Petrophysical and Geological Evaluation of Laminated Clastic Rocks Using Borehole Electrical Image and NMR Data Integration

Abstract

Abstract Determining effective porosity and permeability (filtration capacity properties-FCP) of reservoir rocks represented by thinly laminated depositional sequences is a challenging task. The vertical resolution of most logging tools is coarser than the thickness of individual lamina; hence, these tools usually record averaged formation properties. In the case of NMR measurements, the best vertical resolution (VR) provided by modern NMR tools is 15 cm - 30 cm (VR=1 ft). To enhance the NMR vertical resolution, geological information with a higher VR is required. Electrical micro-imaging tools with VR=0.2?? (0.5cm) can provide high-resolution data which can be used to qualitatively determine the inter-bedding structure in thinly laminated intervals. The integration of these two measurements provides the physical link between detailed lithological data obtained by borehole image measurements and FCP estimated by the NMR tool. The T2 spectra obtained from the majority of water saturated clastic rocks, with negligible diagenetic effects, is mainly a function of grain size distribution (GSD) and depositional environment (i.e. function of pore size distribution). Based on the physical relationship between pore size and grain size in clastic reservoir rocks, the FCPs are mainly controlled by grain size distribution. The new Main Spectral Components (MSC) algorithm for Image-NMR data processing and integration has been developed. The MSC has been used to define the main groups of pores from pore size distributions representing the studied porous media. In this paper, we used the integration of borehole image, NMR data and GR logs as a basis for reservoir rock clustering to predict porosity and permeability with high resolution. Borehole electrical image analysis is used for the identification of clay type distribution (laminated or dispersed) and for preliminary rocks' classification. The developed MSC technique is efficiently used for main geological clusters identification with simultaneous estimation of their FCPs. The newly developed Image-NMR integration technique required the following input data: Statistical distributions of conductivity within the limit of NMR Vertical Resolution The correlations between MSCs determined from NMR spectra and statistically identified MSCs determined based on the borehole image conductivity histograms; and The correlations between T2 values and grain size (GSD) established for the main clusters of clastic rocks These relationships were used for computing T2 values with following prediction of GSD with vertical resolution equivalent to the VR of resistivity image logs. This computation is based on the assumption that each interval (in the limit of the borehole image's VR) is presented by one single pure cluster according to grain size/pore size distribution. The generated high resolution T2 data was then used for the construction of high resolution grain size distribution curves. The obtained GSD data can be used as an input for effective porosity and permeability estimation using a forward modeling technique.