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Unified Fracture Design (UFD) bridges the gap between practices and theory in the hydraulic fracturing industry. It represents a technique to design hydraulic fracturing treatment with a particular amount of proppant. This design could provide the maximum fold-of-increase (FOI) in productivity-index (PI) after hydraulic fracturing treatment. The UFD optimization tool is very effective, but it has assumptions like any other model. One assumption of UFD optimization technique; is a single-layer assumption. This assumption does not align with the limited entry fracturing design concept. In limited entry fracturing, the frictional pressure is employed to offset the stress differences between multi-layers reservoirs to attain fluid injection through these layers, intended to deliver an optimal fracture conductivity in all layers. The drawback of this assumption is the underestimation of the actual value of FOI in PI. This paper aims to recast the original unified fracture design approach to extend the optimal UFD to a multilayer reservoir to predict the FOI in PI after limited entry fracturing treatment. The recasting tool for this problem to find the optimum solution is Artificial Neural Networks (ANN). The architected ANN model is based on actual historical data of limited entry fracturing treatments. A statistical comparison between the proposed ANN model and classical UFD technique demonstrates that ANN model solution has a more reliable estimation of FOI in PI with the actual historical data.

The effect of perforation friction on hydraulic fracturing treating pressure is normally considered negligible. However; this is not the case in limited entry fracturing. In limited entry fracturing, perforation friction is utilized to attain large frictional pressure drop. During the treatment injection, the frictional pressure offsets the stress differences between the zones to ensure fluid injection through every perforation in each interval. The major constraint with this diversion mechanism is perforations erosion when proppant laden slurry pumped through it. The purpose of this paper is to provide a new model to assess the perforation friction after perforation erosion by proppant laden slurry. The methodology involves the application of artificial neural network (ANN) to predict the final hydraulic perforation diameter. In order to train, validate and test the proposed ANN model; real field data of limited entry fracturing treatments have been used. The paper reviews the previous research progress on the assessment of perforation friction. A comparison between the new proposed ANN model and the previous perforation friction correlations demonstrates that the results from ANN model are the most reliable estimation of perforation friction real data. [ABSTRACT FROM AUTHOR]

Marine reserves are gaining attention around the world as a tool to both conserve ocean resources and improve the productivity of fisheries. Using simulation analysis, we investigate the inherent bioeconomic trade-offs in a limited- entry fishery associated with cost-effective designs of marine reserve networks--that is, the trade-offs between the degree of connectedness of a site and the biological and economic heterogeneity. We find in many cases that closing two low-value patches can result in larger biological gains and lower costs than closing one high-value patch. We also simulate biological productivity effects after creation of a reserve and find that under special conditions, aggregate sustainable rents are maximized with the closing of a portion of the fishable habitat. Finally, we find that the biological gains of implementing a more rationalized management system can outweigh the gains from closing multiple subpopulations under open access. [ABSTRACT FROM AUTHOR]