Problems in natural resources are currently handled by applications that require High Performance Computing (HPC) platforms. These applications from areas such as agriculture, oil and gas, weather forecast, mining, among others, require complex mathematical models, which rely heavily on computeintensive methods. Examples of these methods are partial differential equations (PDEs) discretization techniques (e.g. spectral element, finite difference, and finite volume methods), optimization algorithms, and inverse problems. Most of natural resources applications run on HPC clusters or supercomputers. Generally the Total Ownership Costs (TOC) of these infrastructure resources is very high even for medium to large companies. Moreover, small innovative software companies that specialize in computational modeling may not be able to acquire such clusters. Cloud Computing can come as a technology and business model to enable the execution of scientific applications from all companies, while such infrastructure is rented on demand. Initially used for Web applications, Cloud Computing is currently increasing its maturity to execute different types of workloads, including computing-intensive, dataintensive and networking-intensive, scientific and industry applications on HPC resources. This paper describes the challenges we observed while running natural resources applications that require high performance resources, on a Cloud. It also highlights some of the key opportunities that HPC Cloud brings to the natural resources area.