DEVELOPMENT, OPTIMIZATION AND EVALUATION OF PULSATILE DRUG DELIVERY CAPSULES LOADED WITH CARVEDILOL BY APPLYING QUALITY BY DESIGN

Objective: The purpose of this research is to find the best way for designing carvedilol pulsatile drug delivery system capsules. Methods: The research paves the way to improve the method of preparing carvedilol pulsatile drug delivery by adjusting critical material attributes (CMA) such as coating polymer concentration, critical process parameters (CPP) such as inlet temperature and atomizing air pressure, and their impact on critical quality attributes (CQA) like particle size (PS in nm), entrapment efficiency in percentage (% EE) and amount of drug delivered in percent (%ADR) at 12 h in the carvedilol pulsatile pellets filled capsules by applying the Box-Behnken design. By varying the polymer concentration and process parameters, nearly 15 formulations were created. Results: Based on the influence of CMA, CPP on CQA, the formulation CP13 was determined to be the most optimized formulation among the 15 formulations. The optimized levels of CMA were found to be-1 level of coating polymer concentration and CPP was found to be-1 level of inlet temperature, 0 level of atomizing air pressure and it optimized CQA like PS was found to be 1017.5±8.4 nm, % EE was found to be 96.8±2.8 %, % ADR at 12 h was found to be 88.4±3.4 %. Carvedilol Pulsatile drug delivery system was designed by using optimized fluidized bed coater in order to decrease the usage of attributes, decrease the productivity cost and enhance the usage of specific attributes at fixed concentration for further manufacturing scale. Conclusion: By the current results it was concluded that the optimized CMA and CPP that shown in the results are the suitable attributes for the best formulation of carvedilol pulsatile drug delivery system capsules.

Formulation and Evaluation of Transdermal Patch of Thiochochicoside

The aim of the study is to formulate and evaluate transdermal patches of Thiocholchicoside In the present study, matrix type were prepared by moulding techniques. This mode of drug delivery is more beneficial for chronic disorders such as Rheumatoid arthritis which require long term drug administration to maintain therapeutic drug concentration in plasma. Transport of drugs or compounds via skin is a complex phenomenon, which allows the passage of drugs or compounds into and across the skin. In the present work an attempt has been made to formulate and evaluate the transdermal patches of Thiocholchicoside using various blends of polymer. The polymeric combinations EC/PVP and EC/HPMC used for the formulation of transdermal patches showed good film forming property. The patches formed were thin, flexible, smooth and transparent. The weight variation tests showed less variation in weight and suggesting uniform distribution of drug and polymer over the mercury surface. The thicknesses of the transdermal patches were found to increase on increasing concentration of hydrophilic polymers like PVP and HPMC.All the patches showed good flexibility and folding endurance properties. The result suggests that the formulations with increased hydrophilic polymer concentration showed long folding endurance. The moisture content in the patches was found to be low and formulations with more hydrophilic polymer concentrations showed more percentage moisture content. The in-vitro drug release studies showed that formulations TDP2, TDP3, TDP4, and TDP5 with increased concentration of hydrophilic polymer showed rapid release. The drug content analysis showed minimum variations suggesting uniform distribution of drug.

The Effect of Surfactant Polymer Concentration on the Distribution of Oil Drainage Area in Heterogeneous Reservoir

The use of chemical injection has been widely used in the oil field on a large scale. One of the enhanced oil recovery (EOR) methods to increase production from old oil fields is through polymer surfactant injection, which functions to reduce interfacial tension and water-oil mobility ratio. This study focuses on developing a simulation model for chemical injection of polymer surfactant reservoirs by hypothetically making heterogeneous reservoir models in each layer with dimensions of 10x10x4. It consists of one a vertical well which is producer well located at the top of the left corner and one an injection well which is located at the bottom of right corner. This study shows a comparison between surfactant injection, polymer injection and SP injection using the same surfactant and polymer concentration with a concentration of 1000 ppm with 0.3 PV. Oil recovery in polymer injection turned out to be quite high compared to other chemical injections. In polymer injection, the oil recovery was 4.17%. Meanwhile, surfactant injection and SP injection increased by 0.59% and 0.61, respectively.

High Performance Friction Reducer for Slickwater Fracturing Applications: Laboratory Study and Field Implementation

Proppant placement in a tight formation is extremely challenging. Therefore, using a high viscous friction reducer (HVFR) as a fracturing fluid for stimulation treatment in tight gas reservoirs is increasing within the industry because it can transport proppant, help reduce pipe friction generated during hydraulic-fracturing treatments, and efficiently clean up similar to the lower viscosity friction reducers (FRs). In this paper the implementation of the robust HVFR that is building higher viscosity at low concentrations, which minimizes energy loss and promotes turbulent flow within the pipe during the pumping of low viscosity, is discussed in detail. Performance evaluation of the new HVFR was conducted in the laboratory and compared to the lower viscosity FR. The study consisted of viscosity measurements at 70 and 180°F, compatibility with other additives, and proppant transport capabilities. Additionally, the viscosity generated from both FRs was compared using two water sources: water well A and treated sewage water. Viscosity measurements were performed across a wide range of FR and HVFR concentrations and under varying shear rates using a digital viscometer. To validate drag reduction capabilities for this HVFR in the field, the same groundwater with low salinity and low total dissolved solids (TDS) content were used for comparison purposes. The test plan for this new HVFR was for a well to be drilled to a total depth of 17,801 ft MD (10,693 ft TVD) with a 6,016-ft lateral section. Another part of the plan was to complete 41 stages—the first stage with the toe initiator, and subsequent stages using ball drops until Stage 8, were completed using the current FR. For Stage 8, the drag reduction from the new HVFR was evaluated against the current FR only during the pad stage. Then, FR or HVFR concentrations were used, with a gradual reduction from 2 to 1 gpt without compromising proppant placement from stages 9 to 37, alternating current FR and the new HVFR every four stages. From Stage 38 to 41, the same approach was used but with treated sewage water and alternating every other stage using current FR or HVFR at 1gpt. The implementation of the new HVFR showed better friction reduction when using the same concentration of the current FR. Also, achieving better average treating pressures with lower concentration. Based on that it is a cost-effective solution and the performance is better, this lead to reduce the HVFR volume to be pumped per stage compared to the current FR. Applications/Significance/Novelty For this study, drag reduction capabilities for this new HVFR were validated in the field at higher pumping rate conditions, potentially optimizing (reducing) the polymer concentration during a freshwater application. It was shown that lower concentrations of this HVFR provided higher viscosity, which helps improve proppant transport and operation placement.

Intelligent Predictor for Polymer Viscosity to Enhance Support for EOR Processes

Research into the use of polymers for enhanced oil recovery (EOR) processes has been going on for more than 6 decades and is now classified as a techno-commercially viable option. A comprehensive evaluation of the polymer's rheology is pivotal to the success of any polymer EOR process. Laboratory-based evaluation is critical to EOR success; however, it is also a time/capital consuming process. Consequently, any tool which can aid in optimizing lab tests design can bring in great value. Accordingly, in this study a novel predictive correlation for viscosity estimation of commonly used "FP 3330S" EOR polymer is presented through use of cutting-edge machine learning neural networks. Mathematical equation for polymer viscosity is developed using machine learning algorithms as a function of polymer concentration, NaCl concentration, and Ca2+ concentration. The measured input data was collected from the literature and sub-divided into training and test sets. A wide-ranging optimization was performed to select the best parameters for the neural network which includes the number of neurons, neuron layers, activation functions between multiple layers, weights, and bias. Furthermore, the Levenberg-Marquardt back-propagation algorithm was utilized to train the model. Finally, measured and estimated viscosities were compared based on error-analysis. Novel correlation is developed for the polymer that can be used in predictive mode. This established correlation can predict polymer viscosity when applied to the test dataset and outperforms other published models with average error in the range of 3-5% and coefficient of determination in excess of 0.95. Moreover, it is shown that neural networks are faster and relatively better than other machine learning algorithms explored in this study. The proposed correlation can map non-linear relationships between polymer viscosity and other rheological parameters such as molecular weight, polymer concentration, and cation concentration of polymer solution. Lastly, through machine learning validation approach, it was possible to examine feasibility of the proposed models which is not done by traditional empirical equations.

Optimizing Seawater Based Fracture Fluids Rheology Utilizing Chelating Agents

Due to the scarcity and high cost of freshwater, especially in the Gulf region, utilization of seawater as a fracturing fluid gained noticeable interest. However, seawater contains high total dissolved solids (TDS) that may damage the formation and degrade the performance of the fracturing fluids. Numerous additives are required to reduce the damaging effect and improve the viscosity resulting in an expensive and non-eco-friendly fracturing fluid system. Chelating agents, which are environmentally benign, are proposed in this study as the replacement of many additives for seawater fracturing fluids. This study focuses on optimizing chelating agents to achieve high viscosity employing the standard industry rheometers. Carboxymethyl Hydroxypropyl Guar Gum (CMHPG) polymer, which is effective in hydraulic fracturing, was used in this research with 0.5 and 1.0 wt% in deionized water (DW) as well as seawater (SW). It was first tested as a standalone additive at different conditions to provide a benchmark then combined with different concentrations, and pH level chelating agents. In this study the hydration test was conducted through different conditions. It was observed that CMHPG, when tested as a standalone additive, provided slightly higher viscosity in SW compared to DW. Also, increasing polymer concentration from 0.5 to 1.0 wt% provided three folds of viscosity. The viscosity did not show time dependence behavior at room temperature for the aforementioned experiments where all hydration tests were run at 511 1/s shear rate. Temperature, however, had a significant impact on both viscosity magnitude and behavior. At 70 °C, the fluid viscosity increased with time where low viscosity was achieved early on but kept increasing with shearing time. Similarly, high pH chelating agents provided time dependant viscosity behavior when mixed with CMHPG. This behavior is important as low viscosity is favorable during pumping but high viscosity when the fluids hit the formation. The study investigates the possibility of utilizing chelating agents with seawater to replace numerous additives. It acts as a crosslinker at early shearing times, where a gradual increase in viscosity was observed and a breaker in the reservoir harsh conditions. It also captures the divalent ions that are common in seawater, which replaces the need for scale inhibitors. The viscosity increase behavior can be controlled by adjusting the pH level, which could be desirable during operations.

Polymer Concentration Maximizes Encapsulation Efficiency in Electrohydrodynamic Mixing Nanoprecipitation

Encapsulation in self-assembled block copolymer (BCP) based nanoparticles (NPs) is a common approach to enhance hydrophobic drug solubility, and nanoprecipitation processes in particular can yield high encapsulation efficiency (EE). However, guiding principles for optimizing polymer, drug, and solvent selection are critically needed to facilitate rapid design of drug nanocarriers. Here, we evaluated the relationship between drug-polymer compatibility and concentration ratios on EE and nanocarrier size. Our studies employed a panel of four drugs with differing molecular structures (i.e., coumarin 6, dexamethasone, vorinostat/SAHA, and lutein) and two BCPs [poly(caprolactone)-b-poly(ethylene oxide) (PCL-b-PEO) and poly(styrene)-b-poly(ethylene oxide) (PS-b-PEO)] synthesized using three nanoprecipitation processes [i.e., batch sonication, continuous flow flash nanoprecipitation (FNP), and electrohydrodynamic mixing-mediated nanoprecipitation (EM-NP)]. Continuous FNP and EM-NP processes demonstrated up to 50% higher EE than batch sonication methods, particularly for aliphatic compounds. Drug-polymer compatibilities were assessed using Hansen solubility parameters, Hansen interaction spheres, and Flory Huggins interaction parameters, but few correlations were EE observed. Although some Hansen solubility (i.e., hydrogen bonding and total) and Flory Huggins interaction parameters were predictive of drug-polymer preferences, no parameter was predictive of EE trends among drugs. Next, the relationship between polymer: drug molar ratio and EE was assessed using coumarin 6 as a model drug. As polymer:drug ratio increased from <1 to 3–6, EE approached a maximum (i.e., ∼51% for PCL BCPs vs. ∼44% PS BCPs) with Langmuir adsorption behavior. Langmuir behavior likely reflects a formation mechanism in which drug aggregate growth is controlled by BCP adsorption. These data suggest polymer:drug ratio is a better predictor of EE than solubility parameters and should serve as a first point of optimization.

Stabilizing Na-bentonite by Poly(Diallyl Dimethyl Ammonium Chloride) Adsorption

Clay minerals have been modified by polymers for different applications. The polymer addition affects not only the surface propertiesbut also the rheological properties and the stability of the clay-polymer suspension as a whole. In the current study, the electro-chemical properties of bentonite particles in presence of poly diallyl dimethyl ammonium chloride (PDDACl) were investigated. Theseproperties were characterized by as zeta potential, adsorption isotherm, Fourier transform infrared (FTIR) and the apparent viscosityat different solid percent. The results indicated that the viscosity of the bentonite-PDDACl suspension not only increases by raising thepolymer concentration but also by increasing solids %. Adsorption of PDDACl polymer increases the positivity of bentonite surfaceas a function of polymer concentration, which could be explained mainly by electrostatic interaction of deficient metal ions at theoctahedral sheets of bentonite with the cationic head group of the polymer. The PDDACl adsorption isotherm on bentonite fits moreprobably Langmuir than Freundlich isotherm

Core-to-Field-Scale Simulations of Polymer Viscoelastic Effect on Oil Recovery Using the Extended Viscoelastic Model

Being one of the most commonly used chemical EOR methods, polymer flooding can substantially improve both macroscopic and microscopic recovery efficiencies by sweeping bypassed oil and mobilizing residual oil, respectively. However, a proper estimation of incremental oil to polymer flooding requires an accurate prediction of the complex rheological response of polymers. In this paper, a novel viscoelastic model that comprehensively analyzes the polymer rheology in porous media is used in a reservoir simulator to predict the recovery efficiency to polymer flooding at both core- and field-scales. The extended viscoelastic model can capture polymer Newtonian and non-Newtonian behavior, as well as mechanical degradation that may take place at ultimate shear rates. The rheological model was implemented in an open- source reservoir simulator. In addition, the effect of polymer viscoelasticity on displacement efficiency was also captured through trapping number. The calculation of trapping number and corresponding residual-phase saturation was verified against a commercial simulator. Core-scale tertiary polymer flooding predictions revealed the positive effect of injection rate and polymer concentration on oil displacement efficiency. It was found that high polymer concentration (>2000 ppm) is needed to displace residual oil at reservoir rate as opposed to near injector well rate. On the other hand, field-scale predictions of polymer flooding were performed in a quarter 5-spot well pattern, using rock and fluid properties representing the Middle East carbonate reservoirs. The field-simulation studies showed that tertiary polymer flooding might improve both volumetric sweep efficiency and displacement efficiency. For this case study, incremental oil recovery by polymer flooding is estimated at around 11 %OOIP, which includes about 4 %OOIP residual oil mobilized by viscoelastic polymers. Furthermore, the effect of different parameters on the polymer flooding efficiency was investigated through sensitivity analysis. This study provides more insight into the robustness of the extended viscoelastic model as well as its effect on polymer injectivity and related oil recovery at both core- and field-scales. The proposed polymer viscoelastic model can be easily implemented into any commercial reservoir simulator for representative field-scale predictions of polymer flooding.

A Robust Predictive Machine Learning Model for Supercritical CO2 Foam Strength with Integrated Testing Parameters

Unconventional resources have made a significant contribution to fossil energy supply to date, and some specific stimulation techniques have been used in their exploitation. For example, the use of scCO2 foam as a hydraulic fracturing stimulation fluid has sparked considerable interest due to its numerous advantages in terms of fracturing and production performance. The strength of scCO2 foam, an indicator of foam performance, highly depends on the formulation design, foaming properties and operating conditions. Due to complex nature of foam, the quantification of foam strength at downhole conditions is challenging. Specific screening and optimization processes are required to design high performance foam. Although the flow behavior (apparent viscosity) of foam has been extensively studied with empirical models, integrating some essential process parameters into the foam flow behavior evaluation remains challenging. In this study, we present an effective model that incorporates the benefits of a deep learning (DL) approach while taking into account the integration of specific process variables. Several input parameters such as surfactant types and concentration, salinity, polymer concentration, temperature and pressure were used in conjunction with foam quality and shear rate. To predict foam strength while taking the aforementioned parameters into account, a deep neural network (DNN) with optimized hyperparameters was developed. The experimental data for this purpose were obtained using a pressurized foam rheometer. An improved deep learning framework was developed and designed to learn the intrinsic relation among various parameters. The predictive study concludes that, the developed optimized DNN algorithm can provide a reliable and robust prediction with significantly high accuracy. When compared to a shallow network with a standard deviation of less than 5%, the developed optimal deep neural network increased average predictive accuracy to 95.64%. The regression coefficient in the optimized case was found to be nearly one with a low mean square error. The developed DNN algorithm is considered as an improved framework which encompasses several process variables and provides reliable and accurate prediction thus makes it suitable for further integration with fracturing simulator. It would also be helpful for optimizing fracturing process and improving foam formulations.