bluuebunny/crossref_metadata_2025 huggingface dataset:
Efectividad del consumo de productos alimenticios fortificados con hierro en el incremento de hemoglobina en niños de 6 meses a 6 años: revisión sistemática
Authors:   , Alcantara Tolentino Katy Eva , Oblitas Marrufo de Cutipa María Lisbeth , Taype Espinoza Lacey
Published: March 2023

Antecedentes: Durante décadas se ha considerado a la anemia como un problema de salud pública universal por su afectación en la salud individual, social y económica de cada país, siendo las poblaciones más afectadas las mujeres en edad reproductiva y los niños en edad preescolar. Algunas consecuencias de la anemia son el retraso en el crecimiento, el déficit intelectual y la disminución de la respuesta inmunológica en niños pequeños. Para contrarrestar esta enfermedad se ha optado por diversas estrategias, siendo una de ellas la fortificación o enriquecimiento de alimentos con hierro junto con vitaminas y otros minerales lo que, según el reporte de varios estudios actuales, favorece el incremento de la hemoglobina y la reducción de la anemia no solo en niños, sino en poblaciones de otras edades. Objetivo: revisar y sistematizar las evidencias sobre la efectividad del consumo de alimentos fortificados con hierro en el incremento de la hemoglobina en niños menores de 6 años diagnosticados con o sin anemia. Para ello se realizó una búsqueda en 3 bases de datos bibliográficos: MEDLINE(PubMed), LILACS y Scopus. Se siguió directrices PRISMA, excepto la publicación del protocolo en un repositorio. La evaluación de sesgo se realizó mediante la herramienta de ROB 1 de Cochrane. Resultados: Se obtuvieron 599 estudios, de ellos se eligieron 5 estudios de diseño experimental aleatorizados para la presente revisión. Dos artículos se realizaron en Brasil y los demás en Camerún, India y Europa Occidental. Los alimentos fortificados con hierro fueron: pan con harina de trigo, arroz, leche en polvo, fórmula infantil y cereal infantil (soya y harina de trigo). Se halló un incremento en los niveles de Hb y Fe sérico en infantes.
Diseño de un plan de negocio para crear una empresa tipo Startups en Lima
Authors:   , Palomares Ojeda Marisa Stefany
Published: March 2023

El Plan de Negocios presentado ha sido planteado con el propósito de crear una empresa tipo Startups en Lima, siendo de gran potencial por los servicios innovadores que ofrecerá y su cobertura geográfica. Mediante este plan se establece la viabilidad del proyecto a ejecutar, logrando definir la rentabilidad de la misma e incluyendo: i) Plan estratégico; ii) Plan de Marketing; iii) Plan de operaciones; iv) Plan de recursos Humanos y v) Plan financiero, fundamentándose teóricamente en Weinberger (2009). Logrando definir a la empresa Emporio TechPeru como el primer startup en ofrecer servicios de creación de startup excluyendo a las aceleradoras e incubadoras del sistema, creando una nueva modalidad de negocio con enfoque tecnológico, presentando resultados favorables para la idea de negocio, resultando positivo la viabilidad económica-financiera, tasa interna de retorno y el periodo de recuperación es aceptable.
Plan de negocio para la exportación de mantequilla de palta a la ciudad de Madrid – España en el 2024
Authors:   , Vigo Cermeño Iveth del Rosario , Quincho Tacza Katherine , Lazo Garcia Evelyn Fiorella , Moreno Sotelo Kimberly Judith
Published: March 2023

El proyecto de investigación trata sobre exportación de un novedoso producto nuevo e innovador el cual será dirigido al mercado de Madrid España, en donde tendremos como segmentación a un público entre 25 a 40 años, los cuales son denominados como millennials debido a que la gran mayoría de sus compras son mediante las diversas plataformas de ventas. La mantequilla de palta es un producto novedoso debido a que no hay en el mercado, cuenta con competidores directos debido a que es una mantequilla hecha a base de palta Hass, 100% natural, sin embargo, cuenta con competidores similares que son las mantequillas y margarinas. El objetivo de este proyecto es alcanzar un punto de equilibrio para el año 2024, logrando así posicionarse en la mente de los consumidores mediante las redes sociales y las páginas de los distribuidores.
Plan de negocios para la exportación de abrigos para damas hechos de fibra alpaca hacia Estados Unidos
Authors:   , Alarcón Martínez Daniel , Pacheco Ramos Álvaro Neils
Published: March 2023

El plan de negocios consiste en la exportación de abrigos de lujo para damas hacia Estados Unidos. Para ello, se formó la empresa peruana Alpatextil SAC, la cual se dedica al diseño, venta y exportación de ropa hecha con fibra de alpaca y dirigida hacia un público femenino. Nuestra principal ventaja competitiva reside en la diferenciación de producto con respecto a las demás empresas en el mercado internacional. En este trabajo de investigación, veremos cómo el mercado de Estados Unidos es uno de los principales mercados para la exportación de este producto, desarrollaremos estrategias de marketing, operaciones para ver la viabilidad del negocio, y como la empresa genera valor para miras a un crecimiento exponencial a otros mercados internacionales. Nuestra inversión inicial será de S/. 716,882 y nuestras ganancias netas del primer año ascienden a S/.2,181,901.11. El plan financiero nos indica que el proyecto es viable, debido a que nuestro VAN asciende a S/. 4,814,433.99 y nuestro TIR es de 203%, teniendo como aporte principal de los socios con un 84% y del banco Mibanco con un 16%. Finalmente, con la exportación de los abrigos a base de fibra de alpaca se busca un desarrollo económico para el país y crear bienestar para nuestros stakeholders.
Importación de franquicia: Amazon GO
Authors:   , Araujo Ramirez Madduel Enrique , De Rivero Guzman Estefania
Published: March 2023

Amazon Go es un prototipo de supermercado o tienda de conveniencia, administrado por Amazon.com, su primer prototipo se lanzó en Seattle, EEUU en el año 2016, desde ahí ha ido expandiendo su presencia como tienda física alrededor de EEUU. El tipo de negocio simplifica el proceso de compra y brinda un servicio personalizado y rápido a los clientes, si bien es un modelo poco común la posibilidad de crecimiento y desarrollo podría ser considerable, dadas las facilidades y acceso a tecnología actual.
Estudio de caso de las estrategias de publicidad digital de los emprendimientos de repostería saludable "Fit cake" y "Un dulce con Cami" creados durante mayo - agosto en el contexto del Covid-19 en Lima Metropolitana
Authors:   , Huapaya De La Cruz Cynthia Beatriz
Published: March 2023

La presente investigación se enmarca en el estudio sobre publicidad, muestra las estrategias aplicadas por las marcas “Fit Cake” y “Un dulce con Cami”; emprendimientos creados en un blog de Instagram. Mediante esta investigación se destacan las estrategias de publicidad digital para persuadir en los usuarios mediante las publicaciones realizadas por las marcas. El objetivo de este estudio es analizar las estrategias digitales utilizadas por los nuevos emprendimientos del sector de repostería saludable, creados durante el contexto del COVID-19. Para ello, se aplicó la metodología mixta, en donde se realizaron entrevistas semi-estructuradas hacia las creadoras de las marcas, encuestas hacia sus seguidores y un análisis del contenido basado en las acciones identificadas en las cuentas de Instagram de las marcas. El análisis desarrollado fue aplicando la observación no participante. Como resultado, se identificó que las acciones digitales realizadas por ambas marcas son bien percibidas por sus seguidores, entre las más destacadas fue posible identificar: la frecuencia en la publicación de contenido, la interacción con el público, hablar sobre temas de salud integral, y también, el buen aprovechamiento de los formatos de contenido de Instagram, razones por las cuales estas marcas muestran un crecimiento en la cantidad de seguidores y en el aumento de ventas.
El marketing mix y su incidencia en la rentabilidad de la empresa Perfect Store SAC, distrito San Miguel - Lima 2019
Authors:   , Farfan Melendez Maria Elena , Cordova Marchena Deysy Madeleyne
Published: March 2023

El análisis seguidamente desarrollado pretendió determinar la influencia del Marketing Mix aplicado en la Rentabilidad de la organización Perfect Store Sac, Lima-2019; formulándose para esto un análisis cuantitativo, tipo aplicado y diseño no experimental; de tipo correlacional- causal. De acuerdo a eso, fue escogida la encuesta como estrategia para la recabar la información, fue aplicado a 25 empleados de los departamentos; comercial, administración, finanzas de la organización; se aplicó un cuestionario con escala de Likert, los cuales conformaron, además de la muestra, la población del estudio. Así, los resultados conseguidos, permitieron demostrar que las técnicas de mercadotecnia en la organización y la rentabilidad son evaluadas indecisamente por el 40% y el 40% de la muestra, respectivamente. Asimismo, se evidencia que hay correlación considerable entre el marketing mix de los productos aplicados en la compañía y la rentabilidad de la empresa (rho = 0.738; p-valor de 0.000; p < 0.05), igualmente, existe relación muy alta entre la promoción y la rentabilidad (rho = 0.992; p-valor de 0.000; p < 0.05). De igual manera, puede afirmarse que hay una correlación positiva considerable entre los costos de la mercancía y/o servicios y la rentabilidad (rho = 0.684; p-valor de 0.000; p < 0.05). En razón de lo anterior, es posible concluir que, un nivel de 95% de confianza, hay una correlación significativa con el Marketing Mix y la Rentabilidad de Perfect Store Sac, Lima- 2019, obteniéndose una correlación de 0.914 con un p-valor de 0.000 (p < 0.05); observándose una relación positiva alta entre dichas variables.
Determinación del Estado de conservación en función de la contaminación por hidrocarburos alifáticos y residuos sólidos en las Áreas Naturales Protegidas Isla Pachacamac e Isla Asia
Authors:   , Noriega Pacheco Diana Carolina
Published: March 2023

La investigación tuvo como finalidad determinar el estado de conservación relacionado a la contaminación por hidrocarburos alifáticos y residuos sólidos en los sectores de la Isla Pachacamac e Isla Asia (IPA), mediante la optimización de la metodología Efectos por Actividades propuesta por el Servicio Nacional de Áreas Naturales Protegidas por el Estado (SERNANP). La metodología Efectos por Actividades evalúa a gran escala la distribución espacial de las zonas afectadas por contaminación en Áreas Naturales Protegidas (ANP), con el objetivo de determinar su estado de conservación y tomar acciones para mejorarlo. La aplicación de la metodología Efectos por Actividades permitió determinar el estado de conservación en la zona insular de IPA, mientras que la optimización de la metodología permitió determinar el estado de conservación en la zona marina de IPA. Como resultado de la aplicación y optimización de la metodología, se determinó que el estado de conservación relacionado a la presencia de hidrocarburos y residuos sólidos en la zona evaluada de la Isla Pachacamac es del 50% y en el caso de Isla Asia, es del 53%. La mayor cantidad de hidrocarburos encontrados son de origen antrópico y se detectó presencia de residuos sólidos en la zona marina e insular de ambas islas. Se determinó que las actividades antrópicas están impactando en el estado de conservación de las ANP IPA.
Asociación entre el porcentaje de grasa corporal y obesidad sarcopénica en adultos mayores: comparación de impedanciometría y absorciometría de rayos X de energía dual
Authors:   , Ramírez Berríos Luis Alberto , Gutiérrez Velasco Estefanía Cristina
Published: March 2023

Objetivo: Determinar la asociación entre grasa corporal y obesidad sarcopénica (OS) en adultos mayores atendidos en un servicio de geriatría de Lima, Perú. Materiales y métodos: Estudio retrospectivo, transversal y analítico que analiza secundariamente una base de datos de adultos mayores de 60 años atendidos en el “Servicio de Geriatría del Hospital Central de la Fuerza Aérea del Perú” durante los años 2019 – 2020. Resultados: El tamaño muestral del estudio fue de 50 adultos mayores. El promedio de edad de los pacientes fue 77,9 años (DE 7,44), el 62% fueron mujeres y un 38% varones. Se evidencio que el 100% de adultos mayores con OS eran varones. En el análisis de regresión lineal ajustado a la variable sexo, se revelo una mayor asociación entre la OS y el índice que relaciona la grasa tóraco-abdominal con la grasa de las extremidades inferiores (iGTA/GMI), en tanto que con la grasa corporal total no se encontraron resultados estadísticamente significativos. Asimismo, la asociación de otras variables que tienen un impacto significativo con la OS son el sexo y el índice cintura-cadera (ICC), mientras que el número de comorbilidades y la edad no resultaron significativos. Conclusiones: Los adultos mayores de 60 años con OS tenían mayor masa grasa en la región tóraco-abdominal, en comparación con las extremidades inferiores. Por ello, el tratamiento en estos pacientes debe ir dirigido a reducir la grasa en la región tóraco-abdominal, reduciendo el iGTA/GMI.
Satisfacción de la educación a distancia asociado a la depresión y ansiedad en internos de Medicina Humana a nivel nacional durante la pandemia COVID-19
Authors:   , Anton del Rio Karen Mirella , Casas Hermoza Elizabeth Almendra
Published: March 2023

Objetivo: Determinar la asociación entre la satisfacción de la educación a distancia y la ansiedad y depresión en los internos de medicina humana, durante la pandemia COVID-19. Materiales y métodos: Se realizo un estudio transversal, observacional y analítico; contando con una muestra de 345 participantes, recolectados mediante un cuestionario virtual en donde se utilizaron la escala de satisfacción académica (ESA), para la evaluación de educación a distancia y la escala Hospitalaria de Ansiedad y Depresión (HADS). Resultados: Se obtuvo que 197 (57,1%) internos presentaron ansiedad y depresión simultáneamente, de los cuales, el sexo femenino predominaba con un 61,5%, 147 (51,7%) poseía un antecedente psicológico psiquiátrico previo, 73 (54,4%) tuvieron más de 7 horas de sueño y 83 (48,2%) creyeron haber tenido COVID-19; por otra parte 119 (34,4%) de ellos presentaron insatisfacción de la educación a distancia, de las cuales 76 (34,3%) eran de sexo femenino y 92(38,6%) convivieron con su núcleo familiar durante la pandemia. En el análisis bivariado se muestra asociación entre estar insatisfecho con la educación a distancia y presentar ansiedad y depresión simultáneamente (IC 95%: 0,64 – 0,92, valor de p = 0,004), sin embargo, en el análisis multivariado ajustado por las variables confusoras pierde su valor significativo (IC 95%: 0,73 – 1,02, valor de p = 0,096). Conclusiones: Aunque se observa una predisposición mayor a tener ansiedad y depresión simultáneamente en los internos insatisfechos por la educación a distancia esta no llega a ser significativa en el multivariado.
Análisis de los regímenes de contratación de personal en la gestión del sistema de inspección del trabajo y su influencia en el clima laboral en la ciudad de Lima, 2016-2017
Authors:   , Cock Gastañeta Pablo
Published: March 2023

Esta tesis tiene como objetivo analizar los regímenes de contratación de personal que se presentan en la gestión del Sistema Inspectivo del Trabajo, y, cómo influyen en el clima laboral, en la ciudad de Lima, 2016-2017. Tratándose del régimen público, materia de esta investigación, las situaciones dadas para la prestación de la labor han sido reguladas por normas imperativas de alcance general, contra las cuales no cabe el ejercicio de autonomía de la voluntad; ello significa, que tanto el Estado, como los funcionarios, no pueden acordar condiciones distintas, todo ello agregado a los diversos procedimientos y requisitos que aplican para la “contratación de personal”, como son: el régimen público “(Decreto Legislativo Nº 276 y reglamento), la “Contratación Administrativa de Servicios (Decreto Legislativo Nº 1057 y reglamento), la Gerencia Pública (Decreto Legislativo Nº 1024)”, la contratación der personal altamente calificado “(Decreto de Urgencia Nº 051-2011); el Fondo de Apoyo Gerencial de las Naciones Unidas (Decreto Ley Nº 25650); y, los contratos de locación de servicios (Código Civil)”. El presente trabajo resulta relevante, en tanto, corresponde al Estado cautelar los derechos de los trabajadores, haciéndose evidente que, en los últimos años a nivel del régimen público, han surgido problemas para la contratación de personal, lo cual permite advertir una deficiente gestión del capital humano, generando una serie de demandas legales respecto a pretensiones de índole laboral. Estando a ello consideramos como una de las causales, la confluencia de diversos regímenes de contratación laboral, por cuanto la misma deviene en una diversificación de deberes, derechos y beneficios (compensaciones, vacaciones, gratificaciones, entre otros)...
Implementación de "Filtrovisor" e influencia en el aprendizaje del procesamiento macroscópico anátomo patológico de aborto espontáneo temprano en médicos residentes de un Hospital de Lima
Authors:   , Ramírez Valladares María Jesús , Mendoza Rosario Marcos Felix
Published: March 2023

Objetivos: Implementar Filtrovisor como material didáctico e influir en el aprendizaje del procesamiento macroscópico de aborto espontáneo temprano en médicos residentes de un Hospital de Lima. Materiales y métodos: Tipo de estudio: experimental, aplicación de encuestas en dos grupos: Médicos de segunda especialización(residentes) de primer año que realizaron su rotación en el Servicio de Anatomía patológica según programación durante un mes y a quienes se les impartió conocimiento sobre procesamiento macroscópico de aborto espontáneo temprano y las diferentes etiologías. Segunda fase, grupo experimental, se implementó el uso del Filtrovisor en el procesamiento macroscópico de la muestra de aborto espontáneo utilizando agua, las cuales son despejadas del contenido hemático, distendidas, también son medidas con escala milimétrica incorporada permitiendo identificar cada estructura y orientar el diagnóstico, conjuntamente con la información clínica, al finalizar su rotación se aplica la encuesta. Resultados: La implementación del Filtrovisor como material didáctico influye en el aprendizaje del procesamiento macroscópico de aborto espontáneo en médicos residentes, en un promedio de 71%, nos dice que Siempre y Casi siempre tiene mejoras en la identificación de estructuras gracias al uso del Filtrovisor. Conclusiones: La implementación del Filtrovisor incide en el aprendizaje del procesamiento macroscópico de aborto espontáneo lo que ha permitido a su vez elaborar un formato para los datos clínicos y estandarizar el procesamiento macroscópico.
High Performance Computing and Speedup Techniques in Geochemical Modeling of Matrix Acidizing
Authors: Wei Wan , Sanaei Alireza , Bordeaux Rego Fabio , Sepehrnoori Kamy
Published: March 2023

Matrix acidizing is a stimulation treatment during which acid is injected below formation fracture pressure. The purpose of acidizing is to enlarge pore space or create channels through dissolution of plugging particles and formation minerals near the wellbore. Simulation of acidizing process is computationally expensive, especially for geochemical simulation which considers full-species transport and complex reactions. In this paper, geochemical modeling of acidizing process is implemented through coupling two simulation models. One is UTCOMP (a 3D reservoir simulator) which is responsible for calculations of fluid flow and solute transport. The other is IPhreeqc (a geochemical package) which is responsible for calculations of kinetic and equilibrium reactions among minerals and aqueous species. Acidizing simulation through the coupled model UTCOMP-IPhreeqc is computationally expensive, and geochemical calculations through IPhreeqc are the computational bottleneck. To improve the computational efficiency, geochemical calculations which take up the majority of the computational time are parallelized. And speedup techniques are implemented to reduce the number of IPhreeqc calls through monitoring the amount change of geochemical components. We have validated the coupled model UTCOMP-IPhreeqc through comparison with the analytical solution in previous work. Parallel performance is measured by comparing total CPU time, CPU time spent on geochemical calculations, and speedup ratios among simulation runs using different processor numbers. For heterogeneous matrix, different dissolution patterns are generated under different injection rates, and the computational time varies depending on the total injection time and the average time step size. For different dissolution patterns, the overall speedup ratio is up to 6.69 when using 16 processors, reducing 85% of CPU time compared with the case using a single processor. The speedup ratio for geochemical calculations is up to 14.21 when using 16 processors, saving 93% of CPU time compared with the case using a single processor. Besides parallel computing, the speedup techniques also improve the computational efficiency, and obtain optimal performance for wormhole dissolution patterns in which most of the geochemical reactions occur in a localized volume. The computational time is reduced to 49% maintaining 96% accuracy compared with the case without using speedup techniques. The coupled model UTCOMP-IPhreeqc has the modeling ability of full-species transport and complex reactions. On this basis, the presented model significantly improves the computational efficiency of UTCOMP-IPhreeqc through parallel computing and speedup techniques reducing the computational time of geochemical calculations.
A Global-Convergent Newton Optimization Algorithm for the Phase Behavior Calculations with Capillary Pressure Effect for Tight Reservoir Fluids
Authors: Jing Hongbin , Liu Jianqiao , Pan Huanquan , Kuang Tie , Yin Zhilin , Li Bensheng
Published: March 2023

The thermodynamic behavior of a fluid in a tight reservoir differs from that in the conventional environment. A new phase equilibrium algorithm with capillary pressure is presented and formulated using the laws of thermodynamics. At a given temperature, volume, and moles with capillary pressure, this new algorithm is based on the Newton iteration and line search, which guarantees a global convergence. We obtain the Newton direction by utilizing the modified Cholesky factorization to ensure a descending direction and combine line search to facilitate the iterations in the feasible domain. The initial values of the new algorithm originate from Michelsen's two-sided method. All relevant derivatives are computed analytically and automatically through the Automatically Differentiable Expression Templates Library (ADETL), developed at Stanford University. The new algorithm is based on the Helmholtz free energy, and the corresponding energy surface will not be influenced by the pressure inequality between the liquid and vapor phases. We tested our algorithm on several fluids with different pore radii over a wide range of temperatures and total volumes, and no single calculation breakdown occurred. Meanwhile, the new algorithm can also determine the system phase status at a given temperature and pressure. We compared the results between the given temperature and volume and the given temperature and pressure. There is a dispute in effect of the derivatives of capillary pressure with respect to compositions on the phase equilibrium calculation in literature. We compared the results with and without the derivatives at a given temperature and volume and a given temperature and pressure. These results show that our new algorithm exhibits a good convergent performance and a robust solution even if the pore radius decreases to one nanometer, which indicates the potential of our algorithm for simulating the shale reservoir production process.
Nonlinearly Constrained Life-Cycle Production Optimization Using Sequential Quadratic Programming (SQP) With Stochastic Simplex Approximated Gradients (StoSAG)
Authors: Nguyen Quang , Onur Mustafa , Alpak Faruk Omer
Published: March 2023

Life-cycle production optimization is a crucial component of closed-loop reservoir management, referring to optimizing a production-driven objective function via varying well controls during a reservoir's lifetime. When nonlinear-state constraints (e.g., field liquid production rate and field gas production rate) at each control step need to be honored, solving a large-scale production optimization problem, particularly in geological uncertainty, becomes significantly challenging. This study presents a stochastic gradient-based framework to efficiently solve a nonlinearly constrained deterministic (based on a single realization of a geological model) or a robust (based on multiple realizations of the geologic model) production optimization problem. The proposed framework is based on a novel sequential quadratic programming (SQP) method using stochastic simplex approximated gradients (StoSAG). The novelty is due to the implementation of a line-search procedure into the SQP, which we refer to as line-search sequential quadratic programming (LS-SQP). Another variant of the method, called the trust-region SQP (TR-SQP), a dual method to the LS-SQP, is also introduced. For robust optimization, we couple LS-SQP with two different constraint handling schemes; the expected value constraint scheme and minimum-maximum (min-max) constraint scheme, to avoid the explicit application of nonlinear constraints for each reservoir model. We provide the basic theoretical development that led to our proposed algorithms and demonstrate their performances in three case studies: a simple synthetic deterministic problem (a two-phase waterflooding model), a large-scale deterministic optimization problem, and a large-scale robust optimization problem, both conducted on the Brugge model. Results show that the LS-SQP and TR-SQP algorithms with StoSAG can effectively handle the nonlinear constraints in a life-cycle production optimization problem. Numerical experiments also confirm similar converged ultimate solutions for both LS-SQP and TR-SQP variants. It has been observed that TR-SQP yields shorter but more safeguarded update steps compared to LS-SQP. However, it requires slightly more objective-function evaluations. We also demonstrate the superiority of these SQP methods over the augmented Lagrangian method (ALM) in a deterministic optimization example. For robust optimization, our results show that the LS-SQP framework with any of the two different constraint handling schemes considered effectively handles the nonlinear constraints in a life-cycle robust production optimization problem. However, the expected value constraint scheme results in higher optimal NPV than the min- max constraint scheme, but at the cost of possible constraint violation for some individual geological realizations.
Graphics Processing Unit Performance Scalability Study on a Commercial Black-Oil Reservoir Simulator
Authors: Tene Matei , Sekachev Michael , de Brito Dias Daniel , Szyndel Matthew D. E.
Published: March 2023

Commercial reservoir simulators have traditionally been optimized for distributed parallel execution on Central Processing Units (CPUs). Recent advances in Graphics Processing Units (GPUs) have led to the development of GPU-native simulators and triggered a shift towards a hardware-agnostic design in existing CPU solutions. For the latter, the suite of algorithms and data structures employed for a given computation are implemented for each target device. This results in a hybrid approach, where some simulator components inherently expose enough instruction parallelism or memory bandwidth requirements to warrant running on the GPU, while others are more suitable for the CPU. This paper examines the performance characteristics of a commercial black-oil reservoir simulator, which was recently extended with GPU support.

Each simulation case will distribute load on the various modules in a reservoir simulator differently, depending on the target physical properties and the forecasted data desired. To assess this, the scalability of the simulator is measured in detail using the CPU and GPU, for components where both implementations are available, focusing on time spent during model initialization, property calculation, linearization, solver, field management and reporting. This is done using test cases which stress the simulator across several axes: grid resolution, different petrophysical property distributions, well count and the volume of reported data. The synthetic models which form the basis for these studies were designed to represent realistic reservoir engineering scenarios.

The results show that a static partition between CPU- and GPU-assigned tasks, as employed by default in the simulator, is performant for scenarios where the work dedicated to grid cell properties and linear solution vastly outnumbers the effort spent resolving well or aquifer connections, field management and reporting. This is expected for typical simulation cases. However, when one of the latter aspects becomes dominant, the balance can shift, leading to suboptimal hardware utilization. In conclusion, if performance across all possible inputs is to be maintained, then a fully-CPU-and-GPU-capable simulator is needed, employing a dynamic scheduling strategy, where the runtime data locality, volume and parallelism of the corresponding computations are all considered when determining the target device for each operation.

To the authors’ knowledge, a study on the scalability of a commercial reservoir simulator, across two different hardware architectures, has not previously been conducted to this level of detail. The results on realistic models are presented in the hope that they will contribute to the discussion surrounding the benefits of modern computing hardware for reservoir simulation and help drive deployment and design decisions for existing and future developments in both the commercial and academic spheres.
Implementation of Adaptive Localization for Enhancing Ensemble-Based History Matching in Hydrocarbon Reservoir Management
Authors: Pernalete Marta , Lia Oddvar , Popa Tudor , Hanea Remus
Published: March 2023

In reservoir management, the ensemble-based history matching is applied to quantify and update uncertainty in reservoir characterization with the main objective to support high quality decisions. However, the ensemble-based history matching could suffer from statistical problems that make the ensemble unable to quantify uncertainty statistically-correctly. Localization schemes can effectively solve the ensemble-based history matching problems. The aim of this work lies in the research about the practical pros and cons of adaptive over non-adaptive localization schemes, that has not been extensively tested in different settings for ensemble-based history matching of reservoir simulation models. This work proposes a general workflow to guide localization implementation and evaluation. The workflow is developed in five sections: i) the initial ensemble generation; ii) history matching with no localization; iii) history matching with non-adaptive localization (distance-based); iv) history matching with adaptive localization (correlation-based); and v) Comparative result analysis among history matching cases.

The main conclusion from this work is that the history matching with the adaptive localization scheme overperformed the history matching with no localization and with the non-adaptive localization scheme. The history matching with adaptive localization demonstrated to estimate the reservoir dynamics and heterogeneities with more accuracy that can enhance decision quality in reservoir management. In addition, the implemented adaptive localization scheme supports standardization in the implementation of localization. However, the spatial regions for updating of the non-adaptive localization scheme are more intuitive than correlations of the adaptive localization scheme. Although correlations of the adaptive localization scheme embed reservoir dynamics, the understanding and interpretation of the correlations are not as straightforward as spatial regions are. Therefore, the adaptive localization scheme requires understanding of the statistical method and interpreting correlations to build history matching ownership.

The study brought innovative guidelines to best practice for implementing non-adaptive and adaptive localization. The effectiveness of the workflows was tested, evaluated, and contributed to further developing and improving the history matching processes. The identified practical pros and cons will facilitate the adaptive localization implementation in Equinor.
Parameter Inversion in Geothermal Reservoir Using Markov Chain Monte Carlo and Deep Learning
Authors: Zhang Zhen , He Xupeng , Li Yiteng , AlSinan Marwa , Kwak Hyung , Hoteit Hussein
Published: March 2023

Traditional history-matching process suffers from non-uniqueness solutions, subsurface uncertainties, and high computational cost. This work proposes a robust history-matching workflow utilizing the Bayesian Markov Chain Monte Carlo (MCMC) and Bidirectional Long-Short Term Memory (BiLSTM) network to perform history matching under uncertainties for geothermal resource development efficiently. There are mainly four steps. Step 1: Identifying uncertainty parameters. Step 2: The BiLSTM is built to map the nonlinear relationship between the key uncertainty parameters (e.g., injection rates, reservoir temperature, etc.) and time series outputs (temperature of producer). Bayesian optimization is used to automate the tuning process of the hyper-parameters. Step 3: The Bayesian MCMC is performed to inverse the uncertainty parameters. The BiLSTM is served as the forward model to reduce the computational expense. Step 4: If the errors of the predicted response between the high-fidelity model and Bayesian MCMC are high, we need to revisit the accuracy of the BiLSTM and the prior information on the uncertainty parameters. We demonstrate the proposed method using a 3D fractured geothermal reservoir, where the cold water is injected into a geothermal reservoir, and the energy is extracted by producing hot water in a producer. Results show that the proposed Bayesian MCMC and BiLSTM method can successfully inverse the uncertainty parameters with narrow uncertainties by comparing the inversed parameters and the ground truth. We then compare its superiority with models like PCE, Kriging, and SVR, and our method achieves the highest accuracy. We propose a Bayesian MCMC and BiLSTM-based history matching method for uncertainty parameters inversion and demonstrate its accuracy and robustness compared with other models. This approach provides an efficient and practical history-matching method for geothermal extraction with significant uncertainties.
Feature Extraction in Time-Lapse Seismic Using Deep Learning for Data Assimilation
Authors: Exterkoetter Rodrigo , Rachid Dutra Gustavo , Passos de Figueiredo Leandro , Bordignon Fernando , Anozé Emerick Alexandre , Moura Silva Neto Gilson
Published: March 2023

The assimilation of time-lapse (4D) seismic data is challenging with ensemble-based methods because of the massive number of data points. This situation requires an excessive computational time and memory usage during the model updating step. We addressed this problem using a deep convolutional autoencoder to extract the relevant features of 4D images and generate a reduced representation of the data. The architecture of the autoencoder is based on the well-known VGG-19 network, from which we take advantage of the transfer learning technique. Using a pre-trained model bypasses the need of large training datasets and avoids the high computational demand to train a deep network. For further improvements in the reconstruction of the seismic images, we apply a fine-tuning of the weights of the latent convolutional layer. We propose to use a fully convolutional architecture, which allows the application of distance-based localization during data assimilation with the Ensemble Smoother with Multiple Data Assimilation (ES-MDA). The performance of the proposed method is investigated in a synthetic benchmark problem with realistic settings. We evaluate the methodology with three variants of the autoencoder, each one with a different level of data reduction. The experiments indicate that it is possible to use latent representations with major data reductions without impairing the quality of the data assimilation. Additionally, we compare CPU and GPU implementations of the ES-MDA update step and show in another synthetic problem that the reduction in the number of data points obtained with the application of the deep autoencoder may provide a substantial improvement in the overall computation cost of the data assimilation for large reservoir models.
Reservoir Connectivity Identification and Robust Production Forecasting Using Physics Informed Machine Learning
Authors: Nagao Masahiro , Datta-Gupta Akhil , Onishi Tsubasa , Sankaran Sathish
Published: March 2023

Routine well-wise injection/production data contain significant information which can be used for closed-loop reservoir management and rapid field decisions. Traditional physics-based numerical reservoir simulation can be computationally prohibitive for short-term decision cycles, and also requires detailed geologic model. Reduced physics models provide an efficient simulator free workflow, but often have a limited range of applicability. Pure machine learning models lack physical interpretability and can have limited predictive power. We propose a hybrid machine learning and physics-based approach for rapid production forecasting and reservoir connectivity characterization using routine injection/production and pressure data.

Our framework takes routine measurements such as injection rate and pressure data as input and multiphase production rates as output. We combine reduced physics models into a neural network architecture by utilizing two different approaches. In the first approach, the reduced physics model is used for pre-processing to obtain approximate solutions that feed it into a neural network as input. This physics-based input feature can reduce the model complexity and provide significant improvement in prediction performance. The second approach augments the residual terms in the neural network loss function with physics-based regularization that relies on the governing partial differential equations (PDE). Reduced physics models are used for the governing PDE to enable efficient neural network training. The regularization allows the model to avoid overfitting and provides better predictive performance.

Our proposed hybrid models are first validated using a 2D benchmark reservoir simulation case and then applied to a field-scale reservoir case to show the robustness and efficiency of the method. The hybrid models are shown to provide superior prediction performance than pure machine learning models and reduced physics models in terms of multiphase production rates. Specifically, in the second method, the trained hybrid neural network model satisfies the reduced physics model, making it physically interpretable, and provides inter-well connectivity in terms of well flux allocation. The flux allocation estimated from the hybrid model was compared with streamline-based flux allocation, and excellent agreement was obtained. By combining the reduced physics model with the efficacy of deep learning, model calibration can be done very efficiently without constructing a geologic model.

The proposed hybrid models with physics-based regularization and preprocessing provide novel approaches to augment data-driven models with underlying physics to build interpretable models for understanding reservoir connectivity between wells and robust future production forecasting.
Application of Deep Neural Networks to the Operator Space of Nonlinear PDE for Physics-Based Proxy Modelling
Authors: Hadjisotiriou George , Mansour Pour Kiarash , Voskov Denis
Published: March 2023

In this study, we utilize deep neural networks to approximate operators of a nonlinear partial differential equation (PDE), within the Operator-Based Linearization (OBL) simulation framework, and discover the physical space for a physics-based proxy model with reduced degrees of freedom. In our methodology, observations from a high-fidelity model are utilized within a supervised learning scheme to directly train the PDE operators and improve the predictive accuracy of a proxy model. The governing operators of a pseudo-binary gas vaporization problem are trained with a transfer learning scheme. In this two-stage methodology, labeled data from an analytical physics-based approximation of the operator space are used to train the network at the first stage. In the second stage, a Lebesgue integration of the shocks in space and time is used in the loss function by the inclusion of a fully implicit PDE solver directly in the neural network's loss function. The Lebesgue integral is used as a regularization function and allows the neural network to discover the operator space for which the difference in shock estimation is minimal. Our Physics-Informed Machine Learning (PIML) methodology is demonstrated for an isothermal, compressible, two-phase multicomponent gas-injection problem. Traditionally, neural networks are used to discover hidden parameters within the nonlinear operator of a PDE. In our approach, the neural network is trained to match the shocks of the full-compositional model in a 1D homogeneous model. This training allows us to significantly improve the prediction of the reduced-order proxy model for multi-dimensional highly heterogeneous reservoirs. With a relatively small amount of training, the neural network can learn the operator space and decrease the error of the phase-state classification of the compositional transport problem. Furthermore, the accuracy of the breakthrough time prediction is increased therefore improving the usability of the proxy model for more complex cases with more nonlinear physics.
Dissipation-Based Nonlinear Solver for Efficient Implicit Simulation of Compositional and Discrete Fracture Models
Authors: Jiang Jiamin , Pan Huanquan
Published: March 2023

The solution of nonlinear equation-system resulting from the Fully Implicit Method (FIM) remains a challenge for numerically simulating multi-phase flow in subsurface fracture media. The Courant numbers can vary orders of magnitude across discrete fracture- matrix (DFM) models because of the high contrasts in the permeability and length-scale between matrix and fracture. The standard Newton solver is usually unable to converge for big timestep sizes or poor initial guesses.

Limited research has been conducted on nonlinear solver techniques for multi-phase compositional flow-transport in fractured media. We make an extension of a new dissipation-based continuation (DBC) algorithm to compositional and DFM models. Our goal is to prevent time-step cuttings and sustain efficient time-stepping for FIM. The DBC algorithm builds a homotopy of the discretized conservation equations through the addition of numerical dissipation terms. We introduce a continuation parameter for controlling the dissipation and ensuring that accuracy of the computed solution will not be reduced. Under the nonlinear framework of DBC, general dissipation operators and adaptive methods are developed to provide the optimal dissipation matrix for multiphase compositional hyperbolic systems.

We assess the new nonlinear solver through multiple numerical examples. Results reveal that the damped-Newton solver suffers from serious restrictions on timestep sizes and wasted iterations. In contrast, the DBC solver provides excellent computational performance. The dissipation operators are able to successfully resolve the main convergence difficulties. We also investigate the impact of star-delta transformation which removes the small cells at fracture intersections. Moreover, we demonstrate that an aggressive time-stepping does not affect the solution accuracy.
A Numerical Study of Selective Inorganic Precipitation in Fractures to Geoengineer Resilient and Secure Underground Storage Sites
Authors: Fan Shijun , Younis Rami M. , Lu Jun
Published: March 2023

A process to geoengineer hydrodynamic seals is investigates numerically. It is well-documented that upon mixing, Barium- and sulfate-rich brines lead to rapid deposition of impermeable and stable scale. When deposited within open fractures, the mineral scale also holds significant tensile strength. This work investigates the intentional injection of these incompatible ions into faulted formations and around caverns to create mechanically resilient hydrodynamic seals. A coupled hydrological-mechanical-chemical HMC model is calibrated to available bench-scale experimental observations. The model attains a close match with experimental core-flooding observations by fitting reaction rate, solubility, precipitation, and nonequilibrium formation blockage. It is then applied to conduct field-scale investigations of various synthetic operational injection scenarios. The calibrated simulations suggest that the saturation index of the injected brines, injection patterns, and rates have a first-order impact on seal creation and uniformity. With inorganic precipitation, the critical injection rates to triggering seismic nucleation may be reinforced by one to two orders in magnitude due to cohesive mechanical healing.
Integrating Geomechanics Studies to Shale and Tight Phased Development - Application to Delaware Basin
Authors: Khebzegga Ouassim , Chen Yuguang , Rey Alvaro, J , Wang Bin
Published: March 2023

Parent and child wells interference is a major concern in the development of shale and tight reservoirs. Oil and gas operators typically aim to prevent fractures interference during the child well’s stimulation, which connects the parent and child well’s fracture networks. Another aspect of the parent-child well interference lies in the impact of parent-well depletion on child-well stimulation, often leading to the underperformance of child-well production. For these circumstances, reservoir simulation that combines flow and geomechanics is required to predict child-well fractures growth. To achieve this goal, a new workflow combining flow (EDFM), geomechanics (single porosity) and fracturing was developed to predict the reservoir stress change, the growth of the child-well fractures, and the production of the parent and child wells. As one of the applications, this tool was applied to a Delaware basin reservoir and enabled the asset team to better design the pad for the child wells. Multiple scenarios were analyzed for eight child wells located between two parent wells. Using this tool, we were able to predict the asymmetric growth of the fractures in the direction of the parent wells for child wells that were close enough to their parent wells. The impact of this fractures’ asymmetric growth on the production of the child wells was also quantified, based on which a better configuration of child wells was recommended to mitigate the depletion effect of parent wells.