Matrix-acidizing operations have been accounted to be the most hazardous and environmentally harmful among all the well-stimulation techniques. For instance, diesel oil-based emulsified acids have been prohibited from usage due to their high level of toxicity. There is, therefore, a dire need for emulsified acids that are environmentally viable and technically competent to replace the diesel-based emulsified acids. In this study, a novel oil-based environmental friendly emulsified acid has been synthesized from Jatropha curcas oil and, then, compared against diesel and palm oil-based emulsified acids. The technical evaluation of the three acids has been done based on experimental results obtained from thermal stability, droplet size analysis, rheological study, acid solubility, and toxicity screening. In addition, core flooding experiments have been conducted to evaluate the performance of the three emulsified acids as well stimulants. The results revealed that Jatropha oil-based emulsified acid has the potential to replace diesel-based emulsified acid. Jatropha oil-based emulsified acid was found to perform better than the diesel-based emulsified acid as indicated by having greater thermal stability and more popular rheological properties at varying temperatures of ambient, 50 and 70 °C. Furthermore, it possessed a lower toxicity load and a higher retardation effect on acid solubility than that of the diesel oil-based emulsified acid. The core flooding results have also indicated better well-stimulation performance of Jatropha-based emulsified acid as compared with dieselbased emulsified acids.

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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.

Asphaltenes deposition is considered as Achilles's heel in the oil industry. The nucleation, precipitation and deposition of asphaltenes reduce the production rate significantly in affected wells and sometimes it can completely block the flow by plugging the flowlines, tubing and process facilities, in severe cases. This chapter evaluates the extrinsic and the intrinsic (thermodynamic) factors within the heavy crude oil production system. The main consequences of asphaltenes deposition are discussed such as the solvent-to-crude oil dilution ratio, crude oil physical properties (cloud point, pour point and API gravity), chemical solvent type (carbon number, functional group), agitation time and temperature changes. This chapter is expected to become the means for understanding the factors affecting the asphaltenes nucleation, precipitation and deposition.