models, used to evaluate both new and mature fields, and to determine the most efficient method of economic development. Porosity and permeability are also key parameters in basin modeling. Such models help in the interpretation of sedimentary, hydrodynamic, geochemical, and tectonic processes which affected a given area over geologic time. Understanding of processes controlling basin formation

Abstract Porosity and permeability reduction and enhancement in nature are ultimately the cumulative result of physical and chemical processes acting on a given volume of rock. These processes are constrained by the fundamental physical-chemical variables of Pressure (P), Temperature (T), Time (t), and Composition (X). Composition is taken to include not only the chemical composition of the rock expressed as mineralogy but also fluid chemistry and such textural variables as size, sorting, roundness, sphericity, orientation, and packing. Porosity, at any given time for a given unit of rock at depth, is controlled by the complex interaction of these variables. Predictive models range between theoretical chemical models and purely empirical models. Since chemical reactions alone do not describe all of the porosity-controlling processes (e.g., mechanical compaction), predictive equations or models can be considered to lie along a spectrum which can be considered as process-oriented as one end member and effect-oriented as the other. Theoretical chemical and mechanical equations seek to describe and quantify the processes or the individual stages and reactions, the sum total of which result in a given porosity and permeability state. The current chemical models of specific diagenetic reactions are examples of this approach. At the other extreme, most empirical effect-oriented equations seek to predict porosity or permeability directly and are not concerned with the processes which produced the predicted state. Prediction of porosity using depth (a spatial coordinate) is a good example of this approach. Published models represent both ends of the spectrum and a full gamut of

Abstract The usefulness of geochemical modeling in diagenesis lies in the ability to analyze processes that cannot be measured directly in the laboratory, either due to the time element (slow kinetics) or, more typically, metastable equilibria. Important diagenetic process include: albitization (Morad and others, 1990), the illite/smectite transition (Perry and Hower, 1970; Bjørlykke and Aagaard, 1992), zeolitization, development of quartz overgrowths, carbonate cementation (Boles, 1979; Bjørlykke, 1983; Bjørlykke and others, 1989), the role of calcium-plagioclase and other unstable minerals in driving diagenetic reactions (Boles, 1991; Ram-seyer and others, 1992), and the effects of fluid composition on the final diagenetic state. Computer modeling of these processes has reached the stage that it can perform fairly realistic "what if” experiments in which the investigator has control of all pertinent variables and can examine the results from a number of perspectives. All of the reactions mentioned above can be occurring separately or simultaneously, and the models have now reached a state of sophistication that they will accurately keep a mass balance and insure that chemical equilibrium is maintained at all points along the reaction path. At present, there is no other way to dissect these complex and effect relationships other than by invoking the so-called “PATH” models or their variants. They are powerful tools, difficult to master and prone to abuse, but overall constitute a tool for the study of diagenesis that rivals X-rays diffraction analysis and petrographic analysis.

Abstract Most reservoir properties, ranging in scale from megascopic to microscopic, can be ultimately traced to environmental variations within deposi-tional systems. Each depositional environment produces sand bodies which display a certain size and shape, and which exhibit a characteristic range in mineral composition, sedimentary structures, and textures. Following deposition, diagenetic processes affecting reservoir quality of siliciclastic rocks normally proceed along different paths that are directly or indirectly related to environmentally-controlled differences in composition and physical characteristics (Figure 4-1). The effect of depositional facies on reservoir quality in siliciclastics is, obviously, most pronounced in relatively shallow reservoirs. In such rocks, reservoir quality (particularly permeability) is generally controlled by lithofacies (Figure 4-2), which are a product of the depositional environment (e.g., Weber, 1980; Clark and Reinson, 1990). As noted by Weber (1980), in many reservoirs, “it is often possible to work out the train of events leading to present rock properties and to relate these properties to the original sedimentologi-cal characteristics.” The influence of depositional facies in not restricted to shallow burial depths. In many (but not all) deeply-buried reservoirs affected by a moderate to heavy diagenetic overprint, the relative quality of each facies does not change significantly (e.g., Weber, 1980; Harms and others, 1981; Lindquist, 1983; Schotchman and Johnes, 1990). As noted by Weber (1980), even in deeply-buried sandstones, “one often finds the same general contrasts in permeability in the reservoir that existed in the original sandstone body but with an enhancement of the ratio between maximum and minimum permeabilities.” Permeability trends

Abstract This segment of the notes presents an overview of diagenetic processes of porosity and permeability destruction and enhancement. In the limited space available here, it is impossible to cover in any detail the vast literature on this subject. Thus, only the volumetrically important mechanisms of reservoir quality modification will be discussed, along with highlights of some recent studies of particular interest. Major advances have been made in the understanding of clastic diagenesis in the last fifteen years. Many of these advances can be attributed directly to the intense efforts of oil industry research, exploration, and production personnel, as well as academicians, who have been involved in reservoir quality studies centered on the North Sea and offshore Norway. These studies have been greatly enhanced by the extensive coring that has attended drilling operations. Several publications focusing on diagenesis and reservoir quality in the North Sea and adjacent areas have been used widely in the preparation of this text. These are listed in Table 5-1. Other major publications devoted to diagenesis and reservoir quality in clastic rocks are also referenced frequently, and these are listed separately in Table 5-2. In most reservoir sandstones, levels of resrervoir quality are far below initial (time of deposition) levels (porosities of 40 to 50% and permeabilities of 10 to 2,000 darcies). Although diagenesis tends to accentuate the effects of depositional control in most reservoir sandstones (e.g., Nagtegaal, 1979; Weber, 1980), it may also influence reservoir properties in an irregular manner, or even reverse depositional controls (e.g.

Abstract Diagenetic reactions are characterized by mineral dissolution and precipitation. Both can occur separately or simultaneously and can involve one or more minerals. The process, either dissolution or precipitation, can vary over many orders of magnitude, from sub-millimeter to kilometer scale. The dominant scale for diagenetic purposes, however, would seem to be from millimeters (mm) to meters (m), with the mm scale more common in fine grained rocks such as shales and siltstones, and the meter scale more common in sandstones. Megascale phenomena (kilometers) appear to be related to fluid movement along faults and fractures and are associated with tectonic events and thermal anomalies. There is also a slow, persistent, large-scale fluid movement associated with the dewatering of mudstones that begins on initial deposition and persists until effective porosity is lost (Bonham, 1980; Bjørlykke, 1983). The mechanics of this dewatering are reasonably well understood, at least relative to the chemical consequences, but it is still an area of active investigation, particularly as it may involve overpressure development in young, subsiding basins such as the Gulf Coast or the San Joaquin Basin of California. The importance of this dewatering flow is that it is pervasive, it involves huge volumes of rock, and if it becomes focused even slightly, it can concentrate large fluxes of fluid through limited volumes of rock.

Abstract After two decades of research, secondary porosity remains one of the most controversial topics in clastic sedimentary petrology and reservoir quality assessment. Although the ubiquity of such porosity in sandstones of all ages worldwide is unquestionable, fumdamental disagreements remain as to its practical importance, origin, and effect on reservoir quality prediction. It appears that the significance of secondary porosity and its impact on sandstone reservoir quality is frequently overemphasized. This can be attributed to two factors: the subjective nature of the criteria used in defining and quantifying secondary porosity the erroneous assumption that the presence of secondary porosity automatically results in a net increase of total porosity and permeability A critical review of processes thought to be responsible for secondary proposity generation suggests that meteoric water influx provides the most effective leaching mechanism. Importantly, some of this porposity can be preseved during deeper burial, particularly in sandstones with high percentages of non-ductile grains. Silicate hydrolysis is probably the most important porosity-generating process in the deep subsurface. However, porosity created by this process is mostly “redistributional” (the volume increase produced by dissolution of minerals is counterbalanced by a similar volume decrease generated by precipitation of pore-filling diagenetic phases representing products of the dissolution reactions). The importance of organic acids as a quantitatively important porosity- enhancing medium is not clear. The presence of secondary porosity does not appear to significantly affect the accuracy of empirical predictions in many sandstones for two reasons: The extent of secondary porosity and permeability generation and

Abstract The dependence of sandstone reservoir quality on composition has been recognized in numerous empirical studies (e.g., Hayes and others, 1976; Nagtegaal, 1978; Selley, 1978; Seeman and Scherer, 1984; Scherer, 1987; Smosna, 1989) and in experimental studies (Pittman and Larese, 1991). The composition and relative abundance of detrital components in sands greatly influence both physical and chemical diagenesis and, thus, reservoir quality. The purpose of this chapter is to discuss some of the effects of detrital composition on porosity and permeability and then review evaluation of mineral composition of a potential reservoir prior to drilling.

Abstract Evidence purported to document the influence of various controls on diagenetic processes or on reservoir properties abounds in the literature. However, such evidence often consists of simple crossplots in which the influences of only one or, at most, two variables are displayed. In most cases, the influences of other variables to which the same effects might be attributed are not discussed, or they are claimed to have a minimal effect or be held constant. Seldom is the data backing up these claims presented or available to outside workers. As a result, the reader is forced to accept these claims on faith alone, or to base his acceptance on prior knowledge of the unit, area, or relationship being discussed. It is not the intent here to present incontrovertible evidence regarding the relative importance of each of the factors which control the major diagenetic processes. The data to accomplish that are simply not available. As discussed in Chapter 5, many of the controlling processes are still in question, and even where general agreement exists with respect to the importance of a particular process, the factors that control that process are commonly not fully understood. This chapter will, therefore, review examples of evidence presented in the literature which are claimed to support the importance of a particular control. This will give the reader an overview of those variables which are considered by specialists to play a major role in controlling diagenesis. Some of these factors may indeed be very important, while others may

Abstract Ultimately, quantitative prediction of porosity and permeability must be performed using a model. In the most fundamental sense, a model is the simplified symbolic representation of a physical/chemical principle which is based upon meaningful concepts and which is able to reproduce significant elements of those concepts. Creation of a model can be considered to involve five major phases: model design data collection data analysis model development model verification These phases can in turn be subdivided. Figure 10-1 illustrates our recommended approach to the major steps involved in the generation of a predictive model. This and following chapters (11 through 18) deal sequentially with the steps outlined in Figure 10-1. The most critical phase of model generation is the design (formulation phase). Without proper design, a model may lack both accuracy and robustness and may even result in severe misinterpretation. Major concerns in model design include: identification and definition of the important variables (both independent and dependent) and relationships between variables identification of the appropriate structures which can be used to represent both variables or processes (e.g., variable transforms) and the interaction between these variables or model components This chapter discusses important aspects concerning identification and definition of the dependent variable, or target population, and population sampling. This chapter also discusses the fundamental nature ofthe models or structures which can be used to relate porosity to the selected independent variables. Chapters n and 12 discuss important aspects concerning the measurement and operational definitions of the dependent variables, porosity and permeability. Chapter 13 discusses both the identification

Abstract Fundamental to the development of porosity or permeability prediction models is the creation of a calibration database which is used to both develop and test the predictive models. To create a database, it is necessary to obtain quality data for the dependent variable, porosity and/or permeability, and for all of the independent variables, such as pressure, temperature, time, and composition. In empirical modeling, the "operational" definitions of the independent variables and the procedures followed for their measurement are highly flexible. An empirical equation can be developed using an independent variable which is a useful statistical tool but is not a direct measure of a porosity controlling process itself, like depth. The only constraint upon the operational definitions for independent variables is that they must always be measured the same way. This liberty decreases as the models become progressively more process-oriented or theoretical and become measures of real-world processes containing externally defined variables such as pressure and temperature. Though great flexibility is possible in the definition and meaning of the independent variables, the “operational” definition of the dependent variable, porosity for example, is highly important. The measure and type of porosity used as the dependent variable in the developed model will govern the type of porosity which that model predicts. This is the measure of porosity that will be applied to the real world and form the basis for economic decisions. Thus, if a porosity prediction model is developed which predicts, for example, average porosity, then the model cannot be assumed

Abstract Porosity data are an integral part of all databases used to develop predictive models of reservoir quality. In most instances, such data are obtained from core analysis and/or log analysis (discussed in the preceding chapter). However, useful porosity data also can be generated from petrographic point counts. Point counts (modal analyses) provide quantitative data on both total thin-section porosity and specific types of porosity. Four types of porosity occur in sandstones: intergranular, dissolution, micro, and fracture (Pittman, 1978). Intergranular pores can be identified in thin section by their location between detrital grains. Identification of dissolution pores was discussed in detail in the chapter dealing with secondary porosity (Chapter 7). Microporosity is best recognized by using fluorescent microscopy on samples containing fluorescent epoxy injected into the rock's pore system (Yurewicz and Dravis, 1984; Yanguas and Dravis, 1985). Fracture porosity is generally less than 1 to 2% and does not contribute significantly to total porosity. Fracture porosity cannot be estimated reliably from thin sections. Modal analysis of porosity is usually part of a general procedure to quantify the volumes of sandstone elements (grains, cements, and pores). Unlike other techniques, petrographic analysis can also provide information on the processes involved in porosity reduction or enhancement (or both) and thus help in the choice of parameters for predictive equations. Most importantly, petrographic observations provide clues as to the best approach to be used in reservoir quality prediction for a given target population (see Chapters 20 through 22 involving case histories). Other than measurements on unconsolidated

Abstract In order to develop meaningful multivariate predictive functions, it is necessary to correctly delineate those physical and compositional (including textural) variables that constitute major controls on reservoir properties and, therefore, warrant detailed measurement. Many of the pertinent controls may be known from previous studies in the general area of interest or from studies in similar sandstones elsewhere. Or, one could simply choose to input data for each of the variables listed by Scherer (1987) (Table 2-1), or include all variables used by various authors in their regression models (these have been reviewed in Chapter 2). A number of review papers discuss the more common diagenetic processes in sandstones of various types and controls on these processes (e.g., Nagtegaal, 1978; Burley and others, 1985). In a given sandstone unit, however, the more common mechanisms of porosity reduction may not be operative. Thus, it is unwise to assume a standard set of diagenetic mechanisms will dominate in a particular rock unit without conducting at least a preliminary examination. However, when the lithologies of interest have not been subjects of in-depth investigation, or where highly diverse opinions about the controls on reservoir quality exist, it may be necessary to rely on generalized lists such as that prepared by Scherer, and then be prepared to conduct a followup study should other controls emerge not originally incorporated in the analyses.

Abstract As discussed in Chapter 2, Empirical Methods of Reservoir Quality Prediction, porosity and permeability reflect the cumulative result of physical and chemical processes acting on a given volume of rock. These processes are constrained by the fundamental physical-chemical variables of Pressure (P), Temperature (T), Time (t), and Composition (X, where composition includes not only rock chemical composition expressed as mineralogy, but also fluid composition and textural variables such as size, sorting, orientation, and packing). Porosity at any given time for a given unit of rock at depth results from the complex interaction of these variables through time. Fundamental to the development of predictive models is the measurement of these independent variables (or alternate variables which reflect the influence of these fundamental variables). This and the next three chapters will discuss the measurement of these independent variables, as well as some examples of interactive variables which combine the influence of two or more of these basic variables.

Abstract Applied overburden is supported by both the rock framework and by the fluids in the pore network. The effective stress which the rock framework experiences is related to the lithostatic (overburden) stress and fluid (pore) pressure as follows: P e = P 1 -P f where: P e is effective stress, P 1 is lithostatic pressure, and P f is fluid pressure. This relationship assumes that no directed tectonic stress is acting on the system. The following sections will review lithostatic and fluid pressures and their measurement. Major reviews of pressure concepts, terminology, and measurement are available in Fertl (1976), Gretener (1977), Magara (1978), Plumley (1980) Pickering and Delicato (1985), and Hubbard (1988).

Abstract Porosity prediction in an open system is extremely difficult because sources of pore-filling cements and grain- and cement-dissolving solutions are not readily delineated, nor are the volumes of these solutions easily quantifiable. A good example of the problems encountered is the case of creating secondary porosity by dissolution of carbonate cement. In this case, porosity prediction depends upon the ability to predict the conditions conducive to both the development of the porefilling cement and its subsequent dissolution. The occurrence of carbonate cement must be considered in terms of timing with respect to other porosity reduction mechanisms, abundance and distribution within the prospective reservoir sandstone, and the various chemical and biological controls on precipitation. Similarly, late-stage dissolution must be evaluated in terms of timing with respect to both structuring and porosity preservation mechanisms, extent and distribution of secondary pores, and the chemical controls on dissolution. In light of these difficulties, prediction of porosity in a reservoir formed by decementation is highly problematic. A prediction of maximum porosity may be a more easily attained, although highly speculative and less meaningful, value for the explorationist.