Artificial Neural Network Modeling for the Prediction of Oil Production

Elmabrouk, Saber Kh.; Shirif, Ezeddin; Mayorga, René V.

doi:10.1080/10916466.2011.605093

Cited by 22 publications

(9 citation statements)

References 3 publications

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“…The efficiency of the developed ANN model was compared to previous empirical correlations and they concluded that the IFT estimation accuracy can be enhanced essentially using the ANN model. The prediction of reservoir oil production performance [58] , the prediction of ultimate recovery factor by steam-assisted gravity drainage (SAGD) [59] , the forecast of horizontal wells productivity [60] , and the prediction of waterflooding performance in heavy oil reservoirs [61] are other examples of petroleum application studies conducted using this powerful tool. ANN has been also utilised in the chemical EOR studies such as; surfactant–polymer (SP) flooding performance [53] , [62] , curing of polymer flooding [63] , and the formation and stability of oil/water emulsion [64] .…”

Section: Introductionmentioning

confidence: 99%

Experimental investigation, binary modelling and artificial neural network prediction of surfactant adsorption for enhanced oil recovery application

Belhaj

Elraies

Alnarabiji

et al. 2021

Chemical Engineering Journal

111

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Section: Introductionmentioning

confidence: 99%

Experimental investigation, binary modelling and artificial neural network prediction of surfactant adsorption for enhanced oil recovery application

Belhaj

Elraies

Alnarabiji

et al. 2021

Chemical Engineering Journal

111

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“…As such, the application is limited to primary recovery only. Elmabrouk et al [23] studied the use of feedforward backpropagation artificial neural network in the prediction of oil production and employed real data from a Libyan oil field to test the hypothesis. The study proves the potential use of advanced empirical models for better prediction of oil production, which is essential for better reservoir management.…”

Section: System Identification Based Proxy Modelsmentioning

confidence: 99%

System Identification Based Proxy Model of a Reservoir under Water Injection

Negash

Tufa

Ramasamy

et al. 2017

Modelling and Simulation in Engineering

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Simulation of numerical reservoir models with thousands and millions of grid blocks may consume a significant amount of time and effort, even when high performance processors are used. In cases where the simulation runs are required for sensitivity analysis, dynamic control, and optimization, the act needs to be repeated several times by continuously changing parameters. This makes it even more time-consuming. Currently, proxy models that are based on response surface are being used to lessen the time required for running simulations during sensitivity analysis and optimization. Proxy models are lighter mathematical models that run faster and perform in place of heavier models that require large computations. Nevertheless, to acquire data for modeling and validation and develop the proxy model itself, hundreds of simulation runs are required. In this paper, a system identification based proxy model that requires only a single simulation run and a properly designed excitation signal was proposed and evaluated using a benchmark case study. The results show that, with proper design of excitation signal and proper selection of model structure, system identification based proxy models are found to be practical and efficient alternatives for mimicking the performance of numerical reservoir models. The resulting proxy models have potential applications for dynamic well control and optimization.

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“…It quickly recognizes the data patterns and determines the closest findings to the actual values [ 18 ]. Compared to conventional modeling techniques, the ANN is widely used for complex systems that are difficult to approximate, particularly when the data are less than adequate [ 19 ]. Its high predictive capabilities can be attributed to the computing systems that simulate human brain processes called neurons [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

A Neural Network-Based Model for Predicting Saybolt Color of Petroleum Products

Salehuddin

Omar

Ibrahim

et al. 2022

Sensors

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Saybolt color is a standard measurement scale used to determine the quality of petroleum products and the appropriate refinement process. However, the current color measurement methods are mostly laboratory-based, thereby consuming much time and being costly. Hence, we designed an automated model based on an artificial neural network to predict Saybolt color. The network has been built with five input variables, density, kinematic viscosity, sulfur content, cetane index, and total acid number; and one output, i.e., Saybolt color. Two backpropagation algorithms with different transfer functions and neurons number were tested. Mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2) were used to assess the performance of the developed model. Additionally, the results of the ANN model are compared with the multiple linear regression (MLR). The results demonstrate that the ANN with the Levenberg–Marquart algorithm, tangent sigmoid transfer function, and three neurons achieved the highest performance (R2 = 0.995, MAE = 1.000, and RMSE = 1.658) in predicting the Saybolt color. The ANN model appeared to be superior to MLR (R2 = 0.830). Hence, this shows the potential of the ANN model as an effective method with which to predict Saybolt color in real time.

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Artificial Neural Network Modeling for the Prediction of Oil Production

Cited by 22 publications

References 3 publications

Experimental investigation, binary modelling and artificial neural network prediction of surfactant adsorption for enhanced oil recovery application

Experimental investigation, binary modelling and artificial neural network prediction of surfactant adsorption for enhanced oil recovery application

System Identification Based Proxy Model of a Reservoir under Water Injection

A Neural Network-Based Model for Predicting Saybolt Color of Petroleum Products

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