Log-Derived Evaluation of Shaly Clastic Reservoirs
Authors: Fertl Walter H. 
Published: December 2007

Reliable evaluation of hydrocarbon resources in shaly clastic reservoir rocks is an important, although difficult, task. This paper briefly reviews the wide variety of interpretive models that has evolved. The discussion focuses on digital shaly-sand evaluation techniques based on the Waxman-Smits model to provide information on total and effective reservoir porosity and fluid distribution, silt content, volume, type (smectite, illite, and chlorite/kaolinite), and distribution modes (dispersed, laminated. and structural) of the clay minerals present in subsurface formations. Case studies from different geologic environments present field experiences in clastic reservoir rocks that exhibit a wide range of porosity and permeability and various amounts, types, and distribution modes of clay minerals. A log-derived formation damage index is also discussed. Finally, emphasis is placed on improved reservoir evaluation of thinly laminated shale/sand intervals through the integration and enhancement of resistivity data, short-spaced dielectric measurements, and/or analytical data-blocking routines.

Introduction

Few hydrocarbon-bearing clastic reservoirs are essentially free of clay minerals. The significant effect of these minerals on geophysical log responses is well recognized. Ref. 1 represents the first modern collection and comprehensive review of the vast number of technical papers important to the evaluation of shaly clastic reservoir rocks based on geophysical well logging measurements and associated interpretive concepts. For example, Fig. 1 illustrates the generalized response of the various porosity logs to shaliness and hydrocarbon effects.

The basic objective in shaly-sand formation evaluation then is a realistic log-derived description of reservoir quality in terms of petrophysical parameters, type, and volume of hydrocarbon resources in place, and expected production behavior. Such evaluation methods may be simple empirical rules, standard analysis concepts, digital wellsite "quick look" techniques, or advanced digital interpretive models used in single-well or multiwell field studies.

Basic Considerations

In his classic empirical equation, Archie related formation conductivity, formation-water conductivity, and the formation resistivity factor (a function of porosity and cementation exponent) to the formation-water saturation. Archie's equation applies satisfactorily to clean sands, whereas the presence of clay minerals (amount, type, and distribution modes) has a detrimental effect on water saturation calculations. While more than 30 water saturation models have been proposed for shaly-sand reservoir evaluation, most of the major developments can be classified in one of four categories (see Table 1 ). Worthington recently discussed these categories and the evolution of shaly-sand concepts in reservoir evaluation.

Concepts based on clay volume, V, exhibit two constraints: being inexact and requiring reliable log-derived V, estimates. 3 The latter, in itself, is no minor task (see Table 2).

The most common clay minerals, their chemical composition, matrix density, hydrogen index (HI), cation exchange capacity (CEC), and distribution of potassium, thorium, and uranium as shown by natural gamma ray spectral information are summarized in Table 3. Because a typical shaly clastic reservoir rock and/or a typical shale formation may contain different clay minerals in various amounts, no single clay parameter can be used universally to characterize a specific type of argillaceous sediment or shaly reservoir rock.

With the advent of the Waxman-Smits model, reliable water saturation calculations are provided for reservoirs with drastically different clay contents and over a wide range of formation-water salinities. The emphasis is on log-derived evaluation of CEC per total PV, Qv, from specific single or combined well logging parameters. Such QV, estimates from well logs can be based on the spontaneous potential curve, gamma ray, natural gamma ray spectral data (potassium, thorium, and uranium), dielectric measurements, reservoir porosity, gamma ray/reservoir porosity, clay composition/reservoir porosity, etc. It suffices here to state that such correlations faciliLate a continuous computation of CEC and Q v values for a given formation in a geological section or localized area. These techniques, however, all require both well logging and core data over the interval of interest because no single unique mathematical transform can be expected.

To overcome these limitations, advanced digital shalysand analysis techniques, such as CLASS and CLAYS, have recently been developed. Based on the WaxmanSmits model and variations in the basic properties of various clay minerals, CLASS and CLAYS analyses provide information on total and effective porosity; total and effective fluid distribution silt volume; amounts, types, and distribution modes of clay minerals present, and reservoir productivity. Details of these methods can be found in Refs. 6 and 7.

JPT

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