Machine learning prediction of methane, nitrogen, and natural gas mixture viscosities under normal and harsh conditions.

The accurate estimation of gas viscosity remains a pivotal concern for petroleum engineers, exerting substantial influence on the modeling efficacy of natural gas operations. Due to their time-consuming and costly nature, experimental measurements of gas viscosity are challenging. Data-based machine learning (ML) techniques afford a resourceful and less exhausting substitution, aiding research and industry at gas modeling that is incredible to reach in the laboratory. Statistical approaches were used to analyze the experimental data before applying machine learning. Seven machine learning techniques specifically Linear Regression, random forest (RF), decision trees, gradient boosting, K-nearest neighbors, Nu support vector regression (NuSVR), and artificial neural network (ANN) were applied for the prediction of methane (CH4), nitrogen (N2), and natural gas mixture viscosities. More than 4304 datasets from real experimental data utilizing pressure, temperature, and gas density were employed for developing ML models. Furthermore, three novel correlations have developed for the viscosity of CH4, N2, and composite gas using ANN. Results revealed that models and anticipated correlations predicted methane, nitrogen, and natural gas mixture viscosities with high precision. Results designated that the ANN, RF, and gradient Boosting models have performed better with a coefficient of determination (R2) of 0.99 for testing data sets of methane, nitrogen, and natural gas mixture viscosities. However, linear regression and NuSVR have performed poorly with a coefficient of determination (R2) of 0.07 and - 0.01 respectively for testing data sets of nitrogen viscosity. Such machine learning models offer the industry and research a cost-effective and fast tool for accurately approximating the viscosities of methane, nitrogen, and gas mixture under normal and harsh conditions.

Assessment of modified chitosan composite in acidic reservoirs through pilot and field-scale simulation studies.

Chemical flooding through biopolymers acquires higher attention, especially in acidic reservoirs. This research focuses on the application of biopolymers in chemical flooding for enhanced oil recovery in acidic reservoirs, with a particular emphasis on modified chitosan. The modification process involved combining chitosan with vinyl/silane monomers via emulsion polymerization, followed by an assessment of its rheological behavior under simulated reservoir conditions, including salinity, temperature, pressure, and medium pH. Laboratory-scale flooding experiments were carried out using both the original and modified chitosan at conditions of 2200 psi, 135,000 ppm salinity, and 196° temperature. The study evaluated the impact of pressure on the rheological properties of both chitosan forms, finding that the modified composite was better suited to acidic environments, showing enhanced resistance to pressure effects with a significant increase in viscosity and an 11% improvement in oil recovery over the 5% achieved with the unmodified chitosan. Advanced modeling and simulation techniques, particularly using the tNavigator Simulator on the Bahariya formations in the Western Desert, were employed to further understand the polymer solution dynamics in reservoir contexts and to predict key petroleum engineering metrics. The simulation results underscored the effectiveness of the chitosan composite in increasing oil recovery rates, with the composite outperforming both its native counterpart and traditional water flooding, achieving a recovery factor of 48%, compared to 39% and 37% for native chitosan and water flooding, thereby demonstrating the potential benefits of chitosan composites in enhancing oil recovery operations.