Reaction of Simple Organic Acid with Calcite: Effect of Reversible Reactions

Matrix acidizing is widely-used in the petroleum industry as a production enhancement technique. In order to design a successful acidizing job, it is important that all aspects of the reaction between the treating acid and the formation rock are understood. The reaction-rate studies involving organic acids seem to present conflicting results regarding the influence of reversible reactions. The primary objective of this paper is to comprehensively investigate the effects of the backward reactions on the kinetics of the acidizing process. In order to understand how weak acids influence the reaction process, a comparative study of the different mathematical models existing in the literature was conducted and its results have been included in this work. Moreover, experimental data were also generated by carrying out experiments with acetic acid on calcite marble disks at different temperatures (80, 150, 200, and 250°F), acid concentrations (0.5, 1.0, 1.5, and 2.0 molar), and disk rotational speeds (100 - 1700 RPM) using a rotating disk apparatus. These studies suggest that the rock porosity and backward reactions can significantly affect the rate of reaction and should not be neglected. For acidizing processes involving weak organic acids (such as acetic acid, formic acid, lactic acid, etc.), it was observed that the dissolution rates estimated by the different models gave distinct results and varied in two order of magnitudes from each other. This large variation can be attributed to the fact that the rate determination process by one method account for the concentration of all the interfacial ions generated during the reversible reactions, whereas the other approach considers only the presence hydrogen ions as a rate affecting parameter. The inclusion of reversible reaction effects on the kinetics study can improve the accuracy up to 55-60%. Therefore, evaluation of all these aspects can lead us to develop a better field approach intended for the use of weak organic acids for well stimulation jobs. It also emphasizes that strong and weak acid systems have very different surface reaction mechanisms and, therefore, their kinetics cannot be estimated in the same manner

Method and system for stability determination of asphaltenes utilizing dielectric constant measurements

A method of determining if unstable asphaltenes are present in a crude oil sample includes obtaining a crude oil sample and performing a fractional analysis of the crude oil sample. In one embodiment, the method further includes measuring, via a cylindrical capacitor, a dielectric constant of the crude oil sample. Responsive to the measured dielectric constant, presence of unstable asphaltenes within the crude oil sample is determined. Responsive to the determined presence of unstable asphaltenes in an amount above a predetermined value, asphaltene precipitation is mitigated by addition of a chemical additive to the well.U

Method and system for stability determination of asphaltenes utilizing dielectric constant measurements

A method of determining asphaltene stability within a crude oil sample that includes obtaining a crude oil sample and performing a fractional analysis of the crude oil sample. The method further includes measuring, via a cylindrical capacitor, a dielectric constant of the crude oil sample and its fractions. Responsive to the measured dielectric constant, the stability of the asphaltenes within crude oil sample is determined.U

Effect of Asphaltene Characteristics on Its Solubility and Overall Stability

Complex molecular structure, high impurity content, and self-association tendency of asphaltenes make the determination of their phase behavior very difficult. Because asphaltene phase behavior is indicative of asphaltene stability within the bulk oil, it is very important to understand its stability. Various production and flow assurance challenges related to precipitation of unstable asphaltenes can be prevented by proper comprehension of asphaltene stability. This study provides a data set on 11 different asphaltenes, which helps us to understand the complicated nature of the components of asphaltenes and crude oils that play an important role in maintaining the stability of asphaltenes. In addition to the physical and chemical characterizations, elemental analysis and ΔPS parameter, which is the indication of the solubility of asphaltenes in different solvents of the bulk oil samples, were measured and evaluated. The results of this study show that the presence of paraffinic wax and water within the crude oil samples along with impurities in the form of reservoir fines can greatly affect the stability of asphaltenes. The organometallic content of crude oil destabilizes asphaltenes, whereas a high fine content increases the stability of asphaltenes

Molecular dynamics simulations of asphaltene aggregation: machine learning identification of representative molecules, polydispersity and inhibitor performance

Molecular Dynamics simulations have been employed to investigate the effect of polydispersity on the aggregation of asphaltene. To make the large combinatorial space of possible asphaltene blends accessible to a systematic study via simulation, an upfront unsupervised machine learning approach (clustering) was employed to identify a reduced set of model molecules representative of the diversity of asphaltene. For these molecules, monodisperse asphaltene simulations have shown a broad range of aggregation behavior, driven by their structural features: size of the aromatic core, length of the aliphatic chains and presence of heteroatoms. Then, the combination of these model molecules in a series of polydisperse mixtures have highlighted the complex and diverse effects of polydispersity on the aggregation process of asphaltene, which yielded both antagonistic, synergistic and seed effects. These findings illustrate the necessity of accounting for polydispersity when studying the asphaltene aggregation process and have permitted to establish a robust protocol for the in-silico evaluation of the performance of asphaltene inhibitors, as illustrated for the case of a nonylphenol resin

Molecular Dynamics Simulations of Asphaltene Aggregation: Machine-Learning Identification of Representative Molecules, Molecular Polydispersity, and Inhibitor Performance

Molecular dynamics simulations have been employed to investigate the effect of molecular polydispersity on the aggregation of asphaltene. To make the large combinatorial space of possible asphaltene blends accessible to a systematic study via simulation, an upfront unsupervised machine-learning approach (clustering) was employed to identify a reduced set of model molecules representative of the diversity of asphaltene. For these molecules, single asphaltene model simulations have shown a broad range of aggregation behaviors, driven by their structural features: size of the aromatic core, length of the aliphatic chains, and presence of heteroatoms. Then, the combination of these model molecules in a series of mixtures have highlighted the complex and diverse effects of molecular polydispersity on the aggregation process of asphaltene. Simulations yielded both antagonistic and synergistic effects mediated by the trigger or facilitator action of specific asphaltene model molecules. These findings illustrate the necessity of accounting for molecular polydispersity when studying the asphaltene aggregation process and have permitted establishing a robust protocol for the in silico evaluation of the performance of asphaltene inhibitors, as illustrated for the case of a nonylphenol resin