scholarly works

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Analysis of filtration properties of locally sourced base oil for the formulation of oil based drilling mud
(2014) Akintola, S.; Oriji, A. B.; Momodu, M.
Oil based drilling fluids are mixtures of clays, oil and other chemical additives suspended or dissolved such as solids and polymers. The environmental problems associated with oil-based drilling fluids are among the major concerns in the petroleum industry leading to increasing stringent regulations to ensure its environmental friendliness. This study examines the use of locally sourced oil like, groundnut oil, melon oil, vegetable oil, soya oil and palm oil as substitute for diesel oil in formulating oil base drilling fluids relative to filtration properties. The filtrate volumes of each of the oils were obtained for filtration control analysis. With increasing potash and industrial starch quantities during formulation, all the local oils had their filtration properties (filtrate volume and mud cake thickness) tending towards that of diesel oil at ambient temperature and atmospheric pressure. When temperature was increased to 70˚C and above, the filtration abilities of all the local oil reduced and degraded due to the flocculation of the clay suspension. The drilling fluids formulated with the local oil where restored by the addition of thinner and organic polymer which significantly stabilized the clay suspension. The polymer and the thinner clearly improved the filtration properties of the locally formulated oil based drilling fluids even when subjected at high temperature, The ranking from the results showing the order of better and effective filtration properties for the local oils are as shown; Melon Oil; Vegetable Oil; Groundnut Oil; Soya Bean Oil and Palm oil.
An analysis of formation damage during the drilling of deviated wells
(Taylor and Francis, 2013) Akinsete, O. O.; Isehunwa, S. O.
Filtrate losses and filter cake properties of drilling fluids are of concern in the oil industry because they alter near well bore permeability and can reduce well productivity. Therefore, it is desirable to accurately characterize filtration process during oil well drilling. A mathematical model for analyzing mud filtration in deviated wells was developed in this study. The model determined solid pressure distribution within cake, cake thickness, cumulative volume of filtrate and extent of invasion under different conditions. Results show assumptions of isotropy in previous studies greatly overestimate the magnitude of the damage. It was also confirmed that mud filtration tend to be higher in deviated than in vertical wells. The model was validated with experimental data.
Analysis of mud filtration properties using factorial design
(Society of Petroleum Engineers, 1995) Isehunwa, S. O.; Orji, H. I.
Determining the filtration properties of a mud system requires that experiments be run for both the standard API and the high Temperature High Pressure (HTHP) tests at intervals throughout the duration of drilling an oil well. However, cost and hazard considerations cause more emphasis to be placed on the standard API test at ambient conditions, without taking into account the effects of elevated downhole pressures and temperatures on filtration properties. In this work, the factorial design concept was applied to the filtration properties of drilling muds. Different samples of water based bentonitic muds were used for the experimental runs at both Low Temperature - Low pressure (LTLP) and high Temperature - High Pressure (HTHP) conditions. The input variables considered were temperature, pressure, solids content, mud weight and time; while the response variables were fluid loss and cake thickness. The final results are presented in the form of a statistically significant model that enables prediction of filtration properties at both LTLP and HTHP conditions. This method minimizes the inherent risks usually associated with operating filter presses at elevated pressures and temperatures. In addition, it saves time and cost by minimizing the number of experimental runs always required to assess mud quality and maximizes the information obtained from the few experimental runs. This experimental design technique can also be applied to the quality assessment and control of other drilling fluid properties.
Analysis of water cresting in horizontal wells
(Society of Petroleum Engineers, 2009) Okwananke, A.; Isehunwa, S. O.
Horizontal well application has sometimes been employed as a way of minimizing excessive water production arising from coning commonly encountered during oil production in vertical wells. Lots of efforts on water coning in vertical wells have been published. Available predictive models in horizontal wells vary from rather simplistic to complex models. This study investigated the development of practical models that combine ease of use with accuracy. Conformal mapping was used to combine steady state flow, volumetric voidage and pressure drop due to gravity effects in horizontal wells to obtain models that predict critical rates and breakthrough times. The results were compared with some existing correlations under varied reservoir fluid and rock properties. The models were also applied to vertical wells. It was also observed that critical rates and breakthrough times in horizontal wells are affected directly by effective permeability, well length, oil column height, density contrast between wafer and oil, !he height of the water crest. There is however, an inverse relationship with oil viscosity and production rate. It is concluded that simple and accurate correlation that can be applied to coning problems in both horizontal and vertical wells have been developed. They provide a means of comparing the performance of horizontal and vertical wells.
Application of agro–waste materials for improved performance of water–based drilling fluid
(2021-07) Akintola, S. A.; Orisamika, B. O.; Odetola, K. O.
Bio–resources and its derivatives have distinctive potential in various industrial applications and solutions especially for captivating usage in drilling fluid formulations for the petroleum industry. Drilling fluids formulations have tremendously advanced through increasing research and development of unique additives to improve their functionalities and meet specific properties in well design operations. In this article, water–based mud formulated with powdered and ash products derived from plantain and banana peels were evaluated. The effects of varying concentrations of these additives and the blends on the performance of the mud were examined by comparison with Low Viscosity Sodium Carboxymethyl Cellulose (LV CMC) for rheology and sodium hydroxide (NaOH) for pH control. The rheology of the mud improves with increasing concentration of the powdered products in a way similar to that of LV CMC. However, powdered banana peels most effectively improved the rheology of the mud to attain 10cp plastic viscosity (PV), 13lb/100sq.ft yield point (YP), 16lb/100sq.ft and 23lb/100sq.ft gel strength at 10seconds and 10minutes, respectively. Plantain peel ashes compared favourably with NaOH in controlling the pH of the mud. Further modification of the products to achieve the same properties as LV CMC is recommended in subsequent studies.
The application of artificial neural networks to mapping of wind speed profile for energy application in Nigeria
(Elsevier Limited, 2010) Fadare, D. A.
This paper presents the application of Artificial Neural Network (ANN) in modeling the heat transfer coefficient of a staggered multi-row, multi-column, cross-flow, tube-type heat exchanger. Heat transfer data were obtained experimentally for air flowing over a bank of copper tubes arranged in staggered configuration with 5 rows and 4 columns at different air flow rates with throttle valve openings at 10 - 100%. The Reynolds number and the row number were used as input parameters, while the Nusselt number was used as output parameter in training and testing of the multi-layered, feed-forward, back-propagation neural networks. The network used in this study was designed using the MATLAB® Neural Network Toolbox. The results show that the accuracy between the neural networks predictions and experimental values was achieved with Mean Absolute Relative Error (MRE) less than 1 and 4% for the training and testing data sets respectively, suggesting the reliability of the networks as a modeling tool for engineers in preliminary design of heat exchangers.
Application of generalized pressure perturbation principle to cubic equation of state formulation
(Society of Petroleum Engineers, 2005) Isehunwa, S. O.; Falade, G. K.
Cubic equations of state are commonly used for predicting the properties of reservoir fluids. They are simple to use and require few parameters during computations. They have also been found to produce results that are comparable to the more rigorous multi-parameter equations. However, they are still regarded by many as mere comprehensive correlations of fluid properties because of a number of weaknesses and /imitations. This work addresses two weaknesses of cubic equations of state commonly highlighted in literature, viz: that they do not seem to have deep theoretical foundations and are not as accurate as non-cubic equations. A pressure perturbation technique based on a simple adaptation of the Weirtheim's first order thermodynamic perturbation theory has been developed and used to formulate a cubic equation of state. The practical equation formulated was applied to pure fluids and samples of Niger Delta Petroleum fluids. The results show more accurate predictions than the commonly used SRK and PR equations. This work suggests that cubic equations could have deeper theoretical.
Application of Neuro- Fuzzy to palm oil production process
(Nigerian Association of Mathimatical physics, 2009-11) Odior, A. O.; Fadare, D. A.
Palm oil is an important nutritional food requirement and in order to facilitate the production of palm oil for consumption, the production process of palm oil has been investigated. The basic operations involved in the production of edible palm oil include; purchase, transportation and reception of oil palm bunches; bunch threshing and fruit fermentation; sorting and weighing of oil palm fruits; boiling, digestion and pressing of palm oil fruits; clarification and drying of palm oil and palm oil storage. A Neuro-Fuzzy model was used to analyze the performance of palm oil production process as it affects the basic operations involved in the production of edible palm oil. The research work can be applied to any other small or medium scale production firm for better efficiency.
An artificial neural network model for forecasting daily global solar radiation in Ibadan, Nigeria
(2009) Fadare, D. A.; Olugasa, T. T.
Solar radiation, the primary driver for many physical, chemical and biological processes on the earth's surface is considered the most indispensable parameter in the performance prediction of solar power systems. In this study, an artificial neural network (ANN) model was developed for predicting missing solar radiation data for Ibadan (Lat. 7.43°N; Long. 3.9°E; Alt. 227.2m), Nigeria. This study utilized daily solar radiation data for the period of 1984 to 2007 (24 years) from a meteorological station in Ibadan. The ANN model was designed using the Matlab® Neural Network Toolbox and five different structures of the model were investigated. Structure 1 utilized solar radiation data for 5 days to predict the next 25 days expected data; structure 2 utilized data for 10 days to predict the next 20 days; structure 3 used data for 15 days to predict succeeding 15 days; structure 4 used data for 25 days to predict next 5 days data; structure 5 used data for 5 days to predict the next 1 day solar radiation. The different structures were trained by using solar radiation data for 22 years and one year and the prediction accuracies were evaluated using the solar radiation values for year 2007. Results showed that structure 5 with correlation coefficient of 0.73 and 0.79 when trained with 22 years and 1 year, respectively gave the best prediction performance. Thus, indicating the suitability of structure 5 for prediction of solar radiation missing data.
Artificial neural network model for prediction of friction factor in pipe flow
(INSInet Publication, 2009) Fadare, D. A.; Ofidhe, U. I.
Determination of friction factor is an essential prerequisite in pipe flow calculations. The Darcy-Weisbach equation and other analytical models have been developed for the estimation of friction factor. But these developed models are complex and involve iterative schemes which are time consuming. In this study, a suitable model based on artificial neural network (ANN) technique was proposed for estimation of factor to friction in pipe flow. Multilayered perceptron (MLP) neural networks with feed-forward back-propagation training algorithms were designed using the neural network toolbox for MATLAB®. The input parameters of the networks were pipe relative roughness and Reynold’s number of the flow, while the friction factor was used as the output parameter. The performance of the networks was determined based the mean on absolute percentage error (MAPE), mean squared error (MSE), sum of squared errors (SSE), and correlation coefficient (R-value). Results have shown that the network with 2-20-31-1 configuration trained with the Levenberg-Marquardt 'trainlm' function had the best performance with R-value (0.999), MAPE (0.68%), MSE (5.335xI0-7), and SSE (3.414x10-4). A graphic user interface (GUI) with plotting capabilities was developed for easy application of the model. The proposed model is suitable for modeling and prediction of friction to factor in pipe flow for on-line computer-based computations.
Artificial neural network modeling of heat transfer in a staggered cross-flow tube type heat exchanger
(Akamai University, 2008-11) Fadare, D. A.; Fatona, A. S.
This paper presents the application of Artificial Neural Network (ANN) in modeling the heat transfer coefficient of a staggered multi-row, multi-column, cross-flow, tube-type heat exchanger. Heat transfer data were obtained experimentally for air flowing over a bank of copper tubes arranged in staggered configuration with 5 rows and 4 columns at different air flow rates with throttle valve openings at 10 - 100%. The Reynolds number and the row number were used as input parameters, while the Nusselt number was used as output parameter in training and testing of the multi-layered, feed-forward, back-propagation neural networks. The network used in this study was designed using the MATLAB® Neural Network Toolbox. The results show that the accuracy between the neural networks predictions and experimental values was achieved with Mean Absolute Relative Error (MRE) less than 1 and 4% for the training and testing data sets respectively, suggesting the reliability of the networks as a modeling tool for engineers in preliminary design of heat exchangers.
Artificial neural network predictive modeling of uncoated carbide tool wear when turning NST 37.2 steel
(Asian Research Publishing Network, 2012-04) Asafa, T. B.; Fadare, D. A.
We report the development of a predictive model based on Artificial Neural Network (ANN) for the estimation of flank and nose wear of uncoated carbide inserts during orthogonal turning of NST (Nigerian steel) 37.2. Turning experiments were conducted at different cutting conditions on a M300 Harrison lathe using Sandvic Coromant uncoated carbide inserts with ISO designations SNMA 120406 using full factorial design. Cutting speed (v), feed rate (f), depth of cut (d), spindle power (W), and length of cut (l) were the input parameters to both the machining experiments as well as the ANN prediction model while the flank wear (VB) and nose wear (NC) were the output variables. Nine different structures of multi-layer perceptron neural networks with feed-forward and back-propagation learning algorithms were designed using the MATLAB Neural Network Toolbox. An optimal ANN architecture of 5-12-4-2 with the Levenberg-Marquardt training algorithm and a learning rate of 0.1 was obtained using Taguchi method of experimental design. The results of ANN prediction show that the model generalized well with root mean square errors (RMSE) of 3.6% and 4.7% for flank and nose wear, respectively. With the optimized ANN architecture, parametric study was conducted to relate the effect of each turning parameters on the tool wear. The ANN predictive model captures the dynamic behaviour of the tool wear and can be deployed effectively for online monitoring process.
Assessment of household energy utilization in Ibadan, Southwestern Nigeria
(Scientific Research, 2012) Waheed, M. A.; Oni, A. O.|; Fadare, D. A.; Sulaiman, M. A.
Energy and exergy analysis was conducted for a vegetable oil refinery in the Southwest of Nigeria. The plant, powered by two boilers and a 500 kVA generator, refines 100 tonnes of crude palm kernel oil (CPKO) into edible vegetable oil per day. The production system consists of four main group operations: neutralizer, bleacher, filter, and deodorizer. The performance of the plant was evaluated by considering energy and exergy losses of each unit operation of the production process. The energy intensity for processing 100 tonnes of palm kennel oil into edible oil was estimated as 487.04 MJ/tonne with electrical energy accounting for 4.65%, thermal energy, 95.23% and manual energy, 0.12%. The most energy intensive group operation was the deodorizer accounting for 56.26% of the net energy input. The calculated exergy efficiency of the plant is 38.6% with a total exergy loss of 29,919 MJ. Consequently, the exergy analysis revealed that the deodorizer is the most inefficient group operation accounting for 52.41% of the losses in the production processes. Furthermore, a critical look at the different component of the plant revealed that the boilers are the most inefficient units accounting for 69.7% of the overall losses. Other critical points of exergy losses of the plant were also identified. The increase in the total capacity of the plant was suggested in order to reduce the heating load of the boilers. Furthermore, the implementation of appropriate process heat integration can also help to improve the energy efficiency of the system. The suggestion may help the company to reduce its high expenditure on energy and thus improve the profit margin.
Carbon(IV)oxide Capture and Sequestration in Nigeria: Prospects and Challenges
(Society of Petroleum Engineers, 2006) Isehunwa, S. O.; Makinde, A. A.; Olamigoke, O.
The capture and storage of carbon dioxide (CCS) produced during the combustion of fossil fuels now offers one option for attaining large scale reductions in the emissions of greenhouse gases and thus, promote a clean environment. It is now becoming clear that CCS technologies could promote the use or consumption of fossil fuels than otherwise previously thought. This paper presents an overview of the techniques involved in the capture and sequestration of carbondioxide(CO). The opportunities and the challenges of the application of CCS in Nigeria are considered. It is concluded that the development of gas utilization schemes and power plants makes it imperative for Nigeria togive attention to CCS technologies.
Classical modelling of the effect of heterogeneity on reservoir performance of agbada formation
(Nova Science Publishers, Inc., 2015) Akintola, S. A.; Akinsete, O. O.; Akan, O. G.
Understanding the basic mechanisms that govern flow of hydrocarbon in any given reservoir situation is necessary in developing reliable methods of predicting behaviour in that reservoir. Most reservoirs in Agbada Formation of the Niger Delta Basin are anisotropic and therefore heterogeneous, which is a vital parameter in the efficient production of hydrocarbons. This work looked at the effect of permeability anisotropy (Kv/Kh) or heterogeneous distribution and its effects on reservoir performance using windows based IPM-MBAL petroleum engineering software. Results analysis revealed that anisotropy makes reservoir production modelling more realistic than the isotropic scenarios, and degree of heterogeneity improves oil recovery from the reservoir (Kv/Kh = 1, R.F = 49.31%; Kv/Kh = 0.1, R.F = 49.95%; Kv/Kh = 0.001, R.F = 50.60%; Kv/Kh = 0.0001, R.F = 51.24%). Reservoir heterogeneity should be included in reservoir modelling practices because it has a significant effect on hydrocarbon production.
Comparative analysis of different inocular conditions on the performance of a bioreactor in the treatment of operationally exhausted metal working fluids (MWFs)
(2011-01) Ogunjobi, A. A..; Fadare, D. A.; Dauda, O. F.; Fagade, O. E
This work studied the biodegradation of the spent MWFs by using two inocular conditions: Indigenous microorganisms and seeding with Pseudomonas and Bacillus species (both earlier isolated from MWFs) both in a locally designed bioreactor. The performance of each inoculum condition was monitored by total viable bacteria counts, chemical oxygen demand (COD), biological oxygen demand (BOD), total organic carbon (TOC), oil and grease content and heavy metals residue over a 15-day period. For indigenous organisms alone, the total viable counts ranged from 30 × 107 to 5 × 103 cfu/ml; COD, BOD & TOC showed reduction of approximately 34%, 37% and 24% respectively; oil and grease showed approximately 8% reduction and the results for heavy metal residues showed about 20% reduction for all heavy metals analyzed (Cu, Pb, Cd, Cr). Inoculation with the laboratory isolates showed high total viable counts throughout from initial 10 × 107 cfu/ml to 3 × 107cfu/ml at the end of the period; COD, BOC, and TOC showed 44%, 51% and 37% reduction respectively; oil and grease content was reduced by 33% and result for heavy metals showed over 30% reduction for Pb and Cu while Cd and Cr were below 20% reduction. The results showed that bio-augmentation with the laboratory isolates performed better than indigenous microorganism in the degradation of MWFs.
Conversion coating pretreatment enhances pipeline integrity
(2018) Oki, M.; Adediran, A. A.; Ogunsemi, B.; Akintola, S. A.; Charles, E.
It is necessary to coat both the internal and external surfaces of pipelines which transport different types of fluids that are usually contaminated with various percentages of aggressive corrosives. Pipelines pass through various terrains and highly challenging environments hence the need for both internal and external coatings to prevent corrosion and its adverse effects. In order to improve on the longevity of pipelines and the adhesion of the coating system, it is preferable to conversion coat blasted surfaces prior to application of any coating system. Improvements in pipeline coatings applications and the often neglected chromating procedure prior to coating application have been highlighted. This will prolong the lifespan of pipeline networks and secure these strategic assets from being a source of both materials and human resources drain pipes. However, the use of corrosion resistant alloys as internal lining for carbon steel pipes in the petroleum/gas industry is fast gaining recognition.
A correlation for predicting the viscosity of Nigerian crude oils
(Society of Petroleum Engineers, 1990) Amoo, O. A.; Isehunwa, S. O.
An empirical equation that could be used for predicting the viscosity of Nigerian crudes is presented in this paper. The correlation uses oil specific gravity as the main correlating parameter, and also incorporates the effects of reservoir pressure, temperature and oil formation volume factor. Data from well over 400 samples of Nigerian crudes were used for developing the correlation, with most samples however, from reservoirs at or above saturation pressure. The results show however that the correlation could have general applicability. The correlation when compared with some earlier works proved to be more accurate for Nigerian crudes. Graphical and Statistical error analyses undertaken suggest good performance and accuracy. The correlation should prove valid for estimating the viscosity of Nigerian crudes, as well as other crude types having properties that fall within the range of the data used in this work.
A correlation to predict the viscosity of light crude oils
(Society of Petroleum Engineers, 2006) Isehunwa, O. S.; Olamigoke, O.; Makinde, A. A.
Direct viscosity measurements are often expensive or unavailable. Therefore, empirical correlations are often used for predicting the viscosity of crude oils. However, several published correlations are either too simplistic or too complex for routine operational use. Many of the common correlations in use were developed using data from other regions of the world, Empirical correlations for predicting the viscosity of light crude oils in the Niger Delta have been presented in this paper. Data from over 400 oil reservoirs from the Niger Delta were collected. The samples were representative of the two crude oil viscosity regimes: above and below the bubble point. After normal quality checks, non-linear multiple regression with linear partial correlation coefficient techniques were used to establish simple correlations between viscosity, pressure, temperature, oil specific gravity and solution gas oil ratio. Statistical error analysis of the developed correlation showed average absolute relative percentage error of 4.00% and 3.25% and R2 of 0.99 and 0.97 for oil viscosity above and below the bubble point respectively. These results constitute considerable improvements over existing correlations.
Correlations for nusselt number in a staggered cross-flow tube-type heat exchanger
(Obafemi Awolowo University, Ife, 2009) Fadare, D. A. D. A.
Empirical correlations for Nusselt number (Nu) in a staggered multi-row multi-column cross-flow rube-type heat exchanger is presented in this paper. In the experiment, air at ambient temperature was drawn by a centrifugal fan perpendicularly over banks of cylindrical rods arranged in staggered configuration of 5 rows by 4 columns. A test element consisting of a tube of copper with length, internal and external diameters of 0.125, 0.0115 and 0.0125 m was heated to a maximum temperature of about 90°C and inserted into the air stream in the working section. Rate of cooling was measured by thermocouple embedded at the centre, via a digital multimeter which was connected to a computer for monitoring temperature data. A semi-logarithms plot of the data was used to calculate the heat transfer coefficient (h) between the copper element and air, and hence the Nusselt number. The heat transfer coefficient at the centre of each of the four columns at ten different flow rates with throttle valve openings ranging from 10 - 100% were investigated. Results showed that the Nusselt number increased exponentially with increase in air flow rate and also increased in successive columns in the direction of flow at a diminishing rate. Correlations of Nusselt number with Reynolds number (Re) were developed for preliminary design and performance assessment of staggered multi-row multi-column cross-flow tube-type heat exchanger.