Alireza Rostami

One of the main constituents of any reservoir characterization is an accurate forecast of water saturation, which is highly dependent upon the cementation exponent. Even though there have been a lot of studies, the most common correlations depend on total porosity. This means that they do not work as well in heterogeneous carbonate reservoirs, especially tight formations with total porosities less than 10%. This study aims to develop accurate and universal models for estimating the cementation exponent in deep and tight carbonate pore systems located in West Asia. Two heuristic algorithms, including the radial basis function neural network optimized by ant colony optimization (RBFNN-ACO) and gene expression programming (GEP), were employed to calculate the cementation exponent. To do this, we prepared a databank incorporating cementation exponents, total porosity, and various pore types. Then, the databank is classified into the test subset (for model prediction checking) and the train subset (for model construction). The reliability of the new recommended models is inspected by applying several statistical quality measures associated with graphical analyses. So, the consequences of the modeling disclose the large precision of the above-mentioned RBFNN-ACO, GEP Model-I, and GEP Model-II by average absolute percentage relative deviations (AAPRD%) of 6.28%, 6.39%, and 7.45%, respectively. Based on the outliers analysis, nearly 95% of the databank and model estimations are, respectively, valid and reliable. Additionally, the three input variables, including moldic porosity (with a + 70% impact value), non-fabric-selective dissolution (connected) porosity (with a -30% impact value), and interparticle porosity (with a -23% impact value), exhibit the main affecting parameters on the cementation exponent. Comparing current results with traditional literature correlations demonstrates the supremacy of the RBFNN-ACO model (AAPRD = 6.28% and root mean squared error (RMSE) = 0.17) over the examined literature correlations such as Borai’s equation (AAPRD = 12.30% and RMSE = 0.41). In addition, RBFNN-ACO can give better results than Borai’s Eqn. for tight (porosity less than 10%) and deep carbonate samples.

The need to determine permeability at different stages of evaluation, completion, optimization of Enhanced Oil Recovery (EOR) operations, and reservoir modeling and management is reflected. Therefore, various methods with distinct efficiency for the evaluation of permeability have been proposed by engineers and petroleum geologists. The oil industry uses acoustic and Nuclear Magnetic Resonance (NMR) loggings extensively to determine permeability quantitatively. However, because the number of available NMR logs is not enough and there is a significant difficulty in their interpreting and evaluation, the use of acoustic logs to determine the permeability has become very important. Direct, continuous, and in-reservoir condition estimation of permeability is a unique feature of the Stoneley waves analysis as an acoustic technique. In this study, five intelligent mathematical methods, including Adaptive Network-Based Fuzzy Inference System (ANFIS), Least-Square Support Vector Machine (LSSVM), Radial Basis Function Neural Network (RBFNN), Multi-Layer Perceptron Neural Network (MLPNN), and Committee Machine Intelligent System (CMIS), have been performed for calculating permeability in terms of Stoneley and shear waves travel-time, effective porosity, bulk density and lithological data in one of the naturally-fractured and low-porosity carbonate reservoirs located in the Southwest of Iran. Intelligent models have been improved with three popular optimization algorithms, including Coupled Simulated Annealing (CSA), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). Among the developed models, the CMIS is the most accurate intelligent model for permeability forecast as compared to the core permeability data with a determination coefficient (R²) of 0.87 and an average absolute deviation (AAD) of 3.7. Comparing the CMIS method with the NMR techniques (i.e., Timur-Coates and Schlumberger-Doll-Research (SDR)), the superiority of the Stoneley method is demonstrated. With this model, diverse types of fractures in carbonate formations can be easily identified. As a result, it can be claimed that the models presented in this study are of great value to petrophysicists and petroleum engineers working on reservoir simulation and well completion.

This piece of study attempts to accurately anticipate the apparent viscosity of the viscoelastic surfactant (VES) based self-diverting acids as a function of VES concentration, temperature, shear rate, and pH value. The focus not only is on generating computer-aided models but also on developing a straightforward and reliable explicit mathematical expression. Towards this end, Gene Expression Programming (GEP) is used to connect the aforementioned features to and the target. The GEP network is trained using a wide dataset adopted from open literature and leads to an empirical correlation for fulfilling the aim of this study. The performance of the proposed model is shown to be fair enough. The accuracy analysis indicates satisfactory Root Mean Square Error and R-squared values of 7.07 and 0.95, respectively. Additionally, the proposed GEP model is compared with literature published correlations and established itself as the superior approach for predicting the viscosity of VES-based acids. Accordingly, the GEP model can be potentially served as an efficient alternative to experimental measurements. Its obvious advantages are saving time, lowering the expenses, avoiding sophisticated experimental procedures, and accelerating the diverter design in stimulation operations. Article Highlights: The Gene Expression Programming evolutionary algorithm is proposed for modeling the viscosity of Viscoelastic Surfactant-based self-diverting acids. The viscoelastic surfactant viscosity correlation presents high accuracy which is demonstrated through multiple analyses. The Gene Expression Programming algorithm is a reliable tool expediting the diverter design phase of each stimulation operation.

Introduction: Post-pericardiotomy syndrome is a relatively common complication after open heart surgery. In recent years, colchicine had a good effect on treatment of the pericarditis. Objectives: This study aimed to study the effect of colchicine to prevent post-pericardiotomy syndrome in patients who had open heart surgery. Patients and Methods: This double-blind clinical trial study was conducted on 240 patients undergone open heart surgery. Eligible patients were selected by simple sampling and randomly divided into two groups; 120 subjects in the experimental group receive colchicine (1 mg for patients greater than 70 kg and 0.5 mg for patients lower than 70 kg) and 120 subjects in the control group who received the placebo. Patients were followed up regarding the incidence of post-pericardiotomy syndrome after the surgery during hospitalization and in follow up visits in one month and six months after hospital discharges. Results: In total, the incidence rate of post-pericardiotomy syndrome was 33.7% (81 subjects). A significant difference of post-pericardiotomy syndrome between two groups was seen. We found post-pericardiotomy syndrome in 12.1% (29 patients) of colchicine group versus 21.6% (52 patients) in the placebo group (chi-square test). The incidence rate based on the Kaplan-Meier curve, in the second week to one month after the surgery, was lower in colchicine group while in third to the sixth month it had a constant trend. Conclusion: Colchicine is effective for prevention the post-pericardiotomy syndrome without any life-threatening complication.

Mineral scaling has been considered a great concern for developing the oil production from the underground petroleum reservoirs. One of the main causes of this phenomenon is known as the chemical incompatibility of injected brine, frequently sea water, with the reservoir brine leading to the deposition of various supersaturated salts such as calcium carbonate, calcium sulfate and barium sulfate. In present communication, an evolutionary approach namely, Gene Expression Programming (GEP), was employed for rigorous modeling of formation damage by mineral scaling of mixed sulfate salt deposition. At first, a large databank of damaged permeability datapoints as a function of injected volume, injection flowrate, temperature, differential pressure and ionic concentrations of the existing chemical species in the porous media was employed. In this regard, a user-friendly correlation was extended for the first time by the aforementioned technique in the literature. Professional evaluation of the suggested GEP-based model was implemented by different statistical parameters and appealing visualization tools. Having proposed the GEP-based correlation, statistical parameters of the Average Absolute Relative Deviation Percent (AARD%) of 0.640% and determination coefficient (R²) of 0.984 was calculated. Accordingly, it is demonstrated that the proposed model has a superior performance and great potential for efficient prediction of damaged permeability due to the mixed sulfate salt scaling. Moreover, the implemented outlier diagnosis technique verified the validity of the databank used for modeling, as well as the high robustness of the suggested model was confirmed. In conclusion, the developed correlation in this work can be of enormous practical value for skillful engineers and scientists in any academic study and industrial applications dealing with mixed salt deposition.