With growing global demand for hydrocarbons and decreasing conventional reserves, the gas industry is shifting its focus in the direction of unconventional reservoirs. Tight gas reservoirs have typically been deemed uneconomical due to their low permeability which is understood to be below 0.1mD, requiring advanced drilling techniques and stimulation to enhance hydrocarbons. However, the first step in determining the economic viability of the reservoir is to see how much gas is initially in place. Numerical simulation has been regarded across the industry as the most accurate form of gas estimation, however, is extremely costly and time consuming. The aim of this study is to provide a framework for a simple analytical method to estimate gas. Usually during production three variables are readily accessible: production rate, production time, and pressure-volume-temperature properties. This paper develops an analytical approach derived from the dynamic material balance proposing a new methodology to calculate pseudo time, with an interactive technique. This model encompasses pseudo functions accounting for pressure dependent fluid and rock variables. With the dynamic material balance yielding weak results in the linear flow regimes, an additional methodology derived from the volumetric tank model has been taken into consideration whereby equivalent drainage area is linked to total reservoir area. It has been shown even with short production data this volumetric approach yields accurate results. This proposed methodology has been validated against previous literature and additional cases considered to determine the sensitivity of each of it to reservoir parameters. Finally, it is shown that this method works for both fractured and unfractured wells in tight gas reservoirs, however, it is sensitive to the quantity of data based within the pseudo steady state flow period.
This paper proposed a theoretical treatment to study underwater wireless optical communications (UWOC) system with different modulation schemes by multiple input-multiple output (MIMO) technology in coastal water. MIMO technology provides high-speed data rates with longer distance link. This technique employed to assess the system by BER, Q. factor and data rate under coastal water types. The reliability of the system is examined by the techniques of 1Tx/1Rx, 2Tx/2Rx, 3Tx/3Rx and 4Tx/4Rx. The results shows the proposed technique by MIMO can get the better performance compared with the other techniques in terms of BER. Theoretical results were obtained to compare between PIN and APD
A new method for the determination of the drug cefalexin in some Pharmaceuticals using (UV-Vis) and indirect Flame Atomic Absorption Spectrophotometer (FAAS) , Fe III should forms a chelating complex with cefalexin (CEX –Fe III) at pH (1-8) and the best pH for the formation of (CEX –Fe III) chelating complex was (2) .The complex extracted with Methanol and Dimethy-Sulphoxide .The mole-ratio method has been used to determine the structure of chelate (CEX - Fe III) and found to be 2:1 LM ( Ligand : Metal.) .
Keywords : Cefalexin , chelating complex.
For the first time Iron tungstate semiconductor oxides films (FeWO4) was successfully synthesized simply by advanced controlled chemical spray pyrolysis technique, via employed double nozzle instead of single nozzle using tungstic acid and iron nitrate solutions at three different compositions and spray separately at same time on heated silicone (n-type) substrate at 600 °C, followed by annealing treatment for one hour at 500 °C. The crystal structure, microstructure and morphology properties of prepared films were studied by X-ray diffraction analysis (XRD), electron Scanning Electron Microscopy (SEM) and Atomic Force Microscopy (AFM) respectively. According to characterization techniques, a material of well-crystallized monoclinic ph
... Show MoreThis paper proposes a neuro-fuzzy system to model β-glucosidase activity based on the reaction’s pH level and temperature. The developed fuzzy inference system includes two input variables (pH level and temperature) and one output (enzyme activity). The multi-input fuzzy inference system was developed in two stages: first, developing a single input-single output fuzzy inference system for each input variable (pH, temperature) separately, using the robust adaptive network-based fuzzy inference system (ANFIS) approach. The neural network learning techniques were used to tune the membership functions based on previously published experimental data for β-glucosidase. Second, each input’s optimized membership functions from the ANF
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