\r\n\t[2] J. V. Moloney, A. C. Newell. Nonlinear Optics. Westview Press, Oxford, 2004.
\r\n\t[3] M. Kauranen, A. V. Zayats. Nonlinear Plasmonics. Nature Photonics, vol. 6, 2012, pp. 737-748.
\r\n\t[4] P. Dombi, Z. Pápa, J. Vogelsang et al. Strong-field nano-optics. Reviews of Modern Physics, vol. 92, 2020, pp. 025003-1 – 025003-66.
\r\n\t[5] N. C. Panoiu, W. E. I. Sha, D.Y. Lei, G.-C. Li. Nonlinear optics in plasmonic nanostructures. Journal of Optics, 20, 2018, pp. 1-36.
\r\n\t[6] A. Krasnok, A. Alu. Active nanophotonics. Proceedings of IEEE, vol. 108, 2020, pp. 628-654.
\r\n\t[7] M. Lapine, I.V. Shadrivov, Yu. S. Kivshar. Colloquium: Nonlinear metamaterials. Reviews of Modern Physics, vol. 86, 2014, pp. 1093-1123.
\r\n\t[8] Iam Choon Khoo. Nonlinear optics, active plasmonics and metamaterials with liquid crystals. Progress in Quantum Electronics, vol. 38, 2014, pp. 77- 117.
\r\n\t
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.
Rice is such an agricultural commodity that covers the third-highest worldwide production making it one of the most important cereal crops [1]. With its wide geographic distribution extending from 50°N to 35°S, rice is expected to be the most vulnerable cultivated crop to changing climates in future [2, 3]. Rice production is dwindled mainly because of biotic and abiotic stresses due to the complexity of interaction between the stress factors and various molecular, biochemical and physiological phenomena affecting plant growth and development [4, 5]. To battle with these situations, development of adaptive rice varieties is one of the best strategies. Since aboveground parts are often taken into consideration for making stress tolerant varieties, root study remains backward in this aspect. Roots, the hidden portion of the plant have not yet been much focused. Because exploring the root traits of the plant are much more difficult compared to its above-ground traits. But when it comes to the fact of studying the optimal developmental plasticity system and characteristic features of plant growth, the root system is given the first priority [6]. Root system is the site of water and nutrient uptake from the soil, a sensor of abiotic and biotic stresses, and a structural anchor to support the shoot. The root system communicates with the shoot, and the shoot in turn sends signals to the roots [7]. Soil type, moisture and nutrients all strongly influence the architecture of the root system [8, 9, 10]. Recently it has been emphasized that root architectural traits play a decent role for the adaptation of crop varieties under different abiotic stresses [11, 12]. Root interaction with changing environment is a complex phenomenon that differs with genotypes and intensity of stress [13, 14, 15, 16, 17]. For that, different species and also genotypes under the same species may respond contrarily under stress conditions and show different magnitudes of tolerance or susceptibility to stress. These diversities can be exploited by plant breeders to improve stress tolerance in plants. Scientists assume that selection for yield will indirectly select for varieties with the optimum root system. But the fact is, more directed selection for specific root architectural traits could enhance yields for different soil environments [18]. As by 2035, a predicted 26% increase in rice production will be essential to feed the rising population [19], it is imperative to develop high yielding rice cultivars with efficient root systems for better exploitation of natural resources under stressed conditions.
\nBeing the hidden half of the plants, the root system performs several functions like water and nutrient acquisition, mechanical support to the plant and storage of reserve assimilates [7]. In plant, roots are the first organ for sensing the water limitation and the roots are also the signal transmitter to other plant parts through xylem sap and phytohormone which is known as one of the most important root-shoot stress signal mechanism [20, 21, 22, 23]. Development of the root system is a major agronomic trait and proper architecture in a given environment permits plants to survive in water and nutrient deficit conditions and gives the ability to utilize minimum resources efficiently [6].
\nCrop loss in rice production has become severe now-a-days due to abiotic stresses. Therefore, having a clear knowledge about the architecture and development of roots of rice toward optimizing water and nutrient uptake has become crucial for exploitation and manipulation of root characteristics for enhancing yield under unfavorable conditions [24, 25]. In general, root study comprises the study of the entire root system or a large portion of the plant’s root system [26, 27]. To understand the functional characteristic of root system and the necessity to exploit heterogeneous environment, root architecture study has become crucial in plant productivity as root system architecture is strongly linked with plasticity to the plant through which plant can alter its root structure according to its heterogeneous environment [26].
\nElongation and branching are the mode of plant root growth. Local environmental conditions, physiological status of the plants and the type of root determine the magnitude and direction of root elongation [6]. Root system architecture (RSA) is thus the three-dimensional geometry of the root system including the primary root, branch roots, and root hairs [6, 26, 28, 29]. Topological, distributional and morphological features combine to form the root system architecture [8, 26, 30]. Topology denotes the branching pattern of individual roots including features like lengths and diameters, number of roots originating from a node, root insertion angles, magnitude and the altitude of root [31, 32]. Measures of the spatial distribution of roots simplify the dissection of root systems [26]. Root morphology refers to the external features of a root axis and may include properties of roots hairs, root diameter and trend of secondary root emergence. Acceleration or inhibition of primary root growth, increment of lateral roots (LRs) and a rise in root hairs and also the formation of adventitious roots are the ways of modification of root system architecture. The primary root is formed during embryogenesis. This primary root produces secondary roots those in turn produce tertiary roots [6, 33]. Root system architecture has proved to be a critical factor in plant survival, contributing to water and nutrient acquisition efficiency and competitive fitness in a given environment [34]. Composition of soil specially water and mineral nutrients availability and plant species have impact on root architecture [6].
\nMonocot cereals have a complex fibrous root system consisting of an adventitious root (ARs) bunch. Adventitious roots originate from the shoot or subterranean stem. This type of root is sometimes referred to as a nodal or crown root [35]. Root systems of rice plants (Oryza sativa L.) comprise numerous nodal roots of relatively short length: a mature rice plant usually has several hundreds of nodal roots, most of which are less than 40 cm in length [36]. Rice (Oryza sativa L.) is a model cereal crop with seminal roots that die during the growing period [36]. Thus, lateral roots and adventitious roots are the key determinants of nutrient and water use efficiency in rice [37].
\nSeveral embryonic and postembryonic roots including the radicle, the embryonic crown roots, the postembryonic crown roots, the large lateral roots (L-type), and the small lateral roots (S-type) [38] form the rice root systems (see Figure 1). Lateral rice roots can appear on any primary root, including embryonic and crown roots, and can be classified into two main anatomical types [39]. Numerous small lateral roots (S-type) are thin with determinate growth that can be formed from large lateral roots (L-type) and they never bear any lateral roots. Whereas large lateral (L-type) roots are few in number, thinner compared to primary roots that show indeterminate growth. Additionally, lateral elongation of small lateral roots and downward elongation of large lateral roots indicate non-responsiveness of the small lateral roots to gravity. Higher orders of branching can also be observed in the large lateral roots of the crown roots that emerge at later growth stages [40]. These small and large lateral roots exhibit differential growth and lateral root bearing pattern signifying unlike purposes for these two types of lateral roots [37].
\nA typical root system architecture at the tiller axis of Oryza sativa L. Black disks indicate individual root bearing phytomer with progressive development chronologically from top to downward. Root hairs form on main axis and all the lateral roots [41].
The concept of a phytomer was established around 6–7 decades ago [40, 42]. Clear knowledge about phytomer is required for better understanding of plant development and architecture. Many higher plants, including rice, are composed of successive stem segments called phytomer [43, 44, 45]. Each phytomer consists of an internode of the stem with one leaf, one tiller bud and several adventitious (nodal) roots [36]. The phytomer concept has long been recognized among grass scientists [46, 47]. The coordinated development of stem, tiller bud, and adventitious roots in each phytomer corresponds to the phyllochronic time in rice [43, 44, 48]. This indicates that genotypic variation in root-and-shoot growth can be ascribed to the variation of stem and adventitious root development at the phytomer level [49].
\nDetailed study of root morphology and architecture at the phytomer level become more obvious with the attainment of new knowledge about segmental architecture of poaceous crops [50, 51, 52, 53]. As the higher plant structure is formed by the repetitive unit of plant growth called phytomer [54], so phytomer formation, its growth and senescence ultimately determine development of plant canopy [47]. Therefore the phytomer components have become the interest of the plant breeder.
\nRoot axes of rice plants serve functions of anchorage and typically establish overall root system architecture [55]. The lateral roots are the functionally active part of the root system involved in nutrient acquisition and water uptake. The size, type and distribution of lateral roots eventually decide the ultimate length and surface area of an individual root and finally of a whole tiller. Understanding morphology of the lateral roots is therefore important to develop rice cultivars with an efficient root system [11, 56].
\nIn rice, there are two types of lateral roots; long and thick roots, and short and slender roots [57, 58, 59]. It has been designated that the first type as L-type and the latter as S-type [60]. The L-type lateral roots are usually long and thick and are capable of producing higher-order lateral roots, whereas S-type ones are short, slender, and non-branching. In rice plants, these two types of lateral roots are visually distinguishable. The L-type lateral roots show basically identical tissue arrangement with seminal and nodal roots, whereas S-types are anatomically different wherein their vascular systems are simplified [35].
\nIn rice plants, the observed average diameter of S-type lateral roots (first-order) that were produced on mature nodal roots of a one-month-old plant was 80 μm, whereas that of L-type roots was almost double that, i.e., 159 μm. Average length was 7.6 mm for S-type and about 30 mm for L-type. The S-type laterals were almost similar in length, and only very few S-type laterals exceeded 10 mm in length. The L-type laterals varied greatly in length and some of them elongated to more than 300 mm [60]. The small laterals are less effective in water and nutrient uptake than even root hairs [61].
\nThe changes in lateral root development were triggered by changes in water status in the root zone, and these developmental changes were induced by genetic [62, 63] and environmental factors. With regard to the environmental factors, it is shown that phenotypic plasticity promoted lateral root development and that nodal root production was the key trait that ensured stable growth of rice plants grown under changing soil moisture levels [64]. As far as the literature explored, developmental morphology of the individual roots with special reference to different lateral root branches was not studied in detail, probably due to lack of the most appropriate tools and methods [11].
\nRoot hairs are tubular-shaped cells that arise from root epidermal cells called trichoblast; they are thought to increase the absorptive capacity of the root by increasing the surface area [65]. Root hairs contribute as much as 77% of the root surface area of the cultivated crops, forming the major point of contact between the plant and the rhizosphere. Root hair is a long and narrow tube like structure originating from a single cell through tip growth (the deposition of new membrane and cell wall material at a growing tip). For being the major water and nutrient uptake site of plants, root hairs form a progressively significant model system for development studies and cell biology of higher plants [66]. Root hairs had the highest contribution toward total length and surface area of an individual root whereas main axis and first order laterals mostly contributed root volume [11].
\nRoot hairs are localized for many water channels [67], phosphate [68], nitrogen [69], potassium [70], calcium [70], and sulfate transporters [71], all of which are beneficial to water and nutrient uptake by plants [72]. There is significant inter- and intra-specific variation exists for root hair traits, and this has been linked to P uptake. Plants with longer, denser root hairs exhibit greater P uptake and plant growth in P-deficient soils [73, 74, 75]. So, the root hair traits, especially root hair length can be exploited in breeding for improved nutrient uptake and increased fertilizer use efficiency [76]. Considerable researches support an important role for root hairs in P attainment [73, 74, 75, 77, 78]. Root hair length and root hair density (which is usually correlated with root hair length) have clear value for the acquisition of P and probably other diffusion-limited nutrients such as K and ammonium [79].
\nUsually root hair traits have a low heritability and their expression is influenced by soil type resulting in lack of research in this field [6, 80, 81]. It has been proposed that plasticity in root epidermis development as a response to a variety of environmental conditions might reflect a function of root hairs in sensing environmental signals, after which plants adjust themselves to the stress conditions, such as by increasing nutrient acquisition and water uptake or by helping to anchor the plant to the soil [82, 83, 84, 85, 86, 87]. Root hair elongation increases root surface area. Root surface area increment is a common phenomenon when the plants are subjected to the stress condition like salinity, drought or other abiotic stresses [79, 88, 89, 90, 91].
\nPlants recurrently face several stresses like salinity, drought, submergence, low temperature, heat, oxidative stress and heavy metal toxicity while exposed to the nature. Growth and grain production in cereals is often limited by these stresses under field conditions. All these stresses either directly or indirectly impose osmotic stress to plants that ultimately affect the final yield of rice. Root is the first part which can sense these stresses better than other plant parts. So researchers prioritize the fact of understanding the root adaptive responses of plants upon osmotic stress. In the last 30 years, comprehensive studies have been performed focusing on architecture and developmental morphology of roots and their genetic and molecular basis [11]. Morphological and anatomical development of the rice root system was thoroughly reviewed [92] whereas the mystery of root length was also reviewed [93]. A recent study highlighting the growth, development and genetic reasons of root morphology and function of crop plants was provided by [94]. An outstanding study on root system architecture and its molecular and genetic background also greatly contributed to the relevant literature recently [37]. The physiological background of root branching was also studied [7, 33]. The root parameters that are focused by the studies comprising root anatomy, plant height, root-shoot ratio, length, diameter, density, surface area and volume of root, root elongation rate, root branching, expansion of root regarding tiller development, maximum root depth, distribution pattern of root in soil column, root hydraulic conductivity, hardpan penetrability, all of which possess innumerable functional implication [95]. Roots of large diameter show greater penetration ability [96, 97, 98] and branching [8, 99] because of having larger radii of xylem vessel and poorer axial resistance to water flux [100].
\nWater is essential for survival and plant growth. As a sessile organism, plants constantly encounter water deficit, which is the most severe environmental stress limiting plant growth and productivity in natural and agricultural systems [101, 102]. Thus, water stress tolerance has been a fundamental scientific question in plant biology.
\nPlants have evolved complex adaptive mechanisms that enable them to survive drought conditions. Over more than five decades, researchers have identified osmotic adjustment, antioxidant protection, and stomatal movement as key adaptive mechanisms for survival where both osmotic adjustment and reactive oxygen species (ROS) are involved in this plastic development process [103]. To cope with the changing water status in the growing environment, plants have evolved various adaptive mechanisms by which plants can modify root allocation and root system architecture to obtain more water [104].
\nNumerous studies have provided evidence to show that when plants are subjected to water stress, root growth is strongly inhibited, although root development is less sensitive to water stress than that of shoots [105, 106, 107].
\nRoot system architecture is regulated by osmotica [108]. The osmotic potential of the soil alters the depth of the root system, its overall mass, the rate of root elongation and the number of lateral roots in many plants, including Arabidopsis [8, 9, 107, 109, 110].
\nRoot length, root dry weight, and root production are limited by drought stress [111, 112]. Roots are the significant plant part which increase plant adaptability power to soil water deficits by maintaining water uptake under dry conditions [113]. Root and other root components such as root hair, root-shoot ratio, and root length are found to be decreased in drought sensitive varieties. But the resistant varieties which possess tolerance capacity against drought showed increase in root hair, high root to shoot ratio and root length [114]. Roots are considered as the most efficient plant organ which helps plant to uptake water and minerals from the soil and during drought stress. Root proliferation and changes in root parts occurs to take more water from deeper regions of the soil [25]. Different types of changes are observed in root growth of drought resistant rice varieties such as a deeper and highly branched root system than drought- sensitive varieties [115]. Plant also extends its roots for more nutrients (such as phosphorus) and water uptake which results in more root to shoot ratio [116]. In recent years breeding for developing larger and more efficient root systems has become the hotspot in research in some crops such as rice, as there is a relation between root system size and tolerance to water stress [81, 117].
\nThe change in lateral root development, i.e. in the plasticity of the root system, exhibited under water deficit conditions may play an important role in drought stress tolerance [35]. From an agronomical view, the knowledge about lateral root development is useful for breeding varieties with drought stress tolerance [118].
\nThe importance of root system structure is particularly recognizable when its significance in relation to its function is clearly identified. The significance of root system structure in nutrient and water uptake was stressed in previous study [119].
\nUnder waterlogged conditions, the plant roots have to function in anaerobic soil, and there are at least two morphological adaptations that roots exhibit in response to anaerobiosis, i.e., development of new adventitious roots [120, 121] and superficial rooting (i.e., the concentration of new root growth in the upper layers of the soil) [122]. Nodal root production (increase in number) continued to take place, however, in the sense that when adventitious roots in the lower nodal position of the plant’s stem die due to waterlogging injury, new adventitious roots appear at the next highest nodal position. There appears to be a direct relationship between the death of older adventitious roots and the development of new ones. Progressively waterlogged plants generally show smaller root system size than those grown in a well-drained condition. It is considered that the turgor pressure affects the cell elongation and growth of plants [123, 124]. Aerobic cultivars of rice have greater ability for plastic lateral root production than irrigated lowland cultivars under transient moisture stresses [125].
\nWe have a little understanding of the responses of roots and root hairs to salinity stress and their function in stress tolerance. The efficient root system can either avoid or lessen the osmotic stress. Usually, growth, morphology, and physiology of the roots alter first under salinity stress and the whole plant is then affected. Therefore, the responses and characteristics of the roots under saline conditions are of primary importance for plant salt-tolerance [126]. It is supposed that root morphology affects salt accumulation around the roots impeding uptake of water from saline areas. Modification of root morphology has a big potential to develop crop salt tolerance [127]. Root hairs have higher sensitivity to salt than other root traits and shoots [128]. Environmental factors also regulate the root hair development [128]. The development of root epidermal cells has great plasticity where the differentiation programs can be switched from one to another in response to external factors [17]. Plasticity in development of root epidermis as a response to a variety of environmental conditions might reflect a function of root hairs in sensing environmental signals, after which plants adjust themselves to the stress conditions [82, 84, 85, 86, 87, 129].
\nRoot hair growth and development and their physiological role in response to salt stress are largely unknown [128]. The development of root epidermis cells has great plasticity where the differentiation programs can be switched from one to another in response to external factors [17]. Root hairs have higher sensitivity to salinity than do roots and shoots [128]. Systematic study on root hair plasticity induced by salt stress and the possible role in plant adaptation/tolerance to salinity is still lacking [128]. Usually root hair traits have a low heritability and their expression is influenced by soil type resulting in lack of research in this field [6, 80, 81].
\nEarlier many scientists had reported root morphology and its distribution were greatly varied based on genotypes of plant species [13, 14, 15, 16]. There is widespread evidence that root architecture and different root characteristics of many crop species varies among genotypes [14, 130, 131, 132, 133]. In a few quite recent studies, the importance of studying root architectural traits has been emphasized for the adaptation of the crop varieties to various abiotic stress conditions. Genotypic variation has a significant role in adapting the adverse environmental and edaphic effects [14]. Inter- and intra-species variations in root architectural traits are very useful to breed the crops for root features optimum for diverse environmental conditions [134, 135, 136].
\nRoot anatomical and morphological traits have been well studied in rice [92]. Varietal differences in root morphological characteristics such as length and thickness have been reported in cultivated rice (Oryza sativa L.) in various studies [11, 14, 41, 137]. In general, the roots of upland rice cultivars are thicker and penetrate more deeply into the soil than those of lowland cultivars [14]. Root distribution has also been quantitatively characterized by using several traits, including root length, volume, and density in the soil at different depths, and these characteristics differed among cultivars [92, 138, 139, 140].
\nUnderstanding and improvement of root system and its genetics plays a pivotal role to become self-sufficient and to achieve sustainability in rice production. Actually more yields from the limited input rely on our capability to unambiguously manipulate the plants. And exploring the diversity of root architecture both in genetic and phenotypic basis will directly connect to this concern. Although great strides have been made to understand the root morphology but in future, more intense investigations to elucidate the functional implication of root morphological variation may aid in selection of root system with anticipated characteristics.
\nFuture exploration of stress responses regulated by roots at cellular or tissue level will open the door of further breeding research. Besides the modern gene pools, exploration of genes and alleles in wild relatives and landraces will also provide interesting features that will be easier to transfer to cultivated rice. Further it is important to have a better understanding on the epigenetic regulation of roots and root development under stressful conditions. There will be a need for high throughput phenotyping systems coupled with automated data analysis for accelerating the development. Endorsement of approaches including both root ideotype-based screening and selection for grain yield may establish a fruitful screening system. Alongside designing new genetic screening methods based on a better knowledge of the integrated stress responses will be also appreciated. Dynamic root/soil interaction modeling will aid in integrating different functional parameters (e.g. water uptake per length of root) under a variety of environmental conditions. Overall the root system being less accessible and more complex than other agronomic traits, achieving the ambitious goal of future rice root research, coordinated effort and joint resources are required. The sensible and appropriate efforts will have a crucial role to play in future crop production in vulnerable climate and resource scarcity prioritizing the objective of serving food to 9 billion world populations by the year 2050.
\n“The authors declare no conflict of interest.”
Edited by Jan Oxholm Gordeladze, ISBN 978-953-51-3020-8, Print ISBN 978-953-51-3019-2, 336 pages,
\nPublisher: IntechOpen
\nChapters published March 22, 2017 under CC BY 3.0 license
\nDOI: 10.5772/61430
\nEdited Volume
This book serves as a comprehensive survey of the impact of vitamin K2 on cellular functions and organ systems, indicating that vitamin K2 plays an important role in the differentiation/preservation of various cell phenotypes and as a stimulator and/or mediator of interorgan cross talk. Vitamin K2 binds to the transcription factor SXR/PXR, thus acting like a hormone (very much in the same manner as vitamin A and vitamin D). Therefore, vitamin K2 affects a multitude of organ systems, and it is reckoned to be one positive factor in bringing about "longevity" to the human body, e.g., supporting the functions/health of different organ systems, as well as correcting the functioning or even "curing" ailments striking several organs in our body.
\\n\\nChapter 1 Introductory Chapter: Vitamin K2 by Jan Oxholm Gordeladze
\\n\\nChapter 2 Vitamin K, SXR, and GGCX by Kotaro Azuma and Satoshi Inoue
\\n\\nChapter 3 Vitamin K2 Rich Food Products by Muhammad Yasin, Masood Sadiq Butt and Aurang Zeb
\\n\\nChapter 4 Menaquinones, Bacteria, and Foods: Vitamin K2 in the Diet by Barbara Walther and Magali Chollet
\\n\\nChapter 5 The Impact of Vitamin K2 on Energy Metabolism by Mona Møller, Serena Tonstad, Tone Bathen and Jan Oxholm Gordeladze
\\n\\nChapter 6 Vitamin K2 and Bone Health by Niels Erik Frandsen and Jan Oxholm Gordeladze
\\n\\nChapter 7 Vitamin K2 and its Impact on Tooth Epigenetics by Jan Oxholm Gordeladze, Maria A. Landin, Gaute Floer Johnsen, Håvard Jostein Haugen and Harald Osmundsen
\\n\\nChapter 8 Anti-Inflammatory Actions of Vitamin K by Stephen J. Hodges, Andrew A. Pitsillides, Lars M. Ytrebø and Robin Soper
\\n\\nChapter 9 Vitamin K2: Implications for Cardiovascular Health in the Context of Plant-Based Diets, with Applications for Prostate Health by Michael S. Donaldson
\\n\\nChapter 11 Vitamin K2 Facilitating Inter-Organ Cross-Talk by Jan O. Gordeladze, Håvard J. Haugen, Gaute Floer Johnsen and Mona Møller
\\n\\nChapter 13 Medicinal Chemistry of Vitamin K Derivatives and Metabolites by Shinya Fujii and Hiroyuki Kagechika
\\n"}]'},components:[{type:"htmlEditorComponent",content:'This book serves as a comprehensive survey of the impact of vitamin K2 on cellular functions and organ systems, indicating that vitamin K2 plays an important role in the differentiation/preservation of various cell phenotypes and as a stimulator and/or mediator of interorgan cross talk. Vitamin K2 binds to the transcription factor SXR/PXR, thus acting like a hormone (very much in the same manner as vitamin A and vitamin D). Therefore, vitamin K2 affects a multitude of organ systems, and it is reckoned to be one positive factor in bringing about "longevity" to the human body, e.g., supporting the functions/health of different organ systems, as well as correcting the functioning or even "curing" ailments striking several organs in our body.
\n\nChapter 1 Introductory Chapter: Vitamin K2 by Jan Oxholm Gordeladze
\n\nChapter 2 Vitamin K, SXR, and GGCX by Kotaro Azuma and Satoshi Inoue
\n\nChapter 3 Vitamin K2 Rich Food Products by Muhammad Yasin, Masood Sadiq Butt and Aurang Zeb
\n\nChapter 4 Menaquinones, Bacteria, and Foods: Vitamin K2 in the Diet by Barbara Walther and Magali Chollet
\n\nChapter 5 The Impact of Vitamin K2 on Energy Metabolism by Mona Møller, Serena Tonstad, Tone Bathen and Jan Oxholm Gordeladze
\n\nChapter 6 Vitamin K2 and Bone Health by Niels Erik Frandsen and Jan Oxholm Gordeladze
\n\nChapter 7 Vitamin K2 and its Impact on Tooth Epigenetics by Jan Oxholm Gordeladze, Maria A. Landin, Gaute Floer Johnsen, Håvard Jostein Haugen and Harald Osmundsen
\n\nChapter 8 Anti-Inflammatory Actions of Vitamin K by Stephen J. Hodges, Andrew A. Pitsillides, Lars M. Ytrebø and Robin Soper
\n\nChapter 9 Vitamin K2: Implications for Cardiovascular Health in the Context of Plant-Based Diets, with Applications for Prostate Health by Michael S. Donaldson
\n\nChapter 11 Vitamin K2 Facilitating Inter-Organ Cross-Talk by Jan O. Gordeladze, Håvard J. Haugen, Gaute Floer Johnsen and Mona Møller
\n\nChapter 13 Medicinal Chemistry of Vitamin K Derivatives and Metabolites by Shinya Fujii and Hiroyuki Kagechika
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