Exploration Opportunity Identification Along the passive margins of West and Southern Africa

West Africa's transform passive margins and Southern Africa rifted passive margins offer a diverse array of exploration opportunities, particularly within the Cretaceous to late Miocene stratigraphy. Here we share some publicly available seismic cross sections of some recent billion-barrel discoveries in Africa, accompanied by feature maps of the exact play types, albeit from a different survey. These regions have become prominent for their deep-water turbidite channels and basin floor fans, which have been the source of billion-barrel oil fields.

Countries such as Nigeria, Ghana, Gabon, Mauritania, Angola, and Namibia are notable for their significant hydrocarbon potential. The subsurface risks associated with exploration in such areas can be addressed via a strategic and tailored approach. At Bandwidth Pty Ltd we combine direct hydrocarbon indicator's (DHI) with the principles of quantitative seismic stratigraphy (QSS) to unlock value from these complex settings.

But sometimes, the DHI solution presented may not polarize the opportunity due to sub-optimal seismic data (2D vs. 3D, broadband vs conventional seismic). Other times, the DHI solution partly polarizes (i.e. dry well vs. discovery) but the properties and or fluid phase may be out of range. The explanation for this can range from inadequate underlying geological models; to lack of reliable calibration (poor seismic to well tie) such that predrill predictions are inconsistent with the post drill well results. Challenging situations occur where the DHI is unable to polarize residual hydrocarbons (i.e. when a trap has been breached).

Hence DHI workflows vary, but typically include: (i) data conditioning e.g. estimation of appropriate noise levels, filtering of dataset; (ii) 1D OR 2D scenario modelling for brine, oil and gas; (iii) wedge models, updip downdip histograms, amplitude conformance/amplitude-depth shutoff maps; (iv) inverting for the discrete (facies) and elastic properties (impedances, Vp/Vs, LambdaRho) across multiple realizations; (v) using multivariate relationships to co-simulate engineering properties (i.e. porosity, ntg, saturation) with the right vertical and lateral details; (vi) generating histograms and cumulative probabilities from multiple realizations to better understand the different outcomes; (vii) integration and closing the loop i.e. the DHI prediction fits with the trap configuration.