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Oil and gas production from carbonate reservoirs heavily depends on the extent of fracture propagation in the producing formations. Detecting fractures in subsurface rock formations is challenging due to their potential impact on oil production, fluid movement, reservoir connectivity to wells, and hydrocarbon production methods. Therefore, detecting fractures and quantifying their characteristics in carbonate reservoirs is of utmost importance. Detecting fractures in subsurface rock formations is challenging due to their potential impact on oil production, fluid movement, reservoir connectivity to wells, and hydrocarbon production methods. This study identifies natural fractures in the Asmari reservoir (with Eligo-Miocene age and Cenozoic era) of an oil field in Southern Iran. The Asmari carbonate reservoir owes most of its hydrocarbon production to natural fractures. This study uses image logs, drilling cores, maximum flow rate, and mud loss data to analyze fractures in reservoirs due to their complexities and limitations, including high cost, non-directionality, and low recovery coefficient in fractured zones. Data from cores and full-bore Formation MicroImager (FMI) logs acquired from five wells drilled into this reservoir identify fractures and enhance our understanding of their effect on hydrocarbon production. In addition, by analyzing the image logs and creating rose diagrams of fractures, a better interpretation of the dip and direction of the fractures on the fault map is obtained. As a result, it was found that the density of the fractures and faults calculated from image logs corresponds closely with the bubble map of mud loss and maximum flow rate in the production sections of the reservoir. According to the extent of the Asmari reservoir anticline, in three sectors , including northwest, southwest, and central areas, where the highest fracture density is detected, bubble maps of mud loss and flow rate index also show the highest values.

A bit-arc capacity scaling algorithm to solve the maximal flow problem subjected to box constraints on the flow vector in directed network has been presented. The algorithm is mainly based on successive divisions of capacities by multiples of two. It solves the maximal flow problem as a sequence of O(n2) Dijkstra's shortest path between two nodes in the defined residual network with n nodes and m arcs. It is proven that, the algorithm's complexity was estimated to be no more than O(n2mr) arithmetic operations in the worst case to reach the maximum vector flow through the directed network. Where r denotes to the smallest integer greater than or equal to log B, and B denotes to the largest arc capacity of the network. A numerical example has been illustrated using the proposed algorithm.

Abstract We propose a new approach to solve inter-temporal unit aggregation issues under maximum opening size requirements using two models. The first model is based on Model I formulation with static harvest treatments for harvest activities. This model identifies periodic harvest activities using a set of constraints for inter-temporal aggregation. The second model is based on Model II formulation, which uses dynamic harvest treatments and incorporates periodic harvest activities directly into the model formulation. The proposed approach contributes to the literature on spatially constrained harvest scheduling problems as it allows a pattern of unit aggregation to change across multiple harvests over time, as inter-temporal aggregation under a maximum opening size requirement over period-specific duration. The main idea of the proposed approach for inter-temporal aggregation is to use a multiple layer scheme for a set of spatial constraints, which is adapted from a maximum flow specification in a spatial forest unit network and a sequential triangle connection to create fully connected feasible clusters. By dividing the planning horizon into period-specific durations for different spatial aggregation patterns, the models can complete inter-temporal spatial aggregation over the planning horizon under a maximum opening size requirement per duration.

Benefiting from advanced satellite payload technologies, edge computing servers can be deployed on satellites to achieve orbital computing and reduce the mission processing delay. However, geostationary Earth orbit (GEO) satellites are hindered by long-distance communication, whereas low Earth orbit (LEO) satellites are restricted by time windows. Relying solely on GEO or LEO satellites cannot meet the strict quality of service (QoS) requirements of on-board missions while conserving energy consumption. In this paper, we propose a computation offloading strategy for GEO/LEO hybrid satellite networks that minimizes total energy consumption while guaranteeing the QoS requirements of multiple missions. We first innovatively transform the on-board partial computation offloading problem, which is a mixed-integer nonlinear programming (MINLP) problem, into a minimum cost maximum flow (MCMF) problem. Then, the successive shortest path-based computation offloading (SSPCO) method is introduced to obtain the offloading decision in polynomial time. To evaluate the effectiveness and performance of SSPCO, we conduct a series of numerical experiments and compare SSPCO with other offloading methods. The experimental results demonstrate that our proposed SSPCO outperforms the reference methods in terms of total energy consumption, QoS violation degree, and algorithm running time.

An efficient polynomial time algorithm for solving maximum flow problems in directed networks has been proposed in this paper. The algorithm is basically based on successive divisions of capacities by multiples of two; it solves the maximum flow problem as a sequence of O(m) shortest path problems on residual networks with n nodes and m arcs. It runs in O(m2 r) time, where r is the smallest integer greater than or equal to log B, and B is the largest arc capacity of the network. A numerical example has been illustrated using this proposed algorithm.

The precise correction of water and energy balance is a significant difficulty in studying turbulence energy balance and water flow for agricultural purposes. The need for efficient water and energy management is growing as the world struggles to keep up with rising water and energy demands. This research examines artificial intelligence (AI)'s impact on the water flow and energy balance confluence subnetwork of sensing elements from all the original network's nodes. The study proposed an AI-based optimized sensor energy balance model (AI-SEBM) that uses pressure data to maintain energy balance in turbines and save water with the optimized energy source for agriculture usage. This research explores the potential for installing Kalpan hydraulic turbines, which are most effective during half-load operation, and to forecast all loads with little computing effort to balance energy in turbulence. To anticipate daily pressure readings and energy consumption across all nodes in the network, an AI-based optimization wireless sensor network is designed for communication and linked to an energy balance system. Sensors are strategically deployed at the network's nerve centres. The maximum flow algorithm is used for a grid representing the water and energy balance to determine the positions of the virtual nodes.

Abstract We propose a new optimization model to solve spatially constrained harvest scheduling problems with maximum opening size constraints, which utilizes flow network constraints in a maximum flow problem. The idea of the maximum flow problem is to identify aggregated units in each cluster. The proposed model consists of a spatial component for aggregation and connectivity, a temporal component, and an integrated component that binds all the components together by linkage constraints between spatial and temporal features. Spatial connection for forest units in each cluster is conducted by a newly introduced approach of sequential triangle connection, under the adjacency relationship of common boundaries and common corner edges, which gives explicit connectivity in each cluster. Using 18 examples with two small hypothetical forests and two real-life forests, our computational comparison against the bucket formulation without a priori numeration and the path formulation with a priori numeration shows that our proposed model outperforms the bucket formulation in all cases, and outperforms the path formulation in 10 out of the 18 cases. When 2 and 3 green-up periods are considered over a planning horizon of 10 periods, our proposed model shows constant superiority against the bucket and the path formulations.

In this paper, the binary representation of arc capacity has been used in developing an efficient polynomial time algorithm for the constrained maximum flow problem in directed networks. The algorithm is basically based on solving the maximum flow problem as a sequence of O(n2) shortest path problems on residual directed networks with n nodes generated during iterations. The complexity of the algorithm is estimated to be no more than O(n2mr) arithmetic operations, where m denotes the number of arcs in the network, and r is the smallest integer greater than or equal to log B (B denotes the largest arc capacity in the directed network). Generalization of the algorithm has been also performed in order to solve the maximum flow problem in a directed network subject to non-negative lower bound on the flow vector. A formulation of the simple transportation problem, as a maximal network flow problem has been also performed. Numerical example has been inserted to illustrate the use of the proposed algorithm.

Every year, millions of migratory shorebirds fly through the East Asian-Australasian Flyway between their arctic breeding grounds and Australasia. This flyway includes numerous coastal wetlands in Asia and the Pacific that are used as stopover sites where birds rest and feed. Loss of a few important stopover sites through sea-level rise (SLR) could cause sudden population declines. We formulated and solved mathematically the problem of how to identify the most important stopover sites to minimize losses of bird populations across flyways by conserving land that facilitates upshore shifts of tidal flats in response to SLR. To guide conservation investment that minimizes losses of migratory bird populations during migration, we developed a spatially explicit flyway model coupled with a maximum flow algorithm. Migratory routes of 10 shorebird taxa were modeled in a graph theoretic framework by representing clusters of important wetlands as nodes and the number of birds flying between 2 nodes as edges. We also evaluated several resource allocation algorithms that required only partial information on flyway connectivity (node strategy, based on the impacts of SLR at nodes; habitat strategy, based on habitat change at sites; population strategy, based on population change at sites; and random investment). The resource allocation algorithms based on flyway information performed on average 15% better than simpler allocations based on patterns of habitat loss or local bird counts. The Yellow Sea region stood out as the most important priority for effective conservation of migratory shorebirds, but investment in this area alone will not ensure the persistence of species across the flyway. The spatial distribution of conservation investments differed enormously according to the severity of SLR and whether information about flyway connectivity was used to guide the prioritizations. With the rapid ongoing loss of coastal wetlands globally, our method provides insight into efficient conservation planning for migratory species. Cada año, millones de aves costeras migratorias vuelan por la ruta migratoria Asia-Australasia de Oriente entre sus sitios árticos de reproducción y Australasia. Esta ruta incluye numerosos humedales costeros en Asia y el Pacífico que se usan como sitios de parada temporal donde las aves descansan y se alimentan. La pérdida de unos cuántos sitios de parada temporal por medio del incremento en el nivel del mar (SLR, en inglés) podría causar declinaciones poblacionales repentinas. Formulamos y resolvimos matemáticamente el problema de cómo identificar los sitios de paradas temporales más importantes para minimizar las pérdidas de poblaciones de aves a lo largo de rutas migratorias al conservar suelos que faciliten cambios orilla arriba de llanuras de marea en respuesta al SLR. Para guiar una inversión en la conservación que minimice la pérdida de poblaciones de aves migratorias durante la migración, desarrollamos un modelo de ruta migratoria espacialmente explícito acoplado con un algoritmo de flujo máximo. Las rutas migratorias de 10 taxones de aves costeras fueron modeladas en el marco de la teoría de gráficos al representar agrupaciones de humedales importantes como nodos y los números de aves volando entre 2 nodos como bordes. También evaluamos varios algoritmos de asignación de recursos que requirieron sólo información parcial sobre la conectividad de rutas migratorias (estrategia de nodo, basada en los impactos del SLR en los nodos; estrategia de hábitat, basada en cambios de hábitat en los sitios; estrategia de población, basada en cambios de población en los sitios; e inversión al azar). El algoritmo de asociación de recursos basado en la información de rutas migratorias se desempeñó en promedio 15% mejor que las asignaciones simples basadas en patrones de pérdida de hábitat o conteos locales de aves. La región del Mar Amarillo sobresalió como la prioridad más importante para la conservación efectiva de aves costeras migratorias, pero sólo la inversión en el área no puede asegurar la persistencia de especies a lo largo de la ruta migratoria. La distribución espacial de las inversiones de conservación difiere enormemente de acuerdo a la severidad del SLR y dependiendo de si la información sobre la conectividad de las rutas migratorias se usó para guiar las priorizaciones. Con la continua y rápida pérdida de humedales costeros a nivel global, nuestro método proporciona conocimiento sobre la planeación eficiente de la conservación para especies migratorias.

Patients with cystic fibrosis have inspissated mucus that is thought to contribute to the pulmonary exacerbations characteristic of the disease. As compared with treatment with normal saline, twice-daily treatment with inhaled hypertonic saline after the inhalation of a bronchodilator did not affect the linear rate of change in the forced expiratory volume in one second (FEV 1 ) but was associated with improved FEV 1 values and with fewer and shorter pulmonary exacerbations. Twice-daily treatment with inhaled hypertonic saline after the inhalation of a bronchodilator was associated with improved FEV 1 values and with fewer and shorter pulmonary exacerbations. Mutations in the cystic fibrosis gene result in abnormal ion transport across the respiratory epithelium. 1 , 2 In the absence of functional cystic fibrosis transmembrane conductance regulator protein, there is defective chloride secretion and excessive sodium absorption. Among the theories linking this genetic defect to lung disease in patients with cystic fibrosis is the isotonic volume-depletion hypothesis. It proposes that excessive absorption of salt from the airway lumen of patients with cystic fibrosis carries water with it, dehydrating airway mucous secretions and depleting the volume of liquid on the airway surface. These changes disrupt the mucociliary mechanism, with retained mucus becoming . . .