The study aimed to develop energy flow diagram (Sankey diagram) of Sudan for the base year 2014. The developed Sankey diagram is the first of its kind in Sudan. The available energy balance for the base year 2012 is a simple line draw and did not count the energy supply by private and mixed sectors such as sugar and oil industries and marine and civil aviation. The private and mixed sectors account for about 7% of the national grid electric power. Four energy modules are developed: resources, transformation, demand, and export and import modules. The data are obtained from relevant Sudanese ministries and directorates and Sudan Central Bank. “e!Sankey 4 pro” software is used to develop the Sankey diagram. The main primary types of energy in Sudan are oil, hydro, biomass, and renewable energy. Sudan has a surplus of gasoline, petroleum coke, and biomass and deficit in electric power, gasoil, jet oil, and LPG. The surplus of gasoline is exported; however, the petroleum coke is kept as reserve. The deficit is covered by import. The overall useful energy is 76% and the loss is 24%. The useful energy is distributed among residential (38%), transportation (33%), industry (12%), services (16%), and agriculture (1%) sectors.

Most of the corrosion inhibitors that are used in industry contain chemicals that are harmful to health and environment. Corrosion inhibitors derived from green sources are, therefore, believed to be a good option for replacing the chemical corrosion inhibitors. In this work, a green oleochemical corrosion inhibitor derived from Jatropha Curcas is introduced. The paper discusses the methodology of deriving the corrosion inhibitor as well as the experimental test conducted for evaluating its corrosion inhibition efficiency. The new oleochemical corrosion inhibitor was derived via two reactions. Jatropha oil was firstly saponified with sodium hydroxide to yield gras acid and glycerol, which was then esterified with boron fluoride in presence of excess methanol to produce the oil methyl esters, which is used as oleo-chemical corrosion inhibitor. To evaluate the oleo-chemical corrosion inhibitor, the corrosion rate of mild steel in NaCl corrosive medium with CO2 is tested at static condition and two dynamic conditions, namely 500 and 1500 rpm. This is to simulate the transitional and turbulent flow in a pipeline. At each dynamic condition, the proposed corrosion inhibitor was tested at concentration dosages of 0, 50, 100, and 150 ppm. The experiments results revealed a good performance of the new oleochemical corrosion inhibitor. The inhibition efficiency was found to be highly affected by the concentration of corrosion inhibitor. Total corrosion inhibition of the mild steel was noticed by using 150 ppm at dynamic condition of 500 rpm.

Isothermal oil compressibility coefficient is one of the physical properties that requires an exact description for applied and theoretical science applications, especially in the solution of petroleum reservoir engineering problems. Conventional empirical correlations are however inconsistent and yield high error due to high input parameters needed and regional crudes-based development. For a reservoir with pressure below bubble point, the effect of co to the fluid flow is insignificant as it is overshadowed by the presence of large gas compressibility (cg). This study aims to increase the range of applicability and accuracy of the formula used for estimating the co by eliminating the limitations that occur in existing correlations. A new formula for the estimation of the coefficient of isothermal oil compressibility below bubble point pressure is devised using Adaptive Neuro-Fuzzy Inference System (ANFIS). The approach is a combination of neural networks and fuzzy logic. This method targets to model imprecise mode of reasoning in order to make rational decisions in an environment of uncertainty and imprecision. A benchmark has been set based on the best model available in the literature using the current set of data. Trial-and-error approach was followed with the assist of the trend analysis to check a model that represents the true phenomenon. A total number of 369 data points were collected from worldwide fluid samples for the purpose of training and testing the model. Exhaustive trend analysis has been conducted to verify that the proposed ANFIS model honors the true physical behavior. The new proposed model found to follow the correct trend which implies its reliability. In addition, a comparative study was carried out using the best available correlations to confirm the significance of the results of the oil compressibility prediction using ANFIS. Different statistical analyses have been shown to verify the robustness of the newly developed model. The statistical analyses showed a positive outcome whereby the proposed model obtained the lowest average absolute percent relative error of 3.3976% and the highest correlation coefficient of 99.76%. The best model tested among the other models has five input parameters and average absolute percent relative error of 12.07% and a correlation coefficient of 98.27%. The new approach managed to produce the most accurate model to predict the coefficient of isothermal oil compressibility below the bubble point when compared to the best available models in the literature. The new proposed model overcome the limitations described by the locality of some correlations as they are depending on data from certain locations.

In the last few years, water shut off (WSO) treatments in production wells have started to become as a part of standard well service. Decision making, taking into account the uncertainties of reservoir and well data, is a difficult job with regard to WSO treatment especially with the current high oil price. The success of any relative permeability modifier (RPM) treatment is depending on the pre-treatment reservoir and well production parameters. The uncertainty within these parameters leads to improper prediction of the post treatment condition and consequently a wrong WSO treatment decision may be taken. An RPM WSO treatment evaluation model was developed based on Monte Carlo simulation and a developed radial flow equation. The model predicts the post-treatment water cut and production rates specifying a range that extends from minimum output value to a maximum value in order to alienate any chances of associated risks. The simulation inputs and results are also illustrated as probabilistic distributions providing a complement to the previously cited production results. Decision tree model is used to choose the best option among different RPM WSO treatment scenarios.

Well intervention performed on oil or gas well often involves the injection of different stimulating fluids or chemical solutions that aims to increase the production rate. The main objective of this paper is to identify the effect of uncertainty in different variables and parameters used to quantify well productivity and injectivity. Monte Carlo simulation technique is used to develop probabilistic models for radial Darcy's inflow on the one hand and near wellbore water-based chemical injection on the other hand. The probabilistic model is based on assigning probability density function for all variables and parameters used in the governing formulas. Variance-based sensitivity analysis (VBSA) was performed to quantify the contribution and the correlation between different model's inputs and outputs. Results indicate that some rough assumptions for about 60% of injectivity model's parameters and factors, i.e., value with considerable error/uncertainty, can still result in output with small standard deviation in comparison with other parameters. In Darcy's law, the uncertainty in reservoir pressure value affects the calculated flow rate two times higher than the effect of the formation of permeability or produced fluid viscosity. At low drawdown condition, about 50% of Darcy's flow variance is caused by the uncertainty in reservoir pressure input value. Throughout VBSA, it is also found that data accuracy of variables and parameters used in the injectivity model is not of importance as for formation permeability, injected fluid viscosity, pressure, and temperature of the injected fluid. Application of this methodology will focus on the cost of information needed by the decision makers and will save a lot of efforts and resources needed to apply confirmation tests or to validate different data sets.