Bioenergy has emerged to be among the primary choices for the short- and medium-term replacement of fossil fuels and the reduction in greenhouse gas (GHG) emissions. The most practical method for transforming biomass into biofuel is thermochemical conversion, which may be broken down into combustion, torrefaction, pyrolysis, hydrothermal liquefaction, and gasification. In this study, producing biofuels using a biomass pyrolysis process was investigated. This study explored the pyrolysis process and operating conditions to optimize the process parameters to maximize the desired product yields and quality. The pyrolysis process produces three main products, which are bio-oil, bio-char, and gas. There are three classifications for the pyrolysis method, with each of them producing a majority of a certain product. First, slow pyrolysis is conducted in the temperature range of 300–950 °C and residence time of 330–550 s. It produces around a 30% oil yield and 35% char yield, and thus, the majority yield of slow pyrolysis is char. Second, fast pyrolysis produces around 50% oil, 20% char, and 30% gas yields with a temperature range of 850–1250 °C and a residence time of 0.5–10 s. The average yield of flash pyrolysis was found to be 75% bio-oil, 12% bio-char, and 15% gas, which is conducted within less than 1 s. It was reported that the pyrolysis of biomass was simulated using ASPEN Plus, where the effects of several parameters, such as the temperature, heating rate, and residence time, on the product yield and composition were investigated. Pyrolysis was performed under different conditions ranging from 400 to 600 °C. The effects of different catalysts on the pyrolysis process were studied. It was found that the addition of a catalyst could increase the yield of bio-oil and improve the quality of the product. The optimal operating condition for the pyrolysis process was determined to be a temperature of 500 °C, which resulted in a higher bio-oil yield. It was found that the biofuel yield was enhanced by selecting appropriate raw materials, such as rice husk, along with the pyrolysis temperature (e.g., 450 °C) and particle size (350–800 µm), and using a low residence time and pressure.

Currently, biomolecules flooding in the underground reservoirs acquires sustainable interest owing to their availability and eco-friendly properties. The current study reported chemical displacement by xanthan gum as well as xanthan/SiO₂and xanthan grafted with vinylsilane derivatives. Chemical characterization evaluated by traditional spectroscopic methods. Investigation of fluids response to reservoir environment assessed through rheological performance relative to shearing rate, ionic strength, and thermal stability. A sequence of flooding runs generated on 10 sandstone outcrops with different porosity and permeabilities. Core wetness assessed through relative permeability curves at different water saturation. The flooding tests indicate that grafting of the silica derivative overcome the shortage of xanthan solution in flooding operations relative to the reservoir conditions. The ability of the flooding solutions to alter rock wettability explored through relative permeability curves at different water saturation. The results reveal that the synthesized composite was a promised agent for enhancing oil recovery and profile conformance.

Chemical flooding using a polymer and/or surfactant has been widely applied in oilfields worldwide for enhanced oil recovery. Chemical adsorption in reservoirs has a significant effect on the rock permeability and wettability and hence can affect the overall oil production. In this work, two chemicals, namely, the xanthan gum (XG) biopolymer and sodium dodecylbenzenesulfonate (SDBS) anionic surfactant, were used individually as displacement fluids. The amount of chemical adsorption on the rock surface and the residual resistance factor (permeability reduction) were calculated throughout the flooding experiments using an unconsolidated sandstone (SS) pack model. The effects of the injected chemicals' concentration and reservoir salinity on adsorption capacity have been examined. Additionally, the effect of the addition of nanosilica particles (NSPs) to the injected fluid on the rock adsorption was also investigated. The results showed that the amount of XG and SDBS adsorption on the rock surface increased, albeit to a different extent, by increasing the chemical concentration at the applied salinities (0, 3.5, 5, and 10%) of the displacement fluids. Also, the permeability reduction increased with the increase in XG and SDBS concentrations; however, permeability reduction due to SDBS flooding was lower than that of XG in SS. The use of NSPs as a coinjectant to the XG and SDBS displacement fluids increased the adsorption on the SS rock. A plausible mechanism for the adsorption of the XG/NSP and SDBS/NSP blends on the SS surface was proposed. A density function theory calculation was employed to establish a relation between the adsorptivity of NSPs on SDBS and XG and the total energy and dipole moment of the molecules.

The rate of penetration (ROP) is a critical parameter in drilling which it reflects drilling time, efficiency, and costs. Several approaches have been investigated to handle complex and non-linear drilling parameters for precise prediction for this parameter. Machine Learning remains a promising tool for data analysis and for predicting drilling parameters with acceptable accuracy. However, most of ML algorithms are mainly coded using Python, MATLAB, or R, which require proficiency in programing skills to perform this task. In order to reach an easy, prompt, and precise results, this study conducted Dataiku Data Science Studio (DSS) ML software for predicting ROP through in-memory ML algorithms only, without using advanced or hybrid technologies. The case study was being conducted in an offshore directional gas field in the Nile delta, Egypt. The ML models used in this study are Extreme Gradient Boosted Machine (XGBoost), Gradient Boosted Trees (GBT), K-Nearest Neighbors (K-NN), and Support Vector Machine (SVM). The evaluation metrics used in this study are R ² score, Correlation Coefficient (CC), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). All models’ performance has been validated through learning curves and errors distribution analysis. The output results showed very strong overall performance, and demonstrated that XGBoost model performed best testing scores with R ² = 0.965, RMSE = 1.339, followed by GBT with R ² = 0.936, RMSE = 1.809, then K-NN with R ² = 0.849, RMSE = 2.78, then SVM achieved least performance with R ² = 0.801 and RMSE = 3.586. The study further validates feature importance analysis and SHAP values interpretation to identify dominant features governing ROP prediction.

- Inaccurate petrophysical rock typing is a major factor leading to the failure of many Enhanced Oil Recovery (EOR) projects worldwide. Effective reservoir characterization is crucial for guiding field development plans. Hydraulic Flow Unit (HFU) techniques are typically used to model the petrophysical properties of reservoirs, thus aiding improved oil recovery projects. Various models have been developed to describe reservoirs based on available data, often relying on assumptions about the porous medium, such as cementation factor and connate water saturation. These assumptions can be inaccurate and yield inconsistent rock-type classifications within the same medium, leading to unrealistic results when applied from cored to uncored wells. This study introduced a new approach for better-characterizing sandstone and carbonate reservoirs by leveraging the porosity-permeability relationship exclusively, avoiding the need for traditional petrophysical assumptions. The new approach was derived from a permeability equation developed for several pipes in series, and it utilizes a modified normalized porosity, a modified Flow Zone Indicator (FZI), and a Reservoir Quality Index (RQI). The methodology involved detailed analysis and comparison with existing approaches for identifying HFUs and predicting permeability. The new approach demonstrated superior performance, achieving higher correlation coefficients for predicted permeability in both carbonate and sandstone reservoirs. Specifically, the method showed correlation coefficients of 0.95 for carbonate and 0.93 for sandstone reservoirs, compared to 0.85 and 0.82, respectively, for existing methods. Additionally, it identified HFUs with higher accuracy, evidenced by correlation coefficients of 0.92 for carbonate and 0.90 for sandstone reservoirs, as opposed to 0.80 and 0.78 for conventional methods. By eliminating the reliance on inaccurate petrophysical assumptions, this approach enhances the reliability of reservoir characterization, making it a valuable tool for optimizing EOR projects. Further research will explore the applicability of this method across different reservoir types and conditions to fully establish its robustness and versatility.

Polymer flooding is a well-established technique for enhancing oil recovery from reservoirs. In this study, we investigate the performance of two different polymers, Xanthan Gum, and HPAM for secondary oil recovery in the sand pack. The effects of polymer concentration and high salinity on oil recovery are studied. The polymers are characterized using various measurements and tests, including viscosity, shear rate, and interfacial tension. The sand pack flooding experiments are conducted under various conditions, including varying polymer slugs, and nano-silica (silicon dioxide SiO2) concentration. The results are analyzed to determine the optimal conditions for polymer flooding for enhanced oil recovery. The Xanthan Gum’s highest oil recovery was at 1250 PPM (69.1% oil recovery) and the HPAM’s highest oil recovery was at 1625 PPM (69% oil recovery) and when combined with SiO2 with a concentration of 0.02475 wt% which for Xanthan gum at 1250 PPM gave 74.875% oil recovery and for HPAM at 1625 ppm gave 72.768% oil recovery. Xanthan gum had more resistance to viscosity reduction under high salinity conditions which was not the case with HPAM and thus the xanthan gum had higher oil recovery at lower polymer concentrations.