Fahd Saeed Alakbari

Seismic resilient design of infrastructure is crucial in active regions. The infrastructure development of Pakistan is based on the Building Code of Pakistan (BCP-2007), which is based on the Uniform Building Code (UBC-1997). The UBC is further based on deep soil conditions in the western United States, which have low impedance contrast and high seismic conditions. Applying foreign seismic criteria can give in the determination of results that deviate from the target area's actual geotechnical and geological ground conditions, as these conditions vary from place to place. This study adapts a simple yet more precise technique of nonlinear site-specific seismic response analysis for designing seismic resilient structures rather than relying on the results which are based on the Peak Ground Acceleration only. Site-specific seismic amplification factors were calculated using Geographic Information System and a geotechnical engineering dataset of 42 standard penetration test in the metropolitan area of Peshawar City, Pakistan. Shear wave velocities (Vs) were calculated using 50 SPTN-VS correlations, and the results were validated using the available in situ measurements of shear wave velocity data. Furthermore, the average shear wave velocity (VSZ) and shear wave velocity up to 30m (VS30) were calculated, and sub-site classification of the study area was developed for microzonation. The General Quadratic/Hyperbolic (GQ/H) model was used, and a non-linear one-dimensional analysis of sixteen shallow sub-site profiles under twenty (20) ground motions had been carried out using DEEPSOIL software. The resulting site-specific amplification factors were compared with the suggested values from the BCP. It was found that BCP overestimates short-period amplification factors (Fa) for 12 profiles and long-period amplification factors (Fv) for all 16 proposed profiles. This study proposes site-specific GIS-based maps for the bedrock depth, National Earthquake Hazards Reduction Program site classes, short-period amplification factors (Fa) and long-period amplification factors (Fv), which will eventually enhance economic and safety aspects of the future infrastructure designs at the understudy region.

The global warming crisis, traceable to the rise in greenhouse gas emissions, has called for more proactive measures to curb the emission levels. To this effect, several technologies have been suggested. Out of the lot, carbon capture, utilization, and storage have been identified as one of the most feasible and pragmatic methods. This is partly because the concept, technical resources, and facilities are transferable from CO₂ injection and enhanced oil recovery applications. For this reason, studies centered around CO₂ conformance control for subsurface storage have been conducted. Also, reviews on the classification and description of chemical materials utilized for the conformance control have been published. With the limitations of these materials in mind, this review extends the types of sealants that can be applied for CO₂ entrapment in subsurface geologic formations. The group of gelants that generally fall under multicomponent polymer systems and viscoelastic fluids was evaluated. The review describes their properties, behavior, response to CO₂, and other unique features that make them suitable to be considered for reservoir conformance control purposes. Based on the review, recommendations for future studies on these novel materials were given.

Static Poisson’s ratio (ν_s) is an essential property used in petroleum calculations, namely fracture pressure (FP). The ν_s is often determined in the laboratory; however, due to time and cost constraints, quicker and cheaper alternatives are sought, such as data-driven models. However, existing methods lack the accuracy needed for critical applications, necessitating the need to explore more accurate methods. In addition, the previous studies used limited datasets and they do not show the relationships between the inputs and output. Therefore, this study developed a reliable model to predict the ν_s accurately using the nineteen most common learning methods. The proposed models were created based on a large data of 1691 datasets from different countries. The best-performing model of the nineteen models was selected and further enhanced using various approaches such as trend analysis to improve the model’s performance and robustness as some models show high accuracy but show incorrect relationships between the inputs and output because the machine learning model only built based on the data and do not consider the physical behavior of the model. The proposed Gaussian process regression (GPR) model was also compared with published models. After the proposed GPR model was developed, the FP was determined based on the proposed GPR ν_s model and the previous ν_s models to evaluate their accuracy on the FP determinations. The best approach out of the published and proposed methods was GPR with a coefficient of determination (R²) and average-absolute-percentage-relative-error (AAPRE) of 0.95 and 2.73%. The GPR model showed proper trends for all inputs. The cross-plotting and group error analyses also confirmed that the proposed GPR approach had high precision and surpassed other methods within all practical ranges. The GPR model decreased the residual error of FP from 87% to 26%. It is believed that such a significant improvement in the accuracy of the GPR model will have a significant effect on realistic FP determination.

Sand production is one of the major challenges in the oil and gas industry. This problem exists when sand is produced along with oil and gas causing relevant damage to production equipment, thus decreasing in the productivity of wells. Therefore, a comprehensive geomechanical analysis is necessary to mitigate sand production. This study aims to assess the potential of sand production across the Nahr Umr Formation using the 1-D Mechanical Earth Model (MEM). Tech-log software coupled with well log and core data have been employed to accurately determine the possible rock geomechanical parameters, in-situ stresses and pore pressure at which rock failure might occur. Once MEM is complete, the Poro-elastic method is used to figure out the critical drawdown pressure (CDDP) and accurately predict the sand production onset. Additionally, the effect of different well completion types on the value of the CDDP was examined, and thus it was concluded that cased hole completion is the first line of defense against sand production, and can also be considered as a strategy of sand control because it reduces the sand production potential and increases the operation drawdown. Furthermore, to demonstrate the effectiveness and applicability of our method and technique, a case study was conducted to illustrate the reliability of our method in predicting sand-producing intervals under different depletion rates and completion scenarios. The finding showed that the depth 2527.7 m is a potential location for sand production as the CDDP reads a positive value revealing a high potential for rock failure. Moreover, sensitivity analysis has been performed by considering different ranges of Unconfined Compressive Strength (USC), Poisson ratio, minimum horizontal stress (Shmin), maximum horizontal stress (SHmax), vertical stress, sand grain size, perforation diameter, perforation orientations, stress ratio, and hole deviation. These factors play an essential role in optimal decisions related to real-time sand control techniques. Through the results, it is clear that as the UCS, Shmin, and SHmax increase, the sand-free drawdown and depletion also increase, and vice versa. Also, results show that as the depletion rate increases, the CDDP decreases in both cased and open hole conditions, revealing that the onset sanding likely occurs as the depletion rate is elevated. Based on these findings, a necessary modification to the completion design has been made, ensuring sand-free production from a clastic reservoir located in the southern area of Iraq.

Sand production causes many problems in the petroleum industry. The sand production is predicted to control it in the early stages. Therefore, accurate prediction of sand production has been considered substantial in achieving successful sand control. Critical total drawdown (CTD) can indicate the sand production. The main drawback of the previous studies in predicting CTD is their lack of accuracy. Thus, this study aims to develop an accurate CTD estimation prediction model employing a trend analysis and adaptive neuro-fuzzy inference system (ANFIS). The method is chosen because of its higher performance; the model is built based on 23 published datasets from the Adriatic Sea. The developed ANFIS model is evaluated using various methods, namely, trend analyses. Trend analyses are conducted to show the effects of the features on the CTD to present the physical behavior. The model’s performance was also evaluated using statistical error analyses. In addition, the ANFIS and previously published models were assessed. The trend analyses show the correct relationship between all features and the CTD. In addition, the trend analyses for the previous models are discussed. The results show that the proposed ANFIS method outperforms published methods with an R of 0.9984 and an absolute average percentage relative error (AAPRE) of 4.293%.