Sand production can lead to various problems, including erosion in production flow lines that may lead to total production loss for extended periods and costly workover operations. The extent of erosive damage is determined by many factors, among which the flow and sand rates are the most significant. Three main issues must be addressed to ensure an efficient production operation: Sand erosion estimation, Sand monitoring (settling and deposition), and maintaining optimum production rates. If sand production exceeds certain levels, i.e., allowable sand rate, the erosion in the production network becomes problematic. Sand production has been problematic in some wells in Reservoir X. Core, and historical production data was used to build a comprehensive model using Schlumberger PIPESIM™ hydraulic modelling. The software allows for detailed modelling of the production network by which erosion rate, erosion hotspots, and deposition of the produced sand can be quantitatively analyzed. Considering an allowable erosion rate of 0.3 mm/year, the model outcomes indicate that sand erosion is critical in wells J-1, J-2, and L-2. The next step was identifying the hotspots where the produced sand is deposited in the abovementioned wells. The modelling results indicated that sand deposition is primarily severe in the teeline between the platforms. Moreover, the gas-oil ratio was identified as the most influential factor in the sand deposition. Lastly, a sensitivity analysis was conducted on the allowable flow rates and maximum (technical) allowable sand production and erosion rates to find optimum production rates from reservoir X.

CO2 sequestration is among the most anticipated methods to mitigate the already detrimental concentrations of CO2 in the atmosphere. Among sequestration methods, CO2 injection into oceans is of great significance due to the oceans' large sequestration capacity. However, there are concerns about the changes in water pH as CO2 is injected into oceans. Previous studies in conditions representative of CCS in the ocean are scarce. In the current study, we experimentally measure the pH and solubility at pressures up to 400 atm, temperatures between 283 and 298 K, and different aqueous solutions in a high-pressure autoclave reactor. The results indicated that increasing pressure increases the solubility of CO2 in aqueous solutions, resulting in lower pH values. In contrast, increasing salinity and temperature lowers the solubility and, as a result, increases the system's pH. Among all the tested aqueous solutions, the synthetic seawater mimicked that of a potential injection point in the South China sea, exhibiting the highest salting-out effect and, therefore, the lowest solubility (i.e., the highest pH). The experimental dataset of this study was fed to a machine learning algorithm, Group modeling data handling (GMDH), to develop an explainable, white-box solubility model. The model could predict the pH as a function of solubility, temperature, pressure, and salinity with an accuracy of 0.87. The pH values from the model were compared to those from previous studies, and a good agreement among the values was found. Lastly, a parameter importance analysis was conducted to shed further light on the model's performance. Pressure and temperature were found to be the most and the least influential factors, respectively. As the implantation of the technology is currently being considered in China, the current study can pave the way to better understand the interactions and mechanisms involved in conditions representative of ocean sequestration before large-scale operations.

From exploration to production, the permeability of reservoir rocks is essential for various stages of all types of hydrocarbon field development. In the absence of costly reservoir rock samples, having a reliable correlation to predict rock permeability in the zone(s) of interest is crucial. To predict permeability conventionally, petrophysical rock typing is done. This method divides the reservoir into zones of similar petrophysical properties, and the permeability correlation for each zone is independently developed. The challenge of this approach is that the success depends upon the reservoir's complexity and heterogeneity and the methods and parameters used for rock typing. As a result, in the case of heterogeneous reservoirs, conventional rock typing methods and indices fail to predict the permeability accurately. The target area is a heterogeneous carbonate reservoir in southwestern Iran with a permeability range of 0.1-127.0 md. In this work, two approaches were used. First, based on permeability, porosity, the radius of pore throats at mercury saturation of 35% (r35), and connate water saturation (Swc) as inputs of K-nearest neighbors, the reservoir was classified into two petrophysical zones, and then, permeability for each zone was estimated. Due to the heterogeneous nature of the formation, the predicted permeability results needed to be more accurate. In the second part, we applied novel machine learning algorithms, modified group modeling data handling (GMDH), and genetic programming (GP) to develop one universal permeability equation for the whole reservoir of interest as a function of porosity, the radius of pore throats at mercury saturation of 35% (r35), and connate water saturation (Swc). The novelty of the current approach is that despite being universal, the models developed using GP and GMDH performed substantially better than zone-specific permeability, index-based empirical, or data-driven models used in the literature, such as FZI and Winland. The predicted permeability using GMDH and GP resulted in accurate prediction with R2 of 0.99 and 0.95, respectively, in the heterogeneous reservoir of interest. Moreover, as this study aimed to develop an explainable model, different parameter importance analyses were also applied to the developed permeability models, and r35 was found to be the most impactful feature.

In this study, non-isothermal thermogravimetric analysis was conducted on three samples taken from the Bakken Formation at one heating rate (10 degrees C/min). Based on the TGA thermograms, various intervals of weight loss were identified. The differences in weight loss, peak positions, and shapes of the samples' thermograms were attributed to the variations in the composition (quantities of silicates and clay minerals), TOC content, and thermal maturity of the kerogen. Moreover, it was observed that the sample with the highest thermal maturity had the highest number of aromatic rings in its structure. XRD analysis demonstrated that the sample with the lowest silicate concentration and the highest clay mineral content lost the most weight during TGA analysis. Furthermore, CHNS elemental analysis indicated that samples with higher thermal maturity levels have higher amounts of C and H elements and lower content of N and S heteroatoms. Ultimately, our results were corroborated by HRTEM (High Resolution Transmission Electron Microscopy) analysis of p-kerogens, thus confirming the abundance of aromatic rings in the samples and delineating the relationship between aromaticity and thermal maturity.