Application of a New Imaging Technique in Thin Beds Analysis / Sand Counting of Clastic Reservoirs in Oil Base Mud Environment

The identification and quantification of thin bedded net reservoirs is very challenging. Accurate pay thickness requires the integration of high-resolution data. This paper presents an accurate pay estimation method in wells drilled with Oil Base Mud (OBM) across clastic reservoirs where high-resolution image and true formation resistivity logs were utilized to perform advance thin bed analysis. OBM image technology provides photorealistic borehole image, electrical conductivity, and dielectric permittivity. Gauss-Newton algorithm is applied to generate button standoff, formation resistivity and dielectric permittivity images. Also, the new Hayman factor image analysis associates colour spectrum to the various rock and fluid types in the formation. This was used in estimating the reservoir heterogeneity, including open or closed fractures and more importantly the thin beds sand counting. The true formation resistivity was utilized to optimize the saturation / pay across these thin beds. These static measurements are integrated with dynamic results from the formation testing. Quantitative analysis of Advanced OBM Image data across thinly bedded clastic reservoirs has been conducted. Lamination analysis of the high-resolution image data was performed to understand the lamination density and thickness. The inverted resistivity image was applied to differentiate the good quality sand from degraded facies. Both analysis results were integrated with the true formation resistivity to evaluate accurate net pay in thinly bedded sands and subsequently confirmed by hydrocarbon samples. The technique presented in this paper reveals new and accurate method to evaluate and estimate the amount of reservoir heterogeneity. These will help in a more precise estimation of net sand / pay count in thinly bedded sands.