Well Log Data Statistical Processing for Unbiased Qualitative and Quantitative Analyses: Case Study from the Gulf of Guinea

Abstract

Over oil and gas fields exploration and development phases, one of the main challenges of geoscientists and petroleum engineers is the petrophysical characterization of potential or discovered fields or reservoirs. Well logs data play key roles in wells stratigraphic column establishment and the computation of reservoir formations petrophysical parameters. Due to the conditions and the environment of well log data acquisition, they undergo some technical processing. For gamma ray log data for instance, although the technical data processing, the representative minimum and maximum values of recorded GR are required for unbiased qualitative and quantitative analyses. This study aims to propose statistical techniques for gamma ray logs data processing that will contribute to the reduction of biases related to their qualitative and quantitative analyses. A case study has been performed on a Gulf of Guinea’s offshore well gamma ray log data. The results show that the difference between the maximum and minimum values for the semi-processed data is almost twice the one of the processed data, what will lead to the underestimation of formations shale volumes and therefore to the overestimation of reservoirs effective porosity and flow performance. Moreover, the baselines (shaly sand, sandy shale and shale baselines) obtained from the semi-processed data are respectively located more leftward to those from the processed data. The main consequence is that the semi-processed data analysis has hidden the shaliness of formations comparatively to the processed data analysis. A comparative analysis shows that the semi-processed data analysis has globally underestimated the thickness of thicker formations and underestimate the shale volumes of thicker formations and those for which the estimated thicknesses from both analyses are the same or close to each other. In summary, the statistical processing of gamma ray log data prevents from the underestimation of thicker formations thicknesses and formations shale volumes. The main practical advantage is that it will prevent geologists, petro-physicists and reservoir engineers from the overestimation of oil or gas reservoirs effective porosity and flow performance and therefore from the overestimation of oil or gas initially in place and reserves.