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Debris flow mainly happens in mountainous areas all around the world with deadly social and economic impacts. With the speedy development of the mountainous economy, the debris flow susceptibility evaluation in the mountainous areas is of crucial importance for the safety of mountainous life and economy. Yunnan province of China is one of the worst hitting areas by debris flow in the world. In this paper, debris flow susceptibility assessment of Datong and Taicun gully near the first bend of Jinsha River has been done with the help of site investigation and GIS and remote sensing techniques. Eight causative factors, including slope, topographic wetness index, sediments transport index, ground roughness, basin area, bending coefficient, source material, and normalised difference vegetation index, have been selected for debris flow susceptibility evaluation. Analytical hierarchy process combined with Extension method has been used to calculate the susceptibility level of Datong and Taicun gullies. The evaluation result shows that both the gullies have a moderate susceptibility to debris flow. The result suggests that all the ongoing engineering projects such as mining and road construction work should be done with all precautionary measures, and the excavated material should adequately store in the gullies. Doi: 10.28991/cej-2021-03091702 Full Text: PDF

Surface network and subsurface reservoir coupled modeling adds value to asset development assessments when gathering networks and processing units impose significant constraints on production and injection rates. However, it is often difficult and time-consuming to reduce or avoid oscillations in production forecasts that often occur when the surface network and reservoir model are coupled together time step lagged. We present a new technique for IPR calculations which significantly reduces or eliminates these oscillations and provides smoother more realistic production forecasts from time-lagged coupled modeling without significant user intervention. This technique has three steps. First, the conventional IPR curve is calculated by solving the well model in the reservoir simulator multiple times over a range of flow rates or bottom-hole pressures (BHP). Second, a dynamic "region of influence" (ROI) is generated for each well using the Fast Marching Method (FMM). Third, the average pressure in the ROI is used to analytically scale the conventional IPR curve. The analytical scaling process honors the well operating point in the reservoir model at the time of coupling, maintains its existing non-linear shape, and shifts the no-flow BHP of the conventional IPR curve to the average pressure in the ROI. The conventional IPR curve mentioned earlier only considers the cells in which the well is completed and assumes no change in pressure in these cells until the next coupling time step. As a result, it typically predicts well productivity indices (PI) that are too high so that the coupled model forecasts oscillatory production rates over time. Previous studies have identified this problem and proposed improvements by considering regions around wells for IPR calculations. These methods usually require advancing and winding back flow simulations in reservoir or sub-domain models multiple times between coupling time steps. This implementation requires access to reservoir simulation code and the run time cost can be high and in some cases prohibitive. In contrast, the new technique presented in this paper can be implemented independent from the reservoir simulation code and the run time cost is low. In addition, previous studies provide no clear guideline on how to define the region for a particular well and how the region should change over time. In this paper, we use FMM to define the ROI dynamically around wells, so that its extent depends on simulation time, the coupling time interval and reservoir heterogeneity. We believe the ROI obtained this way reasonably captures the region of the reservoir which most significantly influences well performance until the next coupling time step. Therefore, the average pressure in the ROI provides a good estimate of the no-flow BHP which is used as the basis to adjust the IPR curve. We demonstrate the application of this new technique in a field example.

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