Robust and Flexible Hydrocarbon Production Forecasting Considering the Heterogeneity Impact for Hydraulically Fractured Wells

Producing oil and gas from increasingly more difficult reservoirs has become an unavoidable challenge for the petroleum industry because the conventional hydrocarbon resources are no longer able to maintain the production levels corresponding to the global energy demand. As the industrial investments in developing lower permeability reservoirs increase and more advanced technologies, such as horizontal drilling and hydraulic fracturing, gain more attention and applicability, the need for more reliable means of production forecasting also become more noticeable. Production forecasting of hydraulically fractured wells is challenging, particularly for heterogeneous reservoirs, where the rock properties vary dramatically over short distances, significantly affecting the performance of the wells. Despite the recent improvements in well performance prediction, the issue of heterogeneity and its effects on well performance have not been thoroughly addressed by the researchers and many aspects of heterogeneity have yet remained unnoticed. In this paper, a novel empirical approach for production forecasting of multi-fractured horizontal wells is presented in an attempt to effectively include the effect of heterogeneity. This approach is based on the integration of hyperpolic decline curve analysis (DCA) and heterogeneity impact factor (HIF). This newly defined ratio quantifies the heterogeneity impact on the hydraulically fractured well performance and is calculated on the basis of net pressure match interpretation and post-fracture well test analysis. The proposed approach of the decline curve using heterogeneity impact factor (DCH) is validated against data from a southern North Sea field. The results show a maximum of 15% difference between the outcome of the proposed method and the most detailed three-dimensional history-matched model, for a 15 year period of production forecasts. DCH is a novel, fast, and flexible method for making reliable well performance predictions for hydraulically fractured wells and can be used in forecasting undrilled wells and the range of possible outcomes caused by the heterogeneity

Evanescent wave optical trapping and sensing on polymer optical fibers for ultra-trace detection of glucose

Graphene sensitization of glucose-imprinted polymer (G-IP)-coated optical fiber has been introduced as a new biosensor for evanescent wave trapping on the polymer optical fiber to detect low-level glucose. The developed sensor operates based on the evanescent wave modulation principle. Full characterization via atomic force microscopy (AFM), Fourier transform infrared (FTIR) spectroscopy, X-ray diffraction (XRD), scanning electron microscopy (SEM), Raman spectroscopy, high-resolution transmission electron microscopy (HRTEM), and N2 adsorption/desorption of as-prepared G-IP-coated optical fibers was experimentally tested. Accordingly, related operational parameters such as roughness and diameter were optimized. Incorporating graphene into the G-IP not only steadily promotes the electron transport between the fiber surface and as-proposed G-IP but also significantly enhances the sensitivity by acting as a carrier for immobilizing G-IP with specific imprinted cavities. The sensor demonstrates a fast response time (5 s) and high sensitivity, selectivity, and stability, which cause a wide linear range (10–100 nM) and a low limit of detection (LOD = 2.54 nM). Experimental results indicate that the developed sensor facilitates online monitoring and remote sensing of glucose in biological liquids and food samples

Variation in the shell form of the swan mussel, Anodonta cygnea (Linea, 1876) in response to water current

A biometric study was conducted on the populations of swan mussel, Anodonta cygnea, belonged to water bodies with different water current velocity (high current: HC; low current: LC). The shell length, width, height, weight and age of the collected mussels were measured. The two groups had different age-classes distributions. The HC mussels had larger mean and maximal values of the biometric parameters. A high and medium correlation coefficient (width-length, height-length, and weight-length) were found in the HC and LC mussels, respectively. The weight-length relationships showed negative allometric pattern (HC: weight=3.6121x0.8561; LC: weight=3.1362x0.8508). The 1-2 years old mussels had the highest rates of increase in length, width and height in both groups. Based on the results, water current velocity influences biometric features and population structure of A. cygnea

Modeling the Risk of Commercial Failure for Hydraulic Fracturing Projects Due to Reservoir Heterogeneity

Hydraulic fracturing technologies play a major role in the global energy supply and affect oil pricing. The current oil price fluctuations within 40 to 55 USD per barrel have caused diminished economical margins for hydraulic fracturing projects. Hence, successful decision making the for execution of hydraulic fracturing projects requires a higher level of integration of technical, commercial, and uncertainty analyses. However, the complexity of hydraulic fracturing modeling, and the sensitivity and the effects of uncertainty of reservoir heterogeneity on well performance renders the integration of such studies rather impractical. The impact of reservoir heterogeneity on hydraulic fracturing performance has been quantified by the introduction of Heterogeneity Impact Factor (HIF) and formulas have been developed to forecast well performance using HIF. These advances provide a platform for introducing a practical approach for introducing the Risk of Commercial Failure (RCF) due to reservoir heterogeneity in hydraulic fracturing projects. This paper defines such a parameter and the methodology to calculate it in a time-efficient manner. The proposed approach has been exercised on a real project in which a RCF of 20% is computed. The analysis also covers the sensitivity on Capital Expenditure (CAPEX), Operational Expenditure (OPEX), gas price, HIF and discount rate

Beeswax-colophony blend: a novel green organic coating for protection of steel drinking water storage tanks

Beeswax-colophony blend is mainly used as a sealant mixture for preservation applications. The beeswax itself, however, has had a long way in history taking part in conservation processes including mummification. In this research, this blend was used as a protective coating for drinking water distribution tanks. Initially, a layer with 400 μm thickness was applied on a sand blasted mild steel plate. The long-term electrochemical behavior of the coating was investigated by open circuit potential (OCP) and electrochemical microbiological characteristics of the coating, microbial and chemical examinations were performed on drinking water samples that had been in contact with the coating. Furthermore, its behavior in an up-flow anaerobic sludge blanket reactor (UASBR) in a wastewater treatment plant was investigated using the scanning electron microscopy (SEM) technique. Regarding the consistency of experimental results, it was concluded that this proposed recyclable blend could be considered as a novel green organic coating and also a good corrosion barrier even in aggressive environments

Wearable sensors for learning enhancement in higher education

Wearable sensors have traditionally been used to measure and monitor vital human signs for well-being and healthcare applications. However, there is a growing interest in using and deploying these technologies to facilitate teaching and learning, particularly in a higher education environment. The aim of this paper is therefore to systematically review the range of wearable devices that have been used for enhancing the teaching and delivery of engineering curricula in higher education. Moreover, we compare the advantages and disadvantages of these devices according to the location in which they are worn on the human body. According to our survey, wearable devices for enhanced learning have mainly been worn on the head (e.g., eyeglasses), wrist (e.g., watches) and chest (e.g., electrocardiogram patch). In fact, among those locations, head-worn devices enable better student engagement with the learning materials, improved student attention as well as higher spatial and visual awareness. We identify the research questions and discuss the research inclusion and exclusion criteria to present the challenges faced by researchers in implementing learning technologies for enhanced engineering education. Furthermore, we provide recommendations on using wearable devices to improve the teaching and learning of engineering courses in higher education

Alignment of magnetic sensing and clinical magnetomyography

Neuromuscular diseases are a prevalent cause of prolonged and severe suffering for patients, and with the global population aging, it is increasingly becoming a pressing concern. To assess muscle activity in NMDs, clinicians and researchers typically use electromyography (EMG), which can be either non-invasive using surface EMG, or invasive through needle EMG. Surface EMG signals have a low spatial resolution, and while the needle EMG provides a higher resolution, it can be painful for the patients, with an additional risk of infection. The pain associated with the needle EMG can pose a risk for certain patient groups, such as children. For example, children with spinal muscular atrophy (type of NMD) require regular monitoring of treatment efficacy through needle EMG; however, due to the pain caused by the procedure, clinicians often rely on a clinical assessment rather than needle EMG. Magnetomyography (MMG), the magnetic counterpart of the EMG, measures muscle activity non-invasively using magnetic signals. With super-resolution capabilities, MMG has the potential to improve spatial resolution and, in the meantime, address the limitations of EMG. This article discusses the challenges in developing magnetic sensors for MMG, including sensor design and technology advancements that allow for more specific recordings, targeting of individual motor units, and reduction of magnetic noise. In addition, we cover the motor unit behavior and activation pattern, an overview of magnetic sensing technologies, and evaluations of wearable, non-invasive magnetic sensors for MMG

Self-powered implantable CMOS photovoltaic cell with 18.6% efficiency

Harvesters for implantable medical applications need to generate enough energy to power their loads, but their efficiency is reduced when implanted under the tissue. Conventional photovoltaic (PV) cell harvesters made with CMOS technology stack cells in series, which raises output voltage but lowers power conversion efficiency. In addition, it is difficult to assess harvester performance prior to fabrication. To address these challenges, we developed a novel parallel PV cell configuration that fully utilizes all triple-well diodes and responds efficiently to near-infrared light. Using an optimized structure, the PV cells were fabricated through standard TSMC 65-nm CMOS technology, achieving an efficiency of 18.6%, open circuit voltage of 0.45 V, and short circuit current of 1.9 mA cm −2 . These results confirm the ability of the device to generate sufficient energy even when implanted beneath the tissue. Multiphysics finite element modeling (FEM) was used to optimize the stacking structure of the CMOS PV cell, and experimental results showed a successfully delivered power density of 1.2 mW cm −2 (single cell 1.04 mm 2 ) when placed 2 mm below porcine skin. Different array configurations of six PV cells were also experimentally studied using external wire switching, demonstrating the flexibility of the PV array in delivering different output energy for various implantable devices