Methane leaks are a significant component of greenhouse gas emissions and a global problem for the oil and gas industry. Emissions occur from a wide variety of sites with no discernable patterns, requiring methodologies to frequently monitor these releases throughout the entire production chain. To cost-effectively monitor widely dispersed well pads, we developed a methane point instrument to be deployed at facilities and connected to a cloud-based interpretation platform that provides real-time continuous monitoring in all weather conditions. The methane sensor is calibrated with machine learning methods of Gaussian process regression and the results are compared with artificial neural networks. A machine learning approach incorporates environmental effects into the sensor response and achieves the accuracies required for methane emissions monitoring with a small number of parameters. The sensors achieve an accuracy of 1 part per million methane (ppm) and can detect leaks at rates of less than 0.6 kg/h.

The molecular properties of asphaltenes have been the subject of uncertainty in the literature; in particular the molecular architecture is still a matter of debate. Some literature reports provide evidence that the contrast of petroleum asphaltenes versus coal-derived asphaltenes is useful for understanding the governing principles of asphaltene identity. Here, we employ fluorescence correlation spectroscopy to measure the diffusion constants of asphaltenes obtained from the distillation resid from coal liquefaction fluids. Concentrations employed herein are ∼10⁻⁸ molar, precluding asphaltene aggregation. These are compared with the same measurements on petroleum asphaltenes. These results confirm that the molecular sizes of these coal-derived asphaltenes are much smaller than virgin petroleum asphaltenes. Coal-derived asphaltenes are simpler than petroleum asphaltenes and provide correspondingly tighter constraints for understanding asphaltene molecular architecture. The small size, small alkane fraction, and large PAH of coal-derived asphaltenes are consistent with an “island” molecular architecture.

Acquisition of oil samples from the reservoir prior to oil production is essential in order to design production strategies and production facilities. In addition, reservoir compartmentalization and hydrocarbon compositional grading magnify the necessity to map fluid properties vertically and laterally in the reservoir prior to production. Downhole fluid analysis (DFA), performed in situ in the oil well, helps optimize this fluid mapping process. However, for heavy oil reservoirs drilled with water based muds, fluid mapping has been largely precluded due to the formation of stable water‐in‐oil (w/o) emulsions, which add significant complexity to sample acquisition and which can preclude standard DFA measurements. Here, fluorescence measurements are shown to be dependent on oil type but independent of the state of emulsion even at very high water fractions. Thus, downhole fluorescence measurements can be used to perform hydrocarbon fluid mapping in the reservoir. The sensitivities of fluorescence and optical absorption measurements are determined for different excitation wavelengths, oil type, and oil concentration.