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:
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
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Reservoir Quality Assessment and Prediction in Clastic Rocks
This course is designed to emphasize the following topics: (1) Historical perspective on previous and current empirical, and geochemical methods of reservoir quality prediction; (2) Overview of diagenetic processes which significantly impact reservoir quality and those factors which act as major controls on those processes; (3) Proper design of a comprehensive or limited-focus predictive analysis of reservoir quality; (4) Methodologies for the accurate measurement of all major dependent and independent variables; (5) Data analysis techniques involved in quality control and the assessment of variability prior to performing multivariate regression; (6) Steps involved in the generation of a multivariate regression to insure that the model developed provides maximum accuracy using a minimum number of independent variables; (7) Case histories from a variety of settings illustrating application of the recommended approach to reservoir quality prediction.