Drilling activities have progressed to deep and ultra deep seas in recent times and with it comes more challenges. Due to the difficulty of directly obtaining important parameters like in-situ stress and fracture gradient, simple models have been evolved. This study is a novel attempt to make up for the gap inherent in such models namely that they neglect chemical and thermal effects, settling for only effective stress and a time-dependent analysis. The study applied the Neural Network (NN) technology to predict geomechanical parameters. Neural Network (NN) as a branch of Artificial intelligence (AI) possesses the ability of training available parameters to replace data that cannot be immediately or easily acquired. Data of a well drilled in the Niger Delta Region of Nigeria was used as the case study. A training set of input data was used to train the network and a validation set ensured a completely independent measure of network accuracy. A Neural Network model was developed in Neuroph Studio, Java neural network platform and the Netbeans IDE. The model has the advantage of being easy to use, open source, cross-platform and generally designed to save the cost associated with wellbore instability.
The number of multilateral wells drilled in the Niger Delta all geared towards increasing recovery as well as other field development objectives. However, a major challenge facing the successful drilling of these special trajectory wells is hole instability. The cost of instability according to some operators can be as high as $ 600MM per Year. Wellbore instability poses a unique threat in multilateral wells such that the tendency for collapse or fracture increases at the bore hole junctions. Casing deformation, difficulty in through-put of drilling tools and equipments, to mention but a few, are among the major problems associated with this instability. This research revisits the issue of wellbore stability with emphasis on multilateral wells. A modified failure model is developed by including a stress concentration factor based on the junction configurations. The Mogi criterion was applied to account for intermediate stress and predict the minimum mud pressure below which shear failure will occur. A risk and uncertainty factor is attached to this model for sensitive model parameters including stress concentration factors, and lateral bore entry angle. Results from two case studies presented in this work showed that collapse gradient prediction by Mogi based model were more reliable than those of Mohr based model. In case 1, a minimum mud weight of 0.52 psi/ft was predicted to be sufficient to maintain stability. A mud weight of 0.53 was used to drill the lateral section and hole pack offs were encountered at build angle of 30°. However, the Mogi prediction was 0.57 and sufficiently prevented hole collapse. In case 2, the Mogi predictions are all well above the actual mud weight used as against the underestimation with Mohr's.
Crude oil exploration involves various operational processes and practices such as remote sensing methods of mapping. Moreover, radioactive detectors and explosives associated with seismic exploration, drilling and production equipment, LWD, MWD, uncontrollable wellbore instability, stuck pipe and loss of logging tools in hole issues leading to sidetracking, abandonment activities which leaves an impact on the environment. In this work, the radiological hazard indices in an abandoned oil well of Niger Delta was evaluated in the soil/sediment and water samples of the area to ascertain the impacts on the area and possible solutions were recommended. For soil samples, the mean value of Annual Gonadal Equivalent Dose (AGED) of the five locations was 665.25Ϯ65.07mSvy Ϫ1 and that for Annual Effective Dose Equivalent (AEDE) (Outdoor and Indoor) were 115.75Ϯ11.86Svy Ϫ1 and 463.02Ϯ44.44Svy Ϫ1 respectively. The Excess Lifetime Cancer Risk mean value was (0.41Ϯ0.04) ϫ 10 3 . The mean values for Representative gamma index, I␥, the External Hazard index, H ex , and the Internal Hazard Index, H in , were 0.62Ϯ0.01, 0.53Ϯ0.06 and 0.66Ϯ0.02 respectively. The radiation indices for the water at the abandoned well site, and many locations in the village were also calculated. For soil/sediment samples, the mean value for these indices were above the permissible value which showed radiological elevation in the areas. The radiological elevation from the percentage risk analysis signifies a radiological burden on the people and the environment of these areas and there is the possibility of developing cancer due to exposure to radiation before the age of 70 years for the people living in the areas.
Management of chemical instability in troublesome shales has intensified the need for operators to formulate less reactive muds to safely drill to target depths. While Oil Based Muds (OBMs) mitigate chemically induced drilling problems, they pose constraining issues including disposal and government regulations. Synthetic based drilling fluids (SBF) are a relatively new class of drilling muds that are useful particularly for deepwater and deviated hole drilling. While maintaining chemical stability in drilling operations disposal and biodegradability problems are taken care of. Nevertheless, the base oil for formulating SOBMs is imported at high costs. This work proposes the approach of using local materials such as palm kernel oil, soya beans oil, groundnut oil and palm oil as a base fluid in formulating synthetic base mud. The rheological properties of the locally produced pseudo oil based mud systems were characterized and compared with imported POBM based on API specification. After running chemical analysis, testing the rheological properties and stability of the mud, the palm kernel oil, soya beans oil and ground oil showed comparative results. Developing a local SBM is in line with the local content drive of the federal government and will open new opportunities in the oil and gas industry in Nigeria. SOBMs are biodegradable and thus environmental concerns are ruled out. It will also help us earn foreign exchange for the country by exporting surplus in Gulf of Guinea region. Product substitution from local materials will reduce foreign exchange expenditure and job exportation.
The demand for greater efficiency and large capacity for liquefaction process is inevitable for optimization. This study presents the sensitivity analysis of the factors that affects liquefaction processes of natural gas. Some of these factors are the natural gas pressure, temperature and composition on the single mixed refrigerant liquefaction process was simulated using ASPEN HYSYS 8.6 software. The effects of these parameters on specific power, power consumption and refrigerant flow rate of the process were simulations and examined. At constant pressure, temperature decreased from 15 to 5°C resulted in a 15% decrease in specific power and an increase from 15 to 25 °C resulted to 40% increase. At constant temperature, a decrease in natural gas pressure from 60 to 30bar and increase in specific power from 0.387 to 0.452 kWh/kg-LNG was observed which amounts to a 16.80% increase and when increased from 60 to 90bar specific power decreases from 0.387 to 0.348kWh/kg-LNG about 10.08 % decrease. Thus, when natural gas is supplied at a given pressure and temperature, a decrease in supply pressure will increase power consumption and an increase in supply pressure will decrease power consumption. The useful exergy for the system was about 26% of the total energy (46.42MW) available, indicating that about 74% of energy supplied by the compressor ended up as losses in different components in the process liquefaction cycle. However, the largest loss occurred in LNG heat exchanger and cooler which were to 25 and 24% respectively. The simulation results showed that, natural gas supplied at 150MMScf, 60bar and 15°C gave rise to LNG production of about 0.95MTPA.
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