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Rock physics plays an important role in the oil, gas, and water industries by providing essential data for reservoir management. This study focuses on predicting Shear Wave Velocity (Vs), a key geophysical parameter, using the Deep Belief Network (DBN) and Random Forest (RF) algorithms. The dataset is divided for training, validation, and testing, revealing that the DBN model surpasses the RF and empirical models in accuracy. With a low RMSE value of 0.0398 km/s in testing and an R2 of 0.9775, the DBN model demonstrates precision in Vs prediction. The analysis identifies specific parameters—Measure Depth (MD), Caliper (CALL), Gamma Ray (GR), Shallow Electrical Resistivity (RES-S), Medium Electrical Resistivity (RES-M), Deep Electrical Resistivity (RES-D), Compressional Wave Velocity (DCT), Neutron Porosity (NPHI), and Photoelectric Coefficient Index (PEF)—as significantly influencing Vs, while CALI and GR have a relatively lesser effect. The results of the Vs prediction indicated that the DBN algorithm excels in deep feature extraction, unsupervised pretraining, and efficient handling of complex data. Ultimately, the DBN algorithm proves to be a superior and time-saving alternative for Vs prediction and analysis in subsurface reservoirs.

Background and objective: This scientific review involves a sequential analysis of randomized trial research focused on the incidence of shivering in patients undergoing cardiac surgery. The study conducted a comprehensive search of different databases, up to the end of 2020. Only randomized trials comparing magnesium administration with either placebo or no treatment in patients expected to experience shivering were included. The primary objective was to evaluate shivering occurrence, distinguishing between patients receiving general anesthesia and those not. Secondary outcomes included serum magnesium concentrations, intubation time, post-anesthesia care unit stay, hospitalization duration, and side effects. Data collection included patient demographics and various factors related to magnesium administration. Material and methods: This scientific review analyzed 64 clinical trials meeting inclusion criteria, encompassing a total of 4303 patients. Magnesium was administered via different routes, primarily intravenous, epidural, and intraperitoneal, and compared against placebo or control. Data included demographics, magnesium dosage, administration method, and outcomes. Heterogeneity was assessed using the I2 statistic. Some studies were excluded due to unavailability of data or non-responsiveness from authors. Result: and discussion: Out of 2546 initially identified articles, 64 trials were selected for analysis. IV magnesium effectively reduced shivering, with epidural and intraperitoneal routes showing even greater efficacy. IV magnesium demonstrated cost-effectiveness and a favorable safety profile, not increasing adverse effects. The exact dose-response relationship of magnesium remains unclear. The results also indicated no significant impact on sedation, extubation time, or gastrointestinal distress. However, further research is needed to determine the optimal magnesium dose and to explore its potential effects on blood pressure and heart rate, particularly regarding pruritus prevention. Conclusion: This study highlights the efficacy of intravenous (IV) magnesium in preventing shivering after cardiac surgery. Both epidural and intraperitoneal routes have shown promising results. The safety profile of magnesium administration appears favorable, as it reduces the incidence of shivering without significantly increasing costs. However, further investigation is required to establish the ideal magnesium dosage and explore its potential effects on blood pressure, heart rate, and pruritus prevention, especially in various patient groups.

Determination of pore pressure (PP), a key reservoir parameter that is beneficial for evaluating geomechanical parameters of the reservoir, is so important in oil and gas fields development. Accurate estimation of PP is also essential for safe drilling of oil and gas wells since PP data are used as the input for safe mud window determination. In the present study, empirical equations along with machine learning methods, namely random forest algorithm, support vector regression (SVR) algorithm, artificial neural network (ANN) algorithm, and decision tree (DT) algorithm, are employed for PP prediction applying well log data. To this end, 2827 data records collected from three wells (Well A, Well B, and Well C) drilled in one of the Middle East oil fields are used. The dataset of Wells A and B is used for models’ training, validating, and testing, while Well C dataset is applied for evaluating the models’ generalizability in PP prediction in the field under study. To construct the predictive algorithms, 12 input variables are initially considered in the study. A feature selection analysis is conducted to find the most influential input variables set for developing PP predictive models. The results obtained suggest that the 9-input-variable set is the most efficient combination of inputs used in the ML models construction. Among all the four ML algorithms proposed, the DT algorithm presents the most accurate predictions for PP, delivering R2 and RMSE values of 0.9985 and 14.460 psi, respectively. Furthermore, the model generalization analysis results reveal that the 9-input-variable DT model developed can be used for PP prediction throughout the field of study since it presented an excellent accuracy performance in predicting PP when applied to Well C dataset.

The mud weight window (MW) determination is one of the most important parameters in drilling oil and gas wells, where accurate design can secure the drilled well and deliver a stable borehole. In this paper, novel algorithms based on the most influential set of input features are developed to predict pore pressure, including rate of penetration (ROP), deep resistivity (ILD), density (RHOB), photoelectric index (PEF), corrected gamma ray (CGR), compression-wave velocity (Vp), weight on bit (WOB), shear-wave velocity (Vs) and pore compressibility (Cp). The algorithms used in this study are as follows: 1) machine learning algorithms (ML), these are the K-nearest neighbor (KNN) algorithm, weighted K-Nearest Neighbor (WKKNN), and distance weighted KNN (DWKNN); 2) hybrid machine learning algorithms (HML), which include the combination of three ML with particle swarm optimization (PSO) (KNN-PSO, WKNN-PSO and DWKNN-PSO). The 2875-record dataset used in this study was collected from three wells (S1, S2 and S3) in one of the gas reservoirs (Tabnak field) in Iran. After comparing the performance accuracy of all algorithms, DWKNN-PSO has the best performance accuracy compared to other algorithms presented in this paper (for the total dataset of wells S1 and S2: R2=0.9656and RMSE = 12.6773 psi). Finally, the generalizability of the best predictive algorithm for PP, DWKNN-PSO, is evaluated by testing the proposed algorithm on an unseen dataset from another well (S3) in the field under study, where the DWKNN-PSO algorithm provides PP predictions in well S3 with high accuracy, R2 = 0.9765 and RMSE = 9.7545 psi, confirming its ability to be used in PP prediction in the studied field.