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.

Magnesium Sulfate in Pediatric Abdominal Cancer Surgery: Safety and Efficacy in Ultrasound-Guided Transversus Abdominis Plane (US-TAP) Block in Conjugation with Levobupivacaine.

Purpose: Magnesium sulfate (MgSO4) may enhance the effects of local anesthetics when used as an adjuvant in peripheral nerve blocks. Our objective was to evaluate efficiency and safety of utilizing MgSO4 alongside levobupivacaine in bilateral ultrasound-guided transversus abdominis plane (US-TAP) block for postoperative pain in pediatric cancer patients who underwent abdominal surgery. Methodology: A randomized double-blinded controlled trial at South Egypt Cancer Institute, Assiut University, Assiut, Egypt, included that 40 pediatric patients with Wilms' tumor or neuroblastoma were randomly allocated to get bilateral (US-TAP) block and divided into two groups; M group: received US-TAP with 0.6 mL/kg levobupivacaine 0.25% + 2 mg/kg MgSO4 and C group: received with 0.6 mL/kg levobupivacaine 0.25% only. FLACC scores (Face, Leg, Activity, Cry, Consolability) were used to evaluate post-operative pain, first analgesic request, total analgesic consumption, adverse effects, as well as hemodynamics were monitored for 24 h and recorded at time points (2, 4, 6, 8, 12, 18, and 24h). Parent's satisfaction at discharge, also, was evaluated. Results: FLACC score in M group was significantly lower than in C group from 4 h to 24 h with the first analgesic request being longer (15.95 ± 1.99 vs 7.70 ± 0.80 (h); p < 0.001) and lower total analgesic consumption (231.75 ± 36.57 vs 576.00 ± 170.71 (mg); p < 0.001) when comparing M group to C group, respectively. Both groups had insignificant differences regarding hemodynamics, parent satisfaction, postoperative agitation, and side effects except vomiting occurred in two patients in the C group and one patient in the M group. Conclusion: We conclude that adding magnesium sulphate as an adjuvant to local anaesthetic in US-TAP block for pain management in pediatric abdominal cancer surgeries resulted in better and longer analgesia, with less consumption of rescue analgesics with no serious side effects.

Electrical Tortuosity Index: A New Approach for Identifying Rock Typing to Enhance Reservoir Characterization Using Well-Log Data of Uncored Wells.

The world is gradually moving toward a severe energy crisis, with an ever-increasing demand for energy overstepping its supply. Therefore, the energy crisis in the world has shed important light on the need for enhanced oil recovery to provide an affordable energy supply. Inaccurate reservoir characterization may lead to the failure of enhanced oil recovery projects. Thus, the accurate establishment of reservoir characterization techniques is required to successfully plan and execute the enhanced oil recovery projects. The main objective of this research is to obtain an accurate approach that can be used to estimate rock types, flow zone indicators, permeability, tortuosity, and irreducible water saturation for uncored wells based on electrical rock properties that were obtained from only logging tools. The new technique is obtained by modifying the Resistivity Zone Index (RZI) equation that was presented by Shahat et al. by taking the tortuosity factor into consideration. When true formation resistivity (Rt) and inverse porosity (1/Φ) are correlated on a log-log scale, unit slope parallel straight lines are produced, where each line represents a distinct electrical flow unit (EFU). Each line's intercept with the y-axis at 1/Φ = 1 yields a unique parameter specified as the Electrical Tortuosity Index (ETI). The proposed approach was validated successfully by testing it on log data from 21 logged wells and comparing it to the Amaefule technique, which was applied to 1135 core samples taken from the same reservoir. Electrical Tortuosity Index (ETI) values show marked accuracy for representing reservoir compared with Flow Zone Indicator (FZI) values obtained by the Amaefule technique and Resistivity Zone Index (RZI) values obtained by the Shahat et al. technique, with correlation coefficients of determination (R2) values equal to 0.98 and 0.99, respectively. Hence, by using the new technique, the Flow Zone Indicator, permeability, tortuosity, and irreducible water saturation were estimated and then compared with the obtained results from the core analysis, which showed a great match with the R2-values of 0.98, 0.96, 0.98, and 0.99, respectively.

New Models for Predicting Pore Pressure and Fracture Pressure while Drilling in Mixed Lithologies Using Artificial Neural Networks.

Precise prediction of pore pressure and fracture pressure is a crucial aspect of petroleum engineering. The awareness of both fracture pressure and pore pressure is essential to control the well. It helps in the elimination of the problems related to drilling, waterflooding project, and hydraulic fracturing job such as fluid loss, kick, differential sticking, and blowout. Avoiding these problems enhances the performance and reduces the cost of operation. Several researchers proposed many models for predicting pore and fracture pressures using well log information, rock strength properties, or drilling data. However, some of these models are limited to one type of lithology such as clean and compacted shale formation, applicable only for the pressure generated by under compaction, and some of them cannot be used in unloading formations. Recently, artificial intelligence techniques showed a great performance in petroleum engineering applications. Hence, in this paper, two artificial neural network models are developed to estimate both pore pressure and fracture pressure through the use of 2820 data sets obtained from drilling data in mixed lithologies of sandstone, carbonate, and shale. The proposed artificial neural network (ANN) models achieved accurate estimation of pore and fracture pressures, where the coefficients of determination (R 2) for pore and fracture pressures are 0.974 and 0.998, respectively. Another data set from the Middle East was used to validate the developed models. The models estimated the pore and fracture pressures with high R 2 values of 0.90 and 0.99, respectively. This work demonstrates the validity and reliability of the developed models to calculate pore and fracture pressures from real-time surface drilling parameters by considering the formation type to overcome the limitation of previous models.

New Correlation for Calculating Water Saturation Based on Permeability, Porosity, and Resistivity Index in Carbonate Reservoirs.

Water saturation assessment is recognized as one of the most critical aspects of formation evaluation, reserve estimation, and prediction of the production performance of any hydrocarbon reservoir. Water saturation measurement in a core laboratory is a time-consuming and expensive task. Many scientists have attempted to estimate water saturation accurately using well-logging data, which provides a continuous record without information loss. As a result, numerous models have been developed to relate reservoir characteristics with water saturation. By expanding the use and advancement of soft computing approaches in engineering challenges, petroleum engineers applied them to estimate the petrophysical parameters of the reservoir. In this paper, two techniques are developed to estimate the water saturation in terms of porosity, permeability, and formation resistivity index through the use of 383 data sets obtained from carbonate core samples. These techniques are the nonlinear multiple regression (NLMR) technique and the artificial neural network (ANN) technique. The proposed ANN model achieved outstanding performance and better accuracy for calculating the water saturation than the empirical correlation using NLMR and Archie equation with a high coefficient of determination (R 2) of 0.99, a low average relative error of 1.92, a low average absolute relative error of 13.62, and a low root mean square error of 0.066. To the best of our knowledge, the current research establishes a novel foundation using the ANN model in the estimation of water saturation.

Prediction of the Rheological Properties of Invert Emulsion Mud Using an Artificial Neural Network.

Successful drilling operations require optimum well planning to overcome the challenges associated with geological and environmental constraints. One of the main well design programs is the mud program, which plays a crucial role in each drilling operation. Researchers focus on modeling the rheological properties of the drilling fluid seeking for accurate and real-time predictions that confirm its crucial potential as a research point. However, only substantial studies have real impact on the literature. Several AI-based models have been proposed for estimating mud rheological properties. However, most of them suffer from non-being field applicable attractive due to using non-readily field parameters as input variables. Some other studies have not provided a comprehensive description of the model to replicate or reproduce results using other datasets. In this study, two novel robust artificial neural network (ANN) models for estimating invert emulsion mud plastic viscosity and yield point have been developed using actual field data based on 407 datasets. These datasets include mud plastic viscosity (PV), yield point (YP), mud temperature (T), marsh funnel viscosity (MF), and solid content. The mathematical base of each model has been provided to provide a clear means for models' replicability. Results of the evaluation criteria depicted the outstanding performance and consistency of the proposed models over extant ANN models and empirical correlations. Statistical evaluation revealed that the plastic viscosity ANN model has a coefficient of determination (R 2) of 98.82%, a root-mean-square error (RMSE) of 1.37, an average relative error (ARE) of 0.12, and an absolute average relative error of 2.69, while for yield point, this model has a coefficient of determination (R 2) of 94%, a root-mean-square error (RMSE) of 0.76, an average relative error (ARE) of -0.67, and an absolute average relative error of 3.18.