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Presidents and nonprofits: Illustrating compositional modeling of the US nonprofit sector

SOCIAL SCIENCE QUARTERLY 2024年第4期105卷 1014-1028页

作者： Zuhlke, Samantha Univ Iowa Sch Planning & Publ Affairs Iowa City IA 52240 USA

ObjectivesNonprofits sit at the intersection of policy and politics. compositional modeling presents an opportunity for nonprofit scholars to investigate trade-offs within the nonprofit *** U.S. nonprofit and political data from 1993 to 2013, I employ a dynamic pie modeling strategy to illustrate how federal politics affect the composition of the nonprofit ***.S. federal politics, measured as changes in presidential partisanship, correlate with trade-offs in the composition of the nonprofit sector. Democratic presidents are associated with increases in new Human Services (HU), Environment and Animals (EN), and International/Foreign Affairs (IN) nonprofits and decreases in new Arts/Culture/Humanities (AR), Education (ED), Health (HE), Religion Related (RE), and Unknown/Unclassified (UN) nonprofit ***, politics may play a greater role in the nonprofit sector than scholars currently give credence to. Empirically, compositional modeling strategies provide avenues for new theoretical and empirical insights into nonprofit organizations.

关键词： nonprofits compositional modeling dynamic pie partisanship public policy NTEE codes

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compositional modeling of multicomponent gas injection into saline aquifers with the MUFITS simulator

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JOURNAL OF NATURAL GAS SCIENCE AND ENGINEERING 2021年 94卷 103988-103988页

作者： Afanasyev, Andrey Vedeneeva, Elena Moscow MV Lomonosov State Univ Inst Mech Moscow 119192 Russia

Reservoir simulations of the co-injection of natural gas, carbon dioxide, and other non-condensable gases into saline aquifers are in demand in many areas of subsurface utilization, ranging from excess energy and greenhouse gas storage to the substitution of cushion gas with CO2. We aim at extending our reservoir simulator MUFITS for modeling flows in porous media related to these applications. For the accurate prediction of phase equilibria in aqueous systems, we implement a modified method of Soreide and Whitson in the compositional module of the software. The modified equation of state (EoS) involves the most recent literature data on the binary interaction coefficients and several new correlations for volume shifts to improve the accuracy of the EoS for predicting the partial volume of dissolved gases. All the aggregated and developed correlations are built into the simulator to facilitate its usage by the scientific community. The extended software is validated against a benchmark study of pure CO2 injection into a saline aquifer. Furthermore, two more complicated studies concerning the co-injection of several gases are conducted to demonstrate the potential application areas of MUFITS. First, we simulate the injection of impure carbon dioxide into an aquifer and briefly discuss the influence of N2 impurity on the propagation of the gas plume. We show that the plume is significantly wider in the case of impure CO2 injection. Second, we simulate the injection of supercritical CO2 into a synthetic natural gas storage reservoir to substitute cushion gas. We show that the mechanical dispersion can cause a rapid CO2 spread into the wells used for natural gas production.

关键词： Reservoir simulation compositional modeling Aqueous solutions Underground gas storage Flue gas injection Cushion gas

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The main features of compositional modeling algorithms of modern reservoir flow simulators

SOCAR PROCEEDINGS

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SOCAR PROCEEDINGS 2023年第3期 120-130页

作者： Lobanova, O. A. Indrupskiy, I. M. Russian Acad Sci Oil & Gas Res Inst Moscow Russia

Modern industrial reservoir flow simulators are integrated software products consisting of several packages. modeling of gas injection with various compositions, or hydrocarbon recovery from gas-condensate and oil-gas-condensate reservoirs, or thermal recovery methods requires a detailed account for composition of the fluid system. In these cases, compositional (multi-component) flow simulation is used to calculate component distribution between the coexisting phases, the number and the fractions of the phases, together with the flow equations for fluid components.. For this purpose, the flow simulator solves the thermodynamic subproblem using basic methods and algorithms analogous to the fluid modeling (PVT modeling) packages. The paper describes the main principles and specific features of implementation of the compositional modeling algorithms in several modern reservoir flow simulators.

关键词： compositional modeling multicomponent flow model reservoir flow simulator isothermal and non-isothermal compositional modeling

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Application of physics informed neural networks to compositional modeling

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2022年第0期211卷 110175-110175页

作者： Ihunde, Thelma Anizia Olorode, Olufemi Louisiana State Univ Baton Rouge LA 70803 USA

compositional modeling is essential when simulating any process that involves significant changes in the composition of reservoir fluids. This includes modeling the flow of multicomponent hydrocarbons in pipes, surface facilities, and subsurface rocks. However, the rigorous thermodynamics approach to obtain phase composition is computationally expensive. So, various researchers have considered using machine learning models trained with rigorous phase-equilibrium (flash) calculations to improve computational speed. Unlike previous publications that apply classical deep learning (DL) models to flash calculations, this work will demonstrate the first attempt to incorporate thermodynamics constraints into the training of these models to ensure that they honor physical laws. To this end, we generated one million different compositions with a space-filling mixture design and performed two-phase flash to obtain the corresponding phase compositions. We performed seven-fold cross-validation to ensure reliable estimates of model accuracy. We compared the physics-constrained and standard DL model results to quantify the ability of our approach to honor physical constraints. The evaluation of our physics-informed neural network (PINN) model compared to a standard DL model shows that we can incorporate physical constraints without a considerable reduction in model accuracy. Based on the test data, our model evaluation results indicate that both PINN and standard DL models achieve coefficients of determination of 97%. In contrast, the root-mean-square error of the physics-constraint errors in the PINN model is at least two times smaller than in the standard DL model. To further demonstrate that our PINN model out-performs the DL model in terms of honoring physical constraints, we generate phase envelopes using the overall compositions predicted using the PINN and DL models for several fluid mixtures in the test data. These results show the importance of incorporating the thermody

关键词： Physics-informed neural network compositional modeling Artificial intelligence Phase equilibrium calculations Physics-constrained deep learning

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Experimental measurement and compositional modeling of crude oil viscosity at reservoir conditions

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JOURNAL OF THE TAIWAN INSTITUTE OF CHEMICAL ENGINEERS 2020年 109卷 35-50页

作者： Talebkeikhah, Mohsen Amar, Menad Nait Naseri, Ali Humand, Mohammad Hemmati-Sarapardeh, Abdolhossein Dabir, Bahram Ben Seghier, Mohamed El Amine Amirkabir Univ Technol Dept Petr Engn Tehran Iran Sonatrach Div Laboratoires Dept Etud Thermodynam Ave 1er Novembre Boumerdes 35000 Algeria RIPI PVT CORE Res Grp Tehran Iran Shahid Bahonar Univ Kerman Dept Petr Engn Kerman Iran Jilin Univ Coll Construct Engn Changchun Peoples R China Amirkabir Univ Technol Dept Chem Engn Tehran Iran Ton Duc Thang Univ Inst Computat Sci Div Computat Math & Engn Ho Chi Minh City Vietnam Ton Duc Thang Univ Fac Civil Engn Ho Chi Minh City Vietnam

In the present study, experimental and modeling investigations were performed and combined to implement trustworthy paradigms to predict the viscosity value under different circumstances and a wide variety of conditions. The experimental approach was conducted on a considerable number of Iranian crude samples using a Rolling Ball viscometer. Accordingly, more than 1000 experimental points were gained. These latter were utilized as a databank in the modeling approach which included many advanced soft computing techniques, namely radial basis function (RBF) neural network, multilayer perceptron (MLP), support vector regression (SVR), adaptive neuro-fuzzy inference system (ANFIS), decision trees (DTs) and random forest (RF). When performing the modeling tasks using these techniques, two distinct cases were considered: the first includes all available parameters as inputs such as pressure, temperature, API degrees, Mw of C12+ and the mole fractions till C11;whereas in the second case, a grouping scheme was considered to reduce the number of fractions. The obtained results revealed that DTs for the first case is the best implemented model with an overall average absolute relative deviation (AARD) of 3.379%. In addition, the comparison results with the preexisting approaches showed the superiority of the newly proposed model. (C) 2020 Taiwan Institute of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

关键词： Crude oil viscosity compositional modeling Soft computing techniques Reservoir fluid DTS Prediction

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Leveraging compositional Methods for modeling and Verification of an Autonomous Taxi System 2

Leveraging Compositional Methods for Modeling and Verificati...

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2nd IEEE International Conference on Assured Autonomy (IEEE ICAA)

作者： Pinto, Alessandro Corso, Anthony Schmerling, Edward CALTECH NASA Jet Prop Lab Raytheon Technol Pasadena CA 91125 USA Stanford Univ Dept Aeronaut & Astronaut Stanford CA USA

ISBN: (纸本)9798350326017

We apply a compositional formal modeling and verification method to an autonomous aircraft taxi system. We provide insights into the modeling approach and we identify several research areas where further development is needed. Specifically, we identify the following needs: (1) semantics of composition of viewpoints expressed in different specification languages, and tools to reason about heterogeneous declarative models;(2) libraries of formal models for autonomous systems to speed up modeling and enable efficient reasoning;(3) methods to lift verification results generated by automated reasoning tools to the specification level;(4) probabilistic contract frameworks to reason about imperfect implementations;(5) standard high-level functional architectures for autonomous systems;and (6) a theory of higher-order contracts. We believe that addressing these research needs, among others, could improve the adoption of formal methods in the design of autonomous systems including learning-enabled systems, and increase confidence in their safe operations.

关键词： autonomous systems compositional modeling verification aerospace

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Pore-scale compositional modeling of gas-condensate flow: Effects of interfacial tension and flow velocity on relative permeability

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2021年 202卷 108454-108454页

作者： Reis, P. K. P. Carvalho, M. S. Pontificia Univ Catolica Rio de Janeiro Dept Mech Engn BR-22451900 Rio De Janeiro RJ Brazil

Liquid dropout and retention in gas-condensate reservoirs, specially in the near wellbore region, obstruct gas flowing paths and impact negatively the produced fluid volume and composition. Yet, condensate banking forecasting is commonly inaccurate, as experiments seldom reproduce reservoir extreme conditions and complex fluid composition, while most pore-scale models oversimplify the physics of phase transitions between gas and condensate. To address this gap, a fully implicit isothermal compositional pore-network model for gas and condensate flow is presented. The proposed pore-networks consist of 3D structures of pores connected by constricted circular capillaries. Hydraulic conductances are calculated for the capillaries, which can exhibit singlephase flow or two-phase annular flow, according to local gas and liquid saturations, or be blocked by a liquid bridge, when capillary forces overcome viscous forces. A PT-flash based on the Peng-Robinson EoS is performed at control volumes defined for the pores at each time step, updating the phases properties. Flow analyses were carried based on coreflooding experiments reported in the literature, with matching fluid composition and flow conditions, and approximated pore-space geometry. Predicted and measured relative permeability curves showed good quantitative agreement, for two values of interfacial tension and three values of gas flow velocity.

关键词： Gas condensate relative permeabilities Dynamic pore-network modeling compositional modeling

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compositional modeling of enhanced hydrocarbons recovery for fractured shale gas-condensate reservoirs with the effects of capillary pressure and multicomponent mechanisms

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JOURNAL OF NATURAL GAS SCIENCE AND ENGINEERING 2016年第0期34卷 1262-1275页

作者： Jiang, Jiamin Younis, Rami M. Univ Tulsa McDougall Sch Petr Engn Tulsa OK 74104 USA

There is considerable and timely interest in oil and condensate production from liquid-rich regions in shale gas reservoirs, placing emphasis on the ability to predict the behavior of gas condensate bank developments and saturation dynamics. As the pressure in the near-wellbore region drops below the dew-point, liquid droplets are formed and tend to be trapped in small pores. It has been suggested that injection of CO2 into shale gas reservoirs can be a feasible option to enhance recovery of natural gas and valuable condensate oil, while at the same time sequestering CO2 underground. This work develops simulation capabilities to understand and predict complex transport processes and phase behavior in these reservoirs for efficient and environmentally friendly production management. Although liquid-rich shale plays are economically producible, existing simulation techniques fail to include many of the production phenomena associated with the fluid system that consists of multiple gas species or phases. In this work, we develop a multicomponent compositional simulator for the modeling of gas-condensate shale reservoirs with complex fractured system. Related storage and transport mechanisms such as multicomponent apparent permeability (MAP), sorption and molecular diffusion are considered, In order to accurately capture the complicated phase behavior of the multiphase fluids, an Equation of State (EOS) based phase package is incorporated into the simulator. Because of the large capillary pressure that exists in the nanopores of ultra-tight shale matrix, the phase package considers the effect of capillary pressure on vapor-liquid equilibrium calculations. A modified negative-flash algorithm that combines Newton's method and successive substitution iteration (SSI) is used for phase stability analysis. We employ the developed simulator to study the combined effects of capillary pressure and multi component mechanisms on the phase behavior and hydrocarbon recovery in gas-c

关键词： Numerical simulation Multicomponent mechanisms compositional modeling Gas-condensate shale reservoirs CO2 huff-n-puff Discrete fracture-matrix model

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modeling approaches for addressing enigmatic migration patterns for aqueous- and nonaqueous-soluble tracers in an enhanced oil recovery field

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INTERNATIONAL JOURNAL OF GREENHOUSE GAS CONTROL 2025年 140卷

作者： White, Mark Rinehart, Alex Rose, Peter Mella, Michael Esser, Richard Ampomah, William Pacific Northwest Natl Lab Earth Syst Sci Div Richland WA 99354 USA New Mexico Inst Min & Technol Hantush Deju Natl Ctr Hydrol Innovat Socorro NM USA Univ Utah Energy & Geosci Inst Salt Lake City UT USA New Mexico Inst Min & Technol Petr & Nat Gas Engn Socorro NM USA

A series of six aqueous-soluble and four nonaqueous-soluble tracer experiments and corresponding numerical simulations were executed for the Farnsworth Field in Ochiltree County, Texas, USA, a field which is undergoing tertiary enhanced oil recovery with water-alternating-gas (WAG) production. The combination of field experiments and numerical simulations was designed to identify flow pathways between injectors and producers and potential short circuiting of injected fluids. Field recoveries of aqueous-soluble tracers were dependent on the WAG stages of the tracer injection well, with shorter arrival times for strictly waterflooding and delayed arrival times for alternating injection stages. Aqueous-soluble tracer (i.e., 1,3,6-naphthalene trisulfonate, 1,5-naphthalene disulfonate, 1,6-naphthalene disulfonate, 2-naphthalene sulfonate, 2,6-naphthalene disulfonate, and 2,7-naphthalene disulfonate) arrivals for WAG injectors indicated water bypass was occurring during gas injection stages. Nonaqueous-soluble tracer (i.e., perfluoro-1,2-dimethylcyclohexane, perfluoroethylcyclohexane, perfluoromethylcyclohexane, and perfluoromethylcyclopentane) experiments revealed faster migration velocities than for the aqueous-soluble tracers and flow heterogeneities that resulted in the tracers bypassing nearer production wells. Base-case numerical simulations of the tracer experiments used a geologic model of the Morrow B sandstone production interval with parameters calibrated from history matching simulations, with the Morrow B sandstone sub-divided into hydrologic flow units (HFUs). Alternative simulation scenarios investigated HFU-dependent three-phase relative permeability models and dynamic intrinsic permeability enhancement with exposure to aqueous-dissolved CO2. compositional petroleum models with four components were shown to be sufficient for tracer modeling compared against a nine-component model, with a factor of four difference in simulation execution time. HFU-dependent

关键词： Numerical modeling Enhanced oil recovery EOR compositional modeling Tracers Hydrologic flow units Carbon capture utilization and sequestration CCUS Parallel processing MPI

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A compositional Approach for Complex Event Pattern modeling and Transformation to Colored Petri Nets with Black Sequencing Transitions

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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2021年第7期48卷 2584-2605页

作者： Valero, Valentin Diaz, Gregorio Boubeta-Puig, Juan Macia, Hermenegilda Brazalez, Enrique Univ Castilla La Mancha Albacete Res Inst Informat Dept Comp Sci Albacete 02071 Spain Univ Cadiz Dept Comp Sci & Engn Cadiz 11519 Spain Univ Castilla La Mancha Dept Math Albacete 02071 Spain Univ Castilla La Mancha Albacete Res Inst Informat Albacete 02071 Spain

Prioritized Colored Petri Nets (PCPNs) are a well-known extension of plain Petri nets in which transitions can have priorities and the tokens on the places carry data information. In this paper, we propose an extension of the PCPN model with black sequencing transitions (BPCPN). This extension allows us to easily model the ordered firing of the same transition using an ordered set of tokens on one of its precondition places. Black sequencing transitions are then presented as a shorthand notation in order to model the processing of a flow of events, represented by one of their precondition places. We then show how black sequencing transitions can be encoded into PCPNs, and their application to model Complex Event Processing (CEP), defining a compositional approach to translate some of the most relevant event pattern operators. We have developed MEdit4CEP-BPCPN, an extension of the MEdit4CEP tool, to provide tool support for this novel technique, thus allowing end users to easily define event patterns and obtain an automatic translation into BPCPNs. This can, in turn, be transformed into a corresponding PCPN, and then be immediately used in CPN Tools. Finally, a health case study concerning the monitoring of pregnant women is considered to illustrate how the event patterns are created and how the BPCPN and PCPN models are obtained by using the MEdit4CEP-BPCPN tool.

关键词： Analytical models Tools Semantics Petri nets Sequential analysis Syntactics Image color analysis Colored Petri nets CEP EPL compositional modeling model-driven development

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