The main challenge in frontier and near-field exploration is the limited availability of data to interpret the subsurface. In the absence (or prior to drilling) of an appraisal well, possibly only seismic data and prior geology knowledge are at hand. In such data-poor environments, techniques are required that are able to quickly test large amounts of geology scenarios in a high dimensional (many unknown parameters) model space. Markov Chain Monte Carlo optimization techniques have previously shown to be capable of realizing reasonable solutions for large modelling problems, but are less effcient when the problem has very limited prior knowledge available. Optimization techniques as Particle Swarm Optimization and Cuckoo Search may however be able to de-risk the seismic amplitudes with the use of limited prior knowledge, since they may evaluate large sets of potential rock and uid property scenarios in parallel. To validate the applicability of these techniques, they are tested on their capability of solving mineralogy and porosity in a case-study on the Shuaiba Formation, with only limited prior data available. Cuckoo Search in particular has proven to be suitable to achieve reasonable mineralogy and porosity scenarios fitting a seismic section of the Shuaiba Formation. Particle Swarm Optimization has proven to be a robust technique as well, but due to its slightly less exploratory behavior, it has a greater risk of not finding the optimal solution. On the contrary however, if time is limited and effciency is more important than detail, Particle Swarm Optimization may be the preferred tool to use. Both techniques have provided an indication towards a carbonate mineralogy and porosity values only vary in the order of a few percent with respect to prior research. In overall, the model created has shown to be easily applicable by geologists and geophysicists as it requires only limited prior knowledge of the subsurface. The tool may therefore be of great value to the oil and gas business, since it may help decide whether it is worth drilling an appraisal well based on the probabilities of different scenarios, potentially yielding a great economic advantage