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The relative permeability and capillary pressures are used to characterize large-scale multiphase flow encountered in recovery of hydrocarbons. These parameters are acquired via special core flooding experiments. Reservoir engineers calculate these parameters from special core analysis (SCAL) module of reservoir simulation. However, core flooding is an expensive experiment and involves spending lot of time and efforts before Darcy law assumptions are achieved. Therefore, independent simulation of core flooding is necessary during which reservoir engineers can perform faster and simultaneous analysis of relative perms and capillary pressures of multiphase flow. This paper presents 1-D black oil simulation of core flooding using Sendra software to get relative perms and capillary pressures. The approach used in this study was four steps. In the first step, core flooding was simulated for particular scenario of injection using different correlations. In the second step, experimental data of core flooding for differential pressure and production versus time were referenced in the software. Then history matching was performed between estimated and experimental data of core flooding. Based on this, the best fit of correlations was obtained for estimating both relative perms and capillary pressures. The benefits of this approach compared with other methods are that it saves time, user-friendly and faster. It is reliable for estimating relative permeability and capillary pressures at steady state or unsteady state, imbibitions or drainage process either in oil or gas recovery.

This study aims at developing the screen-printed sensors as a viable means of depositing sensing tracks on composites for their on-line structural health monitoring. Conventional silk screen was employed in order to deposit a nano-composite solution comprising of a conductive nano-filler (carbon nano-particles) dispersed in a thermoplastic matrix (high density polystyrene) on laminated composite specimens. The solution was deposited using a squeegee and was allowed to dry. Commercially available metal foil strain gauges were also bonded alongside screen-printed sensor in order to compare the response of the screen-printed sensors with the commercially available strain gauges. The sensing ability of these screen-printed sensors was tested on a universal testing machine (MTS 810) in four-point bending configuration using a load cell of 100 kN. The sensor deposited using screen-printing technique underwent tensile loading at the lower side of the laminate. A data linearization and amplification module comprising of commercially available instrumentation amplifier (INA 118) was used in conjunction with data acquisition module (Keithley KUSB 3100). The results obtained show that the screen-printed sensors have higher gauge factors in tensile loading scenario with reasonably linear response as compared to traditional metal foil strain gauges. The ease of the deposition of a nano-composite solution via screen printing also makes the technique a viable alternative to the traditional resin bonded metal foil strain gauges which have to be bonded on the surface. Moreover, screen printing offers unlimited options for the development of smart composites in various configurations for a multitude of structural applications.

This work reports the development of highly sensitive Piezoresistive flexible strain sensor for human motion detection and speech recognition. Initially, a conductive polymer composite (CPC) solution comprising of thermoplastic polyurethane and Carbon Nanoparticles was prepared using Dimethylformamide as solvent and Chloroform as dispersant with the composition of 50% v/v. The solution was heated to a temperature of 60°C for evaporation of the solvent until it contained 13.5% w/v solvent for steady electrospinning. In this way, the CPC solution was used to develop Electrospun Nanofibrous Yarns (ENFYs) by applying a potential difference of 40 KV between the electrospinning needle and Aluminum collector. A cotton fabric was wrapped on the Aluminum collector to allow twisting of the deposited electrospun nanofibers. This novel collector configuration resulted in the formation of nanofibrous yarns due to the whirling action of the advancing jet of CPC solution. The cotton fabric on the collector facilitated twisting of fibers by allowing them to roll over the fabric. The fabricated ENFY sensors showed remarkable stretchability up to 102% strain while achieving a gauge factor of 70 at 100% strain. Long-term usage necessitates repeatability, which was demonstrated by cyclic loading at a crosshead speed of 40 mm/min for up to 1000 cycles using a custom-developed linear actuator, with no signs of fracture. ENFY strain sensor was attached to different parts of human body such as finger, fist, elbow, knee and ankle and was found capable of measuring and observing angle, position and frequency of motion. Owing to its ultrasensitive behavior, the developed sensor was able to measure heart rate as well. When the developed sensor was attached to Adam’s apple for speech recognition it showed remarkable response towards different utterances and breathing and gulping actions with clearly distinguishable signals. These results demonstrate that the developed novel ENFY flexible strain sensor can be employed for proprioceptive sensing and speech recognition for human-machine interaction, soft robotics and wearable devices etc.

Graphene is an advanced material in the carbon group and offers greater mechanical, electrical, structural, and optical properties. Graphene oxide (GO) and reduced graphene oxide (rGO) nanoparticles were synthesized and characterized and their special effects on enhancing the physio-mechanical characteristics of medium density fiberboard (MDF) were assessed. GO and rGO nanoparticles were added to urea formaldehyde (UF) resin at different weight percentages (1.0, 2.0, and 3.0 wt%) during the dosing process. To manufacture the MDF, nanofillers were created by sonication and combination with natural wood fibers. To observe the behavior of nanoparticles in the nanofillers, microstructure characterizations were conducted. The manufactured nano MDF samples underwent physical and mechanical testing. The incorporation of GO and rGO nanoparticles into UF resin led to significant improvements in the physical and mechanical properties of the MDF. The addition of GO and rGO nanoparticles at different weight percentages (1.0, 2.0, and 3.0 wt%) resulted in a range of improvements in thickness swelling (up to 53.3% and 35.2% for GO and rGO nanoparticles, respectively), water absorption (up to 23.3% and 63.15%, respectively), and thermal conductivity (up to 42.16% and 27.7%, respectively). Additionally, the internal bond and rupture modulus of the MDF was enhanced by 59.0% and 70.0%, respectively, for GO and 41.4% and 48.5% for rGO. The highest value of the modulus of rupture (MoR) was observed at a concentration of 3.0% of rGO nanoparticles (44.7 MPa). The findings also showed that thickness swelling (Ts) and water absorption (WA) exhibited directly proportional relationships for 3.0% GO and rGO. These results suggested that incorporating GO and rGO nanoparticles into UF resin can significantly improve the physical and mechanical properties of nano MDF.

No abstract

It is a well-known fact that the deliverability of condensate reservoirs is a function of condensate formation around the wellbore. Thus, accurate estimation of condensate recovery necessitates properly modeled Equation-of-State (EOS) and subsurface flow phenomena. EOS is a function of numerous parameters of the Reservoir and Fluid contained; however it should be kept in mind that it is almost impossible to exactly match all these parameters, while all the parameters don't obligate equal significance as well.This paper describes the most important parameters in EOS which ultimately control the overall condensate recovery. It involves unique simulation methodology to determine the condensate flow in reservoir using velocity-dependent-relativepermeability (VRP) curves (instead of conventional-relative permeability methods) to determine the effect of velocity stripping near the well bore; and the Pseudo-pressure approach.The modified form of EOS used in the study, along with the VRP curves features accurate condensate flow in the reservoir and its optimization. Moreover, using the pseudo pressure approach, this paper also signifies the reservoir modeling and forecasting methods with minimum amount of time for simulation.A complete and comprehensive strategy has been presented in this paper, to infer the factors having max influence on recovery. Using diverse sensitivities, it is evident that four parameters have a high influence; which includes compositionalvariation with depth, Liquid drop-out, Condensate viscosity and Compressibility, while the rest yield minimal impact. The VPR models also improve history matching and give better forecast; while using pseudo-pressure approach reduces significant time with negligible impact on the results.

This paper presents rigorous theoretical guidelines for durations of flow regimes for multi-fractured horizontal wells in ultra-low permeability reservoirs. Theory and practice lead us to expect four regimes: early ramp-up, transient, transition, and boundary-dominated flow (BDF) in these wells. We must model each of these flow regimes for proper forecasting and for construction of TWPs (aka type wells or type curves), but, without guidance from theory and verification in practice, the durations of these flow regimes are difficult to observe in production histories or to predict when forecasting. These forecasts have significant impact on financial decisions regarding low-permeability reservoir development. We can most readily identify flow regimes using log-log plots of pressure-normalized rate vs. time for wells produced at near-constant bottom-hole pressure. This is adequate to determine the start and end of transient flow, with a straight line whose slope is near −1/2. Diagnosis is enhanced if we add normalized rate vs. material-balance time plots, which transform the well response to an equivalent constant-rate profile, on which we can identify BDF with a straight line with −1 slope. On this plot, the transition flow regime lies between the end of transient flow and the start of BDF. In some wells, with relatively longer production histories, we can readily identify these flow regimes, but many if not most wells in a play will display neither transition nor BDF regimes. To fill this gap in knowledge, we simulated flow histories using analytical solutions, which provide shapes and durations of the flow regimes. Starts and ends of flow regimes depend on arbitrary assumptions about deviations from straight lines, which can be determined in theory using derivatives of the analytical solutions. In practice, wells do not follow theory exactly by any means, but we find in our examination of actual well production histories that theory provides excellent guidance that enhances our understanding of actual production profiles. We present our simulated production histories for wells in terms of dimensionless variables, which generalizes their applicability. For actual situations, with known or estimated reservoir and completion properties, we can use these plots of dimensionless variables to determine approximate durations of flow regimes. Importantly, for the common situation in which no production data are available beyond transient flow, we can estimate the shape of the remaining production profile in a way significantly superior to the common two-segment Arps decline model with an assumed terminal decline rate at an assumed time. Critics of the industry, particularly in the financial community, have suggested that this common approach leads to optimistic production forecasts. Realistic forecasts of production profiles for individual wells, which our workflow based on rigorous theory enhances, can improve the credibility of resource evaluators within and beyond individual companies. This is especially important for TWP construction, on which many important financial decisions are based.