In the upstream oil and gas industry, cloud computing is very immature because the industry has always been challenged by storage and computational capability. However, there is recent evidence for considering high-performance cloud computing (HPCC) because of the promise of benefits such as flexibility, accessibility, and cost reduction. HPCC may create an opportunity for small to midsized upstream companies that do not want to invest in the infrastructure needed for evaluating scientific applications.
The target of this project was to prove the concept of running simulation software in a high-performance computing cloud and use the findings to design a framework or methodology enabling companies to pursue business opportunities iteratively while learning along the way. The outcome of the methodology is a dynamic tactical and strategic roadmap that leverages trends in HPCC.
The following cases were run on a local cluster at an early stage for the purpose of run validations:
The four cases showed identical results for oil-production rate and cumulative oil for the duration of the field history, as expected.
The case with a single CPU was completed in approximately 20 hours. The run times with four and eight CPUs were 7.7 and 5.7 hours, respectively.
Fig. 1 shows that wall-clock time decreased as more CPUs were added, both for calculations performed internally and for those performed on the cloud servers. It was also observed that internal calculations stagnated at more than four CPUs (i.e., sublinear scaling). On the other hand, close to linear scaling was observed when calculations were run on the cloud servers.
Project Tests High-Performance Cloud Computing for Reservoir Simulations
26 June 2016