Anaconda Mining Inc. is experiencing three rock penetration and fragmentation problems that impact costs, recovery and overall feasibility. Through collaboration between Anaconda Mining and the Drilling Technology Laboratory (DTL) at Memorial University of Newfoundland (MUN) and the MITACS graduate internships described in this proposal, these problems will be investigated and appropriate solutions developed. The investigation of these research questions is applicable in the mining as well as oil & gas fields.
Increasing the productivity of agricultural fields is essential to secure our existing food supplies and provide for the growing world population. In this context, pesticides and fertilizers play an essential role in both increasing crop yields, as well as enhancing crop defenses against environmental stresses such as drought, pests, or diseases. However, the application of existing chemical pesticides and fertilizers can lead to eventual crop resistance as well as potential downstream environmental issues.
A critical issue in the oil and gas industry is to quantify the composition of fluids flowing back from the hydraulic fracturing process. This quantification is usually carried out by a manual process (frequently via a visual test) to estimate the water and oil produced from a well flow back process. A sample of these onsite tests are sent to laboratories for chemical analysis. This process has been the status quo for decades. This approach is manual, prone to error, and does not lend itself to sophisticated real time analysis.
Machine learning is the concept where a computer can be trained to recognize data and predict future outcomes based on the trends that exist in the data. This method of analysis has not been used on engine data, specifically in-line oil. Oil is an engine’s lifeblood and a lot of data can be collected and engine health can be predicted based on these measurements. This project aims to deploy machine learning concepts in the area of engine failure prediction.
The precise prediction of fluid behavior is required in many fields of engineering. Fluid flows are governed by a complex system of continuous partial differential equations (PDEs) which rarely have an exact analytical solution. Computational Fluid Dynamics (CFD) has emerged as a leading method of analyzing fluid flows, by numerically solving the respective PDEs. Current methods in finite volume schemes of CFD on unstructured meshes have two major sources of errors: noise in the reconstructed gradients and lack of cancellation during flux integration.
Permeation of CO2 gas through the inner layer in multi-layer fiber reinforced pipes (FRPs) destructively reduces pipe
durability. FRPs generally consist of three or more layers of polymer and reinforcing fibers. Gas permeation thorough
the polymer layer and its accumulation in reinforcing layer leads to pipe failure during depressurizing cycles. Using clay
nano-platelet can lead to decrease gas permeability in polymer layers. Good dispersion and good adhesion between
clay nano-layers and polymer are key features for optimization of gas permeability.
The fuel-grade ethanol obtained from corn-based feedstocks, utilizes about 33% of the total carbon present in corn based feedstocks. The remaining fraction is converted into dry distillers grains (DDG) and carbon dioxide, which is then converted to syngas (CO+H2). In this research the syngas from ethanol plant will be converted to transportation fuel and derived chemicals using our patented Fischer- Tropsch (FTS) catalyst. The catalysts will be pelletized and tested in 5 cc micro-reactor. The process parameters such as, temperature, pressure will be evaluated to obtain optimal yields.
Despite the abundance of natural gas resources and relatively lower price of gas per unit energy compared to electricity gas-fired heat pumps (GHPs) have not been widely used in Canada. This project will study the feasibility of two types of (GHPs), i.e., gas engine-driven heat pump (GEHP) and gas-fired absorption heat pump (GAHP) for buildings located in Canada. The project will include making theoretical models for prediction of performance and energy savings, which would be verified by comparison with actual performance data.
Direct Contact Steam Generators (DCSGs) for use in Steam Assisted Gravity Drainage generate flue gas containing steam and C02 which can be injected into reservoirs to aid bitumen recovery with part of the C02 remaining underground. The objectives of this project are to understand mechanisms of C02-stearn bitumen rate enhancement and determine the amount of C02 stored during the recovery process. Reservoir simulation modeling of C02 and steam injection will be done in parallel to Suncor's steam-C02 co-injection field pilot.
Steam-Assisted Gravity Drainage is the most commonly used in-situ thermal method for recovering bitumen from oil sands formations in western Canada. In this process, two parallel horizontal wells, about 5 m apart vertically, are drilled near the bottom of the formation. Before production, the bitumen between the two wells has to be heated, to become mobile, by circulating steam through the wells for several months. A new technology has been developed to make the inter-well bitumen flow in only a week, in which water is forced into the sands to dilate the pores, followed by steam injection.