Post graduate dissertations (Theses) of Department of Petroleum and Mineral Resources Engineering (PMRE) http://lib.buet.ac.bd;localhosthttp://:8080/xmlui/handle/123456789/1255 2026-04-21T17:16:46Z 2026-04-21T17:16:46Z Das, Himadri Shakher http://lib.buet.ac.bd;localhosthttp://:8080/xmlui/handle/123456789/7050 2025-04-20T03:50:13Z 2024-11-20T00:00:00Z

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 Shaumik Rahman Ayon, Md http://lib.buet.ac.bd;localhosthttp://:8080/xmlui/handle/123456789/7009 2025-03-10T05:08:15Z 2024-06-01T00:00:00Z

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 Didar Hossain, Mohammad http://lib.buet.ac.bd;localhosthttp://:8080/xmlui/handle/123456789/6775 2024-07-09T07:53:59Z 2022-12-03T00:00:00Z

Design and analysis of a pipeline system for optimal transmission of natural gas from proposed payra LNG terminal to the southwestern zone of Bangladesh Mahbubur Rahman, Dr. Mohammed; Didar Hossain, Mohammad; 0416132013; 665.74/DID/2022 Bangladesh has set a target to become a high-income country by 2041. Industrialization and job creation are the key factors for its steady growth and development. For industrialization, the country needs a reliable and quality supply of energy at an affordable rate. To achieve its targeted GDP 587,665 million USD by 2041, the country's total energy demand needs to be 130,827 toe. Bangladesh also has a long-term plan to achieve an electricity generation capacity of 40,000 MW by 2030 and 60,000 MW by 2041. At present, 60.44% of the country's electricity is generated by natural gas, and almost 62% of the commercial energy is provided from natural gas. According to current statistics, the remaining natural gas reserve in the country is only 9.3 TCF as of June 2022. It is projected that the daily demand for natural gas in Bangladesh will be approximately 8,346 MMscfd by 2041. If no new major gas discoveries are made, this demand will have to be met by importing Liquefied Natural Gas (LNG). The proposed LNG infrastructure development plans mainly focus on the Chittagong and Cox's Bazaar areas. This will require significant investments in pipelines to transport natural gas to the Southwestern regions of the country. So, to promote the overall economic development of the country, the authorities are aiming to construct a 1,000 MMscfd LNG terminal at Payra, Patuakhali. With the availability of LNG at Payra, it will become necessary to transport LNG to the ultimate consumers in the Southwestern zone. The most economical, easiest, and safest way of continuously transporting such a huge volume of gas is through pipelines. The objective of this research is to evaluate different options for gas transmission facilities that can ensure reliable gas supply to the Southwestern region. To establish a sustainable gas supply infrastructure, a virtual model has been developed to simulate pipeline performance and suggest solutions for future gas demand. The gas network originates from Payra, Patuakhali, and extends downstream to Khulna and Langalbandh. A detailed study is conducted to assess the current demand and supply of gas, future growth forecasts, and existing gas infrastructure in the Southwestern region. After analyzing the gas supply situation with simulation software, it was concluded that 309 km transmission pipeline network, comprising a 42-inch diameter 157 km long Payra-Barisal-Takerhat pipeline and a 36-inch diameter 152 km long Khulna-Gopalganj-Takerhat-Langalbandh pipeline can be constructed to supply gas at the right pressure and quantity to the major load centers in the Southwestern region. Nine alternative scenarios were considered to develop a sustainable gas supply infrastructure and this was deemed to be the best option. After a couple of years, when these pipelines become saturated, installing a 111 km 36-inch Barisal-Jhalkathi-Bagherhat-Khulna pipeline can help overcome the bottleneck of the Southwestern Zone network.

2022-12-03T00:00:00Z Aminur Rahman, Md. http://lib.buet.ac.bd;localhosthttp://:8080/xmlui/handle/123456789/6759 2024-06-30T05:46:36Z 2023-01-28T00:00:00Z

Comparative study of power delivery by water, air, SC-CO2 and foam during drilling operation by coiled tubing method Mohammad Mahbubur Rahman, Dr.; Aminur Rahman, Md.; 0419132006; 622.3382/AMI/2023 Coiled tubing drilling has become one of the most emerging technological developments and a preferred technique in oilfields. Drilling fluid is required to provide sufficient power to the drilling motor and downhole drill bits and to circulate drill cuttings out of the wellbore. Water is Common used drilling fluid is water. Water is a high density and viscosity fluid, so its pressure loss inside the coiled tubing is high. Additionally, water is an incompressible fluid. Water has no internal energy and can only deliver hydraulic power. This is a big problem when drilling small, deep holes in hard formations.Water cannot supply sufficient power to the drill bit because much of its energy is lost in the tubing due to friction. Therefore, different fluids are being tested and used as an alternative to water. Hydraulic power delivery of compressible fluids to drilling bit is higher than water. Hence, a comparative study of power delivery in coiled tubing drilling by using water, air, supercritical carbon dioxide (SC-CO2), nitrogen, and foam as drilling fluid is proposed. Calculations were performed to evaluate the pressure loss and power delivery by these fluids. It was found that, high injecting pressure of compressible fluids delivered higher enthalpy and power to the drill bit in coiled tubing drilling. Density and viscosity of compressible fluid is lower than water. As a result, frictional pressure loss of compressible fluid is low. Supercritical CO2 delivers higher power than other fluids to drill bit inside the coiled tubing. SC-CO2 density is higher than air but smaller than water, so it can produce enough torque to drive a downhole motor. Frictional pressure loss is lower than water for low viscosity of SC-CO2 fluid. So, SC-CO2 is the most suitable fluid for Coiled tubing drilling.

2023-01-28T00:00:00Z