Designing Smart drilling fluids using modified nano silica to improve drilling operations in Geothermal wells

Martin, C.; Babaie, Meisam; Nourian, Amir; Nasr, G. G.

doi:10.1016/j.geothermics.2022.102600

Cited by 10 publications

(2 citation statements)

References 63 publications

(84 reference statements)

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“…Among various types of nanoparticles, silica, titanium dioxide, carbon nanotubes, copper, clay, and aluminum oxide are the most extensively utilized nanoparticles in the oil and gas and energy sector, pharmaceuticals, the automobile industry, the construction sector, and textile and paint sectors [ 16 , 36 , 40 , 41 , 42 ]. Silica nanoparticles are widely utilized in the petroleum industry due to their advantageous combination of small size, high surface area, and versatility, making them effective at improving oil recovery, fines migration control, drilling fluid stability, and removing contaminants from crude oil [ 10 , 14 , 49 , 50 , 51 , 52 , 53 , 54 , 55 ].…”

Section: Introductionmentioning

confidence: 99%

Unlocking the Power of Artificial Intelligence: Accurate Zeta Potential Prediction Using Machine Learning

et al. 2023

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Nanoparticles have gained significance in modern science due to their unique characteristics and diverse applications in various fields. Zeta potential is critical in assessing the stability of nanofluids and colloidal systems but measuring it can be time-consuming and challenging. The current research proposes the use of cutting-edge machine learning techniques, including multiple regression analyses (MRAs), support vector machines (SVM), and artificial neural networks (ANNs), to simulate the zeta potential of silica nanofluids and colloidal systems, while accounting for affecting parameters such as nanoparticle size, concentration, pH, temperature, brine salinity, monovalent ion type, and the presence of sand, limestone, or nano-sized fine particles. Zeta potential data from different literature sources were used to develop and train the models using machine learning techniques. Performance indicators were employed to evaluate the models’ predictive capabilities. The correlation coefficient (r) for the ANN, SVM, and MRA models was found to be 0.982, 0.997, and 0.68, respectively. The mean absolute percentage error for the ANN model was 5%, whereas, for the MRA and SVM models, it was greater than 25%. ANN models were more accurate than SVM and MRA models at predicting zeta potential, and the trained ANN model achieved an accuracy of over 97% in zeta potential predictions. ANN models are more accurate and faster at predicting zeta potential than conventional methods. The model developed in this research is the first ever to predict the zeta potential of silica nanofluids, dispersed kaolinite, sand–brine system, and coal dispersions considering several influencing parameters. This approach eliminates the need for time-consuming experimentation and provides a highly accurate and rapid prediction method with broad applications across different fields.

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Section: Introductionmentioning

confidence: 99%

Unlocking the Power of Artificial Intelligence: Accurate Zeta Potential Prediction Using Machine Learning

et al. 2023

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“…At present, the main categories of plugging materials are inorganic plugging materials and polymer-based plugging materials [ 24 , 25 ]. Inorganic materials have excellent temperature resistance and are not sensitive to salt, mainly calcium carbonate nanoparticles, silicon dioxide nanoparticles and graphene oxide [ 26 , 27 , 28 ]. However, nanomaterials are easy to aggregate and not easy to deform, resulting in poor compatibility with pores and a poor plugging effect.…”

Section: Introductionmentioning

confidence: 99%

A Novel Amphoteric Ion-Modified, Styrene-Based Nano-Microsphere and Its Application in Drilling Fluid

Zhang,

Liu,

Sun

et al. 2023

Materials

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With the gradual depletion of shallow oil and gas, deep oil and gas has become the focus of development. However, deep formations generally face the challenge of high-temperature and high-salinity, and drilling fluid agents are prone to failure, leading to drilling fluid intrusion into the formation that can cause serious drilling accidents such as well bore collapse. For this, a styrene-based nano-microsphere (SSD) modified with amphoteric ions was developed, with a particle size of 228 nm which could resist temperatures up to 200 °C and sodium chloride (NaCl) up to saturation. SSD has significant salt-responsive properties and its aqueous dispersion becomes transparent with increasing salinity. The SSD provided superior plugging performance in solutions containing NaCl, with a core plugging efficiency of 95.2%, and it was significantly better than the anion-modified microspheres. In addition, in drilling fluids under high temperature and high-salinity conditions, the SSD promotes particle gradation of drilling fluids and improves the zeta potential through its own plugging and synergistic effect with clay, which significantly improves the comprehensive performance of drilling fluids, such as stability, rheological performance, and filtration reduction performance. The development of SSD provides a new idea for research of high-temperature and high-salinity-resistant drilling fluid agents.

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Clay mineral–nanoparticle interactions in drilling fluids

Fan,

Zhuang

2024

Clay Science in Drilling and Drilling Fluids

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Designing Smart drilling fluids using modified nano silica to improve drilling operations in Geothermal wells

Cited by 10 publications

References 63 publications

Unlocking the Power of Artificial Intelligence: Accurate Zeta Potential Prediction Using Machine Learning

Unlocking the Power of Artificial Intelligence: Accurate Zeta Potential Prediction Using Machine Learning

A Novel Amphoteric Ion-Modified, Styrene-Based Nano-Microsphere and Its Application in Drilling Fluid

Clay mineral–nanoparticle interactions in drilling fluids

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