Although volatile arsenides in natural gas are usually in trace amounts, the service life of a catalyst in natural gas processing can still be shorten by them. Environmental pollution is caused by distribution of As into the atmosphere through gas burning. Health and safety issues happen for workers in close contact with unprocessed gas during exploration and handling. As an important part of natural gas product quality inspection, the technology of arsenic content detection in natural gas needs to break through immediately. We confirmed the feasibility of the two solutions as the adsorption of arsenic through choosing an arsenic adsorbent in natural gas and exploring multi-stage, circulating, and subsequent instrument methods. We selected concentrated nitric acid and silver nitrate solution as the adsorbents and tested the solutions by inductively coupled plasma mass spectrometry (ICP−MS). The results were obtained that the highest absorption efficiency of silver nitrate solution was 98.7%, the average of which was 80.31%, of which concentrated nitric acid could be up to 45%. Detection was also taken in the gas production plant using the methods mentioned above. Moreover, sandstone gas from fields of different provinces was also collected to be detected in the laboratory. We could draw the conclusion from the data that the two absorbents could reach the minimum detection value of the instrument in both gas field and the laboratory. The method of silver nitrate solution was better, and concentrated nitric acid should be used when detecting high H 2 S gas. Some wells were of higher arsenic content than the safety standard line, in which proper treatment should be taken.

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Over the past decade, deep learning frameworks such as convolutional neural networks (CNNs) have made major research inroads in upstream oil and gas, with applications in seismic processing/imaging, velocity model building, petrophysics, geological seismic interpretation, all the way to development, production and supply chain logistics. CNN fault prediction centers around the idea of image edge detection and, for improved prediction results, three data-driven steps are recommended. First, pre-condition the seismic data to increase signal-to-noise ratio as much as possible: iterative dip-steered median filtering and principal component filtering are adopted to further improve signal-to-noise ratio and sharpness for edge detection. Second, a fault probability volume obtained through deep learning (DL) -based fault detection using a U-Net architecture, taking synthetic seismic models as samples that can improve DL-based fault prediction in lieu of readily available labeled fault sets that may be prohibitively time-consuming to generate. Finally, edge enhancement is performed on the inference results to improve precision and fault continuity. A comparative analysis between related edge enhancement technologies is also presented. Results for three different faulting modes (normal, reverse and strike-slip) over three real seismic field data sets from China demonstrate the robustness of the proposed workflow.