مجله ژئومکانیک نفت
سال ششم شماره 4 (زمستان 1402)

As the most significant factor determining the final success of a hydraulic fracturing operation, fracture conductivity is influenced by proppant operating variables including mesh size, proppant concentration, and crushing behavior under minimum horizontal stress. Outcrop samples of the Ilam Formation were cut and prepared in the desired dimensions to simulate fracture in one of the Iranian reservoirs. As natural proppants, two groups of silica sand were collected from Firozkoh and Hamadan mines. Three mesh sizes were used to screen the proppants: 16/30, 20/40, and 30/50. In conductivity tests, Firozkoh proppants were used at two concentrations of 2 and 0.5 pounds per square foot. Experiments were carried out with a conductivity measuring apparatus by injecting nitrogen gas into the conductivity cell at different flow rates. This was done to simulate gas flow through fracture media. As a final step, the crushed percentage of proppants was determined and Forchheimer's equation was used to calculate conductivity values. As determined by the study, a significant decrease in conductivity values was observed with increasing closure stress. Firouzkoh 20/40 displayed better conductivity than Hamedan 20/40 proppant and performed better under the same stress conditions. Higher values of sphericity and roundness explain this. A comparison of the conductivity of 16/30, 20/40, and 30/50 proppants from Firuzkoh sand revealed that the 30/50 proppant had the lowest conductivity reduction compared to the other two samples up to a stress of 7251 psi because there are more contact points between the particles, the stress distribution is better between the particles, and the proppants do not crush as easily. A comparison of two amounts of Firouzkoh 20/40 proppant showed that more particles formed in the fracture as the proppant concentration increased. Consequently, the crushed percentage was reduced and conductivity values were maintained at higher proppant concentrations.

Carbonate reservoirs are very complex and heterogeneous. Overcoming heterogeneity is important and necessary for accurate reservoir characterization. Dalan-Kangan formations, as the largest non-associated gas reservoir of the world, are heterogeneous and complex due to the influence of the sedimentary environment and diagenesis processes. Carbonate reservoirs are routinely studied using laboratory data. The use of well logging instead of using laboratory methods is very cost-effective in reducing time and cost. To overcome the heterogeneity by acoustic log, samples of Dalan-Kangan carbonate formations were prepared. A total of 87 limestone thin sections were evaluated by petrography, routine core analysis, and sonic velocity. Porosity, permeability, sedimentary textures, pore types, and diagenetic processes were determined precisely. From the studied well, well logging and acoustic logs were also available. After data quality control, the acoustic log was converted to velocity. Velocity-porosity model was constructed based on the differential effective medium (DEM) approach for different values of equivalent pore aspect ratio (EPAR). The results show that moldic, vuggy, interparticle, and microporosity pores have the largest aspect ratio, respectively. Due to their spherical shape and high aspect ratio at the given porosity, moldic and vuggy pores have higher velocity than flat and narrow pores. Through the geometrical shape and pore type and by using the acoustic log, the rock types were determined. These rock types clearly showed the porosity evolution, permeability changes and diagenetic processes that have been occurred in Dalan-Kangan reservoirs.

Due to the significance of the sand production (SP) issue in sandstone hydrocarbon reservoirs, the main objective of this study is to evaluate the Asmari formation layers along well No. 469 in the Ahwaz hydrocarbon field in terms of SP causes and its potential capacity to provide suitable solutions for its reduction from a geomechanical perspective. The evaluation was carried out using the Techlog software. The required parameters for constructing a one-dimensional geomechanical reservoir model were estimated from available data. The Mohr-Coulomb failure criterion was adopted considering the scale effect for perforated cavities under non-hydrostatic stress conditions. After constructing the one-dimensional model, the Critical DrawDown Pressure (CDDP) curve was plotted for both open hole and perforation completions, and the susceptible SP zones were identified. The M2 layer was selected as one of the most susceptible zones for SP sensitivity analysis due to its low strength, porosity, and permeability compared to other layers. The sensitivity analysis was conducted based on well geometry, formation rock properties, field stress conditions, and perforated cavity characteristics. The analysis was performed at depths of 2822 and 2837 meters in the open hole and the perforation completions, respectively, with a dominant sand diameter of 200 microns in the potential SP zone. The Critical Bottom Hole Pressure (CBHP) and the Critical Reservoir Pressure (CRP) were estimated to be 1898 and 2735 pounds per square inch, respectively, in the maximum horizontal stress direction with a 0.4-inch perforation diameter and 861 and 2115 pounds per square inch, respectively, in the direction perpendicular to the maximum horizontal stress direction with a 0.3-inch perforation diameter. By defining and determining Transitional Deviation Angle (TDA), Minimum Safe Deviation Angle (MSDA), and Critical Perforation Orientation Angle (CPOA) based on sensitivity analyses, a novel design approach for perforation operations in sand-prone reservoirs has been introduced.

Elastoplastic criteria are very important in many topics related to petroleum geomechanics, geotechnics, and rock mechanics. Due to the importance of these criteria, their numerical implementation is considered essential. Although some of the existing software includes the stated criteria, due to the lack of access to the coding core of the software, the accuracy of the modeling done with them is practically not fully assured. Therefore, considering the importance of these criteria and of course their complexity for implementation, in this research a comprehensive numerical model to improve the elastoplastic integration algorithm of the Mohr-Coulomb criterion was presented and described in detail. The proposed integration algorithm includes two steps elastic trial step and the plastic corrector step. In the proposed model, if the elastic trial step is in the elastic region or on the yield surface, the answer of elasticity is accepted. Otherwise, if the trial stress in the first step cannot confirm the acceptable conditions, it is provided by the return-mapping algorithm. This process is done for all surfaces of the Mohr-Coulomb criterion and the top of the model comprehensively and of course separately until the Mohr-Coulomb model can present the elastoplastic behavior of the material during loading. The presented model for rock was investigated and the validity of the proposed model was confirmed by comparing the numerical results with the experimental data.

Today, the economies of Middle Eastern countries rely heavily on increasing their crude oil production and exploitation rates. With the understanding that the discovery of large hydrocarbon fields will become increasingly rare in the future, oil companies' planning focuses on optimal production from existing fields. The Gadwan formation is one of the important reservoirs in the Abadan Plain, composed of shale and sandstone sequences. Drilling production wells in this formation has always been a challenge due to the weak nature of the shale sequence. More than 90% of the well problems in the Gadwan formation are due to wellbore instability, including collapse, narrowing, and sticking of the drill bit, resulting in increased non-productive time (NPT) and drilling costs. The main objective of this study is to evaluate the stability of a well located in southwestern Iranian oilfields using a one-dimensional geomechanical model based on well log data. After constructing a one-dimensional geomechanical model, the Mohr-Coulomb and Mogi-Coulomb criteria were used to evaluate wellbore stability. The results showed that the Mohr-Coulomb criterion provides a better estimate of collapse pressure. Furthermore, the orientation of the identified collapses on the image log was northwest-southeast, indicating the direction of minimum horizontal stress. According to the sensitivity analysis results, the optimal drilling direction for the Gadwan formation is northwest-southeast, and for vertical drilling in this formation, the safe mud weight estimated according to the well's geomechanical model is 14.9 pounds per gallon (LB/G). The results of this study can be used as a reference for determining the optimal mud weight window in planning future wells in this field to deal with drilling stability problems.

This study aims to employ supervised Advanced machine learning for the classification of lithological facies from geophysical log data in wells without drilling core samples. For this purpose, a dataset from seven wells in a training set from one of the oil fields in southern Iran has been utilized. This dataset includes natural gamma ray (SGR), corrected gamma ray (CGR), bulk density (RHOB), neutron porosity (NPHI), compressional wave slowness (DTSM), and shear wave slowness (DTCO), which directly influence the classification of geomechanical facies. These parameters are employed as independent variables, while lithological facies serve as the dependent variable for classification. This dataset pertains to depths ranging from 3000 to 4000 meters in the Ilam and Sarvak fractured limestone formations (Bangestan Limestone) of the subsurface. As the title suggests in this article, Initially, through artificial intelligence clustering methods and laboratory studies, these formations were categorized into five distinct lithological facies After this stage, eight supervised machine learning methods were employed, including Regression Logistic, K Neighbors Classifier, Decision Tree Classifier, Random Forest Classifier, Gaussian NB, Gradient Boosting Classifier, Extra Trees Classifier, and Support Vector Machine (SVM), to predict lithological facies in wells without existing classifications. The dataset of these wells underwent training and testing stages with each of these algorithms to construct an appropriate model.As a result, facies labels were predicted. The performance of the models was evaluated using multiple metrics including Accuracy, Precision, F1-Score, and Recall through confusion matrices and ROC curves. The Extra Trees Classifier, Gradient Boosting Classifier, and K Neighbors Classifier showed superior results among these methods. Finally, the model's performance in predicting lithological facies of unseen or out-of-sample wells was presented.