Enhancing Fracturing Fluid Viscosity in High Salinity Water: A Data-Driven Approach for Prediction and Optimization

Othman, Amro; Tariq, Zeeshan; Aljawad, Murtada Saleh; Yan, Bicheng; Kamal, Muhammad Shahzad

doi:10.1021/acs.energyfuels.3c02272

Cited by 5 publications

(4 citation statements)

References 52 publications

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“…In this section, the use of gas hydrate field data for machine learning models is discussed to provide state-of-the-art knowledge and advance machine learning in gas hydrate applications. For hydrate predictions, just like other predictive models in drilling, fracturing, and shale studies, ANN models are mostly used. − An ANN and SVR machine learning model was developed by Qin using field data from a dry tree facility in the Gulf of Mexico. The models were programmed to predict or detect gas hydrate formation plugs and the formation conditions.…”

Section: Machine Learning In Hydrate Field Data Predictionmentioning

confidence: 99%

“…For hydrate predictions, just like other predictive models in drilling, fracturing, and shale studies, ANN models are mostly used. 84 − 88 An ANN and SVR machine learning model was developed by Qin 50 using field data from a dry tree facility in the Gulf of Mexico. The models were programmed to predict or detect gas hydrate formation plugs and the formation conditions.…”

Section: Machine Learning In Hydrate Field Data Predictionmentioning

confidence: 99%

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Research Advances in Machine Learning Techniques in Gas Hydrate Applications

Osei,

Bavoh,

Lal

2024

ACS Omega

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The complex modeling accuracy of gas hydrate models has been recently improved owing to the existence of data for machine learning tools. In this review, we discuss most of the machine learning tools used in various hydrate-related areas such as phase behavior predictions, hydrate kinetics, CO 2 capture, and gas hydrate natural distribution and saturation. The performance comparison between machine learning and conventional gas hydrate models is also discussed in detail. This review shows that machine learning methods have improved hydrate phase property predictions and could be adopted in current and new gas hydrate simulation software for better and more accurate results.

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Section: Machine Learning In Hydrate Field Data Predictionmentioning

confidence: 99%

Section: Machine Learning In Hydrate Field Data Predictionmentioning

confidence: 99%

Research Advances in Machine Learning Techniques in Gas Hydrate Applications

Osei,

Bavoh,

Lal

2024

ACS Omega

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“…Due to the complex nature and poor understanding of foam behavior, the prediction of foam viscosity under harsh reservoir conditions is still a challenging task. ,− Recently, artificial intelligence (AI) tools have attracted special interest in oil and gas applications due to their ability to solve nonlinear problems with high accuracy. Othman et al predicted the fracturing fluid viscosity by considering different parameters such as polymer, temperature, and salinity, utilizing 86 experiments. They used six machine learning models, including decision trees (DTs), fully connected neural networks (FCNN), gradient boosting (GB), random forest (RF), extreme GB (XGB), and adaptive gradient boosting (AGB; AdaBoost).…”

Section: Introductionmentioning

confidence: 99%

Prediction of Foam Rheology Models Parameters Utilizing Machine Learning Tools

Al-Darweesh,

Aljawad,

Tariq

et al. 2024

ACS Omega

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Rheological models are usually used to predict foamed fluid viscosity; however, obtaining the model constants under various conditions is challenging. Hence, this paper investigated the effect of different variables on foam rheology, such as shear rate, temperature, pressure, surfactant types, gas phase, and salinity, using a high-pressure high-temperature foam rheometer. Power-law, Bingham plastic, and Casson fluid models fit the experimental data well. Therefore, the data were fed to different machine learning techniques to evaluate the rheological model constants with different features. In this study, seven different machine learning techniques have been applied to predict the rheological models' constants, including decision tree, random forest, XGBoost (XGB), adaptive gradient boosting, gradient boosting, support vector regression, and voting regression. We evaluated the performance of our machine learning models using the coefficient of determination (R 2 ), cross-plots, root-mean-square error, and average absolute percentage error. Based on the prediction outcomes, the XGB model outperformed the other ML models. The XGB model exhibited remarkably low error rates, achieving a prediction accuracy of 95% under ideal conditions. Furthermore, our prediction results demonstrated that the Casson model accurately captured the rheological behavior of the foam. Additionally, we used Pearson's correlation coefficients to assess the significance of various properties in relation to the constants within the rheological models. It is evident that the XGB model makes predictions with nearly all features contributing significantly, while other machine learning techniques rely more heavily on specific features over others. The proposed methodology can minimize the experimental cost of measuring rheological parameters and serves as a quick assessment tool.

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“…However, for hydraulic fracturing treatments to be successful, the fluids must have other specialized qualities . Fracturing fluids should have better cleanup and breakup properties in the postfracture treatment, provide excellent fluid-loss control, have low friction pressure while pumping, and be as ecologically friendly as possible, in addition to displaying the appropriate viscosity in the fracture. , The various types of fracturing fluids are water-based, oil-based, acid-based, and multiphase. , …”

Section: Introductionmentioning

confidence: 99%

Impact of Hydrocarbons on the Rheological Properties of Seawater-Based Fracturing Fluids

Janjua,

Bashir,

Murtaza

et al. 2024

Energy Fuels

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The success of extracting hydrocarbons from unconventional reservoirs heavily relies on the properties of the fracturing fluids. These fluids, typically waterbased and containing polymers and cross-linkers, are essential for enhancing the solution's viscosity. With concerns over operational costs and resource availability, the oil and gas industry is increasingly turning to seawater as an alternative to freshwater in fracturing processes. The primary focus of this study is to examine how the presence of hydrocarbons affects the rheological properties of polymer-based fracturing fluids. In this study, seawater is mixed with varying concentrations of synthetic and crude oils (50, 100, 250, and 500 ppm), specifically decane (C 10 H 22 ), dodecane (C 12 H 26 ), tetradecane (C 14 H 30 ), and light crude oil. Carboxymethyl hydroxypropyl guar gum (CMHPG) was used as the polymer, which was hydrated in these oil-laden solutions. After polymer hydration, a cross-linker (XL-Z2) was introduced. The rheological properties were analyzed using a Chandler HTHP Viscometer 5550 under the following conditions: a temperature of 70 °C, 500 psi pressure, and a shear rate of 100 s −1 . The study reveals an increase in viscosity with increasing concentrations of decane and dodecane and a slight decrease in viscosity with increasing concentrations of tetradecane. The increase in viscosity is attributed to the elongation of carbon chains and the strengthening of intermolecular forces, resulting in enhanced viscosity. However, there is a sharp decrease in the viscosity in the presence of light crude oil. This decrease is due to the presence of heavy hydrocarbon atoms in the crude oil, which can be attributed to the asphaltene content in the crude oil. Additionally, over a period of 180 min, the study observed a significant increase in viscosity in the presence of synthetic oils (decane and dodecane), whereas a decrease was noted with tetradecane and light crude oil, likely due to its heavier hydrocarbon components such as asphaltenes. In conclusion, the study highlights the significant influence of hydrocarbons on the viscosity and overall rheological characteristics of polymer-based fracturing fluids in seawater. These findings offer valuable insights for the oil and gas industry, aiding in the selection and optimization of fracturing fluids by understanding the effects of different hydrocarbon types and concentrations.

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Enhancing Fracturing Fluid Viscosity in High Salinity Water: A Data-Driven Approach for Prediction and Optimization

Cited by 5 publications

References 52 publications

Research Advances in Machine Learning Techniques in Gas Hydrate Applications

Research Advances in Machine Learning Techniques in Gas Hydrate Applications

Prediction of Foam Rheology Models Parameters Utilizing Machine Learning Tools

Impact of Hydrocarbons on the Rheological Properties of Seawater-Based Fracturing Fluids

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