Accurate and simple techniques for measurement of fluid rheological properties are important for field operations in the oil industry. Marsh Funnels are popular quality-control tools used in the field for drilling fluids and they offer a simple, practical alternative to viscosity measurement. In the normal measurements, a single point (drainage time) is used to determine an average viscosity; little additional information is extracted regarding the non-Newtonian behavior of the fluid.
Here, a new model is developed and used to determine the rheological properties of drilling muds and other non-Newtonian fluids using data of fluid density and drainage time collected from a Marsh Funnel as a function of viscosity. The funnel results for

In this study, two types of mixes were adopted by using two grading of coarse aggregate. The practical side of this study was to produce no-fine aggregate concrete by using crushed clay brick aggregates. The durability of the produced concrete and internal sulfate attack was studied.      For durability assessment, it is found that the no-fine concrete made with crushed brick aggregate lost about (15-25) % of its compressive strength after being subjected to 60 cycles of wetting and drying with age 120 days. The curing condition showed that the water curing improved the compressive strength with a rate higher than that when sealed or air dry curing were used. The crushed brick no-fine concrete de

Modeling data acquisition systems (DASs) can support the vehicle industry in the development and design of sophisticated driver assistance systems. Modeling DASs on the basis of multiple criteria is considered as a multicriteria decision-making (MCDM) problem. Although literature reviews have provided models for DASs, the issue of imprecise, unclear, and ambiguous information remains unresolved. Compared with existing MCDM methods, the robustness of the fuzzy decision by opinion score method II (FDOSM II) and fuzzy weighted with zero inconsistency II (FWZIC II) is demonstrated for modeling the DASs. However, these methods are implemented in an intuitionistic fuzzy set environment that restricts the ability of experts to provide mem

Predicting vertical stress was indeed useful for controlling geomechanical issues since it allowed for the computation of pore pressure for the formation and the classification of fault regimes. This study provides an in-depth observation of vertical stress prediction utilizing numerous approaches using the Techlog 2015 software. Gardner's method results in incorrect vertical stress values with a problem that this method doesn't start from the surface and instead relies only on sound log data. Whereas the Amoco, Wendt non-acoustic, Traugott, average technique simply needed density log as input and used a straight line as the observed density, this was incorrect for vertical computing stress. The results of these methods

This article proposes a new strategy based on a hybrid method that combines the gravitational search algorithm (GSA) with the bat algorithm (BAT) to solve a single-objective optimization problem. It first runs GSA, followed by BAT as the second step. The proposed approach relies on a parameter between 0 and 1 to address the problem of falling into local research because the lack of a local search mechanism increases intensity search, whereas diversity remains high and easily falls into the local optimum. The improvement is equivalent to the speed of the original BAT. Access speed is increased for the best solution. All solutions in the population are updated before the end of the operation of the proposed algorithm. The diversification f

Market share is a major indication of business success. Understanding the impact of numerous economic factors on market share is critical to a company’s success. In this study, we examine the market shares of two manufacturers in a duopoly economy and present an optimal pricing approach for increasing a company’s market share. We create two numerical models based on ordinary differential equations to investigate market success. The first model takes into account quantity demand and investment in R&D, whereas the second model investigates a more realistic relationship between quantity demand and pricing.