The mechanistic modeling of mineral crystallization is essential for the understanding and control of many natural and industrial processes. In the past century, many mechanisms and models have been proposed to explain observations in different crystallization stages. However, most models only focus on a certain step or mechanism (e.g., nucleation, aggregation) and lack a comprehensive view. Incorporating nucleation, aggregation, and surface reaction together, this study developed an analytical two-stage crystallization model to simulate the particle size and number concentration versus time and correlate them with the measured solution turbidity. Through measuring solution turbidity in real time, this model can reproduce the crystallization process by predicting the key parameters: nucleation rate, particle size, number concentration, surface tension, induction time, and particle linear growth rate. Most of these values for barite crystallization match with literature data and our direct cryo-transmission electron microscopy (cryo-TEM) measurements. Moreover, the established relationships of these key parameters versus temperature and supersaturation enable this model to predict barite crystallization kinetics based only on the initial supersaturation and temperature. This study is a potential starting point to more quantitatively and comprehensively analyze and control mineral crystallization, important to various science and engineering applications.

Additives play an important role in crystallization controls in both natural and industrial processes. Due to the lack of theoretical understanding of how additives work, the use and design of additives in various disciplines are mostly conducted empirically. This study has developed a new theoretical model to predict the additive impacts on crystallization based on the classical nucleation theory and regular solution theory. The new model assumes that additives can impact the nucleus partial molar volume and the apparent saturation status of the crystallization minerals. These two impacts were parametrized to be proportional to additive concentrations and vary with inhibitors. As a practical example, this new model has been used to predict barite induction times without inhibitors from 4 to 250 °C and in the presence of eight different scale inhibitors from 4 to 90 °C. The predicted induction times showed close agreement with the experimental data published previously or produced in this study. Such agreement indicates that this new theoretical model can be widely adopted in various disciplines to evaluate mineral formation kinetics, elucidate mechanisms of additive impacts, predict minimum effective dosage (MED) of additives, and guide the design of new additives, to mention a few.

A reliable anoxic plug flow reactor has been developed to study iron sulfide (FeS) precipitation kinetics, solubility, and phase transformation simultaneously. The effects of temperature (23–125 °C), ionic strength (0.00886–5.03 mol/kg), and ferrous iron [Fe­(II)] to sulfide [S­(-II)] concentration ratio (1:20 to 1:5) were studied. The kinetics of FeS precipitation was found to be a pseudo-first-order reaction with respect to Fe­(II) concentration, when Fe­(II) concentration is significantly lower than the S­(-II) concentration. FeS precipitation kinetics can be accelerated by high temperature and high ionic strength but not be affected by the Fe­(II) to S­(-II) concentration ratio at the tested ratio range. A model for predicting FeS solubility and precipitation was developed by using FeS solubility data measured in this study and data from literature. At a temperature ≤100 °C, freshly precipitated FeS was found to be mackinawite. Mackinawite can transform to troilite at a temperature ≥50 °C, and low ionic strength favors the phase transformation. Also, mackinawite formed from steel corrosion seems to be easier to transform to troilite than the mackinawite formed from precipitation. This study presented a new approach for iron sulfide study and contributed valuable FeS thermodynamics and kinetics data for FeS prediction and control in industry.

Scale inhibitors are widely used to prevent mineral scale deposition and precipitation in industrial processes. In this study, the performance of scale inhibitors is systematically investigated in a flow loop apparatus, designed to mimic the barite deposition in pipelines. The inhibitors are tested in tubing with and without a precovered barite scale layer. DTPMP (diethylenetriamine-pentamethylene phosphonic acid), PPCA (phosphinopoly carboxylic acid), and SPCA (sulfonated poly­(carboxylic acid)) are the main inhibitors tested in this work. The heterogeneous deposition rate constants at 120 °C in a clean tubing in the presence of 0.25 ppm of DTPMP/PPCA/SPCA are 0.00, 10.3 × 10–5, and 32.1 × 10–5 cm/s, respectively. The deposition rate constants at 120 °C in a tubing fully covered with barite in the presence of 2.0 ppm of DTPMP, PPCA, or SPCA are 5.97 × 10–5, 36.8 × 10–5, and 28.0 × 10–5 cm/s, respectively. In tubing fully covered with barite, all three inhibitors require a higher concentration to reach the same level of inhibition as observed in the uncoated tubing. The experiments in fully covered tubing were conducted in a wide temperature range (50–150 °C) with various dosages of inhibitors (0.02–40 ppm). The impact of inhibitors on deposition rate constants is modeled with a Langmuir-type equation by using an adsorption equilibrium constant K eq, fractional occupancy θ, and unoccupiable fraction θ0 in the fully covered tubing. θ represents the fraction of active sites occupied by the inhibitors, and θ0 represents the fraction that cannot be occupied by the inhibitors. The adsorption isosteric enthalpies of functional groups on the inhibitor molecules are calculated from the fitted associate equilibrium constants. A kink site adsorption mechanism has been proposed as the inhibition mechanism.

Summary Numerous saturation indices and computer algorithms have been developed to determine whether, when, and where scale will form. However, scale prediction can still be challenging because the predictions from different models often differ significantly at extreme conditions. Furthermore, there is a great need to accurately interpret the partitioning of water (H2O), carbon dioxide (CO2), and hydrogen sulfide (H2S) between different phases, as well as the speciations of CO2 and H2S. This paper summarizes current developments in the equation-of-state (EOS) and Pitzer models to accurately model the partitioning of H2O, CO2, and H2S in hydrocarbon/aqueous phases and the aqueous ion activities at ultrahigh-temperature, ultrahigh-pressure, and mixed-electrolytes conditions. The equations derived from the Pitzer ion-interaction theory have been parameterized by regression of more than 10,000 experimental data from publications over the last 170-plus years using a genetic algorithm on the supercomputer DAVinCI at Rice University. With this new model, the 95% confidence intervals of the estimation errors for solution density are within 4×10–4 g/cm3. The solubility predictions of CO2 and H2S are accurate to within 4%. The saturation-index (SI) mean values for calcite (CaCO3), barite (BaSO4), gypsum (CaSO4·2H2O), anhydrite (CaSO4), and celestite (SrSO4) are accurate to within ±0.1—and for halite the values are within ±0.01—most of which are within experimental uncertainties. This model accurately defines the pH value of the production tubing at various temperature and pressure regimes and the risk of H2S exposure and corrosion. Furthermore, our model is able to predict the density of soluble chloride and sulfate (SO42−) salt solutions within ± 0.1% relative error. The ability to accurately predict the density of a given solution at temperature and pressure allows one to deduce when freshwater breakthrough will occur. In addition, accurate predictions can only be reliable with accurate data input. The need to improve the accuracy of scale prediction with quality data will also be discussed.