Accurate Prediction of Gas Hydrate Suppression Capability of Water-Based Drilling Fluids in Deepwater Drilling

An important aspect of hydrocarbon drilling is the usage of drilling fluids, which remove drill cuttings and stabilize the wellbore to provide better filtration. To stabilize these properties, several additives are used in drilling fluids that provide satisfactory rheological and filtration properties. However, commonly used additives are environmentally hazardous; when drilling fluids are disposed after drilling operations, they are discarded with the drill cuttings and additives into water sources and causes unwanted pollution. Therefore, these additives should be substituted with additives that are environmental friendly and provide superior performance. In this regard, biodegradable additives are required for future research. This review investigates the role of various bio-wastes as potential additives to be used in water-based drilling fluids. Furthermore, utilization of these waste-derived nanomaterials is summarized for rheology and lubricity tests. Finally, sufficient rheological and filtration examinations were carried out on water-based drilling fluids to evaluate the effect of wastes as additives on the performance of drilling fluids.

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Application of Hybrid Silicate as a Film-Forming Agent in High-Temperature Water-Based Drilling Fluids

ACS Omega ◽

10.1021/acsomega.1c02725 ◽

2021 ◽

Author(s):

Ying Li ◽

Maosen Wang ◽

Xianfeng Tan ◽

Yinghui An ◽

Huanan Liu ◽

...

Keyword(s):

High Temperature ◽

Drilling Fluids ◽

High Temperature Water ◽

Film Forming ◽

Water Based ◽

Hybrid Silicate ◽

Temperature Water

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Wet Drilled Cuttings Bed Rheology

Energies ◽

10.3390/en14061644 ◽

2021 ◽

Vol 14 (6) ◽

pp. 1644

Author(s):

Camilo Pedrosa ◽

Arild Saasen ◽

Bjørnar Lund ◽

Jan David Ytrehus

Keyword(s):

Drilling Fluid ◽

Drilling Fluids ◽

Parallel Plates ◽

Single Particles ◽

Cuttings Transport ◽

Transport Studies ◽

Water Based ◽

Fluid Properties ◽

External Forces ◽

Cuttings Bed

The cuttings transport efficiency of various drilling fluids has been studied in several approaches. This is an important aspect, since hole cleaning is often a bottleneck in well construction. The studies so far have targeted the drilling fluid cuttings’ transport capability through experiments, simulations or field data. Observed differences in the efficiency due to changes in the drilling fluid properties and compositions have been reported but not always fully understood. In this study, the cuttings bed, wetted with a single drilling fluid, was evaluated. The experiments were performed with parallel plates in an Anton Paar Physica 301 rheometer. The results showed systematic differences in the internal friction behaviors between tests of beds with oil-based and beds with water-based fluids. The observations indicated that cutting beds wetted with a polymeric water-based fluid released clusters of particles when external forces overcame the bonding forces and the beds started to break up. Similarly, it was observed that an oil-based fluid wetted bed allowed particles to break free as single particles. These findings may explain the observed differences in previous cutting transport studies.

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Prediction of velocity profile of water based copper nanofluid in a heated porous tube using CFD and genetic algorithm

Scientific Reports ◽

10.1038/s41598-021-90201-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Tiziana Ciano ◽

Massimiliano Ferrara ◽

Meisam Babanezhad ◽

Afrasyab Khan ◽

Azam Marjani

Keyword(s):

Artificial Intelligence ◽

Genetic Algorithm ◽

Velocity Profile ◽

Fluid Velocity ◽

Accurate Prediction ◽

Inference System ◽

Porous Tube ◽

Intelligence Algorithm ◽

Water Based ◽

Artificial Intelligence Algorithm

AbstractThe heat transfer improvements by simultaneous usage of the nanofluids and metallic porous foams are still an attractive research area. The Computational fluid dynamics (CFD) methods are widely used for thermal and hydrodynamic investigations of the nanofluids flow inside the porous media. Almost all studies dedicated to the accurate prediction of the CFD approach. However, there are not sufficient investigations on the CFD approach optimization. The mesh increment in the CFD approach is one of the challenging concepts especially in turbulent flows and complex geometries. This study, for the first time, introduces a type of artificial intelligence algorithm (AIA) as a supplementary tool for helping the CFD. According to the idea of this study, the CFD simulation is done for a case with low mesh density. The artificial intelligence algorithm uses learns the CFD driven data. After the intelligence achievement, the AIA could predict the fluid parameters for the infinite number of nodes or dense mesh without any limitations. So, there is no need to solve the CFD models for further nodes. This study is specifically focused on the genetic algorithm-based fuzzy inference system (GAFIS) to predict the velocity profile of the water-based copper nanofluid turbulent flow in a porous tube. The most intelligent GAFIS could perform the most accurate prediction of the velocity. Hence, the intelligence of GAFIS is tested for different values of cluster influence range (CIR), squash factor(SF), accept ratio (AR) and reject ratio (RR), the population size (PS), and the percentage of crossover (PC). The maximum coefficient of determination (~ 0.97) was related to the PS of 30, the AR of 0.6, the PC of 0.4, CIR of 0.15, the SF 1.15, and the RR of 0.05. The GAFIS prediction of the fluid velocity was in great agreement with the CFD. In the most intelligent condition, the velocity profile predicted by GAFIS was similar to the CFD. The nodes increment from 537 to 7671 was made by the GAFIS. The new predictions of the GAFIS covered all CFD results.

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