Prediction of the critical flow rate that will result in sand bed formation in multiphase flow is a critical aspect of multiphase production. Many correlations have been developed for solids transportation in multiphase flow; however, all of them treat the multiphase flow in an ad hoc way that does not respect the complexity of the phenomenon. Further, many approaches rely on correlations that have been developed for much higher solids loading than would occur in oil and gas production. In this paper, first a correlation for liquid-solid transport is developed, based on data taken in the SINTEF STRONG JIP. A good fit to both sand bed height and measured pressure drop is obtained. A critical aspect of the model is the assumption that there is a critical slip velocity between the sand and liquid which remains relatively constant over a wide range of flow velocities. Second, the particle diameter is used to augment the surface roughness. A critical velocity correlation is developed, based on solid and fluid properties, and pipe diameter. An essential feature of the model is that the critical slip between the liquid and solid phases is unaffected by the presence of gas. Good fit to the data is obtained. A sand model is developed using OLGA2000 which does a good job in fitting sand hold-up against the experimental data. Such a model can be used to predict sand bed formation potential in field lines. Introduction Sand is often produced out of the reservoir in both onshore and offshore production systems, particularly in reservoirs that have a low formation strength. Sand production may be continuous, or sudden - as when a gravel pack fails. In the case that no downhole sand control is done, or that a sandcontainment strategy fails, a sand-management strategy must be employed, with operations designed to tolerate a certain amount of sand production. Deposition of sand beds poses several risks, including increased frictional pressure losses, increased risk of corrosion due to microbial attack under the sand bed, and increased risk of equipment failure due to sand. Sand transport in near-horizontal pipelines has four main regimes, depending on the fluid flow rate. Below a critical velocity, sand will drop out of the carrier fluid and form a stable, stationary sand bed. As the sand bed builds over time, the fluid above the bed is forced into a smaller cross-sectional area, causing the fluid velocity to increase. When the velocity reaches a critical value, sand is transported in a thin layer along the top of the sand bed. A steady-state is reached, such that the sand eroded from the top of the bed is replaced by new sand production from upstream. At higher velocities, the sand bed begins to break up into a series of slow-moving dunes, with sand particles transported from the upstream to the downstream side of the dune. As the flow velocity increases still further, the dunes break up entirely, and the sand forms a moving bed along the bottom of the pipe.
Many state water-quality agencies use biological assessment methods based on lotic fish and macroinvertebrate communities, but relatively few states have incorporated algal multimetric indices into monitoring programs. Algae are good indicators for monitoring water quality because they are sensitive to many environmental stressors. We evaluated benthic algal community attributes along a landuse gradient affecting wadeable streams and rivers in Maine, USA, to identify potential bioassessment metrics. We collected epilithic algal samples from 193 locations across the state. We computed weighted-average optima for common taxa for total P, total N, specific conductance, % impervious cover, and % developed watershed, which included all land use that is no longer forest or wetland. We assigned Maine stream tolerance values and categories (sensitive, intermediate, tolerant) to taxa based on their optima and responses to watershed disturbance. We evaluated performance of algal community metrics used in multimetric indices from other regions and novel metrics based on Maine data. Metrics specific to Maine data, such as the relative richness of species characterized as being sensitive in Maine, were more correlated with % developed watershed than most metrics used in other regions. Few communitystructure attributes (e.g., species richness) were useful metrics in Maine. Performance of algal bioassessment models would be improved if metrics were evaluated with attributes of local data before inclusion in multimetric indices or statistical models.
Hydrodynamic slug flow is the prevailing flow regime in oil production, yet industry still lacks a comprehensive model, based on first principles, which fully describes hydrodynamic slug flow. This paper describes a very simple, time-dependent, two-phase (gas-liquid) model which is capable of producing hydrodynamic slugging from first principles. The new method can be applied for evaluation of slugging potential for oil pipelines. The model is capable of producing slug lengths and frequencies, as well as slug hold-ups. The model has been compared to the published Prudhoe Bay field data gathered by Brill et al. The model has been able to predict the transition from homogeneous to slug flow, and also give information about slug lengths and frequencies. Such a simple model could be a very excellent jumping off point for building more complex models capable of predicting slug distributions from first principles, without any need for additional user input. No such model currently exists. Theoretical Background While hydrodynamic slug flow is an inherently transient phenomenon, it has historically been modeled as a ‘pseudo steady-state’ which ignores the fundamentally transient nature of the flow. Even in instances where the individual slugs are introduced and tracked in a Lagrangian frame (so-called ‘slug tracking’)1, the results have been somewhat disappointing, in that the ultimate slug distributions are heavily influenced by user input. This paper examines another approach - where the fundamental transient nature of hydrodynamic slug flow is accounted for in the model. In order to formulate a model for slug flow, we must first develop a ‘point model’ for an individual pipeline segment, or computational cell, in a pipeline. This pipeline segment must then be joined with other pipeline segments upstream and downstream of it to form a ‘steady-state’ model for the pipeline. Lastly, these steady-state solutions must be implemented into a transient scheme; a proper model of hydrodynamic slug flow absolutely requires that slugging not be treated as a pseudo-steady-state, but as an inherently transient phenonenon.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.