http://lib.buet.ac.bd;localhosthttp://:8080/xmlui/handle/123456789/1255 Post graduate dissertations (Theses) of Department of Petroleum and Mineral Resources Engineering (PMRE) 2026-06-04T11:40:46Z http://lib.buet.ac.bd;localhosthttp://:8080/xmlui/handle/123456789/7322 Experimental study on rheological properties of water based drilling fluid and its impact on drilling operations Mahbubur Rahman, Dr. Mohammed; Sumon Chowdhury, Md.; 1018132012; 622.3381/SUM/2024 Most encountered problems like fluid loss, wellbore stability, well control, poor capacity of cuttings transport, poor torque performance, increased drag, and stuck pipe can occur during drilling due to the improper design of the drilling mud, which can increase the cost of drilling. This study looks into the rheological properties of ten water-based drilling mud and their impact on drilling operations. A viscometer is used to conduct the analysis in the laboratory. The density of the prepared mud ranges from 8.7 ppg to 10.01 ppg. This experimental study focuses on determining the viscosity, gel strength, and yield point of ten water-based drilling mud which are formulated under different barite concentrations. The plastic viscosity of the ten mud samples ranges from 10 cp to 18 cp, yield point ranges from 5 lb/100ft2 to 12.75 lb/100ft2 and gel strength ranges from 2 lb/100ft2 to 9 lb/100ft2. The effect of density on viscosity, gel strength, and yield point is also observed in this study. Key findings indicate that the viscosity, gel strength, and yield point of the drilling fluid are significantly influenced by the density of mud at constant pressure and temperature. Five drilling mud rheological models such as Newtonian, Bingham plastic, Power law, API, and Herschel-Bulkley are analyzed to select the most suitable fluid model and measure the total frictional pressure drop in the wellbore, considering the suitable model. The error analysis of experimental/measured shear stress and theoretical/modeled shear stress is done to choose the most perfect fluid model. The minimum error indicates the best fitted rheological fluid model. This study found the error between experimental/measured shear stress and theoretical/modeled shear stress maximum for Newtonian model (7% to 18%) and minimum for API model (up to 0.16%). The mud samples are preferable for the API model to calculate the standpipe or pump pressure. Data matching is done to compare the experimental and real pressure loss data, SBHP and FBHP. There is good scope in the future to study the effect of some chemical additives on the rheological properties of water based drilling mud in different pressure and temperature. 2024-05-28T00:00:00Z http://lib.buet.ac.bd;localhosthttp://:8080/xmlui/handle/123456789/7321 Reservoir performance analysis of the gas fields of Bangladesh Mahbubur Rahman, Dr. Mohammed; Nazmul Islam, Md.; 1015132031; 665.773095492/NAZ/2024 Reservoir performance prediction is an iterative process that incorporates various data points, such as production rates, pressure, fluid properties, and geological characteristics. Techniques like decline curve analysis, material balance, and reservoir simulation are commonly used for evaluating a reservoir. The Reserve Performance Indicator (RPI) analyzes parameters such as production rates, pressure data, and fluid properties to evaluate the performance of a reservoir over time. Till now 29 gas fields was discovered in Bangladesh. There isn't any literature or publication that addresses a consistent approach of ranking these reservoirs based on their performance. In this study, an approach is taken to rank the reservoirs according to various indicators used for analyzing the reservoir performance and to identify more prolific and problematic reservoirs. After collecting all the available reports from the public domain (Annual Reports, MIS Reports), reservoirs are ranked by initial reserves, cumulative production (Gas, Condensate), Gas Recovery. The Jalalabad gas field has retrieved more gas than its initial reserve which suggests the necessity of reserve re-estimation. For the majority of discovered fields, the last reserve estimation was completed 14 years ago. Although there is a noticeable reserve in the Kailashtila and Rashidpur fields, just 22.21% and 19.24% of the gas has been recovered, respectively, suggesting that their field development approach is inadequate. Potential gas recovery is possible from these fields. In addition to displaying inadequate development strategies for such fields, only 1 well was drilled in Meghna field during its 26-year production life, while 2 wells were drilled in Narsingdi field over its 27-year production life. Suspended wells of Titas, Habiganj, Bakhrabad, and Kailashtila fields are examined further and the wells of Titas, Bakhrabad, and Kailashtila fields having the potential for workover operation on a priority basis are also identified. Finally, the top 4 fields that are performing well are categorized as Category-I fields, and the 4 fields whose performance was poor and need to change the field development tactics are categorized as Category-II fields. 2024-05-28T00:00:00Z http://lib.buet.ac.bd;localhosthttp://:8080/xmlui/handle/123456789/7050 Hydrocarbon production forecasting in conventional and unconventional reservoirs using different models Rahman, Dr. Mohammed Mahbubur; Das, Himadri Shakher; 0422132036; 658.5/DAS/2024 In the oil and gas industry, accurate prediction of hydrocarbon production is still a major problem. While certain empirical and analytical decline analysis approaches were developed to address this problem, they are limited by their assumptions making them less reliable for every reservoir and flow conditions. There is no single decline analysis method that can handle all kinds of data and reservoir types. Reservoir simulation can also be used but it demands extensive data, accurate geo-modeling of the reservoir, and a long production-pressure history. Moreover, it is computationally expensive and time-consuming. In this context, data-driven approaches offer a promising avenue for a more robust solution. This study explores hydrocarbon production forecasting using empirical, analytical, and machine learning methods. The analysis used three distinct datasets: one from the Volve oil field, a conventional oil field with production, time, and downhole pressure data collected over 95 months, another from the Marcellus shale gas field, an unconventional gas reservoir with production and time data over 130 months, and the other from a conventional gas field of Bangladesh consisting production rate and time over 100 months. The study involves three methodologies: empirical decline curve analysis using Arps’ Decline Curve Analysis for the conventional oil field and gas field and the Duong model for the Marcellus shale gas field, analytical modeling using the Topaze module in KAPPA workstation specifically including Blasingame type curve analysis, and machine learning and deep learning models including Autoregressive Linear Regression (LR), Support Vector Regression (SVR), and Long Short-Term Memory (LSTM) networks. The empirical analysis resulted in an estimated ultimate recovery (EUR) of 3.99 million Sm3 for the Volve oil field at the end of 120 months, 60.176 Bscf for the conventional gas field after 124 months of production, and 3.03 Bscf for the Marcellus shale gas field after 240 months of production. Analytical modeling for the Volve oil field provided additional insights with a forecasted EUR of 3.88 million Sm3. Machine learning models exhibited significant outcomes in forecasting accuracy. For the Volve oil field, the LR, SVR, and LSTM models achieved Root Mean Squared Error (RMSE) values of 481.765 Sm3/month, 419.049 Sm3/month, and 361.072 Sm3/month respectively on the test dataset with corresponding EUR values of 4.009 million Sm3, 4.0219 million Sm3, and 3.956 million Sm3. In case of the gas field, the LR, SVR, and LSTM models attained RMSE values of 8.256 MMscf/month, 8.291 MMscf/month, and 17.034 MMscf/month respectively on the unseen data with respective EUR values of 61.751 Bscf, 61.754 Bscf, and 61.127 Bscf. For the Marcellus shale gas field, the LR, SVR, and LSTM models achieved RMSE values of 126.7783 Mscf/month, 127.3119 Mscf/month, and 237.0362 Mscf/month respectively on the test dataset with respective EUR values of 3.1341 Bscf, 3.1357 Bscf, and 2.957 Bscf. The results are very close to the outcomes obtained from empirical and analytical methods. The errors in the oil and shale gas datasets are much less considering the mean and standard deviation of the production data. However, on the conventional gas data both the empirical and the machine learning models produced more errors because of the nature of the dataset. To evaluate the models, the study also considered the coefficient of determination (R2 score) and Relative RMSE (RRMSE) metrics. Regarding the R2 score, the SVR model performed better than the two others on the unseen data, greater than 0.98 for the Volve oil field dataset. On the other hand, for the Marcellus shale dataset, LR and SVR performed almost the same achieving R2 scores of 0.9154 and 0.9149 respectively. On the gas field dataset, the LR model explained the variance in the unseen data better than the other models. LSTM did not perform very well both on the gas field and Marcellus shale dataset both in terms of RMSE and R2 values. The significance of this work lies in its direct comparison of empirical, analytical, and machine learning techniques using diverse datasets, shedding light upon their respective strengths and limitations. Through this research, a more generalized and robust tool for hydrocarbon production forecasting has been developed. 2024-11-20T00:00:00Z http://lib.buet.ac.bd;localhosthttp://:8080/xmlui/handle/123456789/7009 Application of physics-informed neural networks for solution of the diffusivity equation and interpretation of well response Rahman, Dr. Mohammed Mahbubur; Shaumik Rahman Ayon, Md; 0422132022; 622.3382/SHA/2024 The diffusivity equation is a widely accepted mathematical model for addressing the complex phenomenon of fluid flow through porous media. It has numerous applications in petroleum engineering, including reservoir simulation, reservoir characterization, and pressure and rate transient analysis. While analytical solutions to the diffusivity equation are available for simple boundary conditions, more complex problems typically require employment of numerical methods. Recently, there has been a growing focus on Physics- Informed Neural Networks (PINNs) due to their ability to integrate physical laws into the learning process, offering a more generalized approach compared to traditional numerical solvers. This study presents the effectiveness of Physics-Informed Neural Networks (PINNs) in solving flow through porous media problems and compares the results with available analytical and semi-analytical solutions. The proposed PINN framework demonstrates a mean percentage error of 0.04 % for simple one-dimensional linear flow scenarios and maintains errors below 0.5 % for various radial flow cases. Additionally, the effectiveness of transfer learning in solving the inverse problem of well test analysis is highlighted, with 0.1 % error in predicting the permeability and 11.96 % in skin factor prediction from noisy well test data. This research identifies the critical role of the physics loss weight factor (λP DE) in solution accuracy and its correlation with computational domain size. Furthermore, the study identifies the limitations and advantages of PINN models, providing a foundation for future advancements in the application of neural networks to complex fluid flow problems in porous media. 2024-06-01T00:00:00Z