Development of artificial neural network models to calculate the areal sweep efficiency for direct line, staggered line drive, five-spot, and nine-spot injection patterns

Gomaa, Sayed; Soliman, Ahmed Ashraf; Nasr, Karim; Emara, Ramadan; El-hoshoudy, A.N.; Attia, Attia

doi:10.1016/j.fuel.2022.123564

Cited by 9 publications

(8 citation statements)

References 55 publications

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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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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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“…Enhanced oil recovery (EOR) techniques are utilized to increase the recovery of oil even further . Accurately predicting water saturation and areal sweep efficiency, along with conducting thorough reservoir characterization, is crucial for the success of enhanced oil recovery techniques, as they have the potential to increase production rates and ultimately improve oil recovery rates. − EOR techniques encompass chemical flooding, which involves the utilization of polymers, surfactants, alkalis, or a combination of these chemicals, − gas flooding, which utilizes carbon dioxide, , and thermal injection that involves the application of steam or in situ combustion. − Alkalis and surfactants are commonly utilized in chemical-based oil recovery techniques to enhance both the microscopic and macroscopic sweep efficiency of oil. , …”

Section: Introductionmentioning

confidence: 99%

Sulfonamide Derivatives as Novel Surfactant/Alkaline Flooding Processes for Improving Oil Recovery

Soliman,

ElSahaa,

Elsaeed

et al. 2023

ACS Omega

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Over time, oil consumption has increased along with a continuous demand for petroleum products that require finding ways to increase hydrocarbon production more economically and effectively. So, enhanced oil recovery technologies are believed to be very promising and will serve as a key to meeting the future energy demand. This paper aims to introduce an innovative method to boost the EOR by using two novel types of surfactants synthesized from sulfonamide derivatives. Types I and II surfactants were analyzed using Fourier transform infrared spectroscopy, and their characterization was further performed using 1H NMR and 13C NMR spectroscopy. Additionally, the evaluation of these surfactants included interfacial tension measurements at concentrations up to 0.9 wt %. The combination of types I and II surfactants with alkaline (NaOH) was also investigated by the measurements of interfacial tension. A series of coreflood and sandpack tests under high-salinity conditions were carried out to assess the effects of a surfactant alone and alkaline–surfactant as a combination on improving oil recovery. The rock wettability was evaluated using relative permeability saturation curves, and the oil displacement efficiency was determined using fractional flow curves. The coreflood results demonstrated that alkaline–surfactant flooding with the chemical formula 0.2 wt % surfactant type II plus 0.5 wt % NaOH achieved a higher oil recovery of 74% OOIP compared to surfactant flooding with the chemical formula 0.5 wt % surfactant type II (64% OOIP) and waterflooding (saline solution with a 35,000 ppm salinity: 48% OOIP). Moreover, the experimental results showed that under both core and sandpack flood conditions, there was a noticeable reduction in oil–water interfacial tension, a change in rock wettability to more water-wet, and higher efficiency of oil displacement when alkaline was added to the surfactant. Based on current research, the alkaline–surfactant formulation is strongly recommended for chemical flooding because of its high efficacy and relatively low cost.

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“…Thus, reservoir characterization is essential for decreasing uncertainty, quantitatively assigning reservoir properties, and recognizing geological information . Besides, reservoir characterization is very important in dealing with reservoir rock and enhanced hydrocarbon recovery techniques. − Reservoir characterization provides critical data that is necessary for creating models that accurately predict production outcomes, including the areal sweep efficiency for different injection patterns and water saturation. , Therefore, the development of artificial neural network models is dependent on the accuracy of the reservoir characterization process, highlighting the essential role it plays in improving oil recovery.…”

Section: Introductionmentioning

confidence: 99%

“… 2 − 5 Reservoir characterization provides critical data that is necessary for creating models that accurately predict production outcomes, including the areal sweep efficiency for different injection patterns and water saturation. 6 , 7 Therefore, the development of artificial neural network models is dependent on the accuracy of the reservoir characterization process, highlighting the essential role it plays in improving oil recovery.…”

Section: Introductionmentioning

confidence: 99%

Electrical Tortuosity Index: A New Approach for Identifying Rock Typing to Enhance Reservoir Characterization Using Well-Log Data of Uncored Wells

et al. 2023

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The world is gradually moving toward a severe energy crisis, with an ever-increasing demand for energy overstepping its supply. Therefore, the energy crisis in the world has shed important light on the need for enhanced oil recovery to provide an affordable energy supply. Inaccurate reservoir characterization may lead to the failure of enhanced oil recovery projects. Thus, the accurate establishment of reservoir characterization techniques is required to successfully plan and execute the enhanced oil recovery projects. The main objective of this research is to obtain an accurate approach that can be used to estimate rock types, flow zone indicators, permeability, tortuosity, and irreducible water saturation for uncored wells based on electrical rock properties that were obtained from only logging tools. The new technique is obtained by modifying the Resistivity Zone Index (RZI) equation that was presented by Shahat et al. by taking the tortuosity factor into consideration. When true formation resistivity (R t) and inverse porosity (1/Φ) are correlated on a log–log scale, unit slope parallel straight lines are produced, where each line represents a distinct electrical flow unit (EFU). Each line’s intercept with the y-axis at 1/Φ = 1 yields a unique parameter specified as the Electrical Tortuosity Index (ETI). The proposed approach was validated successfully by testing it on log data from 21 logged wells and comparing it to the Amaefule technique, which was applied to 1135 core samples taken from the same reservoir. Electrical Tortuosity Index (ETI) values show marked accuracy for representing reservoir compared with Flow Zone Indicator (FZI) values obtained by the Amaefule technique and Resistivity Zone Index (RZI) values obtained by the Shahat et al. technique, with correlation coefficients of determination (R 2) values equal to 0.98 and 0.99, respectively. Hence, by using the new technique, the Flow Zone Indicator, permeability, tortuosity, and irreducible water saturation were estimated and then compared with the obtained results from the core analysis, which showed a great match with the R 2-values of 0.98, 0.96, 0.98, and 0.99, respectively.

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Development of artificial neural network models to calculate the areal sweep efficiency for direct line, staggered line drive, five-spot, and nine-spot injection patterns

Cited by 9 publications

References 55 publications

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

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

Sulfonamide Derivatives as Novel Surfactant/Alkaline Flooding Processes for Improving Oil Recovery

Electrical Tortuosity Index: A New Approach for Identifying Rock Typing to Enhance Reservoir Characterization Using Well-Log Data of Uncored Wells

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