bluuebunny/crossref_metadata_2025 huggingface dataset:
Nötrofil / lenfosit oranı kronik hepatit B hastalarında karaciğer fibrozisini saptamada kullanılabilir mi?
Authors: ÖZER SARI Sevil , KÖSEOĞLU Hüseyin , AKAR Mustafa , Solakoğlu Tevfik , ERSOY Osman
Published: April 2019

Amaç: Kronik hepatit B (KHB) hastalarında fibrozis değerlendirmesi siroz ve hepatosellüler kanser gelişimini
önlemek açısından önemlidir. Karaciğer biyopsisi fibrozisi değerlendirmek için altın standarttır. Karaciğer biyopsisinin
invaziv olması ve komplikasyonlarının olması nedeniyle karaciğer histopatolojisini gösterecek noninvaziv testler önem
kazanmıştır. Nötrofil/lenfosit oranının (NLO) nonalkolik steatohepatitli ve KHB'li hastalarda fibrozis ile ilişkisini
gösteren bazı çalışmalar bulunmaktadır. Bu çalışmanın amacı KHB’li hastalarda NLO ile karaciğer fibrozisi arasındaki
ilişkiyi değerlendirmektir.
Gereç ve Yöntem: Hepatoloji polikliniğimizce takip edilen ve karaciğer biyopsisi olan 67 KHB’li hasta retrospektif
olarak değerlendirildi. Tüm hastaların demografik verileri, laboratuvar parametreleri ve karaciğer biyopsisinde
saptanan değerler kaydedildi.
Bulgular: Çalışmaya dahil edilen hastaların yaş ortalaması 48,6±11,7 yıl idi. 17 hasta kadın (%25,4), 50 hasta erkekti
(%69,6). Fibrozis dereceleri 8 hastada 0, 15 hastada 1, 8 hastada 2, 18 hastada 3, 7 hastada 4, 9 hastada 5, 2
hastada 6 olarak değerlendirildi. KHB hastalarında karaciğer biyopsisinde saptanan fibrozis ile NLO arasındaki
ilişkiye bakıldığında iki parametre arasında anlamlı bir ilişki saptanmadı (p=0,54).
Sonuç: NLO oranı KHB'li hastalarda fibrozisi göstermede etkin bir parametre olarak görülmemiştir.
Modelling Built-Settlements between Remotely-Sensed Observations
Authors: Nieves Jeremiah J. , Sorichetta Alessandro , Linard Catherine , Bondarenko Maksym , Steele Jessica , Stevens Forrest , Gaughan Andrea E. , Carioli Alessandra , Clarke Donna , Esch Thomas , Tatem Andrew J.
Published: April 2019

Mapping settlement extents at the annual time step has a wide variety of applications in demography, public health, sustainable development, and many other fields. Recently, while more multitemporal urban feature or human settlement datasets have become available, issues still exist in remotely-sensed imagery due to coverage, adverse atmospheric conditions, and expenses involved in producing such feature sets. These challenges make it difficult to increase temporal coverage while maintaining high fidelity in the spatial resolution. Here we demonstrate an interpolative and flexible modeling framework for producing annual built-settlement extents. We use a combined technique of random forest and spatio-temporal dasymetric modeling with open source subnational data to produce annual 100m x 100m resolution binary settlement maps in four test countries of varying environmental and developmental contexts for test periods of five-year gaps. We find that in the majority of years, across all study areas, the model correctly identified between 85-99% of pixels that transition to built-settlement. Additionally, with few exceptions, the model substantially out performed a model that gave every pixel equal chance of transitioning to the category “built” in each year. This modelling framework shows strong promise for filling gaps in cross-sectional urban feature datasets derived from remotely-sensed imagery, provide a base upon which to create future built/settlement extent projections, and further explore the relationships between built area and population dynamics.
Thermodynamic Analysis of Solar Cooling System for a Residential Buildings Application Capital of Iran
Authors: Asgari Mohamad Javad , Mossafa Amirhossein , Fallah Mohsen
Published: April 2019

The global industrial development, the increasing demand for energy, the limited availability of resources for the future generations of fossil fuels, and the prevention of environmental damage caused by their burning have led to public concern. Increasing energy consumption by buildings has led the wider global attention to its social, environmental, and economic implications. In the present study, in order to reduce the power consumption of the summer, a solar vacuum tube-collecting system, with its high efficiency, with a hot water tank and an absorption refrigeration system in a 5-story residential building in In Tehran Capital of Iran, has been investigated. In addition, the results also indicate that the main source of exergy destruction is solar collectors. In the solar collector, we have 93.22% of the exergy destruction, which accounts for 92.8% of the total exergy cascade. In addition, 18.45% of the exergy destruction occurred in the generator, which was equivalent to 1.598% of the total exergy casualties. Also, the coefficient of performance and exergy efficiency of the whole system were 0.746 and 23.5%, respectively, which showed a significant increase in total exergy efficiency compared to the conventional refrigeration system.
Real Time Control Strategy for Hybrid Electric Vehicles
Authors: Niknamian Sorush
Published: April 2019

Based on the complex structure of electric hybrid car and uncertainty in driving force structure of electric hybrid car, different strategies have been presented for optimal management of energy based on smart methods. In this study by the decision making nature of fuzz logic, a movement map for Parallel Hybrid Electric Vehicle (PHEV) is made based on the required path. In a parallel hybrid car, recharging control of battery and auxiliary torque of electric engine are used as the key points of movement. Based on the disadvantages of pure electric car, to increase the life of battery and its easy use, we need a movement strategy balancing the battery charge for a movement path. If the battery is charged at no load by the combustion engine, NOx emission is increased and the battery charge is not good and adequate for HEV performance under no-load condition by the energy retrieval power and combustion engine. For a movement structure, it is hard to define the conversion point between the motor performance and generator performance exactly. By a drive strategy based on crisp methods, the battery charge is sensitive to the moving samples of driver, path condition and load conditions. Using fuzzy control strategy to control varied non-linear systems is very suitable and it is robust against the changes of components of sub-systems and inexact measurements. New York City Cycle (NYCC) is considered to perform simulation. As shown in paper, the fuzzy control strategy can keep the charge stage of batteries at good range.
Análise de custo por resposta de adalimumabe, etanercepte, guselcumabe, infliximabe, ixequizumabe, secuquinumabe e ustequinumabe para tratamento de psoríase em placas moderada a grave sob a perspectiva do Sistema de Saúde Suplementar brasileiro
Authors: Fioratti Cyntia , Valencia-Mendoza Atanacio , Rachid Maria Luiza , Rosim Ricardo , Junqueira Marcela
Published: April 2019

Objetivo: O objetivo deste estudo é avaliar o custo por resposta das terapias biológicas disponíveis no Brasil para o tratamento da psoríase em placas moderada a grave na perspectiva do Sistema de Saúde Suplementar. Métodos: A resposta PASI 90 foi o desfecho avaliado neste estudo. Dados clínicos foram calculados com base na razão de risco de uma metanálise em rede, comparando adalimumabe, etanercepte, infliximabe, ixequizumabe, secuquinumabe e ustequinumabe a guselcumabe, cujo dado foi obtido no estudo clínico. Foram considerados apenas os custos de medicamentos. O caso-base avaliou o custo por resposta do ano de indução do tratamento. Além disso, conduziu-se uma análise de orçamento fixo. Em um cenário alternativo, analisou-se o custo por resposta do ano de manutenção. Uma análise de sensibilidade avaliou incertezas dos dados clínicos. Resultados: O menor custo por resposta foi de guselcumabe (R$ 98.643), seguido de ixequizumabe (R$ 112.549), ustequinumabe (R$ 124.078), secuquinumabe (R$ 160.930), infliximabe (R$ 208.039), adalimumabe (R$ 208.686) e etanercepte (R$ 639.124). Resultados similares foram observados no cenário alternativo, considerando os custos no ano de manutenção. Guselcumabe demonstrou ser a terapia que tratou mais pacientes com sucesso, considerando um cenário de orçamento fixo. Conclusão: O presente estudo demonstrou que, na perspectiva do Sistema de Saúde Suplementar brasileiro, guselcumabe possui o menor custo por resposta entre as terapias biológicas para psoríase em placas moderada a grave, além de tratar com sucesso mais pacientes em um cenário de orçamento fixo.
O desafio da equidade: fatores associados aos gastos com a produção ambulatorial na atenção secundária
Authors: Liebel Graziela , Sá Junio Antonio , Campos Estela , Wang Yuan-Pang , Dias Patrick , Chaoubah Alfredo
Published: April 2019

Objetivo: Analisar quais as variáveis socioeconômicas, estruturais e demográficas possuem maior associação com a produção e os gastos ambulatoriais de média complexidade no estado de Minas Gerais no ano de 2014. Métodos: Estudo ecológico, analítico observacional em uma população de 853 municípios do estado de Minas Gerais agregados por 77 Comissões Intergestoras Regionais (CIRs). Foram realizadas análises descritivas, bivariada e multivariada, entre o gasto com a produção e variáveis explicativas sociodemográficas e econômicas, cobertura da estratégia saúde da família, número de médicos especialistas e de atenção básica, e oferta (equipamentos) dos serviços em média complexidade. Resultados: A caracterização socioeconômica, demográfica e de estrutura dos serviços de saúde das CIRs indicou heterogeneidade nessas variáveis. Foi evidenciada correlação positiva entre a produção e o gasto per capita ambulatorial em média complexidade com as variáveis: produto interno bruto (PIB), renda média domiciliar per capita, índice de desenvolvimento humano municipal (IDHM), condição de habitação, sexo feminino e médicos especialistas e de atenção básica. Evidenciaram-se ainda correlações negativas com as variáveis: taxa de analfabetismo e cobertura da estratégia saúde da família. Na regressão linear múltipla, a variável condição de habitação apresentou maior valor preditivo em relação à produção per capita em média complexidade e as variáveis “médicos especialistas”, “condições de habitação” e “equipamento RX” mostraram maior valor preditivo para gasto per capita em média complexidade. Conclusão: Os resultados fornecem evidências de que a infraestrutura e a condição socioeconômica das CIRs estão determinando a maior produção e gastos ambulatoriais de média complexidade, pois regiões com menor infraestrutura e condição socioeconômica tendem a gastar menos.
Transport of HPAM Solutions in low Permeability Porous Media: Impacts of Salinity and Clay Content
Authors: Guetni Imane , Marliere Claire , Rousseau David , Bihannic Isabelle , Pelletier Manuel , Villieras Frédéric
Published: April 2019

Chemical EOR is now considered as an attractive option for low permeability reservoirs, in particular where lack of gas supply does not allow gas injection processes. However, its application can be challenging for permeabilities below 100 mD, as poor injectivity and high chemical retention are frequently observed in these cases. This work aimed at investigating the impact of both chemical and mineralogical parameters on the transport of polymer solutions in well-controlled low permeability porous media.

The intrinsic viscosity and hydrodynamic size of partially hydrolyzed polyacrylamide (HPAM) solubilized in brines of variable ionic strengths and hardnesses were firstly investigated. Polymer injection corefloods were then conducted using granular packs (sand and clays mixtures) with similar petrophysical characteristics (permeability 60-80 mD) but having several controlled mineralogical compositions. The granular packs were especially characterized in terms of structure (SEM) and specific surface area (BET) before and after polymer injections. The main observables from the coreflood tests were the resistance and residual resistance factors generated by the polymer, the polymer inaccessible pore volume and its irreversible retention.

Homogenous and reproducible granular packs were successfully prepared thanks to a dedicated methodology and using different ratios of quartz and clays (kaolinite and illite separately).

Results from the viscometric analysis showed that the intrinsic viscosity of the HPAM solutions decreased with increasing total salinity, as expected from charge screening, and that it decreased sharply in presence of divalent cations, even at low ionic strength, which was less expected.

Coreflood experiments showed that polymer retentions, resistance factors and irreversible resistance factors increased significantly:–with increasing ionic strength and hardness for porous media of a given mineralogical composition. This appeared consistent with the outcomes of the viscometric study and confirmed the major impact of hardness;–in presence of kaolinite and illite, even at low ionic strength and hardness.

The polymer inaccessible pore volume was significantly impacted by the presence of clays, but not by the ionic strength and hardness. Analysis of the results indicated that these effects could not be attributed only to polymer adsorption linked to the increase of specific surface area, but that more complex polymer adsorption/retention mechanisms occur depending on the clay type (layer charge and expandability).

This systematic study allows dissociating the impacts of salinity, hardness and clay contents/types on the transport of polymer solutions in low permeability porous media. The results obtained should be of interest to the chemical EOR industry as they provide guides to help tuning the injection brine composition and polymer concentration to the reservoir properties.
Machine-Learning-Based Petrophysical Property Modeling
Authors: Vallabhaneni Sridharan , Saraf Rahul , Priyadarshy Satyam
Published: April 2019

The current cycle for reservoir management requires several months to years to update static and dynamic models as additional data from the field [logs, production, pressures, core, four-dimensional (4D), etc.] are obtained. These delays in updating the models result in increased risk and contribute to a significant loss of economic value. The ultimate goal for next-generation reservoir management is to reduce the cycle from several months to a few days. The current challenges for developing a proactive/real-time reservoir management solution include but are not limited to the time and manual intervention involved in conditioning and interpreting the logging-while-drilling (LWD) and well log data acquired during and after drilling a well; updating three-dimensional (3D) petrophysical/static models; and the computational cost and time involved in generating reservoir models from static and production data (history matching). However, the current widespread use of machine-learning and cloud-computing capabilities leads to faster and more accurate models, enabling real-time or near-real-time decision making. Using machine learning, one of these challenges—updating the 3D static models—was successfully addressed, namely, updating porosity prediction in a 3D model after new information comes to light, such as logging from a newly drilled well. The conventional geostatistical approach does not always honor geological variations in the subsurface formations, because only one or two seismic attributes can be used for co-simulation, and only with first-order interactions. Additionally, and most important, generating hundreds of realizations on a 3D grid is computationally intense and time consuming. Typically, several weeks are necessary to generate these static models before feeding them into the reservoir model. The proposed solution is a machine-learning-based approach that integrates 3D spatial availability of seismic data with petrophysical properties. One important goal of reservoir management is to understand reservoir uncertainties before they adversely affect field development. This machine learning solution proved to be computationally less costly, more accurate, and much faster than the conventional geostatistical approach.
Geomechanical Model of a Gas Field for Seismic Risk Analysis
Authors: Berentsen Cas , de Pater Cornelis , Nieuwland Ferry
Published: April 2019

The Slootdorp field has a complex structure with most reserves in Rotliegend sandstone, which is communicating with gas bearing Zechstein carbonates. The Rotliegend reservoir is bounded by a large fault, which might become seismogenic during depletion. A 3D geomechanical model was built, based on the faults and horizons in the geological model. Both the Rotliegend and Zechstein reservoirs were included in the model. The model was populated with geomechanical properties derived from logs, LOT's (leak off tests) and regional data on the stress field. Also, overburden properties from previous studies on nearby fields were used.

The pressure input was obtained from reservoir simulation. It is important to include the water leg pressure in the pressure input since the Rotliegend gas reservoir is in contact with an active aquifer. Pressure reduction drives the compaction of the reservoir, which induces stresses on the faults causing slippage. Since the water is quite incompressible, a large pressure reduction in the water leg may be caused temporarily by a rising gas water contact.

It turned out that slippage is not expected at the lowest gas pressure using a conservative estimate of the critical friction coefficient on the fault of 0.55. Sensitivity analysis on the most important input parameters was performed with a range that can be expected for such a field. The result was that the maximum critical stress ratio could range between 0.46 and 0.53 for the expected uncertainty of input parameters. The geomechanical modeling shows that an active aquifer has a dominant, mitigating effect on seismic risk, which can explain why many reservoirs show no seismicity in the Netherlands, although other effects could also play a role.
Attempt of Lithology Prediction from Surface Drilling Data and Machine Learning for Scientific Drilling Programs
Authors: Inoue Tomoya , Tanaka Ryuta , Ishiwata Junya
Published: April 2019

Japan Agency for Marine-Earth Science and Technology (JAMSTEC) operates the scientific deep-sea drilling vessel Chikyu for scientific studies on past climate changes, long-term crust transportation, biological and abiotic processes associated with hydrocarbon production, and earthquakes, the last being one of the most important research topics. Chikyu conducted the Japan Trench Fast Drilling Program (JFAST) to obtain core samples from sediment layers under the seabed for study on the large Tohoku earthquake which triggered the devastating tsunami in 2011. Chikyu has also started to conduct the Nankai Trough Seismogenic Zone Experiment (NanTroSEIZE) as the Nankai Trough is one of the most active earthquake zones. As one of the primary goals of scientific drilling is to evaluate sediment properties, it is highly beneficial to characterize the lithology of the drilling layer during drilling operations. Even an approximation of these properties could potentially provide valuable information for conducting coring operations. Thus, a previous study attempted to discuss the properties of sediment layers using surface drilling data, and estimated the shear stress of the sediment. In this study, we summarize the method applied in the previous paper for estimating the torque at the drill bit from the surface measured drilling data for NanTroSEIZE deep riser drilling operation. This data was then used to directly calculate the shear stress of the sediment. The drilling torque at the bit differed from the surface torque, where this difference increased with increasing drilling depth. Our estimations of the drilling torque were validated using torque data obtained from logging while drilling (LWD) operations. We also used machine-learning approaches to predict the lithology, where learning data was created from surface drilling data and lithology information from core samples obtained during past scientific drilling operations. Machine learning was then applied using neural network algorithms by tuning the number of layers to create a predictive model. This paper discusses the preliminary attempt to predict the lithology using machine-learning approaches for NanTroSEIZE and JFAST data.
Evaluation and Optimisation of Chemically Enhanced Oil Recovery in Fractured Reservoirs Using Dual-Porosity Models
Authors: Al-Rudaini Ali , Geiger Sebastian , Mackay Eric , Maier Christine , Pola Jackson
Published: April 2019

We propose a workflow to optimise the configuration of multiple interacting continua (MINC) models and overcome the limitations of the classical dual-porosity model when simulating chemically enhanced oil recovery processes. Our new approach captures the evolution of the concentration front inside the matrix, which is key to design a more effective chemically enhanced oil recovery projects in naturally fractured reservoirs. Our workflow is intuitive and based on the simple concept that fine-scale single-porosity models capture fracture-matrix interaction accurately and can hence be easily applied in a commercial reservoir simulator. Results from the fine-scale single-porosity system are translated into an equivalent MINC method that yields more accurate results than the classical dual-porosity model or a MINC method where the shells are arbitrarily selected.

Our approach does not require the tuning of capillary pressure curves ("pseudoisation"), diffusion coefficients, MINC shells, or the generation of recovery type curves, all of which have been suggested in the past to model more complex recovery processes. A careful examination of the fine-scale single-porosity model ("reference case") shows that a number of nested shells emerge, describing the advance of the concentration and saturation fronts inside the matrix. The number of shells is related to the required degree of refinement, i.e. the number of shells, in the improved MINC model. Using the results from a fine-scale single-porosity simulation to set up the shells in the MINC model is easy and requires only simple volume calculations. It is hence independent of the chosen simulator.

Our improved MINC method yields significantly more accurate results compared to a classical dual-porosity model, a MINC method with equally sized shells, or a MINC model with arbitrarily refined shells for a number of recovery scenarios that cover a range of matrix wettabilities and permeabilities. In general, improved results can be obtained when selecting five or fewer shells in the MINC. However, the actual number of shells is case-specific. The largest improvement is observed for cases when the matrix permeability is low.

The novelty of our approach is the easy-to-use method to define shells for a MINC model to predict chemically enhanced oil recovery from naturally fractured reservoirs more accurately, especially in cases where the matrix has low permeability. Hence the improved MINC method is particularly suitable to model chemical EOR processes in (tight) fractured carbonates.
Novel Method For Mitigating Injectivity Issues During Polymer Flooding at High Salinity Conditions
Authors: Schmidt Julia , Mirzaie Yegane Mohsen , Dugonjic-Bilic Fatima , Gerlach Benjamin , Zitha Pacelli
Published: April 2019

Synthetic high molecular weight polymers have been utilized for enhanced oil recovery applications. Improving their injectivity remains an important issue for field applications. Large entangled polymer chains can clog pore throats, leading to injectivity decline. We investigated an emulsion polymer system and have developed a series of processing techniques to condition an acrylamide-based copolymer inverse emulsion system at a salinity of 50,000 ppm TDS before injection into porous media. The investigated polymer solution contained 4,000 ppm active emulsion polymer and 2,400 ppm inverter surfactant.

The un-conditioned polymer system and test conditions were chosen to clearly demonstrate the impact of processing techniques on the injectivity behavior. The polymer solution was sheared with two agitators, a disperser and Ultra-Turrax, at different intensities and with a pressure-driven flow into a thin capillary to reduce the size of the largest polymer chains and disentangle the polymer chains while maintaining its viscosifying power. The injectivity of such differently sheared solutions was evaluated by performing filtration tests using a 1-micron membrane and sand-pack flooding tests.

Our experiments have established a master curve showing viscosity and screen factor dependences on accumulated energy during pre-shearing, regardless of the mode of shearing. The un-sheared polymer solution had an unfavorable behavior in filtration test and sand-pack flooding experiment. After pre-shearing, the filtration behavior of polymer solution and the injectivity in sand-packs improved significantly. Polymer solutions sheared with a disperser at an energy input of 15 MJ/m3 improved the injectivity gradient (e.g. the ratio of the resistance factor over 30 pore volumes injected) from 3.7 to 1.6, while the viscosifying power was reduced by only 2%. To reach the same injectivity improvement with Ultra-Turrax, an energy input of 31 MJ/m3 were required, which reduced the viscosity by 11%. Shearing the solution using a capillary at an energy input of 50 MJ/m3, did not reduce the injectivity gradient while viscosity was reduced by 19%. This indicates that the injectivity performance is shear-origin dependent and the resulting polymer structure, when sheared through contractions, has a different alignment as compared to shearing with the agitators, the disperser and Ultra-Turrax.
Optimizing Well Spacing and Fracture Design Using Advanced Multi-Stage Fracture Modeling and Discrete Fractured Reservoir Simulation in Tight Oil Reservoir
Authors: Tian Changbing , Lei Zhengdong , Jiang Qingping , Chang Tianquan , Chen Dongliang , Lu Zhiyuan , Li Sheng
Published: April 2019

Large platforms, long horizontal sections, small well spacings and dense cutting have become economical and effective development means for tight oil reservoirs. Well spacing and fracture design are critical parameters impacting production and Internal rate of return (IRR) of tight oil reservoirs. In order to maximize the total stimulated reservoir area and fracture-controlled reserves, the well spacing and fracture spacing should be small enough. However, in order to minimize the chance of fracture hits caused by offset wells and the overlapping drainage area of a nearby well to avoid Asset spillover, the spacing well should large enough.

Based on minifrac data and microseismic fracture mapping results, a natural/hydraulic fracture network was generated and input into an unstructured-grid-based discrete fracture reservoir simulation model. Its accuracy was calibrated with the well production history. For each group of fracture design and well spacing, well interference was determined by estimating ultimate recovery (EUR) difference between a single well and a middle well among multiple wells. Based on actual information of tight oil developments, the pressure interference were examined by field trail data and well spacing simulations. The real scenarios were selected to study effects of well spacing on EUR and ultimate IRR. Effects of reservoir permeability and fracture half-length on optimal well spacing were also analyzed.

It was found that the decrease in Long-term EURs for different well spacings is a good indicator for well spacing optimization. Based on the reservoir simulation and economic analysis, the maximum IRR of the tight oil reservoir with permeability of 0.23mD can achieved when the well spacing is 260m. Meanwhile, the detailed results were also illustrated to show the effects of fracture half-length, reservoir permeability as well as oil price variation on IRR.

The paper demonstrates an effective method and a workflow to optimize well spacing and fracture treatments design through integrating advanced multi-stage fracture modeling with discrete fracture reservoir simulation in the area of unconventional resource developments. Such optimization studies contribute to minimize operation cost and improve the economy of resource development.
New Geosteering Technology Boosts Wellbore Data and Seismic Properties Integration: A Case History From Norway
Authors: Cossa Alessandro , Manassero Elena , Mele Maurizio , Musca Claudio , Tarchiani Cristiano , Arrigoni Veronica , Halset Gjertrud
Published: April 2019

In a deep-water green field's development, horizontal wells are drilled to exploit the reservoir with the aim of reducing drilling cost and time to first oil.

During geosteering operations, Ultra Deep Azimuthal Electro-Magnetic (E.M.) measurements permit investigating the reservoir around the borehole up to a maximum depth of 30 m, in a good resistivity contrast environment. Acquired data are inverted on a vertical section, providing multi-boundary reservoir mapping along well path.

With such a depth of investigation, the reservoir mapping is an excellent bridge between conventional logging-while-drilling (LWD) and seismic images. The integration of acquired wellbore data with high-resolution attributes, from seismic inversion, maximizes well placement results when operating in complex subsurface geology and expands the perspectives of geosteering application.

A workflow to calibrate the reservoir structural and stratigraphyc setting has been assessed, via integration of seismic and Borehole Data. Enhancement of reservoir geometry interpretation during geosteering provides revised structural surfaces suitable for a quick update of the velocity model and a depth-calibration of all the seismic attributes used to steer wells.

We describe an application of the workflow to an infill well, targeting channel and crevasse splays deposits drilled through a structurally complex oil field in the Norwegian offshore. The availability of seismic attributes (probabilities of facies and petrophysical properties) allowed improving the overall results of the well placement operation.

Reservoir mapping identifies in real time Geo-bodies crossed by the well and within the range of investigation of Ultra Deep E.M. tool based on tool configuration, frequencies analysed and resistivity contrasts of the rocks. Stratigraphic correlation with offset wells, using conventional LWD data supported by Image Log interpretation, allowed allocating resistivity boundaries in terms of stratigraphic surfaces. These data are then integrated in near real time to depth calibrate maps, update the velocity model, hence the depth image of seismic attributes.

After depth calibration, Geo-bodies recognized on seismic show a good correspondence with those identified on the resistivity inversion and a detailed correlation of the heterogeneous fluvial sand was possible, even in presence of minor faults. In this challenging structural and stratigraphic environment, the correlation supported decision making during well operations to target the well on the pay sand.

The application proves that a detailed stratigraphic interpretation is an achievable goal in real time to steer successfully the well and to be used afterwards in a detailed reservoir model update.
Application of Algebraic Multigrid in Fully Implicit Massive Reservoir Simulations
Authors: Kayum Suha , Cancelliere Michel , Rogowski Marcin , Al-Zawawi Ahmed
Published: April 2019

Algebraic Multigrid (AMG) methods have proven to be efficient when numerically solving elliptic Partial Differential Equations (PDE). In reservoir simulation, AMG is used together with the Constrained Pressure Residual (CPR) method to solve a partially decoupled pressure system. Recently, effort has been focused on improving the robustness of the AMG-CPR solver. This paper presents the performance of different AMG-CPR strategies for massive reservoir models. In addition, a solver selection analysis is conducted, proving that dynamic selection of solvers has the potential of increasing the overall efficiency and robustness of the simulation.

Numerous decoupling/preconditioning algorithms exist and have been shown to influence the pressure matrix properties, some resulting in matrices more suitable to the characteristics favorable to AMG. Several decoupling/preconditioning strategies are investigated, such as Alternate Block Factorization (ABF), Quasi-IMPES (QI), and Dynamic Rowsum (DRS). The extracted pressure matrix could be suitable or unsuitable for AMG, depending on the matrix row sum, the diagonal signs, and the signs of the off-diagonal values.

The advantage of using AMG as a preconditioner is demonstrated by running the SPE10 case. The recommended AMG settings that result in the optimal performance for SPE10 are shared. A speedup is seen of up to 4X when using AMG with optimal settings versus the default solver in the in-house reservoir simulator with the improvement range depending on the number of processors used. SPE10 is a highly heterogeneous model resulting in matrices favorable for AMG, i.e., pressure decoupling produces positive definite pressure matrices, which is not necessarily representative of industry models. A comparison is then made with a selection of models with a wide range of characteristics and finally an examination of the convergence behavior of key industry cases with different decoupling strategies is presented. The overall convergence behavior of the pressure and full system are shown and the top decoupling algorithms for the particular models are discussed. Finally, the applicability and performance gain of selectively using AMG during a run is demonstrated.

Recent developments have been made in regard to AMG methods, but their applicability in a wide range of massive real cases is yet to be explored. In this work, different decoupling methods are tested, the AMG behavior on real field massive models is analyzed, the scalability is investigated, and AMG is selectively activated during a simulation run shedding light on the potential of future work entailing the use of Artificial Intelligence (AI) to dynamically select the optimal solver choice.
Waterflooding Management and Optimization for Reservoir Simulation with Coupled Geomechanics and Dynamic Fractures Using Streamline-Based Information
Authors: Lei Zhengdong , Xie Qichao , Tao Zhen , He YouAn , Zhu Zhouyuan , Peng Yan , Liu Canhua
Published: April 2019

Waterflooding of fractured low permeability reservoirs are often associated with poor sweep and high water cut due to existence of natural fractures, hydraulic fractures, and artificially induced fractures. Therefore, reservoir simulation with coupled geomechanics and dynamic fractures is required for this application. In this work, we present the use of streamline-derived flux information to improve overall waterflooding performance in such complex simulation problems.

This work shows the waterflooding optimization workflow of a fractured low-permeability reservoir in ChangQing Oilfield, China. First, the finite difference simulator considering stress field and geomechanical properties is used to simulate the growth of dynamic fractures. Then, the newly formed fracture properties are included into the dual porosity/permeability reservoir simulation model. Afterwards, streamlines can be traced based on the velocity field of this model, which represent a snapshot of the inter-well fluxes. Finally, with the goal of minimizing field water production, we implement linear programming algorithms to optimize the waterflooding operation by considering the inter-well connectivity and well allocation factors.

Through reservoir simulation coupled with geomechanics, we have found that induced fracture growth rate is relatively limited at reasonable injection rate, which is also validated by field empirical observations. This can avoid fracture propagation and reduce the risk of rapid water breakthrough. We deploy our streamline tracing and linear programming based optimization program to work together with this simulation model. A controlled and cautious increase in injection rate has resulted in a positive production response in 28 producers in the pilot area. Reallocation of water to high-efficiency injectors improves sweep efficiency in the reservoir. Finally, the optimized scenario has resulted in more than 15% incremental swept volume as compared to the basic development case.

This work provides a comprehensive case study for better understanding the impact fracture growth on waterflooding performance in fractured low-permeability reservoirs. It further establishes the workflow of using streamline-based flux information for oil production optimizations in these complex simulation problems.
Assessment of Enhanced Oil Recovery and CO2 Storage Capacity Using Machine Learning and Optimization Framework
Authors: You Junyu , Ampomah William , Kutsienyo Eusebius Junior , Sun Qian , Balch Robert Scott , Aggrey Wilberforce Nkrumah , Cather Martha
Published: April 2019

This paper presents an optimization methodology on field-scale numerical compositional simulations of CO2 storage and production performance in the Pennsylvanian Upper Morrow sandstone reservoir in the Farnsworth Unit (FWU), Ochiltree County, Texas. This work develops an improved framework that combines hybridized machine learning algorithms for reduced order modeling and optimization techniques to co-optimize field performance and CO2 storage.

The model's framework incorporates geological, geophysical, and engineering data. We calibrated the model with the performance history of an active CO2 flood data to attain a successful history matched model. Uncertain parameters such as reservoir rock properties and relative permeability exponents were adjusted to incorporate potential changes in wettability in our history matched model.

To optimize the objective function which incorporates parameters such as oil recovery factor, CO2 storage and net present value, a proxy model was generated with hybridized multi-layer and radial basis function (RBF) Neural Network methods. To obtain a reliable and robust proxy, the proxy underwent a series of training and calibration runs, an iterative process, until the proxy model reached the specified validation criteria. Once an accepted proxy was realized, hybrid evolutionary and machine learning optimization algorithms were utilized to attain an optimum solution for pre-defined objective function. The uncertain variables and/or control variables used for the optimization study included, gas oil ratio, water alternating gas (WAG) cycle, production rates, bottom hole pressure of producers and injectors. CO2 purchased volume, and recycled gas volume in addition to placement of new infill wells were also considered in the modelling process.

The results from the sensitivity analysis reflect impacts of the control variables on the optimum results. The predictive study suggests that it is possible to develop a robust machine learning optimization algorithm that is reliable for optimizing a developmental strategy to maximize both oil production and storage of CO2 in aqueous-gaseous-mineral phases within the FWU.
Development and Application of an Artificial Neural Network Tool for Chemical EOR Field Implementations
Authors: AlAbdullah Mohammad , Emami-Meybodi Hamid , Ertekin Turgay
Published: April 2019

The field-scale design of chemical enhanced oil recovery (cEOR) processes requires running complex numerical models that are computationally demanding. This paper provides an efficient screening platform for the cEOR feasibility study by presenting five artificial neural network (ANN) based models. We constructed 1,100 ANN training cases using CMG-STARS to capture the variation in reservoir petrophysical properties and the range of injected chemicals properties for a five-spot pattern. The design parameters were coupled with the reservoir properties using several functional links to optimize the ANN models and improve their performances. The training cases were employed using back-propagation methods to construct one forward model (Model #1) and four inverse models. Model #1 predicts reservoir response (i.e., oil rate, water cut, injector bottomhole pressure, cumulative oil) for known reservoir characteristics (i.e., permeability, thickness, residual oil saturation, chemical adsorption) and project design parameters (i.e., pattern size, chemical slug size and concentration), Model #2 predicts reservoir characteristics by history matching the reservoir response, and Model #3 predicts project design parameters for known reservoir response and characteristics. Models #4 and #5 predict project design parameters for a targeted cumulative oil volume and project duration time, which is useful for economical evaluation before the implementation of cEOR projects.

The validation results show that the developed ANN-based models closely predict the numerical results. In addition, the models are able to reduce the computational time by four orders of magnitude, which is significant considering the complexity of cEOR modeling and the need for reliable and efficient tools in building cEOR feasibility studies. In terms of accuracy, Model #1 has a prediction error of 5% whereas the error for other four inverse ANN models is about 20–40%. To enhance the performance of the inverse ANN models, we changed the ANN structure, increased training cases, and used functional links, which slightly reduced the error. Further, we introduced a back-check loop that uses the predicted parameters from the inverse ANN models as inputs in the forward ANN model. A comparison of back-check results for the reservoir response with the numerical results delivers a relatively small error of 10%, revealing the non-uniqueness of solutions obtained from the inverse ANN models.
Polymer Injectivity: Influence of Permeability in the Flow of EOR Polymers in Porous Media
Authors: Al-Shakry Badar , Shaker Shiran Behruz , Skauge Tormod , Skauge Arne
Published: April 2019

Application of polymer flooding as a chemical Enhanced Oil Recovery (EOR) has increased over recent years. The main type of polymer used is partially hydrolyzed polyacrylamide (HPAM). This polymer still has some challenges especially with shear stability and injectivity that restrict its utility, particularly for low permeability reservoirs. Injectivity limits the possible gain by acceleration in oil production due to polymer flooding. Hence, good polymer injectivity is a requirement for the success of the operation. This paper aims to investigate the influence of formation permeability on polymer flow in porous media.

In this study, a combination of core flooding with rheological studies is presented to evaluate the influence of permeability on polymer in-situ rheology behavior. The in-situ flow of HPAM polymers has also been studied for different molecular weights. The effect of polymer preconditioning prior to injection was studied through exposing polymer solutions to different extent of mechanical degradation.

Results from this study reveal that the expected shear thinning behavior of HPAM that is observed in rheometer measurements is not observed in in-situ rheology in porous media. Instead, HPAM in porous media exhibits near-Newtonian behavior at low flow rates representative of velocities deep in the reservoir, while exhibiting shear thickening behavior at high flow rates representative of velocities near wellbore region. The pressure build-up associated with shear thickening behavior during polymer injection is significantly higher than pressure differential during water injection. The extent of shear thickening is high during the injection of high Mw polymer regardless of cores' permeability. In low permeable Berea cores, shear thickening and mechanical degradation occur at lower velocities although the degree of shear thickening is lower in Berea to that observed in high permeable Bentheimer cores. This is ascribed to high polymer retention in Berea cores that results in high residual resistance factor (RRF). Results show that preshearing polymer before injection into porous media optimizes its injectability and transportability through porous media. The effect of preshearing becomes favorable for the injection of high Mw polymers into low permeability formation.

This study discusses polymer in-situ rheology and injectivity, which is a key issue in the design of polymer flood projects. The results provide beneficial information on optimizing polymer injectivity, in particular, for low permeability porous media.
How to Use Analytical Tools to Forecast Injectivity in Polymer Floods
Authors: Delamaide Eric
Published: April 2019

One of the main uncertainties when designing polymer floods is the polymer injectivity, an important parameter that can affect the economics of the process. Reservoir simulation can be used to forecast injectivity, but the process is not straightforward and can be affected by grid size and other factors. Analytical methods are also available for that purpose, but they are considered too simplistic to deal with realistic reservoir conditions. The aim of this paper is to show that this is not the case and that simple analytical tools can be accurate and of great help to predict or history match polymer injectivity.

The analytical method has been developed by Lake in his classical textbook on Enhanced Oil Recovery, but few applications are documented in the literature. This paper will review the method and corresponding equations before presenting several actual field cases of injectivity in polymer flood pilots or tests from several countries that have been matched analytically.

Although it has not been used very often, the method has been found to give very good results in most of the field cases tested in a variety of situations; these cases will be presented along with recommendations on how to apply the method and a discussion of the results. Sensitivities to the various parameters will also be presented. Once the equations are programmed in a spreadsheet, the matching process takes only a few minutes and it is easy to run various scenarios and sensitivities.

Polymer injectivity remains one of the less understood and less predictable aspects of polymer flood projects. This paper will encourage engineers who are planning such projects to use simple yet accurate analytical tools before embarking in more complex and time-consuming reservoir simulations.