Prediction of the Rheological Properties of Invert Emulsion Mud Using an Artificial Neural Network

Gouda, Abdelrahman; Khaled, Samir; Gomaa, Sayed; Attia, Attia

doi:10.1021/acsomega.1c04937

Cited by 12 publications

(4 citation statements)

References 63 publications

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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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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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“… 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). 42 …”

Section: Methodsmentioning

confidence: 99%

“…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; estimate the areal sweep efficiency ( E A ) at different well patterns; estimate gas condensate dew point pressure in the reservoir condition; and predict the water saturation in carbonate formations using the nonlinear multiple regression (NLMR) and ANN model . 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%

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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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Evaluating the influence of graphene nanoplatelets on the performance of invert emulsion drilling fluid in high-temperature wells

Arain

Ridha

Ilyas

et al. 2022

J Petrol Explor Prod Technol

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The oil-based mud is preferred to drill highly technical and challenging formations due to its superior performance. However, the inadequate chemical and thermal stability of conventional additives have greatly influenced the performance of oil-based mud at high-temperature conditions. Therefore, it is critical to design an oil-based mud with additives that withstand and improve its performance at high-temperature conditions. The nanoparticles have emerged as an alternative to the conventional additives that can significantly enhance the rheological and filtration characteristics of oil-based mud at high-temperature conditions. In this research study, a novel formulation of OBM enhanced with GNP is formulated, and its performance at high-temperature conditions is investigated. An extensive experimental study has been performed to study the effect of graphene nanoplatelets on the rheological and filtration properties along with flow behaviour, viscoelastic properties, electrical stability and barite sagging of oil-based mud at high temperatures. The graphene nanoplatelets are characterised to ascertain their purity and morphology. The result shows that the graphene nanoplatelets exhibited efficient performance and improved the rheological and filtration properties of oil-based mud. The plastic viscosity and yield point are improved by 11% and 42%, with a concentration of 0.3 ppb. Similarly, the gel strength and barite sagging tendency are enhanced by 14% and 2%, respectively. The filtration loss is also significantly decreased by up to 62% and 46%, with 0.5 ppb concentration at 100 and 120 °C. The addition of GNP results in the formation of a thin mud cake compared to the base mud sample. The rheological modelling recommends the shear-thinning behaviour of oil-based mud (n < 1), which is correlated with the Herschel–Bulkley model. An Artificial Neural Network model is developed to predict the viscosity of OBM based on the four input parameters (concentration of nanoparticles, temperature, shear rate and shear stress). The results demonstrate that graphene nanoplatelets have a favourable impact on the performance of oil-based mud. The addition of graphene nanoplatelets, even at small concatenation, has significantly improved the properties of oil-based mud at high-temperature. Graphical abstract

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Prediction of the Rheological Properties of Invert Emulsion Mud Using an Artificial Neural Network

Cited by 12 publications

References 63 publications

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

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

Evaluating the influence of graphene nanoplatelets on the performance of invert emulsion drilling fluid in high-temperature wells

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