Drilling process is one of the main operations in the extraction of hydrocarbons from petroleum reservoirs. It comes right after the exploration processes. Drilling fluids are necessary for controlling the wells and performing different functions during the drilling operation. They perform many roles in lifting the cuttings from the bottom of the well to the surface and cooling/lubricating the drill pipes and bit. Furthermore, they provide the desired hydrostatic pressure to overbalance pore pressure in addition to produce a thin/impermeable filter cake that can prevent or reduce the possible damage to the formations. It is mandatory to keep monitoring, enhancing, and optimizing the properties of the drilling fluids. Recently, different additives, among which nanoparticles (NPs), have been investigated to improve, and maximize the benefits of the drilling fluids accordingly to meet the new challenges. The rheological behavior of such complex fluids has shown different enhancements up on the utilization of those additives. The rheological properties of the drilling fluids are accurately measured on the surface; however, the behavior of those properties may change with time and under harsh drilling conditions, such as high pressure/high temperature environments. For that, different models are introduced and used to predict and optimize the rheological characteristics of such fluids. Bingham, Herschel-Bulkley, Power Law, Casson and others are commonly used as rheological models to predict the drilling fluid behavior. In the last decade, a new trend of developing new models and correlations using the artificial neural networks (ANN) have been introduced to the petroleum field. Mathematical formulas can be developed using ANN, which then can be used to predict the behavior of certain parameter(s) by knowing other ones. Using ANN have shown to be more reliable and accurate in predicting the rheological properties of the drilling fluids, such as apparent viscosity (AV), plastic viscosity (PV), yield point (YP), maximum shear stress, and change in the mud density at various conditions. This work aims at using ANN technique to develop suitable models that can predict the rheological behavior of nano-based drilling fluids. The effect of NPs-type, -size, -concentration, and drilling fluid formulations will be considered, which may pave the road for new applications and efficient utilizations.

All over the world, externally bonded fiber-reinforced polymer systems used to strengthen concrete elements improve building sustainability. However, reports issued by the American Concrete Institute Committee 440 called for heavy scrutinizing before actual field implementation. The very limited number of proposed equations lacks reliability and accuracy. Thus, further investigation in this area is needed. In addition, machine learning techniques are being implemented successfully in developing strength models for complex problems. This study aims to provide a reliable machine learning model based on an experimental database. The proposed model was developed and validated against the experimental database and the very limited models in the literature. The model showed improved agreement with the experimental results compared to the previous models.

All over the world, shear strengthening of reinforced concrete elements using external fiber-reinforced polymer jackets could be used to improve building sustainability. However, reports issued by the American Concrete Institute called for heavy scrutiny before actual field implementation. The very limited number of proposed shear equations lacks reliability and accuracy. Thus, further investigation in this area is needed. In addition, machine-learning techniques are being implemented successfully to develop strength models for complex problems including shear, flexure, and torsion. This study aims to provide a reliable machine-learning model for reinforced concrete beams strengthened in shear using externally reinforced fiber polymer sheets. The proposed model was developed and validated against the experimental database and the very limited models in existing literature. The model showed better agreement with the experimentally measured strength compared to the previous models, which accounted for the effect of various parameters including but not limited to: the element geometry, strengthening details, and configurations. The model could guide the further developments of design codes and mechanical models.

Mud filtrate invasion is a vital parameter that should be optimized during drilling for oil and gas to reduce formation damage. Nanoparticles (NPs) have shown promising filtrate loss mitigation when used as drilling fluid (mud) additives in numerous recent studies. Modeling the influence of NPs can fasten the process of selecting their optimum type, size, concentration, etc. to meet the drilling conditions. In this study, a model was developed, using artificial neural network (ANN), to predict the filtrate invasion of nano-based mud under wide range of pressures and temperatures up to 500 psi and 350 °F, respectively. A total of 2,863 data points were used in the development of the model (806 data points were collected form conducted experiments and the rest were collected form the literature). Seven different types of NPs with size and concentration ranges from 15 to 50 nm and 0 to 2.5 wt%, respectively, had been included in the model to ensure universality. The dataset was divided into 70 % for training and 30 % for validation. A total of 6,750 different combinations for the model’s hyperparameters were evaluated to determine the optimum combination. The N-encoded method was used to convert the categorical data into numerical values. The model was evaluated through calculating the statistical parameters. The developed ANN-model proofed to be efficient in predicting the filtrate invasion at different pressures and temperatures with an average absolute relative error (AARE) of less than 0.5 % and a coefficient of determination (R2) of more than 0.99 for the overall data. The ANN-model covers wide range of pressures, temperatures as well as various NPs’ types, concentrations, and sizes, which confirms its useability and coverability. HIGHLIGHTS Artificial neural network (ANN)-model was developed to predict the volume of filtrate of water-based mud (WBM) modified with nanoparticles (NPs) A total of 2,863 data points were collected to build the ANN-model from both experimental work and literature considering 3 types of WBM modified with 7 types of NPs (SiO2, TiO2, Al2O3, CuO, MgO, ZnO, Fe2O3) with size and concentration ranges from 15 to 50 nm and 0 to 2.5 wt%, respectively, under wide range of pressures and temperatures up to 500 psi and 350 °F A total of 6,750 different combinations for the model’s hyperparameters were evaluated to determine the optimum combination and the N-encoded method was used to convert the categorical data into numerical values The ANN-model proofed to be efficient with an average absolute relative error (AARE) of less than 0.5 % and a coefficient of determination (R2) of more than 0.99 for the overall data GRAPHICAL ABSTRACT

All over the world, externally bonded fiber-reinforced polymer systems used to strengthen concrete elements improve building sustainability. However, reports issued by the American Concrete Institute Committee 440 called for heavy scrutinizing before actual field implementation. The very limited number of proposed equations lacks reliability and accuracy. Thus, further investigation in this area is needed. In addition, machine learning techniques are being implemented successfully in developing strength models for complex problems. This study aims to provide a reliable machine learning model based on an experimental database. The proposed model was developed and validated against the experimental database and the very limited models in the literature. The model showed improved agreement with the experimental results compared to the previous models.