Evolution of petroleum systems and hydrocarbon migration in the Dahomey Basin: Insights from basin modeling, machine learning and geochemical analysis

An integrated geochemical, machine learning (ML) and 3-D basin modeling investigation was carried-out on the petroleum system evolution of the frontier Dahomey Basin, Nigeria, West Africa. The poor understanding of the basin's petroleum system and its complex tectono-stratigraphic evolution has hindered effective exploration of the hydrocarbons and the evaluation of the resources within the basin. This study aims to provide a detailed evaluation of the basin's hydrocarbon potential by integration geochemical techniques, ML algorithms, and 3-D basin modeling. A total of 237 source rock samples from 18 wells within a 450 km onshore-offshore transect across Cretaceous to Paleogene Formations were analyzed by using compound-specific isotope analysis (CSIA), quantitative biomarker methods, and ML based maturation modeling. The source rocks data revealed that the Maastrichtian-Paleocene Araromi Formation have a mean Total organic carbon (TOC) of 4.12 wt% and a hydrogen index (HI) of 245–675 mg HC/g, and the Paleocene Ewekoro Formation has a mean TOC of 2.31 wt% and HI of 45–634 mg HC/g as prime sources of petroleum in the basin. The CSIA values of (δ 13 C −25.2 to −28.1 %) and the biomarkers distribution suggest Type II marine organic matter deposited in an anoxic-suboxic conditions. Three hydrocarbon kitchens with the highest generation occurring in Oligocene-Miocene thermal peaks, which were preferentially migrating along the Paleocene unconformities and the Cretaceous fault networks. The diachronous thermal maturation modeling presents a peak oil window of 20-15 Ma eastern kitchens and 15-8 Ma western kitchens, respectively. The total oil volume of 2.1–4.7 billion barrels of oil equivalent (P50: 3.2 BBOE) with 60 % confidence intervals is concentrated in three large kitchen-fairway systems. The Eastern Kitchen (1.4 BBOE) is the most prospective due to source rock maturity (VRo 0.75–0.95 %) and effective migration in the networks of Paleocene faults. The contribution of Central and Western Kitchens is 1.1 and 0.7 BBOE, respectively. The ML algorithm has successfully predicted the quality of source rock with 89 % validation accuracy and mapping of resources basin-wide in spite of sparse well control which remains a persistent challenge of frontier exploration. The integration of ML, high-resolution geochemistry and 3-D modelling has overcome challenges of data scarcity constraints, define the petroleum prospectivity of the Dahomey Basin, and provides a conceptual framework to assess unexploited passive margins in other parts of the world.