Integrated geological data, 3D post‐stack seismic inversion, depositional modelling and geostatistical modelling towards a better prediction of reservoir property distribution for near‐field exploration: A case study from the eastern Sirt Basin, Libya

Abstract

De‐risking the hydrocarbon potential in near‐field exploration is one of the most important procedures in the exploration of hydrocarbons, and it requires the integration of various data to predict the reservoir characteristics of the prospect area more accurately. In this work, wells and 3D seismic data from the Libyan producing oil fields were utilized to demonstrate how well this technique worked to improve and describe the hydrocarbon potential of the carbonate geobody that corresponds to the Palaeocene Upper Sabil Formation, which was revealed by new seismic data. This study integrates different types of data, including 3D seismic, seismic acoustic impedance, depositional history and geostatistical analysis, to predict the facies, reservoir porosity and permeability distributions and then visualize them in a 3D reservoir model. The 3D seismic data analysis revealed the presence of a clear seismic anomaly geobody (GB) that has never been penetrated by any well. The sedimentological analysis for the well adjacent to the GB indicated a deep‐water depositional environment as turbidites surrounded by deep‐water mud dominated facies. The Upper Palaeocene interval in the study area was subdivided based on the depositional facies and seismic stratigraphy into eight zones that were used to build the reservoir model framework. According to the porosity permeability relationships, the carbonate facies has been classified into five E‐Facies, that is, soft highly argillaceous limestone, hard argillaceous limestone, porous limestone (<20% porosity, and >30% shale volume), medium quality limestone (10–20% porosity, and >30% shale volume) and tight limestone (<10% porosity, and >30% shale volume). The rock physics and inversion feasibility analysis indicated that the acoustic impedance (AI) can be used to predict the porosity but not the lithology or the fluid content. The Bayesian classification has shown excellent results in predicting and modelling the reservoir facies distribution within the study area, utilizing the integration of gross depositional maps (GDEs), wells and seismic data. The reservoir quality of the GB was predicted by using the post‐stack seismic inversion, which indicated a high porosity interval (25%–30%). Moreover, the statistical analysis integrated with the well and seismic data was used to predict the GB permeability. The predicted permeability was reasonably high (40–60 mD). The final E‐facies show an excellent match with the input well data and an excellent match with the blind wells that were used for result quality control (QC) with higher vertical resolution. The developed model can be used as a guide for de‐risking the studied GB hydrocarbon potential in the studied basin, and it can be applied in other similar geological conditions worldwide for exploring underexplored reservoirs and de‐risking their hydrocarbon potential.