\\n\\n
Dr. Pletser’s experience includes 30 years of working with the European Space Agency as a Senior Physicist/Engineer and coordinating their parabolic flight campaigns, and he is the Guinness World Record holder for the most number of aircraft flown (12) in parabolas, personally logging more than 7,300 parabolas.
\\n\\nSeeing the 5,000th book published makes us at the same time proud, happy, humble, and grateful. This is a great opportunity to stop and celebrate what we have done so far, but is also an opportunity to engage even more, grow, and succeed. It wouldn't be possible to get here without the synergy of team members’ hard work and authors and editors who devote time and their expertise into Open Access book publishing with us.
\\n\\nOver these years, we have gone from pioneering the scientific Open Access book publishing field to being the world’s largest Open Access book publisher. Nonetheless, our vision has remained the same: to meet the challenges of making relevant knowledge available to the worldwide community under the Open Access model.
\\n\\nWe are excited about the present, and we look forward to sharing many more successes in the future.
\\n\\nThank you all for being part of the journey. 5,000 times thank you!
\\n\\nNow with 5,000 titles available Open Access, which one will you read next?
\\n\\nRead, share and download for free: https://www.intechopen.com/books
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
Preparation of Space Experiments edited by international leading expert Dr. Vladimir Pletser, Director of Space Training Operations at Blue Abyss is the 5,000th Open Access book published by IntechOpen and our milestone publication!
\n\n"This book presents some of the current trends in space microgravity research. The eleven chapters introduce various facets of space research in physical sciences, human physiology and technology developed using the microgravity environment not only to improve our fundamental understanding in these domains but also to adapt this new knowledge for application on earth." says the editor. Listen what else Dr. Pletser has to say...
\n\n\n\nDr. Pletser’s experience includes 30 years of working with the European Space Agency as a Senior Physicist/Engineer and coordinating their parabolic flight campaigns, and he is the Guinness World Record holder for the most number of aircraft flown (12) in parabolas, personally logging more than 7,300 parabolas.
\n\nSeeing the 5,000th book published makes us at the same time proud, happy, humble, and grateful. This is a great opportunity to stop and celebrate what we have done so far, but is also an opportunity to engage even more, grow, and succeed. It wouldn't be possible to get here without the synergy of team members’ hard work and authors and editors who devote time and their expertise into Open Access book publishing with us.
\n\nOver these years, we have gone from pioneering the scientific Open Access book publishing field to being the world’s largest Open Access book publisher. Nonetheless, our vision has remained the same: to meet the challenges of making relevant knowledge available to the worldwide community under the Open Access model.
\n\nWe are excited about the present, and we look forward to sharing many more successes in the future.
\n\nThank you all for being part of the journey. 5,000 times thank you!
\n\nNow with 5,000 titles available Open Access, which one will you read next?
\n\nRead, share and download for free: https://www.intechopen.com/books
\n\n\n\n
\n'}],latestNews:[{slug:"stanford-university-identifies-top-2-scientists-over-1-000-are-intechopen-authors-and-editors-20210122",title:"Stanford University Identifies Top 2% Scientists, Over 1,000 are IntechOpen Authors and Editors"},{slug:"intechopen-authors-included-in-the-highly-cited-researchers-list-for-2020-20210121",title:"IntechOpen Authors Included in the Highly Cited Researchers List for 2020"},{slug:"intechopen-maintains-position-as-the-world-s-largest-oa-book-publisher-20201218",title:"IntechOpen Maintains Position as the World’s Largest OA Book Publisher"},{slug:"all-intechopen-books-available-on-perlego-20201215",title:"All IntechOpen Books Available on Perlego"},{slug:"oiv-awards-recognizes-intechopen-s-editors-20201127",title:"OIV Awards Recognizes IntechOpen's Editors"},{slug:"intechopen-joins-crossref-s-initiative-for-open-abstracts-i4oa-to-boost-the-discovery-of-research-20201005",title:"IntechOpen joins Crossref's Initiative for Open Abstracts (I4OA) to Boost the Discovery of Research"},{slug:"intechopen-hits-milestone-5-000-open-access-books-published-20200908",title:"IntechOpen hits milestone: 5,000 Open Access books published!"},{slug:"intechopen-books-hosted-on-the-mathworks-book-program-20200819",title:"IntechOpen Books Hosted on the MathWorks Book Program"}]},book:{item:{type:"book",id:"1221",leadTitle:null,fullTitle:"Pituitary Adenomas",title:"Pituitary Adenomas",subtitle:null,reviewType:"peer-reviewed",abstract:"Pituitary Adenomas is a comprehensive book about the most common pathology of the pituitary gland in the sellar region. 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\r\n\tIn the last years, blockchain and distributed ledger technologies (DLTs) have evolved significantly with the objective of providing secure communications, data privacy, cyber-attack resilience and easy deployment/maintenance in multiple fields. Such an evolution, together with the advances in smart contracts, can play a relevant role in the future of decentralized applications. Moreover, Artificial Intelligence (AI) and its different sub-disciplines (e.g., Machine Learning, Deep Learning, Bayesian networks) can enhance blockchains and DLTs in order to provide advanced features like autonomous decision-support systems or intelligent blockchain-enabled services. Furthermore, AI-based applications can benefit from blockchains and DLTs thanks to their ability to allow for accessing trustworthy shared data in insecure environments like the Internet. In this complex scenario, this book looks for shedding light on the potential of the joint use of blockchain and AI, both from a theoretical and a practical point of view, in order to guide the next generation of researchers and developers of AI-enabled blockchain/DLT-based applications.
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Fernández-Caramés (Senior Member, IEEE) has\nworked since 2016 as an Assistant Professor in the area of\nElectronic Technology at the University of A Coruña (UDC)\n(Spain), where he obtained his MSc degree and PhD degrees in\nComputer Science in 2005 and 2011. Since 2005 he has worked\nin the Department of Computer Engineering at UDC: from 2005\nto 2009 through different predoctoral scholarships and between\n2007 and 2016 as Interim Professor. His current research interests include IoT/IIoT\nsystems, RFID, wireless sensor networks, augmented reality, embedded systems\nand blockchain, as well as the different technologies involved in the Industry 4.0\nparadigm. In such fields, he has contributed to 40 papers for conferences, to 35\narticles for JCR-indexed journals and to two book chapters. Due to his expertise in\nthe previously mentioned fields, he has acted as peer-reviewer and guest editor for\ndifferent top-rank journals, and as project reviewer for national research bodies\nfrom Austria, Croatia, Latvia and Argentina.",institutionString:"University of A Coruña",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"University of A Coruña",institutionURL:null,country:{name:"Spain"}}}],coeditorOne:{id:"193724",title:"Dr.",name:"Paula",middleName:null,surname:"Fraga-Lamas",slug:"paula-fraga-lamas",fullName:"Paula Fraga-Lamas",profilePictureURL:"https://mts.intechopen.com/storage/users/193724/images/system/193724.jpg",biography:"Paula Fraga-Lamas (Senior Member, IEEE) received her MSc\ndegree in computer engineering from the University of A\nCoruña (UDC) in 2009, and her MSc and PhD degrees in the\njoint program Mobile Network Information and Communication\nTechnologies from five Spanish universities: University of the\nBasque Country, University of Cantabria, University of Zaragoza, University of Oviedo, and University of A Coruña, in 2011\nand 2017, respectively. She holds an MBA and postgraduate studies in business\ninnovation management (Jean Monnet Chair in European Industrial Economics,\nUDC), Corporate Social Responsibility (CSR) and social innovation (Inditex-UDC\nChair of Sustainability). Since 2009, she has been with the Group of Electronic \nTechnology and Communications (GTEC), Department of Computer Engineering\n(UDC). She has over 70 contributions in indexed international journals, conferences and book chapters, and holds four patents. Her current research interests\ninclude Internet of Things (IoT), cyber-physical systems (CPS), augmented/mixed\nreality (AR/MR), fog and edge computing, blockchain and distributed ledger\ntechnology (DLT), cybersecurity, as well as the different technologies involved\nin mission-critical scenarios under the Industry 4.0 paradigm. She has also been\nparticipating in over 30 research projects funded by regional and national government as well as research and development contracts with private companies. She is\nactively involved in many professional and editorial activities, acting as reviewer,\nadvisory board member, topic/guest editor of top-rank journals and TPC member\nof international conferences",institutionString:"University of A Coruña",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"University of A Coruña",institutionURL:null,country:{name:"Spain"}}},coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"9",title:"Computer and Information Science",slug:"computer-and-information-science"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"297737",firstName:"Mateo",lastName:"Pulko",middleName:null,title:"Mr.",imageUrl:"https://mts.intechopen.com/storage/users/297737/images/8492_n.png",email:"mateo.p@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. Whether that be identifying an exceptional author and proposing an editorship collaboration, or contacting researchers who would like the opportunity to work with IntechOpen, I establish and help manage author and editor acquisition and contact."}},relatedBooks:[{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophanides",surname:"Theophile",slug:"theophanides-theophile",fullName:"Theophanides Theophile"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3092",title:"Anopheles mosquitoes",subtitle:"New insights into malaria vectors",isOpenForSubmission:!1,hash:"c9e622485316d5e296288bf24d2b0d64",slug:"anopheles-mosquitoes-new-insights-into-malaria-vectors",bookSignature:"Sylvie Manguin",coverURL:"https://cdn.intechopen.com/books/images_new/3092.jpg",editedByType:"Edited by",editors:[{id:"50017",title:"Prof.",name:"Sylvie",surname:"Manguin",slug:"sylvie-manguin",fullName:"Sylvie Manguin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3161",title:"Frontiers in Guided Wave Optics and Optoelectronics",subtitle:null,isOpenForSubmission:!1,hash:"deb44e9c99f82bbce1083abea743146c",slug:"frontiers-in-guided-wave-optics-and-optoelectronics",bookSignature:"Bishnu Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1373",title:"Ionic Liquids",subtitle:"Applications and Perspectives",isOpenForSubmission:!1,hash:"5e9ae5ae9167cde4b344e499a792c41c",slug:"ionic-liquids-applications-and-perspectives",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/1373.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"57",title:"Physics and Applications of Graphene",subtitle:"Experiments",isOpenForSubmission:!1,hash:"0e6622a71cf4f02f45bfdd5691e1189a",slug:"physics-and-applications-of-graphene-experiments",bookSignature:"Sergey Mikhailov",coverURL:"https://cdn.intechopen.com/books/images_new/57.jpg",editedByType:"Edited by",editors:[{id:"16042",title:"Dr.",name:"Sergey",surname:"Mikhailov",slug:"sergey-mikhailov",fullName:"Sergey Mikhailov"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"371",title:"Abiotic Stress in Plants",subtitle:"Mechanisms and Adaptations",isOpenForSubmission:!1,hash:"588466f487e307619849d72389178a74",slug:"abiotic-stress-in-plants-mechanisms-and-adaptations",bookSignature:"Arun Shanker and B. Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4816",title:"Face Recognition",subtitle:null,isOpenForSubmission:!1,hash:"146063b5359146b7718ea86bad47c8eb",slug:"face_recognition",bookSignature:"Kresimir Delac and Mislav Grgic",coverURL:"https://cdn.intechopen.com/books/images_new/4816.jpg",editedByType:"Edited by",editors:[{id:"528",title:"Dr.",name:"Kresimir",surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3621",title:"Silver Nanoparticles",subtitle:null,isOpenForSubmission:!1,hash:null,slug:"silver-nanoparticles",bookSignature:"David Pozo Perez",coverURL:"https://cdn.intechopen.com/books/images_new/3621.jpg",editedByType:"Edited by",editors:[{id:"6667",title:"Dr.",name:"David",surname:"Pozo",slug:"david-pozo",fullName:"David Pozo"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"40513",title:"Novel Formulation of Environmentally Friendly Oil Based Drilling Mud",doi:"10.5772/51236",slug:"novel-formulation-of-environmentally-friendly-oil-based-drilling-mud",body:'The term drilling fluids or drilling muds generally applies to fluids used to help maintain well control and remove drill cuttings (rock fragments from underground geological formations) from holes drilled in the earth. Drilling fluids are fluids used in petroleum drilling operations. These fluids are a mixture of clays, chemicals, water, oils. These fluids are used in a borehole during drilling operations for[1]:
Hole cleaning
Pressure control
Cooling and lubrication of the bit
Corrosion control (especially for oil-based muds)
Formation damage control
Wellbore stability maintenance
Transmission of hydraulic energy to BHA (Bottom Hole Assembly)
Aid in cementing operations
Minimize environmental impact
Inhibit gas hydrate formation in the well.
Avoid loss of circulation and seal permeable formations.
Considering each of the uses, the primary use of drilling fluids is to conduct rock cuttings within the well. If these cuttings are not transported up the annulus between the drillstring and wellbore efficiently, the drill string will become stuck in the wellbore. The mud must be designed such that it can, carry the cuttings to surface while circulating, suspend the cuttings while not circulating, and drop the cuttings out of suspension at surface [1-5].
The hydrostatic pressure exerted by the mud column must be high enough to prevent an influx of formation fluids into the wellbore, but the pressure should not be too high, as it may fracture the formation. The instability caused by the pressure differential between the borehole and the pore pressure can be overcome by increasing the mud weight. The hydration of the clays can only be overcome by using non water-based muds, or partially addressed by treating the mud with chemicals which will reduce the ability of the water in the mud to hydrate the clays in the formation. These muds are known as inhibited muds. While drilling, the rock cutting procedure generates a lot of heat which can cause the bits, and the entire BHA (Bottom Hole Assembly) wear out and fail, and the drilling muds help in cooling and lubricating the BHA. These fluids also help in powering the bottom hole tools. In cementing operations, drilling fluids are used to push and pump the cement slurry down the casing and up the annular space around the casing string in the hole.
The drilling fluid must be selected and or designed so that the physical and chemical properties of the fluid allow these functions to be fulfilled. However, when selecting the fluid, consideration must also be given to [5-6]:
The environmental impact of using the fluid
The cost of the fluid
The impact of the fluid on production from the reservoir
Drilling fluids are classified according to the continuous phase [1,3]
The WBM (Water Based Muds), with water as the continuous phase.
The OBM (Oil Based Muds), with oil as their continuous phase.
The Pneumatic fluids (with gases or gas-liquid mixtures as their continuous phase)
This chapter narrows our focus to oil based drilling fluids (OBM).
In general, OBM are drilling fluids which have oil as their dominant or continuous phase. A typical OBM has the following composition:
Clays and sand about 3%, Salt about 4%, Barite 9%, Water 30%, Oil 50-80%.
OBM have a whole lot of advantages over the conventional WBM. This is due to the various desirable rheological properties that oils exhibit. Since the 1930s, it has been recognized that better productivity is achieved by using oil rather than water as the drilling fluid. Since the oil is native to the formation it will not damage the pay zone by filtration to the same extent as would a foreign fluid such as water. We shall outline some of the desirable properties of oil based muds, which include [4]:
Shale Stability: OBM are most suited for drilling shaly formations. Since oil is the continuous phase & water is dispersed in it, this case results in non-reactive interactions with shale beds.
Penetration Rates: OBM usually allow for increased penetration rates.
Temperature: OBM can be used to drill formations where BHT (Bottom Hole Temperatures) exceed water based mud tolerances. Sometimes up to over 1000 degrees rankine.
Lubricity: OBM produce thin mud cakes, and the friction between the pipe and the well bore is minimized, thus reducing the pipe differential sticking. Especially suitable for highly deviated and horizontal wells.
Ability to drill low pore pressured formations is accomplished, since the mud weight can be maintained at a weight less than that of water (as low as 7.5 ppg).
Corrosion control: Corrosion of pipes is reduced since oil, being the external phase coats the pipe. This is due to the fact that oils are non conductive, thermally stable, and more often, do not permit microbial growth.
OBM can be re used, and can also be stored for a long period of time since microbial activity is suppressed.
The basic kind of oil used in formulating OBM is the diesel oil, which has been in existence for a long time, but over the years, diesel oil based muds have posed various environmental problems.
Water-based muds (WBMs) are usually the mud of choice in most drilling operation carried out in sandstone reservoir, however some unconventional drilling situations such as deeper wells, high temperature/pressure formation, deepwater reservoir, alternative shale-sand reservoir and shale resource reservoir require use of other mud systems such as oil based mud to provide acceptable drilling performance [5-8].
OBM is needed where WBM cannot be used especially in hot environment and salt beds where formation compositions can be dissolved in WBM. OBM have oil as their base and therefore more expensive and require more stringent pollution control measures than WBM.
It is imperative to propagate the use of environmentally friendly and biodegradable sources of oil to formulate our OBM, thereby making it less expensive and environmentally safe and equally carry out the basic functions of the drilling mud such as maintenance of hydrostatic pressure, removal of cuttings, cooling and lubricating the drill string and also to keep newly drilled borehole open until cementing is carried out.
Environmental problems associated with complex drilling fluids in general, and oil-based mud (OBM) in particular, are among the major concerns of world communities. Among others are the problems faced by some host communities in the Niger Delta region of Nigeria. For this reason, the Environmental Protection Agency (EPA) and other regulatory bodies are imposing increasingly stringent regulations to ensure the use of environmentally friendly muds [7-8].
Throughout the 1970s and 1980s, the EPA and other regulatory bodies imposed environmental laws and regulations affecting all aspects of petroleum-related operations from exploration, production and refining to distribution. In particular, there has been increasing pressure on oil and gas industry stakeholders to find environmentally acceptable alternatives to OBMs. This has been reflected in the introduction of new legislation by government agencies in almost every part of the world.
The researches and surveys conducted came up with possibilities of having environmentally friendly oil based mud. Stakeholders in the oil and gas industry have been tasked with the challenge of finding a solution to this problem by formulating optimum drilling fluids and also reduce the handling costs and negative environmental effects of the conventional diesel oil based drilling fluid. An optimum drilling fluid is one which removes the rock cuttings from the bottom of the borehole and carries them to the surface, hold cuttings and weight materials in suspension when circulation is stopped (e.g during shut in), and also maintain pressure. An optimum drilling fluid also does this at minimum handling costs, bearing in mind the HSE (Health, Safety, Environment) policy in mind [6].
In response to the harmful effects of diesel oil on the environment and on the ozone layer (as a result of the emission of greenhouse gases), researches and surveys have gone on in the past two to three decades, and have come up with mud formulations based on the use of plant oils as diesel substitutes. Over the years, plant oils have become increasingly popular in the raw materials market for diesel substitutes. The most popular being: Rapeseed oil, Jatropha oil, Mahua oil, Cottonseed oil, Sesame oil, Soya bean oil, palm oil etc. This brings about the importance of agro allied intervention in the energy industry. Hence, the contribution of non-edible oils such as jatropha oil, canola oil, algae oil, moringa seed oil and Soapnut will be significant as a plant oil source for diesel substitute production.
This chapter describes the formulation of environmental friendly oil based mud (using plant oil such as jatropha oil, algae oil and moringa seed oil) that can carry out the same functions as diesel oil based drilling fluid and equally meet up with the HSE (Health, Safety and Environment) standards. Mud tests have been carried out at standard conditions on each plant oil sample so as to ascertain the rheological properties of the drilling fluid formulations. The conventional diesel oil based mud would serve as control.
Drilling mud is in varying degrees of toxicity. It is difficult and expensive to dispose it in an environmentally friendly manner. Protection of the environment from pollutants has become a serious task. In most countries like Nigeria, the drilling fluids industries have had numerous restrictions placed on some materials they use and the methods of their disposal. Now, at the beginning of the 1990\'s, the restrictions are becoming more stringent and restraints are becoming worldwide issues. Products that have been particularly affected by restrictions are oil and oil-based mud. These fluids have been the mud of choice for many environments because of their better qualities. Initially, the toxicity of oil-based fluids was reduced by the replacement of diesel oil with low-aromatic mineral oils. In most countries today, oil-based mud may be used but not discharged in offshore or inland waters. Potential liability, latent cost, and negative publicity associated with an oil-mud spill are economic concerns. Consequently, there is an urgent need for the drilling fluids industry to provide alternatives to oil-based mud.
Four different mud samples were mixed, and the base fluid was varied. The base fluids were algae, moringa, diesel and jathropha oils used in formulating the muds in an oil water ratio of 70:30, where diesel based mud served as the control.
The following equipment and materials were used to carry out the experiment:
Materials | Equipment |
Pulverized bentonite Barite Diesel oil Canola oil Castor oil Jatropha seeds Water n-hexane Filter paper Threads Universal pH paper strips Algae | Weighing balance Retort Halminton Beach Mixer Condenser Mud balance Round bottom flask Rotary viscometer Resistivity meter API filter press pH meter Soxhlet extractor Heating mantle Vernier Caliper Reagent bottles |
The plant seeds (jatropha, moringa and algae) were collected from the western part of Nigeria, peeled and dried in an oven at about 55°C for seventy minutes. The dried seeds were then de-hulled, to remove the kernels. The brownish inner parts of the kernels were ground in a blender (to increase the surface area for the reaction).
The method employed in this study is solvent extraction. Solvent extraction is a process which involves extracting oil from oil-bearing materials by treating it with a low boiling point solvent as opposed to extracting the oils by mechanical pressing methods (such as expellers, hydraulic presses, etc.). The solvent extraction method recovers almost all the oils and leaves behind only 0.5% to 0.7% residual oil in the raw material. Here the equipment used was the Soxhlet extractor. A Soxhlet extractor is a piece of laboratory apparatus invented in 1879 by Franz von Soxhlet. It was originally designed for the extraction of a lipid from a solid material.
Soxhlet extractor assembly.
The extraction procedure is given below:
50g of crushed plant seeds were measured out, and tied in filter papers.
The sample was loaded into the main chamber of the Soxhlet extractor and poured in about 300ml of n-Hexane through the main chamber.
The chamber is fitted into a flask containing 300ml of n-Hexane.
The heating mantle was turned on and the system was left to heat at 70o C. The solvent was heated to reflux. The solvent vapour travelled up a distillation arm, and flooded into the chamber housing the solid wrapped in filter papers. The condenser condensed the solvent vapour, and the vapour dripped back down into the chamber housing the solid material.
Then at a certain level, the siphon emptied the liquid into the flask.
This cycle was repeated until the sample in the chamber changed colour to a considerable extent, and collected the fluid mixture in glass reagent bottles.
The mixture was separated via the use of simple distillation, as shown in the set up in Fig. 2.
The distillation took place at 70oC; the hexane was recovered and re-used while the oil was stored.
Set-up for distillation.
The densities of the various base fluids (water, algae oil, moringa oil, jatropha oil and diesel) were measured using the mud balance shown in diagram 3
Using the weighing balance, the various quantities of materials as shown in Table 2 below were measured.
The quantities of water and oil were measured using measuring beakers.
Using the Hamilton Beach Mixer, the measured materials were thoroughly mixed until a homogenous mixture was obtained.
The mud samples were aged for 24 hours.
Mud Balance
The aged mud samples were agitated for 2 minutes using the Hamilton Beach Mixer.
The clean, dry mud balance cup was filled to the top with the newly agitated mud.
The lid was placed on the cup and the balance was washed and wiped clean of overflowing mud while covering the hole in the lid.
The balance was placed on a knife edge and the rider moved along the arm until the cup and arm were balanced as indicated by the bubble.
The mud weight was read at the edge of the rider towards the mud cup as indicated by the arrow on the rider and was recorded.
Steps 2 to 5 were repeated for the other samples.
The mud was poured into the mud cup of the rotary viscometer shown in Diagram 4, and the rotor sleeve was immersed exactly to the fill line on the sleeve by raising the platform. The lock knot on the platform was tightened.
The power switch located on the back panel of the viscometer was turned on.
The speed selector knob was first rotated to the stir setting, to stir the mud for a few seconds, and it was rotated at 600RPM, waiting for the dial to reach a steady reading, the 600 RPM reading was recorded.
The above process was repeated for 300 RPM, 200 RPM, 100 RPM, 60 RPM, 30 RPM and 6 RPM.
Steps 7 to 10 were repeated for other samples.
Rotational Viscometer
The speed selector knob was then rotated to to stir the mud sample for a few seconds, then it was rotated to gel setting and the power was immediately shut off.
As soon as the sleeve stopped rotating, the power was turned on after 10 seconds and 10 minutes respectively. The maximum dial was recorded for each case.
Steps 12 and 13 were repeated for other samples.
The assembly is as shown in fig 5
Each part of the cell was cleaned, dried and the rubber gaskets were checked.
The cell was assembled as follows: base cap, rubber gasket, screen, filter paper, rubber gasket, and cell body.
API Filter Press
A freshly stirred sample of mud was poured into the cell to within 0.5 inch (13 millimeters) to the top in order to minimize contamination of the filtrate. The top cap was checked to ensure that the rubber gasket was in place and seated all the way around and complete the assembly. The cell assembly was placed into the frame and secured with the T-screw.
A clean dry graduated glass cylinder was placed under the filtrate exit tube.
The regulator T-screw was turned counter-clockwise until the screw was in the right position and the diaphragm pressure was relieved. The safety bleeder valve on the regulator was put in the closed position.
The air hose was connected to the designated pressure source. The valve on the pressure source was opened to initiate pressurization into the air hose. The regulator was adjusted by turning the T-screw clockwise so that a pressure was applied to the cell in 30 seconds or less. The test period begins at the time of initial pressurization.
At the end of 30 minutes the volume of filtrate collected was measured. The air flow through the pressure regulator was shut off by turning the T-screw in a counter-clockwise direction. The valve on the pressure source was then closed and the relief valve was carefully opened.
The assembly was then dismantled, and the mud was removed from the cup.
The filter cake was measured using a vernier caliper, and the measurements were recorded.
The above procedures were carried out for the other mud samples.
A short strip of pH paper was placed on the surface of the sample.
After the color of the test paper stabilized, the color of the upper side of the paper, which had not contacted the mud, was matched against the standard color chart on the side of the dispenser.
Steps 26 and 27 were carried out on other samples.
After the oil based mud samples have been formulated, each is then tested on a growing plant (that is on beans seedling), to see the effects on the plant growth and the living organisms in the soil. Bean seed was planted and exposed to 100ml of three different mud samples, with the following base fluids; diesel, canola and jatropha, the growth rate was measured, and the number of days of survival.
The results as obtained from measurements of density using the mud balance are contained in Table 2 below.
SAMPLE | MEASURED DENSITY (ppg) | CALCULATED DENSITY (ppg) | ERROR | Barite (g) |
Diesel | 8.26 | 8.261 | 0.01 | 119.1 |
Algae | 7.81 | 7.815 | 0.005 | 126.5 |
Jatropha | 8.32 | 8.326 | 0.06 | 154.5 |
Moringa | 8.30 | 8.307 | 0.007 | 149.3 |
Canola | 8.47 | 8.470 | 0 | 150.6 |
Mud density values
Mud density ρ is calculated using eqn
e.g for Jatropha
From the above table, the error differences between the calculated and measured densities all lie below 0.1, thus the readings obtained using the mud balance have a high accuracy. It also showed that the denser the base oil, the higher the amount of barite needed to build.
Viscosity readings obtained from the experiment carried out on the rotary viscometer are contained in Table 3.
The dial reading values (in lb/100ft2) are tabulated against the viscometer speeds in RPM.
Viscosity values are calculated with equations
Apparent viscosity= Dial Reading at 600RPM (θ600)/2
Dial speed (RPM) | Diesel | Algae | Jatropha | Moringa | Canola |
600 | 185 | 122 | 154 | 169 | 128 |
300 | 170 | 114 | 133 | 158 | 120 |
200 | 169 | 96 | 124 | 149 | 115 |
100 | 163 | 88 | 114 | 143 | 114 |
60 | 152 | 82 | 107 | 140 | 113 |
30 | 143 | 74 | 98 | 136 | 111 |
6 | 122 | 62 | 92 | 120 | 110 |
3 | 81 | 55 | 76 | 79 | 60 |
Viscometer Readings for Diesel, Jatropha and Canola OBM’s
Rheological Properties | Diesel | Algae | Jatropha | Moringa | Canola |
Plastic Viscosity | 15 | 8 | 21 | 11 | 8 |
Apparent Viscosity | 92.5 | 61 | 77 | 84.5 | 64 |
Gel Strength | 50/51 | 52/43 | 54/55 | 52/53 | 60/72 |
Plastic Viscosities, Apparent Viscosities, Gel Strength
Diesel OBM had the highest apparent viscosity, followed by Moringa, then Jatropha, Canola and algae OBM’s
Viscometer Plot for Diesel OBM
Viscometer Plot for Jatropha OBM
Viscometer Plot for Moringa OBM
Viscometer Plot for algae OBM
Viscometer Plot for Canola OBM
Combined viscometer plot for Diesel, Algae, and jatropha OBM’s
It can be seen that the plots on Figures 6 to 11, generated from the dial readings of all the mud samples are similar to the Bingham plastic model. This goes to prove that the muds have similar rheological behaviour.
However, not all the lines of the plot are as straight as the Bingham plastic model. This can be explained by a number of factors such as: possible presence of contaminants, and the possibility of behaving like a different model such as Herschel Bulkley.
A Bingham plastic fluid will not flow until the shear stress τ exceeds a certain minimum value τy known as the yield point9 (Bourgoyne et al 1991). After the yield has been exceeded, the changes in shear stress are proportional to changes in shear rate and the constant of proportionality is known as the plastic viscosity µp.
From Figures, the yield points of the different muds can be read off. The respective yield points are the intercepts on the vertical (shear stress) axes.
For reduced friction during drilling, algae OBM gives the best results, followed by Jatropha OBM then moringa OBM.
This means Diesel OBM offers the greatest resistance to fluid flow. Algae, Jatropha, Moringa and Canola OBM’s pose better prospects in the sense that their lower viscosities will mean less resistance to fluid flow. This will in turn lead to reduced wear in the drill string [10].
The filtration tests were carried out at 350 kPa due to the low level of the gas in the cylinder.
The mud cakes obtained from the API filter press exhibited a slick, soft texture.
From Table 5 and Figures 12 to 15, we can infer that Diesel OBM had the highest rate of filtration and spurt loss. Comparing this to a drilling scenario, this means that the mud cake from Diesel OBM is the most porous, and the thickest.
From these inferences, we can see that Algae, Jatropha, Moringa and Canola OBM’s are better in filtration properties than Diesel OBM as inferred from thickness and filtration volumes.
Filtration Volumes for Diesel, Algae, Jatropha and Moringa OBM’s
Filtration Volumes for Diesel, Jatropha and Canola OBM’s
Mud Cake Thicknesses for Diesel, Algae, Canola OBM’s
Mud Cake Thicknesses for Diesel, Jatropha and Canola OBM’s
Filtration Properties | DIESEL | ALGAE | JATROPHA | MORINGA | Canola |
Total Fluid Volume | 6.9ml | 6.2ml | 6.3ml | 7.2ml | 6.0 ml |
Oil volume | 2.3ml | 1.1ml | 1.1ml | 2.5ml | 1.0 ml |
Water Volume | 4.6ml | 5.1ml | 4.2ml | 4.7ml | 4.3 ml |
Cake Thickness | 1.0mm | 0.9mm | 0.8mm | 0.9mm | 0.78mm |
Mud Filtration Results
Problems caused as a result of excessive thickness include4:
Tight spots in the hole that cause excessive drag.
Increased surges and swabbing due to reduced annular clearance.
Differential sticking of the drillstring due to increased contact area and rapid development of sticking forces caused by higher filtration rate.
Primary cementing difficulties due to inadequate displacement of filter cake.
Increased difficulty in running casing.
The problems as a result of excessive filtration volumes include4:
Formation damage due to filtrate and solids invasion. Damaged zone too deep to be remedied by perforation or acidization. Damage may be precipitation of insoluble compounds, changes in wettability, and changes in relative permeability to oil or gas, formation plugging with fines or solids, and swelling of in-situ clays.
Invalid formation-fluid sampling test. Formation-fluid flow tests may give results for the filtrate rather than for the reservoir fluids.
Formation-evaluation difficulties caused by excessive filtrate invasion, poor transmission of electrical properties through thick cakes, and potential mechanical problems running and retrieving logging tools.
Erroneous properties measured by logging tools (measuring filtrate altered properties rather than reservoir fluid properties).
Oil and gas zones may be overlooked because the filtrate is flushing hydrocarbons away from the wellbore, making detection more difficult.
Drilling muds are always treated to be alkaline (i.e., a pH > 7). The pH will affect viscosity, bentonite is least affected if the pH is in the range of 7 to 9.5. Above this, the viscosity will increase and may give viscosities that are out of proportion for good drilling properties. For minimizing shale problems, a pH of 8.5 to 9.5 appears to give the best hole stability and control over mud properties. A high pH (10+) appears to cause shale problems.
The corrosion of metal is increased if it comes into contact with an acidic fluid. From this point of view, the higher pH would be desirable to protect pipe and casing (Baker Hughes, 1995).
The pH values of all the samples meet a few of the requirements stated but Diesel OBM with a pH of less than 8.5 does not meet with specification. Algae, Jatropha, Moringa and Canola OBM’s show better results since their pH values fall within this range.
Type of Oil | DIESEL | ALGAE | JATROPHA | MORINGA |
pH Value | 8 | 9 | 8.5 | 9 |
pH Values
Only three drilling-fluid parameters are controllable to enhance moving drilled solids from the wellbore:Apparent Viscosity (AV) density (mud weight [MW]), and viscosity. Cuttings Carrying Index (CCI) is a measure of a drilling fluid’s ability to conduct drilled cuttings in the hole. Higher CCI’s, mean better hole cleaning capacities.
From the Table, we can see that Jatropha OBM showed best results for CCI iterations.
Diesel | Jatropha | Canola | |
CCI | 15.901 | 19.067 | 17.846 |
Cuttings Carrying Indices (CCI’s)
The Bingham plastic model is the standard viscosity model used throughout the industry, and it can be made to fit high shear- rate viscosity data reasonably well, and is generally associated with the viscosity of the base fluid and the number, size, and shape of solids in the slurry, while yield stress is associated with the tendency of components to build a shear-resistant.
Diesel | Jatropha | Canola | |
Drill Pipe | 829 | 277.39 | 250.65 |
Drill Collar | 177.35 | 173.75 | 157.0 |
Drill Collar (Open) | 161.35 | 158.15 | 142.9 |
Drill Pipe (Open) | 14.1 | 13.81 | 12.48 |
Drill Pipe (Cased) | 9.28 | 9.10 | 8.22 |
Total | 1191.98 | 706.45 | 571.25 |
Bingham Plastic Pressure Losses in Psi
It can be seen from the table that Jatropha and Canola OBM’s gave better pressure loss results than Diesel OBM as a result of lower plastic viscosities, and hence should be encouraged for use during drilling activities.
Samples of 100ml of each of the selected oils were exposed to both corn seeds and bean seed and the no of days which the crop survived are as indicated in Figure 16. The growth rate was also measured i.e the new length of the plant was measured at regular time intervals. For the graph of toxicity of diesel based mud the reduced growth rate indicates when the leaves began to yellow, and the zero static values indicate when the plant died.
From the results indicated by the figure 16, it can be concluded that jatropha oil has less harmful effect on plant growth compared to canola and diesel. Bean seeds were planted and after one week, they were both exposed to 100ml of both jathropha formulated mud and diesel formulated mud. The seeds exposed to jatropha survived for 18 days, while that exposed to diesel mud survived for 6 days and then withered. When the soil was checked, there was no sign of any living organisms in diesel mud sample while that of the jatropha mud, there were signs of some living organisms such as earth worms, and other little insects. This shows that jatropha mud sample is environmentally safer for both plants and micro animals than diesel mud sample.
From the figure 17, it can be seen that the seeds exposed to jatropha had the highest number of days of survival which indicates its lower toxicity while that of diesel had the lowest days of survival which indicates its high toxicity. The toxicity of diesel can be traced to high aromatic hydrocarbon content. Therefore, replacements for diesel should either eliminate or minimize the aromatic contents thereby making the material non toxic or less toxic. Biodegradation and bioaccumulation however depend on the chemistry of the molecular character of the base fluids used. In general, green material i.e plant materials containing oxygen within their structure degrade easier.
Comparison of Growth Rate Curve of Different Mud Types
Densities were measured for the various samples at temperatures ranging from 30OC to 80OC and are summarized in Table 9.
Toxicity of different mud types
Temperature | Diesel | Jatropha | Canola |
30OC | 10 | 10 | 10 |
40OC | 10.1 | 10.05 | 10.05 |
50OC | 10.17 | 10.1 | 10.05 |
60OC | 10.2 | 10.15 | 10.1 |
70OC | 10.2 | 10.15 | 10.15 |
80OC | 10.25 | 10.2 | 10.17 |
Density Changes in ppg at Varying Temperatures.
The mud samples were heated at constant pressure, and in an open system, hence the density increment.
At temperatures of 60OC and 70OC, the densities of Diesel and Jatropha OBM’s were constant, while that happened with Canola OBM at a lower range of 40OC and 50OC. This is shown in Figure 18. This could be due to the differences in temperature and heat energy required to dissipate bonds, which vary with fluid properties (i.e the continuous phases).
Density against Temperature (Diesel, Jatropha and Canola OBM’s)
After the results were recorded, extrapolations were made and hypothetical values were derived for temperatures as high as 320OC, to enhance the prediction using Artificial Neural Network (ANN).
These values are summarized Tables 10 to 12
Diesel | Jatropha | Canola | |
30OC | 10 | 10 | 10 |
40OC | 10.1 | 10.05 | 10.05 |
50OC | 10.17 | 10.1 | 10.05 |
60OC | 10.2 | 10.15 | 10.1 |
70OC | 10.2 | 10.15 | 10.15 |
80OC | 10.25 | 10.2 | 10.17 |
90OC | 10.31133 | 10.24333 | 10.20667 |
100OC | 10.35648 | 10.2819 | 10.24095 |
110OC | 10.40162 | 10.32048 | 10.27524 |
120OC | 10.44676 | 10.35905 | 10.30952 |
130OC | 10.4919 | 10.39762 | 10.34381 |
140OC | 10.53705 | 10.43619 | 10.3781 |
150OC | 10.58219 | 10.47476 | 10.41238 |
160OC | 10.62733 | 10.51333 | 10.44667 |
170OC | 10.67248 | 10.5519 | 10.48095 |
180OC | 10.71762 | 10.59048 | 10.51524 |
190OC | 10.76276 | 10.62905 | 10.54952 |
200OC | 10.8079 | 10.66762 | 10.58381 |
210OC | 10.85305 | 10.70619 | 10.6181 |
220OC | 10.89819 | 10.74476 | 10.65238 |
230OC | 10.94333 | 10.78333 | 10.68667 |
240OC | 10.98848 | 10.8219 | 10.72095 |
250OC | 11.03362 | 10.86048 | 10.75524 |
260OC | 11.07876 | 10.89905 | 10.78952 |
270OC | 11.1239 | 10.93762 | 10.82381 |
280OC | 11.16905 | 10.97619 | 10.8581 |
290OC | 11.21419 | 11.01476 | 10.89238 |
300OC | 11.25933 | 11.05333 | 10.92667 |
310OC | 11.30448 | 11.0919 | 10.96095 |
320OC | 11.34962 | 11.13048 | 10.99524 |
Hypothetical Temperature-Density Values (extrapolated from regression analysis).
From the Artificial Neural Network Toolbox in the MATLAB 2008a, the following results were obtained:
60% of the data were used for training the network, 20% for testing, and another 20% for validation.
On training the regression values, returned values are summarized in Table 11
Diesel | Jatropha | Canola | |
Training | 0.99999 | 0.99999 | 0.99995 |
Testing | 0.99725 | 0.99056 | 0.99898 |
Validation | 0.99706 | 0.98201 | 0.99328 |
All | 0.99852 | 0.99414 | 0.99675 |
Regression Values.
Since all regression values are close to unity, this means that the network prediction is a successful one.
The graphs of training, testing and validation are presented below:
The values were returned after performing five iterations for each network. This also goes to say that the Artificial Neural Network, after being trained and simulated, is a viable and feasible instrument for prediction.
Figures 19 to 31 present the plots of Experimental data against Estimated (predicted) data for training, testing and validation processes from MATLAB 2008.
Diesel OBM Validation values
Diesel OBM Test values
Diesel OBM Training values
Diesel OBM Overall values
Diesel OBM Overall values
Jatropha OBM Validation values
Jatropha OBM Test values
Jatropha OBM Training values
Jatropha OBM Overall values
Canola OBM Validation values
Canola OBM Test values
Canola OBM Training values
Canola OBM Overall values
We can see from the Figures 19 to 31 that the data points all align closely with the imaginary/arbitrary straight line drawn across. This validates the accuracy of the network predictions and this also gives rise to the high regression values (tending towards unity) presented in Table 11
Errors, estimated values and experimental values are summarized in Tables 12 to 14
Temperature oC | Exp Values | Est Values | Errors |
30 | 10 | 10.049 | 0.049 |
40 | 10.1 | 10.1407 | 0.0407 |
50 | 10.17 | 10.1794 | 0.0094 |
60 | 10.2 | 10.2022 | 0.0022 |
70 | 10.2 | 10.2236 | 0.0236 |
80 | 10.25 | 10.24 | -0.01 |
90 | 10.31133 | 10.287 | -0.02433 |
100 | 10.35648 | 10.3579 | 0.001424 |
110 | 10.40162 | 10.3904 | -0.01122 |
120 | 10.44676 | 10.4222 | -0.02456 |
130 | 10.4919 | 10.4835 | -0.0084 |
140 | 10.53705 | 10.5204 | -0.01665 |
150 | 10.58219 | 10.5455 | -0.03669 |
160 | 10.62733 | 10.6133 | -0.01403 |
170 | 10.67248 | 10.687 | 0.014524 |
180 | 10.71762 | 10.7202 | 0.002581 |
190 | 10.76276 | 10.7714 | 0.008638 |
200 | 10.8079 | 10.8335 | 0.025595 |
210 | 10.85305 | 10.8611 | 0.008052 |
220 | 10.89819 | 10.8991 | 0.00091 |
230 | 10.94333 | 10.9623 | 0.018967 |
240 | 10.98848 | 10.9955 | 0.007024 |
250 | 11.03362 | 11.0273 | -0.00632 |
260 | 11.07876 | 11.085 | 0.006238 |
270 | 11.1239 | 11.1195 | -0.0044 |
280 | 11.16905 | 11.1474 | -0.02165 |
290 | 11.21419 | 11.2049 | -0.00929 |
300 | 11.25933 | 11.2432 | -0.01613 |
310 | 11.30448 | 11.2545 | -0.04998 |
320 | 11.34962 | 11.2674 | -0.08222 |
Errors, Experimental Values, and Estimated Values for Diesel OBM
Temperature oC | Exp Values | Est Values | Errors |
30 | 10 | 10 | 0 |
40 | 10.05 | 10.05 | 0 |
50 | 10.1 | 10.0998 | -0.0002 |
60 | 10.15 | 10.1485 | -0.0015 |
70 | 10.15 | 10.2556 | 0.1056 |
80 | 10.2 | 10.3232 | 0.1232 |
90 | 10.24333 | 10.3143 | 0.070967 |
100 | 10.2819 | 10.2851 | 0.003195 |
110 | 10.32048 | 10.281 | -0.03948 |
120 | 10.35905 | 10.3147 | -0.04435 |
130 | 10.39762 | 10.3985 | 0.000881 |
140 | 10.43619 | 10.4526 | 0.01641 |
150 | 10.47476 | 10.4769 | 0.002138 |
160 | 10.51333 | 10.5126 | -0.00073 |
170 | 10.5519 | 10.5544 | 0.002495 |
180 | 10.59048 | 10.5884 | -0.00208 |
190 | 10.62905 | 10.63 | 0.000952 |
200 | 10.66762 | 10.6665 | -0.00112 |
210 | 10.70619 | 10.7025 | -0.00369 |
220 | 10.74476 | 10.741 | -0.00376 |
230 | 10.78333 | 10.7559 | -0.02743 |
240 | 10.8219 | 10.7655 | -0.0564 |
250 | 10.86048 | 10.803 | -0.05748 |
260 | 10.89905 | 10.8872 | -0.01185 |
270 | 10.93762 | 10.9375 | -0.00012 |
280 | 10.97619 | 10.9644 | -0.01179 |
290 | 11.01476 | 11.0148 | 3.81E-05 |
300 | 11.05333 | 11.0533 | -3.3E-05 |
310 | 11.0919 | 11.0747 | -0.0172 |
320 | 11.13048 | 11.1305 | 2.38E-05 |
Errors, Experimental Values, and Estimated Values for Jatropha OBM
Temperature oC | Exp Values | Est Values | Errors |
30 | 10 | 9.8841 | -0.1159 |
40 | 10.05 | 10.0044 | -0.0456 |
50 | 10.05 | 10.048 | -0.002 |
60 | 10.1 | 10.0925 | -0.0075 |
70 | 10.15 | 10.1449 | -0.0051 |
80 | 10.17 | 10.1681 | -0.0019 |
90 | 10.20667 | 10.1987 | -0.00797 |
100 | 10.24095 | 10.2489 | 0.007948 |
110 | 10.27524 | 10.2745 | -0.00074 |
120 | 10.30952 | 10.2972 | -0.01232 |
130 | 10.34381 | 10.3445 | 0.00069 |
140 | 10.3781 | 10.377 | -0.0011 |
150 | 10.41238 | 10.4003 | -0.01208 |
160 | 10.44667 | 10.4539 | 0.007233 |
170 | 10.48095 | 10.4994 | 0.018448 |
180 | 10.51524 | 10.519 | 0.003762 |
190 | 10.54952 | 10.5537 | 0.004176 |
200 | 10.58381 | 10.5952 | 0.01139 |
210 | 10.6181 | 10.6145 | -0.0036 |
220 | 10.65238 | 10.6444 | -0.00798 |
230 | 10.68667 | 10.6888 | 0.002133 |
240 | 10.72095 | 10.7105 | -0.01045 |
250 | 10.75524 | 10.7365 | -0.01874 |
260 | 10.78952 | 10.7895 | -2.4E-05 |
270 | 10.82381 | 10.8224 | -0.00141 |
280 | 10.8581 | 10.8465 | -0.0116 |
290 | 10.89238 | 10.8971 | 0.004719 |
300 | 10.92667 | 10.9337 | 0.007033 |
310 | 10.96095 | 10.945 | -0.01595 |
320 | 10.99524 | 10.9562 | -0.03904 |
Errors, Experimental Values, and Estimated Values for Canola OBM
The minute errors encountered in the predictions further justify the claim that the ANN is a trust worthy prediction tool.
The Experimental outputs were then plotted against their corresponding temperature values, and also fitted into the polynomial trend line of order 2.
The Equations derived are7:
Diesel OBM:
Jatropha OBM:
Canola OBM:
Also by comparing the networks created with that of Osman and Aggour12 (2003), we can see that this work is technically viable in predicting mud densities at varying temperatures as the network developed in the course of this project showed regression values close to those proposed by Osman and Aggour [12].
Errors, percentage errors and average errors as compared with Osman and Aggour12 are relatively lower, thus guaranteeing the accuracy of the newly modeled network.
Table 15 shows the regression values of Osman and Aggour for oil based mud density variations with temperature and pressure [12].
Training | Testing | Validation | All |
0.99978 | 0.99962 | 0.99979 | 0.9998 |
Table Showing the Regression Values from Osman and Aggour [12]
Temperature | Diesel | Jatropha | Canola |
30 | 0.49 | 0 | 1.159 |
40 | 0.40297 | 0 | 0.453731 |
50 | 0.092429 | 0.00198 | 0.0199 |
60 | 0.021569 | 0.014778 | 0.074257 |
70 | 0.231373 | 1.040394 | 0.050246 |
80 | 0.097561 | 1.207843 | 0.018682 |
90 | 0.235986 | 0.692808 | 0.078054 |
100 | 0.013748 | 0.031076 | 0.077606 |
110 | 0.107859 | 0.382504 | 0.007183 |
120 | 0.235115 | 0.428105 | 0.119538 |
130 | 0.080107 | 0.008473 | 0.006675 |
140 | 0.157991 | 0.157237 | 0.010553 |
150 | 0.346719 | 0.020412 | 0.116025 |
160 | 0.132049 | 0.006975 | 0.069241 |
170 | 0.136087 | 0.023647 | 0.176011 |
180 | 0.024081 | 0.019604 | 0.035776 |
190 | 0.080259 | 0.00896 | 0.039587 |
200 | 0.23682 | 0.01049 | 0.107622 |
210 | 0.074195 | 0.03447 | 0.03386 |
220 | 0.008346 | 0.035012 | 0.074922 |
230 | 0.173317 | 0.254405 | 0.019963 |
240 | 0.06392 | 0.521209 | 0.097495 |
250 | 0.057271 | 0.529223 | 0.174223 |
260 | 0.056307 | 0.108703 | 0.000221 |
270 | 0.039597 | 0.001088 | 0.013022 |
280 | 0.193818 | 0.107419 | 0.106789 |
290 | 0.082846 | 0.000346 | 0.043324 |
300 | 0.143289 | 0.000302 | 0.064369 |
310 | 0.442092 | 0.155111 | 0.145538 |
320 | 0.724421 | 0.000214 | 0.355045 |
Table of the Relative Deviations
Table 17 compares the Average Absolute Percent Error abbreviation (AAPE), Maximum Average relative deviation (Ei) and Minimum Ei for Diesel, Jatropha and Canola OBM’s as well as the values from Osman and Aggour.
Diesel | Jatropha | Canola | Osman et al | |
Minimum Ei | 0.008346 | 0.000214 | 0.000221 | 0.102269 |
Maximum Ei | 0.724421 | 1.207834 | 1.159 | 1.221067 |
AAPE | 0.172738 | 0.193426 | 0.124949 | 0.36037 |
Table Comparing Maximum Ei, Minimum Ei, and AAPE
The lower viscosities of jatropha, moringa and canola oil based mud (OBM’s) make them very attractive prospects in drilling activities.
The results of the tests carried out indicate that jatropha, moringa and canola OBM’s have great chances of being among the technically viable replacements of diesel OBM’s. The results also show that additive chemistry must be employed in the mud formulation, to make them more technically feasible. In addition, the following conclusions were drawn:
From the viscosity test results, it can be inferred that the plastic viscosity of jatropha OBM can be further stepped down by adding an adequate concentration of thinner. This method can also be used to reduce the gel strengths of jatropha, moringa and canola OBM’s.
The formulated drilling fluids exhibited Bingham plastic behavior, and from the pressure loss modeling, canola OBM gave the best results, and next was jatropha OBM.
The tests of temperature effects on density: The densities increased and became constant at some point, and began increasing again (these temperature points of constant density varied for the different samples). The diesel OBM showed the highest variation range, while the canola OBM showed the lowest.
Artificial Neural Network works well for prediction of scientific parameters, due to minimized errors returned.
The temperature-density tests were carried out at surface conditions under an open system and at a constant pressure due to the absence of a pressure unit thus, the equations developed are not guaranteed for down-hole circulating conditions.
During the temperature-density tests, it was observed that some of the mud particles settled at the base of the containing vessel, and this reduced the accuracy of the readings.
The accuracy of the temperature-density readings is also reduced because of the use of an analogue mud balance (calibrated to the nearest 0.1 ppg).
The mud samples were aged for only 24 hours, hence the feasibility of older muds may not be guaranteed.
This work should further be tested and investigated for the effect of temperature on other properties of the formulated drilling fluids.
The temperature-density tests should also be carried out at varying pressures, to simulate downhole conditions.
We wish to thank all members of staff Department of Petroleum Engineering Covenant University, Nigeria for their technical support in carrying out this research work especially Mr Daramola. We also acknowledge the support of Environmental Research Group, Father-Heroes Forte Technology Nigeria for their commitment.
Blood is known to be one of the connective tissues in the human body as the blood connects every single cell, tissue, and organ in the body together [1]. All necessary substances are transported through the vascular system. The science of blood flow and the mechanics of blood flow is known as hemodynamics. Hemodynamics is a significant element of cardiovascular mechanics and engineering as it simply clarifies the physical laws that direct the bloodstream within the blood vessels [2]. A considerable number of dysfunctions that occur due to cardiovascular diseases and disorders such as hypertension and congestive heart failure are linked to systemic hemodynamics. For example, clinical studies advocate that local wall shear stress rates and forms moderate the location and the advancement of atherosclerotic plaques.
\nThe shear stress and wall shear stress in the bloodstream throughout the entire cardiovascular system is significantly impacted by the physics and mechanics of the blood. In particular, these values play and important role in the design and development of medical devices for cardiovascular applications. In the area of cardiovascular engineering and technology and medical devices, hemodynamics and the mechanics of blood are undisputedly vital to be well understood and considered.
\nThe major components of blood are considered to be plasma, RBCs, WBCs, and platelets. The liquid component of the blood, known as plasma, is made up of water, salt, sugar, fat, and protein and is responsible for transportation of blood cells throughout the body. Antibodies, oxygen, waste products, chemical messengers such as hormones and proteins, and clotting proteins are also transported throughout the body within the blood. WBCs which are responsible for protection of the body against infection are much fewer in number than RBCs (1 to ~800)[3]. Platelets are not considered as cells but rather a small fragment of cells. Platelets mainly participate in the clotting process, also known as coagulation. They gather at the site of injury and stick to the lining of the injured vessel, forming a frame-like structure on which blood coagulation can take place. This process is also known as the formation of a fibrin clot in which the wound is covered and the leakage of blood is stopped. Fibrin also contributes to the structure of a scaffold upon which new tissue can grow and form, a step known as the healing process.
\nRed blood cells: RBCs are about 40–45% of the blood volume. Their shape is like a biconcave disk with a flattened center [4]. RBCs are made up of a compound known as hemoglobin which is a protein for carrying oxygen. Hemoglobin consists of two alpha subunits and two beta units. Each subunit holds a heme group and each heme holds a Fe2+ ion which can bind to an O2 molecule. Oxyhemoglobin is essentially a hemoglobin that has molecules for bonding to oxygen molecules. RBCs undergo two major states while circulating through the body, oxygenated and deoxygenated. Oxygenated cells are bright red color and contain large quantities of oxyhemoglobin. They circulate through the body to deliver oxygen to the body tissues. When an RBC reaches the intended tissue, oxygen molecules are removed from hemoglobin. The first two O2 molecules are easier to remove than last two, which cause a gradient of release. Deoxygenated cells have less oxyhemoglobin existing in the hemoglobin compound, however blood is never actually deoxygenated as not all oxygen is removed [5].
\nBlood disorders: Some common blood disorders include anemia, malaria, and cancer. Anemia occurs when the number of red blood cells is comparatively low. Common causes of anemia include iron deficiency, B12 deficiency, chronic diseases of the kidney or bones, and red blood cell destruction due to shearing forces. Malaria is a mosquito-borne infectious disease. Minor symptoms include fever, fatigue, vomiting, and headaches, however severe symptoms can include seizures, coma, and possibly death. The main cancer associated with blood is leukemia which begins in the bone marrow and results in high numbers of abnormal white blood cells. These abnormal white blood cells, known as blasts, are not fully developed and cannot function properly. This causes symptoms such as bleeding and bruising, fatigue, fever, and an increased risk of infection. Certain disorders such as thalassemia and leukemia have varying types for which symptoms and ideal treatment varies.
\nThalassemia: Thalassemia is an inherited blood disorder where the hemoglobin produced by the body is abnormal and does not function properly. Thalassemia develops due to a genetic mutation in one of the genes involved in the production of hemoglobin. The disorder leads to the destruction of RBCs which results in anemia [6].
\nThere are three main forms of thalassemia known as alpha thalassemia, beta thalassemia, and thalassemia minor [6]. Thalassemia minor is the less severe form, whereas alpha thalassemia and beta thalassemia are more serious conditions. Alpha thalassemia occurs when at least one of the alpha globin genes has a mutation, while beta thalassemia occurs due to mutations in the beta globin genes. Alpha and beta thalassemia also have two subtypes. Beta thalassemia has the subtypes major and intermedia. Beta thalassemia major is the more severe form of this disease and is generally diagnosed early on when the child is in infancy. Patients with beta thalassemia major have a complete lack of beta globin genes and experience the most severe symptoms. In addition to severe anemia which can be life-threatening, other symptoms may include paleness, poor appetite, jaundice, and enlarged organs [6]. Beta thalassemia intermedia is the less severe form of beta thalassemia and arises due mutations in the beta globin genes. Unlike beta thalassemia major, the genes are present within the DNA just not in their normal form.
\nAlpha thalassemia has the two subtypes hemoglobin H and hydrops fetalis. Hemoglobin H is developed when a patient is missing up to three alpha globin genes or has mutations in up to these alpha globin genes. Complications from this disease can cause bone issues where the cheeks, forehead, and jaw overgrow. Individuals may also experience jaundice, malnourishment, and an extremely enlarged spleen [6]. Alpha hydrops fetalis thalassemia is an extremely severe form of the disease and occurs in the developing fetus. This condition develops when all four alpha globin genes are altered or missing [6]. Due to such early development babies with this form of thalassemia are usually stillborn or die shortly after birth.
\nThe only treatment for patients with thalassemia is regular blood transfusions every 2–4 weeks. This causes the patients to have excess iron which can cause iron overload in the body and lead to dangerous side effects. Due to this, patients also need to take drugs called iron-chelating agents that bind to excess iron and help the body remove it from their systems [7, 8].
\nLeukemia: In leukemia the DNA of immature blood cells, most commonly, WBCs is damaged which causes WBCs to grow and replicate continuously. Unlike healthy WBCs, these abnormal blood cells continue to accumulate in the bloodstream, forcing out healthy cells. As the damaged WBCs grow, they start to affect the normal functions of healthy WBCs by filling up large amounts of space within the blood. Individuals with leukemia generally suffer from poor blood clotting, anemia, and weak immune systems. Other symptoms that can be experienced are nausea, fever, chills, night sweats, flu-like symptoms, weight loss, bone pain, and tiredness [8].
\nLeukemia is split into two sets of types classified as acute or chronic and lymphocytic or myelogenous. Chronic leukemia is a rapidly moving form of cancer, whereas acute leukemia progresses significantly slower. Once divided into either acute or chronic, leukemia is then subdivided by the type of affected blood cell. Lymphocytic leukemia describes cancerous cells affecting the bone marrow that makes lymphocytes. Whereas myelogenous leukemia covers cancers that occur in the bone marrow that produce other types of white blood cells, platelets, and RBCs.
\nTreatments for leukemia vary depending on the classification of the cancer as well as the age and general health of the patient. Treatments for acute leukemia should be started as soon as possible due to the aggressive nature of the cancer and include chemotherapy and bone marrow transplants. Chronic leukemia is treated differently depending on the stage. The types of treatment include targeted therapy, interferons, chemotherapy, radiation therapy, and stem cell transplants [8].
\nThe life cycle of RBCs: RBCs are produced in the bone marrow through a process known as erythropoiesis. The production of RBCs involves erythropoietin, monosaccharides, lipids, vitamin B12, amino acids, folic acid, and iron. RBCs are released into the bloodstream once they are developed and have a lifespan of 120 day after which they expire due to mechanical or structural damage. Dead RBCs are then removed from the bloodstream through the spleen, liver, and bone marrow. The dead cells are crushed into their main components known as heme, comprised of iron and bilirubin, and globin, comprised of amino acids. Amino acids and iron are reused by bone marrow, whereas the bilirubin is removed through feces and urine [9].
\nRBC configuration: RBCs have a discrete biconcave shape, similar to a disk (Figure 1). Normal cells are 7.5–8.0 μm in diameter and ~2.0 μm in height, however RBCs must adapt their shape in order to pass through capillaries as some capillaries are only ~3 μm wide [9]. This adaptation means RBCs feel significant passive deformation through their 120-day lifespan. The properties of a RBC must be physically and mechanically stable so that to resist disintegration and the mechanical properties have to do significantly with their deformation in terms of the bending, shear, area expansion moduli, and relaxation times.
\nThe geometry of human RBCs. (a) The normal biconcave-shaped RBC cut in half along the y-z plane. (b) A RBC with the cytoplasm [10].
Microfluidic channels are implemented to simulate human capillaries and study RBC deformability. Microfluidic channel are formed from stationary DPD particles and are filled with fluid particles containing RBCs. The microfluidic channels are comprised of two wide channels on each other edge and a cuboid channel in between. The wide channels are normally 20.0 μm wide and 3.0 μm high whereas the cuboid channel is 4.0 μm wide, 3.0 μm high, and 30.0 μm long [9]. Adaptive boundary conditions for fluid DPD particles are used to control density fluctuations. An individual RBC undergoes a continuous and severe transition from its normal biconcave shape to an ellipsoidal shape. This deformation includes elongation in the flow direction (longitudinal axis), and shortening in the cross-flow direction (transverse axis). The RBC enters into the narrow channel by undergoing these deformations. Once the entire RBC enters the constriction, it deforms further to pass through the microfluidic channel.
\nRBC deformation: Three distinct cellular components contribute to RBC’s deformability—cell geometry, cytoplasm viscosity, and membrane elasticity.
\nCell geometry: The geometry of a RBC determines the ratio of cell surface area to cell volume. The deformation of a cell is directly related to the surface area to cell volume ratio. Therefore, cells that achieve a higher value of surface area to volume ratio can deform with more ease.
\nCytoplasm viscosity: The cytoplasm viscosity is regulated by the mean corpuscular hemoglobin concentration (MCHC). This suggests that the viscosity is directly related to alterations in cell volume. Therefore, cells with greater volumes have a higher cytoplasm viscosity and will deform with more difficulty.
\nMembrane elasticity: The membrane of RBCs consists of a lipid bilayer supported by an attached spectrin-based cytoskeleton. The resistance of the lipid bilayer to bending elasticity is controlled by the bending rigidity (kc). The spectrin network’s resistance to shear strain is characterized by the in-plane shear modulus, (μs). The deformability of the membrane, along with the mechanical stability of the cell, can be attributed to the elastic modulus, bending modulus, and yield stress.
\nMembrane simulations: RBC membrane properties can reveal the complex behavior that takes place within the membrane when it deforms. Twisting torque cytometry (TTC) is used to simulate membrane rheology and can obtain RBC membrane properties such as yield stress, shear thinning, and viscoelasticity. A microbead is bonded to the surface of a cell membrane and a magnetic twisting cytometry applies both static and oscillating magnetic field. The wall adhesion is simulated by keeping 15% of vertices stationary on the bottom of the lipid bilayer component of the RBC membrane. Microbead adhesion is simulated by including several RBC vertices in the lipid bilayer component near the bottom of the microbead in its rigid motion.
\nThe complex elastic moduli of a RBC can be computed from the phase angle between the storage and loss moduli as such:
\nHere, \n
Blood circulation: Blood flow in the circulatory system is determined by the pulsing drive that is developed from the heart, the individual mechanical and flow properties of the fluid, and the structure and mechanical properties of blood vessels. These factors combined at appropriate levels ensure that the cells of the body receive adequate amounts of oxygen as well as maintain waste management.
\nFlow pulse development: The main function of the heart is to circulate blood throughout the human body. It is composed of four chambers: two chambers known as ventricles on the lower half of the heart and two chambers known as atria composing the upper section as shown in Figure 3. Upon the propagation of bioelectricity through these different components of the heart, contraction of each chamber occurs, moving blood throughout the body in a system known as the cardiac cycle [11]. The cardiac cycle can be easily separated into two main time events: systole and diastole. These two events refer to the action of either the heart pumping blood into the circulatory system or receiving blood from the venous system. In addition to these events, other factors of the blood such as velocity are initialized from the cardiac output of the heart.
\nSystole and diastole: Systole is when the pressure in the circulatory system is the highest due to the force of the heart that is used to pumping blood into the aorta and pulmonary artery, whereas during diastole the blood is moved into the heart due to a pressure difference between the vena cava and the right atrium, and pressure is lowest [12]. These periodic variations in pressure is what causes blood flow to be considered “pulsatile” [13]. This pulsatile action is what makes blood flow unable to be effectively modeled by standard flow models unless specific assumptions are applied.
\nCardiac output: The amount of blood that flows out of the heart in 1 minute is known as the cardiac output and varies dependent on the weight of an individual. Standard values of cardiac output are within the range 4.0–8.0 L/min [13]. Cardiac output is dependent on four main components: heart rate, contractility, preload, and afterload.
\nThe heart rate is directly proportional to the velocity of the blood moving throughout the body because under normal circumstances the blood maintains a constant volume. As heart rate increases, the velocity increases, which affects the viscosity and turbulent effects of the flow. A similar relationship is seen with contractility as the greater the force the heart initially enacts while emptying the left ventricle, the larger the pressure will be that is pushing the blood from the heart, increasing the initial blood velocity. Preload is the degree of myocardial extension prior to shortening which maintains a direct relationship with cardiac output [3]. Afterload is the force that the ventricle must overcome in order to push the blood into the system of blood vessels. These components combined affect the initial velocity, pressure, and forces applied on the blood flow. However, this system is only effective if the components of the heart are working properly and can differ if there are defects present in the heart.
\nHeart murmurs: A heart murmur is a sound that is developed in the heart that occurs due to the presence of turbulent flow near the heart valves [14]. Heart murmurs can be classified into two main types: innocent and abnormal murmurs. These murmurs can then be categorized based on if they occur during systole or diastole and by what type of flow characteristic they possess, namely, regurgitation or ejection [15]. Due to the fact that heart murmurs occur due to turbulent flow, they have a tendency to be increased in those who are diagnosed with anemia (due to the decreased hematocrit) and those with heart valve defects. In addition to these two cases, anything that causes irregular or disturbed flow has the potential to cause increased turbulence in the flow and therefore increase the possibility of heart murmurs. Examples of such include heart valve replacements which introduce new stresses and area contact points in the flow and heart valve infections which cause inflammation [15].
\nBlood circulation begins by the heart pumping deoxygenated RBCs to the lungs which are then oxygenated and released back to the heart through the pulmonary circuit. These oxygenated RBCs are then pumped through the systemic circuit to deliver oxygen to various tissues. The RBCs become deoxygenated after releasing and depositing oxygen within the tissues and travel back to the heart through the system circuit to repeat the cycle.
\nThe flow of fluid within the circulatory system is dependent on a variety of factors but can be characterized by considering the laminar and turbulent properties of the flow. In addition to the laminar and turbulent properties of the flow, it is also important to consider the motion of the suspended particles within the heterogenous fluid allowing it to cohere to adequate blood flow needs.
\nLaminar and turbulent blood flow: In homogeneous fluids, flows are laminar up to a Reynolds number of roughly 2300 and become turbulent at a Reynolds number of 4000. This logic cannot be applied to the flow of blood as blood is not a homogenous fluid and blood vessels are not perfectly cylindrical and possess viscoelastic properties. Though directly corresponding to Reynolds numbers will not accurately represent the type blood flow, it is generally considered that the possibility of turbulence will increase as the Reynolds number increases, regardless of the precise critical Reynolds number values for transition. Adhering to this logic, as seen in Eq. (3), the possibility for turbulent flow will increase as the velocity increases, the diameter increases, the density increases, or as the viscosity decreases:
\nAs a flow develops into turbulence unsteady vortices appear and interact with each other leading to the development of eddy currents, small currents where the flow differs from that of the general flow. Turbulence occurs naturally in locations of the circulatory system where the Reynolds numbers are comparatively elevated such as the ventricles and ascending aorta. In addition to this, turbulence can also be initiated due to branches or curves in the flow, irregularities due to surgical implants, and improper function of circulatory valves [16]. Under diseased or abnormal conditions, other segments in the circulatory system can experience turbulent flow which can have negative effects on epithelial function [16].
\nIn individuals with conditions affecting the viscosity of their blood, such as anemia, due to the decreased hematocrit in the blood the opportunity for one’s blood to enter turbulence is increased [5]. Other individuals experience increased turbulence opportunity due to a foreign object being placed in the circulatory system such as a replaced heart valve, as turbulence is developed from an increased contact area between the blood flow and the valve. Due to the chaotic nature of the flow, turbulent flows require more energy to properly travel throughout the system as much of the energy is lost due to misaligned flows and eddy currents. Even though turbulent flows occur in the circulatory system, RBCs go through a series of motions and deformations which help sustain the efficiency of blood flow.
\nMovement of RBCs: Dependent on shear rate, RBCs can move throughout the circulatory system in one of three manners known as tumbling, swinging, and tank-treading [17]. At the lowest shear rates RBCs have the tendency to move in a pattern known as tumbling (Figure 2). This is where the RBCs spin completely around their axis and maintain little to no deformation. As shear stress is increased RBCs experience a transition motion known as swinging where they experience quasi-deformation and their rotation abilities alter from 180° at tumbling to a range of 5–26° while swinging [5]. Following this, RBC motion develops completely into tank-treading motion which is defined by large amounts of RBC deformation and quasi-steady motion. During tank-treading, RBCs explore a small volume of the flow, leading to less collisions and disturbances, which decreases the viscosity of the fluid. This phenomenon coheres with the fact that as shear rate increases, the viscosity of the blood decreases.
\nTwo conceptual motions are considered for RBCs, (1) linear shear field, the solid-like, also known as tumbling motion (top), and the vesicular, also known as tank treading motion (bottom) [18].
Viscosity: The viscosity of the blood is due to the internal friction between the flow, incorporating the effects of the suspended particles present in the blood, inclusive of RBCs, WBCs, and platelets. As this internal friction increases, more force is required from the heart in order for it to maintain the desired cardiac output of the blood in the circulatory system. This requires a heightened contractility from the muscles of the heart which can result in the fatigue of the heart and, in major cases, heart collapse [19]. The opposite case where there is a lack of proper internal friction in blood flow will cause a decrease in the ability of one’s blood to clot, which imposes risk when blood vessels are damaged and the blood continues to flow out of the site of damage for a prolonged period of time.
\nThe viscosity of blood is dependent on many factors such as the properties of blood plasma, the hematocrit levels, and the individual mechanical properties and influence of the suspended particles in the flow; however, this is inherently dependent on whether blood is considered as a Newtonian or non-Newtonian fluid. The true nature of blood is that it exhibits non-Newtonian properties under specific conditions; however, these conditions arise at very few locations throughout the circulatory system.
\nNewtonian and non-Newtonian conditions: Blood possesses non-Newtonian properties when the shear rate is above 100 s−1 [20] and follows shear-thinning effects. High shear rates occur in the capillaries and the larger arteries coming directly from the heart because the shear rate of a fluid through a vessel increases with increasing velocity and decreasing diameter. Due to the fact that the main non-Newtonian properties of blood occur in small diameter vessels, it is argued that the non-Newtonian effects that occur within the largest arteries can be ignored [21, 22]. In blood vessels that possess a diameter in the more medial range or where there is decreased velocity, such as veins and arterioles, these non-Newtonian factors have minute effects on the properties of the flow, causing them to be neglected for these areas as well:
\nEq. (4) is not an accurate representation of proper blood flow as blood flow is subject to fluctuations based on the viscoelastic properties of the vessel walls, the alternating pressures from systolic and diastolic, and when present the non-Newtonian properties of the blood itself; however, it is used here to outline the relationship between the shear rate and vessel size and diameter.
\nConsidering the above, the non-Newtonian effects of blood are only active within a small portion of overall blood flow and acquire more importance when blood flow at those selected areas are specifically studied. When being considered as a Newtonian fluid, the viscosity of blood in addition to being impacted from the vessel size and blood velocity is also affected by the blood plasma and the concentration of suspended particles in the flow.
\nFactors of blood as a Newtonian fluid: The blood plasma is mainly composed of water (roughly 91% by volume) proteins, hormones, and glucose and acts as a Newtonian fluid with standard values between 1.1 and 1.3 mPas at the human body temperature of 37°C [9]. Blood plasma accounts for approximately 55% of the volume of the blood in the body and due to the incredibly high water content in the substance, the viscosity of plasma is highly affected by the hydration levels of the individual. As a human becomes dehydrated this percentage decreases and the blood becomes more viscous [23]. In addition to hydration levels, blood plasma viscosity is also directly affected by the amount of proteins and lipids in the blood post consumption. The higher the concentration of these elements, the more viscous the plasma will become [24].
\nIn addition to the direct effects on plasma, the shear in the flow is also affected by the amount of suspended particles in the flow. In heterogeneous fluids where particulates are present, these particulates alter the velocity profile of the fluid due to the increased shear at the fluid particle interface. As aside from plasma, the majority of the remaining volume of the blood is composed of RBCs, RBCs are the particles that impose this effect to the flow in the greatest magnitude. Aside from the direct viscosity of the plasma itself, plasma also affects the viscosity of the blood by the housing of certain proteins such as fibrinogen that cause aggregation in the suspended particles [25].
\nFactors of blood as a non-Newtonian fluid: The percentage comparison of the volume of blood cells to the total volume of blood is known as hematocrit and is the main factor contributing to the viscosity of the blood. This is because the blood’s ability to flow is highly dominated the ease of movement of RBCs. At high shear rates the deformability of RBCs is what effectively determines the viscosity of the fluid; however, at low shear rates the viscosity is controlled by the unique property of RBCs to aggregate [26, 27].
\nPhysical capabilities and tendencies of RBCs: The deformability of RBCs is controlled by three main factors: the relatively high surface area to volume ratio due to RBCs enucleated nature, the viscosity of the cytoplasm, and the viscoelastic properties of the cell membrane [28]. The viscoelastic properties of the cell membrane are dominated by three moduli known as the shear elastic modulus, the area compressibility modulus (\n
Modulus type | \nDefinition [29] | \nTested value [4] | \n
---|---|---|
Shear elastic modulus | \nThe ratio of shear stress to shear strain | \n5.5 +/− 3.3 (μN/m) | \n
Area compressibility modulus | \nThe energy per unit area required to uniformly stretch an interface to produce an area change according to Hooke’s law | \n399 +/− 110 (mN/m) | \n
Bending modulus | \nThe energy per unit area required to produce a mean curvature (H) according to Eqs. (3) and (4) | \n1.15 +/− 0.9 (× 10−19Nm) | \n
Viscoelastic factors for RBCs.
During the same experiment as the calculation for the elastic moduli, the cytoplasmic viscosity was tested as well, producing a value with an average that is approximately six times greater than the viscosity of plasma.
\nThis viscosity is important because it outlines how quickly the cell can reshape itself. Similar to the viscosity of plasma, this is dependent on the hydration levels of the individual in which the blood is present [30]. Deformability of RBCs is relevant in locations of high shear rates such as the capillaries because, in order to maintain proper blood flow they must adhere to the vessel to sustain motion. To do this effectively, the RBCs fully elongate into ellipsoids and align with the flow, reducing the possibility of collisions, decreasing the overall viscosity.
\nAt low shear rates, RBCs have the tendency to aggregate together, most commonly into stacks called Rouleaux. It is suggested that this specific formation occurs due to the incredibly high surface area RBCs possess. This combining of RBCs severely increases the frictional resistance between flow streamlines, increasing the viscosity of the fluid. However, as seen in Figure 3 at high shear forces this tendency is overruled and the blood cells separate and align in the direction of the flow [31].
\nShear rate to RBC aggregation comparison [31].
Aside from RBCs, other suspended particles such as platelets and WBCs are present in the blood which also maintain aggregative properties; however due to the fact that they compose roughly 1/800th and 1/600th of the volume of the blood, respectively, they are often not considered a vital part of the viscosity of the blood [31, 32, 33, 34].
\nFlow effects on RBCs: As blood moves from a large vessel to a vessel less than 0.3 mm [35] the RBCs realign to the center of the vessel. Due to this, the velocity of the centric RBCs is increased relative to the layer of plasma present at the wall of the vessel and the RBCs leave the vessel at a faster rate at which they enter them. This causes the hematocrit level to decrease through the vessel, also known as the Fahraeus effect. This causes another effect known as the Fahraeus-Lindkvist effect which states that the viscosity of the blood decreases as the vessel size decreases. Though as viscosity in extremely small vessels is affected by the deformability of RBCs as discussed earlier, the increase in velocity of the RBCs increases the velocity of the entire flow, respectively, causing the viscosity to decrease.
\nHemostasis: Hemostasis is the process by which bleeding is stopped and it can be broken up into three main steps: vasoconstriction, platelet activation, and blood clot formation [36]. The clotting process begins by the blood vessel contracting in order to reduce the flow of blood into the injured vessel. After a rupture, tissue factor is released which causes platelets to aggregate to each other and the walls of the vessel. As platelets become sticky, they help to impede the flow of blood through the rupture. In addition chemicals are released from small sacs inside the platelets called granules [37] that attract more cells to the site and further the clumping of platelets creating a platelet plug. On the surface of these activated platelets, many clotting factors work together in a series of complex chemical reactions known as the coagulation cascade which results in the formation of a fibrin clot [38].
\nBlood diluters: Blood diluters are commonly used in the medical industry for a variety of reasons. The main reason that a doctor may prescribe a blood thinner to a patient is if the patient has a high risk of blood clots, which can cause organ damage or in some cases death. Blood thinners are commonly used for patients suffering from heart disease, poor blood circulation, abnormal heartbeat, and congenital heart defects [6, 8, 39, 40, 41]. There are two types of blood thinners, anticoagulants and antiplatelets. Anticoagulants inhibit the coagulation cascade, whereas antiplatelets prevent platelets from aggregating. Antiplatelet drugs are commonly issued for patients with heart disease or have had prior heart attacks and anticoagulant drugs are used before open heart surgery on heart valves or congenital heart defects [41]. Using blood thinners inhibits vital aspects of the human body which can cause many side effects. Patients can suffer from increased bruising, red- or pink-colored urine, bloody stools, increased bleeding during menstrual period, purple toes, and blackish areas in their fingers, toes, or feet [9]. Common anticoagulants include heparin and warfarin. Well-known antiplatelet drugs are clopidogrel and ticagrelor [42].
\nViscoelasticity of blood: Viscoelasticity is the property of materials that exhibit both viscous and elastic characteristics when undergoing deformation [43, 44, 45, 46, 47, 48, 49]. Viscous materials resist shear flow and strain linearly with time when a stress is applied, while elastic materials strain when stretched and quickly return to their original shape after the stress is removed. The blood is viewed most commonly as a viscous fluid, but due to RBCs ability to store elastic energy, it actually displays viscoelastic properties. Elastic energy is most commonly stored in RBCs as a result of the heart pumping the blood.
\nThe factors contributing to the viscoelastic properties of blood are the plasma viscosity, plasma composition, temperature, shear rate, and the level of hematocrit. When conducting experiments on blood to test the viscoelasticity properties there are two main control factors, the level of hematocrit and the temperature.
\nHematocrit is a significant factor as RBCs are the main reason for the elastic properties of blood. Figure 4 shows the level of viscoelasticity with respect to the amount of RBCs present in the blood. It is easy to discern from the graph that as the amount of RBCs present in the blood increases, the viscoelasticity properties of the blood increase as well.
\nViscosity and elasticity measured at 2 Hz, 22°C, and at a shear rate of 10/s rms [43].
It is important for scientists to properly recreate the same environment experienced within the human body to achieve accurate results. As shown in Figure 5, the temperature affects the levels of viscosity and elasticity within the blood.
\nViscoelastic properties of blood with respect to shear rate. Hematocrit is set to %45 and measurements are performed at 2 Hz in three arrangements: cylindrical tube of diameter of 0.10 cm and length of 6 cm, in a microtube with a diameter of 0.005 cm and a length of 0.120 cm and porous medium with an equivalent diameter of 0.00812 cm and a length of 0.165 cm.
Blood clot factors: In stagnant flow regions or where the blood flow moves very slowly, the risk for blood to clot increases [50]. This occurs due to a high exposure time of RBCs to large variation in shear stress. It has been shown that the pulsatile flow is significant in the regulation of the stagnation areas regarding blood clot formation [50, 51]. In addition, blood clotting is known to occur because of both the jet velocity and turbulent shear stress where Re number is high in the stagnation region [52]. Factors that are said to be in charge of triggering blood clot formation are listed in Table 2.
\nFactor | \nTriggering criteria for blood clots | \n
---|---|
Cavitation | \nWater hammer and squeeze flow | \n
Reynolds shear stress | \n>>200 dynes/cm2 [54] | \n
Cardiac output | \nSlow movement of leaflet | \n
Stagnant flow | \nIf occurs adjacent to the valves, it could promote the deposition of damaged blood elements, leading to thrombus formation on the prosthesis [55] | \n
Vortex shedding | \nYields repeated vortex pairing within the wake, which is responsible for the formation of larger platelet aggregates [56] | \n
Recirculation | \nAllows many platelets to be trapped [57] | \n
Pressure drop | \nA larger pressure drop means that the heart with the MHV prosthesis has to work harder [58], thereby reducing cardiac output | \n
Blood clot factors [53].
Numerical models for blood clot formation: The process of blood clotting begins by activated platelets which aggregate with a damaged blood element. It is well known that the level of platelet activation and blood cell damage are significantly impacted by the magnitude and duration of the applied shear stress, known as residual time [59].
\nThere have been a few models developed based on the measured residual time and the amount of shear stresses as outlined in Table 3.
\nModel | \nExpression | \n\n |
---|---|---|
Linear damage accumulation/BDI | \n\n\n | \n[60] | \n
Platelet activation state (PAS) | \nNondimensional level of platelet activation within the interval of [0, 1], in which 0 and 1 correspond to nonactivated and fully activated platelets, respectively | \n[61] | \n
Power-law model | \n\n\n | \n[62] | \n
\n\n | \n\n\n | \n[63, 64] | \n
Adhesion model | \n\n\n | \n[65] | \n
Available models for the estimation of blood clot formation and threshold [53].
Biological processes are amazing in their complexity and optimization. The blood, being no exception, is extremely evolved and adapted to the different scenarios necessary to maintain life. Consisting of plasma, WBCs, platelets, and RBCs, it is able to transport vital molecules around the body including oxygen, and clot in case of injury. Since RBCs make up approximately half the volume of blood, blood flow mechanics are largely related to the properties of red blood cells defined under a soft solid. Large deformability is essential in the life cycle and function of red blood cells as capillaries are extremely small. Blood clotting is a very important function of blood, in which platelets are the main contributors to the clotting process. When platelets come into contact with damaged tissue, the platelets activate and construct a web to coagulate blood. Due to vWF’s shear dependent binding, under high shear stresses platelets can be bound together and form clots without activating.
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