Fracture packing is a well-known completion technique used in the hydraulic fracturing of low-permeability reservoirs. As much as fracture packs are very effective, the proppant-pack permeability damage formed from pa...
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Fracture packing is a well-known completion technique used in the hydraulic fracturing of low-permeability reservoirs. As much as fracture packs are very effective, the proppant-pack permeability damage formed from particle intrusion reduces that effectiveness because it causes low well productivity. It is important to address the issue of permeability damage caused by formation-particle intrusion. An analytical model was developed in this study to predict the permeability of proppant packs in hydraulic fractures with consideration of different levels of invasion damage of formation sand. The accuracy of the model was verified by model comparison with data from the Eagle Ford Shale field. The model result shows that for the Eagle Ford field and the corresponding proppant size used, three blocking levels were achieved that correspond to high proppant-pack permeability. Three case studies were considered in this study: California sand, Gulf Coast sand, and South China Sea silt. The proppant-pack permeability damage was calculated using the analytical model for three levels of invasion for all case studies. The results from applying the analytical model on the three case studies showed the amount of invasion that is possible in each sand according to the proppant size used. The level of invasion is a factor of the sand distribution and the initial proppant size chosen. More analysis showed that for two of the case studies, only Levels 1 and 2 blockings can develop, while for the last case study, three blocking levels considered can develop. This study, for the first time, gives an insight into how selecting the optimal proppant size can improve sand-control performance while enhancing fracture conductivity.
There is a significant range of ongoing challenges in artificial intelligence (AI) dealing with reasoning, planning, learning, perception and cognition, among others. In this scenario, many-valued logics emerge as one...
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There is a significant range of ongoing challenges in artificial intelligence (AI) dealing with reasoning, planning, learning, perception and cognition, among others. In this scenario, many-valued logics emerge as one of the topics in many of the solutions to some of those AI problems. This special issue presents a brief introduction to the relation between logics and AI and collects recent research works on logic-based approaches in AI.
Reaction Systems (RSs) are a successful computational framework inspired by biological systems. A RS pairs a set of entities with a set of reactions over them. Entities can be used to enable or inhibit each reaction, ...
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Reaction Systems (RSs) are a successful computational framework inspired by biological systems. A RS pairs a set of entities with a set of reactions over them. Entities can be used to enable or inhibit each reaction, and are produced by reactions. Entities can also be provided by an external context sequence to simulate in silicobiological experiments. In this paper we define an extension of RSs considering nondeterministic and recursive context operators, and give an original labelled transition system (LTS) for extended RSs in the structural operational semantics (SOS) style. Thanks to extended contexts, a single LTS can now account for several biological experiments. The rich information recorded in transition labels is useful to guarantee the compositionality of SOS inference rules as well as to define an assertion language to tailor behavioural and logical equivalences on some specific properties or entities. The SOS rules have been also exploited to design a flexible prototype implementation in logic programming that allows to inspect the LTS and to extract useful information when performing experiments on a RS. Our implementation provides a rapid prototyping tool for (extensions of) RSs, with a user friendly online interface to our interpreter. A parser allows to introduce the logical formulas and the contexts using the usual comfortable concrete syntax. The user can visualise and inspect the LTS for a RS and make some analysis of its underlying computation patterns, can check if the main RS satisfies a given property and if it is equivalent to a second adversarial RS. Finally, the SOS approach is suited to drive additional enhancements of RSs. (C) 2021 Elsevier B.V. All rights reserved.
An H-partition of a finite undirected simple graph G is a labeling of G’s vertices such that the constraints expressed by the model graph H are satisfied. These constraints concern the adjacency or nonadjacency of ve...
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Datalog is a carefully restricted logic programming language. What makes Datalog attractive is its declarative fixpoint semantics: Datalog queries consist of simple Horn clauses, yet Datalog solvers efficiently comput...
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Deductive formalisms have been strongly developed in recent years;among them, answer set programming (ASP) gained some momentum and has been lately fruitfully employed in many real-world scenarios. Nonetheless, in spi...
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Deductive formalisms have been strongly developed in recent years;among them, answer set programming (ASP) gained some momentum and has been lately fruitfully employed in many real-world scenarios. Nonetheless, in spite of a large number of success stories in relevant application areas, and even in industrial contexts, deductive reasoning cannot be considered the ultimate, comprehensive solution to artificial intelligence;indeed, in several contexts, other approaches result to be more useful. Typical bioinformatics tasks, for instance classification, are currently carried out mostly by machine learning (ML)-based solutions. In this paper, we focus on the relatively new problem of analyzing the evolution of neurological disorders. In this context, ML approaches already demonstrated to be a viable solution for classification tasks;here, we show how ASP can play a relevant role in the brain evolution simulation task. In particular, we propose a general and extensible framework to support physicians and researchers at understanding the complex mechanisms underlying neurological disorders. The framework relies on a combined use of ML and ASP, and is general enough to be applied in several other application scenarios, which are outlined in the paper.
This paper proposes using a logic programming language to disentangle X.509 certificate validation policy from mechanism. Expressing validation policies in a logic programming language provides multiple benefits. Firs...
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Answer Set programming (ASP) is a framework in artificial intelligence and knowledge representation for declarative modeling and problem solving. Modern ASP solvers focus on the computation or enumeration of answer se...
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We generalize the notion of "not subject to occur-check" (NSTO), on which most known results on avoiding the occur-check in logic programming are based. NSTO means that unification is performed only on such ...
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We generalize the notion of "not subject to occur-check" (NSTO), on which most known results on avoiding the occur-check in logic programming are based. NSTO means that unification is performed only on such pairs of atoms for which the occur-check never succeeds in any run of a nondeterministic unification algorithm. Here we show that "any run" can be weakened to "some run". We present some related sufficient conditions under which the occur-check may be safely omitted. We show examples for which the proposed approach provides more general results than the approaches based on wellmoded and nicely moded programs (this includes cases to which the latter approaches are inapplicable). We additionally present a sufficient condition based on NSTO, working for arbitrary selection rules.& COPY;2023 Published by Elsevier B.V.
It is generally agreed upon that so-called strong persistence (SP) captures best the essence of forgetting in logic programming. While classes of operators, such as FR and FSP, that satisfy immediate relaxations of (S...
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