Effect of silicon-based nanoparticles on enhanced oil recovery: Review

Nasr, Mahdi Shayan; Esmaeilnezhad, Ehsan; Choi, Hyoung Jin

doi:10.1016/j.jtice.2021.04.047

Cited by 26 publications

(1 citation statement)

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“…The first category is fresh water, the second category is a low saline, which is assigned to monovalent salts, and the third category is a high saline, which is assigned to salts that contain both monovalent and divalent ions [6]. Mobility ratio, which is roughly defined as the ratio of flooded fluid (oil) to flooding fluid (polymer solution) [14], is an important influencing parameter that should be considered during performance evaluations of EOR processes. As mentioned previously, the viscosity of polymer solutions is dependent on several parameters that are considered as inputs for the models and, therefore, it cannot be considered directly because putting dependent parameters as inputs in the model will impose huge complexity on the model versus fewer gains.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network to Forecast Enhanced Oil Recovery Using Hydrolyzed Polyacrylamide in Sandstone and Carbonate Reservoirs

2021

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Polymer flooding is an important enhanced oil recovery (EOR) method with high performance which is acceptable and applicable on a field scale but should first be evaluated through lab-scale experiments or simulation tools. Artificial intelligence techniques are strong simulation tools which can be used to evaluate the performance of polymer flooding operation. In this study, the main parameters of polymer flooding were selected as input parameters of models and collected from the literature, including: polymer concentration, salt concentration, rock type, initial oil saturation, porosity, permeability, pore volume flooding, temperature, API gravity, molecular weight of the polymer, and salinity. After that, multilayer perceptron (MLP), radial basis function, and fuzzy neural networks such as the adaptive neuro-fuzzy inference system were adopted to estimate the output EOR performance. The MLP neural network had a very high ability for prediction, with statistical parameters of R2 = 0.9990 and RMSE = 0.0002. Therefore, the proposed model can significantly help engineers to select the proper EOR methods and API gravity, salinity, permeability, porosity, and salt concentration have the greatest impact on the polymer flooding performance.

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Section: Introductionmentioning

confidence: 99%