مجله ژئومکانیک نفت
سال چهارم شماره 3 (Autumn 2021)

Borehole breakouts can be modelled by the propagation of cracks in the vicinity of wellbores. Coalescence of these cracks with a series of formed sub-parallel cracks leads to a breakout. Using fracture mechanics principles, the propagation of cracks in the vicinity of wellbores resulting in the final formation of breakouts is investigated. An indirect BEM with higher order elements has been used to numerically simulate cracks propagation. Various configurations of cracks placement around a wellbore are analyzed. Numerical results showed that the ratio of stresses normal to wellbores’ axis has a significant effect on breakouts final shape. For a given lower normal stress, higher stress ratios lead to wider breakouts, eventually leading to wellbore instability. Furthermore, for a given higher normal stress, higher stress ratios tend to form deeper breakouts with a limited width increase. Hydrostatic stress field can be completely stable or unstable depending on a threshold stress value.

Petroleum reservoirs contain many physics that play role in multiple scales. Fluid flow and deformation of solid phase are main physics that influence the production rate. However, a full description of flow and deformation that includes all these scales exceeds the current computational capabilities. In order to overcome this deficiency, each physical effect should be treated separately on its area of influence. In the present paper, the fluid transport and deformation of porous media are determined through separate frameworks in different scales. The Enhanced Multiscale Multiphysics Mixed Geomechanical Model (EM3GM) have been developed and utilized to determine the production rate of deformable reservoirs. The EM3GM not only maintains advanced features of Multiscale Finite Volume (MSFV) in flow patterns but also improves with properties of Elastic-Plastic framework in the solid domain. Finally, in order to show the accuracy of the model and also to reveal the effect of the plastic deformations in production rate, indicative test cases were analyzed and reasonable results were achieved. The plastic deformation will lead to decrease in oil production rate with respect to energy loses during plastic deformation which is more close to the real situation. The numerical results show that neglecting solid deformation could overestimate the production rate from one to four times higher at the earlier stage of production for the hard rock and this amount would be increase for the loose rock with respect to higher energy loss.

Shear wave velocity (Vs) is one of the key geomechanical parameters effective in the drilling of hydrocarbon reservoirs. In this study, a novel machine learning (extra learning machine (ELM)) approach is developed to predict Vs based on four input variables obtained from well log, including neutron porosity (NPHI), bulk density (RHOB) and gamma-ray (GR). Two algorithms multi-layer perceptron (MLP) and ELM and various empirical equations (Brocher, Eskandari et al., Castagna et al. and Pickett) have been used to predict Vs in this paper. The results show that the performance accuracy for these models includes: ELM> MLP> Castagna et al. > Eskandari et al. > Pickett> Brocher. So, the result that shows the ELM model has higher accuracy than the other machine learning (MLP) approach and also other empirical equations (RMSE = 0.0444 km/s and R2 = 0.9809). Some advantages to the other artificial neural network approach include higher accuracy and performance characteristics, simple algorithm learning, improved performance, nonlinear conversion during training, no stuck in local optimal points, and it is over fitting. The novelty used in this paper is the type of newly implemented artificial model (ELM) and the number of input parameter. This approach possesses to the higher power, speed and accuracy than the methods used by other researchers to predict Vs.

Development of mature oil fields has been increasingly attractive in recent years as a significant amount of world oil and gas production is being extracted from these formations. Hydraulic fracturing (either as a selective corrective stimulation method or as a preliminary completion approach) is a well-established technique in mature oil field rejuvenation to improve productivity and deliverability of such a diminishing field. After many years of successful production in A1 and A2 reservoirs, A3 and A4 reservoirs were developed with only one hydraulically fractured vertical well (Well #1). As the production from well #1 in A3/A4 reservoirs was below the expectation, the well was shut down after 3 years of production. Therefore, the main objective of this research paper is to investigate re-development options for A3/A4 reservoirs due to the low deliverability and productivity of the vertical well #1. Sensitivity analysis for history matching, critical conductivity, and optimum dimensionless fracture conductivity (Cfd) was performed followed by forecasting and multistage hydraulic fracturing. Numerical results showed that there is a critical conductivity beyond which production is insensitive to the conductivity, for a specific propped length and production time. Results also showed that critical conductivity increased with propped length and decreased with production time. After 25 years of forecasting, the recovery factor for the 900m lateral with eight fractures and 110m spacing was the highest at 2.65%. The corresponding values for the 300m and 600m laterals were 2.37% and 2.42%. Therefore, the study suggests that horizontal wells with a longer length and optimized number of fractures and spacing will provide maximum well recovery.

Oil-contaminated soil should be remediated or can be used as filling materials .The evaluation of bearing capacity of geocell-reinforced soil abutment wall is the purpose of present study under conditions of the backfill contaminated soil through numerical modeling based on PLAXIS 2D. The behavior of the wall is studied based on changes in the amount of oil, the distance between the strip footing and the wall facing (D), the height (hg) the length (L) and the number of geocell layers as well as the wall slope. The numerical results showed that the maximum length geocell layer required is 2.16 times the footing width and the optimum geocell length is equal to 1.0 times the wall height (H). The increase in the geocell height and number of geocell layers leads to increase in the soil stiffness, leading to increase in the bearing capacity of footing and decrease in the horizontal displacement of wall. The results showed that reducing the slope of the wall is very effective in reducing the horizontal displacement of the wall. In general, the soil contamination due to the oil has a negative effect on wall performance. In other words, an increase in the amount of oil reduces the percentage improvement in the wall behavior due to an increase in the height, length and the number of geocell layers. For example at hg/B = 0.6 and settlement equal to 10% of the footing width, the bearing capacity of footing for soil contamination with 9% oil reduces by 35%.

The drilling of hydrocarbon wells is a process in which the drilling team deals with the numerous challenges to access hydrocarbon resources. Understanding the formation pore pressures is important to develop a successful and comprehensive drilling plan that minimize cost and maximize safety. This study evaluates the performance of some empirical models for calculating pore pressure based on petrophysical variables as input parameters. This research also compares the estimated performance of empirical models, efficiency assessment, and limitations caused by the petrophysical. The model presented in this study uses LSSVM-PSO artificial intelligence optimized neural networks as powerful tools in solving complex problems to identify complex relationship between petrophysical input data and the actual measured pore pressure with a modular formation dynamic measurement. Among the proposed network models, LSSVM-PSO, the most accurate model from performance and metric error, is a candidate for sensitivity analysis evaluation on 15 different classes categorized by type and number of petrophysical input data. The best predictive approach among the specified classes belongs to the classes in which gamma-ray log petrophysical data participated as input nodes. This study confirms the effect of gamma log data as an influential factor in estimating the formation pore pressure parameter using artificial intelligence sensitivity analysis to the parameters assigned to the input variables.As can be seen in the results, the amount of RMSE = 1.13895 and R2 = 1.0000 for class -15 and for the total data used, which compared to other classes, these error parameters are much higher.Researchers in future studies can evaluate the results of this study as an efficient mathematical model.