New correlations to calculate vertical sweep efficiency in oil reservoirs using nonlinear multiple regression and artificial neural network

Gomaa, Sayed; Emara, Ramadan; Mahmoud, Omar; El-hoshoudy, A.N.

doi:10.1016/j.jksues.2021.07.010

Cited by 13 publications

(10 citation statements)

References 21 publications

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“…But in this paper, the possibilities of using well-known universal modeling tools, such as Simscape MultibodyTM, are explored. The use of statistical approaches for modeling nonlinear systems and processes is proposed in [18], where the main tool is correlation analysis. However, the correlation coefficients of 0.9999 show that the studied variants are of a deterministic nature.…”

Section: Formal Methods and Risk Assessment Modelsmentioning

confidence: 99%

Formalization of risk management in the context of digital business transformation

Duraiswamy

Karlygash

Beglan

et al. 2023

IJEECS

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The aim of the article is to develop a formal methodology for quantitative assessment of the quality of аcontrol in a closed system with feedback in the context of digital transformation. In the proposed study, attention is focused on assessing the quality of management in organizational and technical systems on the example of the aviation industry. The following hypotheses were adopted in the study: in the digital management of business processes of an economic entity, the role of intellectual support is acquired by methods of formal description of processes: control, decision-making and corrective action on the control object. In critical situations, the psychotype of the person making the decision acquires a decisive role. The study solves two scientific and practical problems: development of a formal method for quantitative assessment of the quality of management of a complex multi-criteria organizational and technical system under the conditions of statistical uncertainty of management agents, taking into account feedback in the management of an object; formalization of the process of quantitative assessment of decision-making risks in the environment of statistical uncertainty of control agents and psychological factors of the decision maker.

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Section: Formal Methods and Risk Assessment Modelsmentioning

confidence: 99%

Formalization of risk management in the context of digital business transformation

Duraiswamy

Karlygash

Beglan

et al. 2023

IJEECS

View full text Add to dashboard Cite

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“…Back-propagation refers to an algorithm for estimating the derivative of an error function that propagates errors backward across the network. This approach has been utilized and evaluated in a variety of studies, as evidenced by refs − . Finally, using the weights and bias according to activation and transfer functions, mathematical equations may be developed to apply in the future for computing the output from the input data without having to establish a neural network.…”

Section: Methodsmentioning

confidence: 99%

New Correlation for Calculating Water Saturation Based on Permeability, Porosity, and Resistivity Index in Carbonate Reservoirs

et al. 2022

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Water saturation assessment is recognized as one of the most critical aspects of formation evaluation, reserve estimation, and prediction of the production performance of any hydrocarbon reservoir. Water saturation measurement in a core laboratory is a time-consuming and expensive task. Many scientists have attempted to estimate water saturation accurately using well-logging data, which provides a continuous record without information loss. As a result, numerous models have been developed to relate reservoir characteristics with water saturation. By expanding the use and advancement of soft computing approaches in engineering challenges, petroleum engineers applied them to estimate the petrophysical parameters of the reservoir. In this paper, two techniques are developed to estimate the water saturation in terms of porosity, permeability, and formation resistivity index through the use of 383 data sets obtained from carbonate core samples. These techniques are the nonlinear multiple regression (NLMR) technique and the artificial neural network (ANN) technique. The proposed ANN model achieved outstanding performance and better accuracy for calculating the water saturation than the empirical correlation using NLMR and Archie equation with a high coefficient of determination (R 2) of 0.99, a low average relative error of 1.92, a low average absolute relative error of 13.62, and a low root mean square error of 0.066. To the best of our knowledge, the current research establishes a novel foundation using the ANN model in the estimation of water saturation.

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“… 28 Several researchers utilized artificial neural networks in different petroleum engineering applications. In reservoir engineering, ANN is used to estimate and predict the vertical sweep efficiency ( E V ) in terms of mobility ratio, water–oil ratio, and reservoir permeability variation; 38 estimate the areal sweep efficiency ( E A ) at different well patterns; 39 estimate gas condensate dew point pressure in the reservoir condition; 40 and predict the water saturation in carbonate formations using the nonlinear multiple regression (NLMR) and ANN model. 41 In drilling engineering applications, ANN helps in calculating the characteristics of the invert emulsion mud, such as yield point (YP) and plastic viscosity (PV).…”

Section: Methodsmentioning

confidence: 99%

New Models for Predicting Pore Pressure and Fracture Pressure while Drilling in Mixed Lithologies Using Artificial Neural Networks

et al. 2022

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Precise prediction of pore pressure and fracture pressure is a crucial aspect of petroleum engineering. The awareness of both fracture pressure and pore pressure is essential to control the well. It helps in the elimination of the problems related to drilling, waterflooding project, and hydraulic fracturing job such as fluid loss, kick, differential sticking, and blowout. Avoiding these problems enhances the performance and reduces the cost of operation. Several researchers proposed many models for predicting pore and fracture pressures using well log information, rock strength properties, or drilling data. However, some of these models are limited to one type of lithology such as clean and compacted shale formation, applicable only for the pressure generated by under compaction, and some of them cannot be used in unloading formations. Recently, artificial intelligence techniques showed a great performance in petroleum engineering applications. Hence, in this paper, two artificial neural network models are developed to estimate both pore pressure and fracture pressure through the use of 2820 data sets obtained from drilling data in mixed lithologies of sandstone, carbonate, and shale. The proposed artificial neural network (ANN) models achieved accurate estimation of pore and fracture pressures, where the coefficients of determination ( R 2 ) for pore and fracture pressures are 0.974 and 0.998, respectively. Another data set from the Middle East was used to validate the developed models. The models estimated the pore and fracture pressures with high R 2 values of 0.90 and 0.99, respectively. This work demonstrates the validity and reliability of the developed models to calculate pore and fracture pressures from real-time surface drilling parameters by considering the formation type to overcome the limitation of previous models.

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New correlations to calculate vertical sweep efficiency in oil reservoirs using nonlinear multiple regression and artificial neural network

Cited by 13 publications

References 21 publications

Formalization of risk management in the context of digital business transformation

Formalization of risk management in the context of digital business transformation

New Correlation for Calculating Water Saturation Based on Permeability, Porosity, and Resistivity Index in Carbonate Reservoirs

New Models for Predicting Pore Pressure and Fracture Pressure while Drilling in Mixed Lithologies Using Artificial Neural Networks

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