Mikhail Gladkikh

A Pore-Level Approach to Petrophysical Interpretation of Well Logging Measurements

An accurate description of water- or oil-bearing reservoirs and the assessment of reserves strongly depend on a robust determination of their petrophysical parameters, e.g., porosity, permeability and fluid distribution, reflecting fluid type, content, and mobility. Downhole measurements provide means to formation evaluation; however, they do not directly provide the petrophysical properties of interest. To interpret well logging data, a range of empirical models are usually employed. These empirical relationships, however, lack scientific basis and usually represent generalizations of the observed trends. To provide a link between a detailed description of the physical processes occurring at the pore scale and the macroscopic properties of sedimentary rocks, a new pore-level approach to petrophysical interpretation of logging measurements is suggested in this work. A powerful means to create such a link is to develop quantitative relationships between the petrophysical properties and the geologic processes involved in forming the rocks. Here we describe the use of simple but physically representative models of the results of several rock-forming processes, e.g., sedimentation, cementation, and the formation of authigenic clay minerals. The key feature of these models is that they are geometrically determinate or precisely defined based on knowing the location of every grain comprising the model rock and hence the morphology of the pore space at the grain scale. We outline a method for computing macroscopic petrophysical properties using the proposed rock models. Unlike many approaches to pore-level modeling, our approach introduces no adjustable parameters and thus can be used to produce quantitative, a priori predictions of the rock macroscopic behavior. These a priori predictions, in turn, allow for successfully inverting and interpreting logging data to obtain petrophysical parameters of sedimentary rocks, such as absolute and relative permeabilities as well as capillary pressure curves. For example, NMR (Nuclear Magnetic Resonance) logs contain information about grain size, allowing for an accurate petrophysical interpretation by means of the pore- level approach presented in this work. The proposed methodology is also applied to real field data and the corresponding interpretation results are included in this paper.