The conventional methods for controlling excess water production in oil/gas wells can be classified on the basis of the mechanism (pore-blocking mechanism and relative permeability modification) used. Gel systems developed on the basis of a pore-blocking mechanism completely block the pores and stop the flow of both oil and water, whereas a relative permeability modifier (RPM) only restricts the flow of a single phase of the fluid. The gel working on the basis of the pore-blocking mechanism is known as a total blocking gel. An invert emulsified (PAM–PEI) polymer gel is a relative permeability modifier system. The same invert emulsion system is tested as a total blocking gel system in this research work. The dual-injection technique (1st injection and 2nd injection) was used for this purpose. In this research work, the emulsion system was tested at a temperature of 105 °C. The core sections with drilled holes and fractures were used for the core flooding experiments, representing a highly fractured reservoir. The developed emulsified gel system was characterized using a dilution test, an inverted bottle test, microscopic images, and FTIR images. The emulsified polymer gel was tested using a core flooding experiment. After the 2nd injection, the postflood medical CT and micro-CT images of the core sections clearly showed the presence of two different phases in the core section, i.e., the oil phase and the gel phase. The core flooding experiment result indicates that the gel formed after the 2nd injection of the emulsion system can withstand a very high differential pressure, i.e., above 2000 psi. The gel did not allow any oil or water to be produced. Hence, the developed emulsified polymer gel system with the help of a dual-injection technique can be efficiently used as a total blocking gel for high-temperature reservoirs.
The importance of permeability cannot be under-stimated. It is used in crucial equations used to determine quantities analysed by reservoir, drilling, and production engineers. Using permeability along with other properties is important to understanding reservoir behaviour when wells are drilled, to calculate the rate of the fluid flow, as illustrated by Darcy's equation that relates fluid flow to permeability. Measuring permeability in the laboratory with the conventioanl available steady-state equipment can be time consuming, especially if it was done by gas injection which requires measuring at different pressure points to satisfy Klinkenberg equation. A quick measuring equipment called the prob permeameter have been used for many years, it quantitatively performs a permeability point measurement as a function of position on either a whole core, slabbed core or a rock slab. However, despite of its prompt and easy measurement, most of the results represents a general idea about the actual permeability and sometimes even falls out of the range, which makes it unrelaiable. Series of experiments were conducted for a variety of rock samples with a wide range of permeability ranging from tight to permeable, to compare the generated results between both of the above equipment. The results were graphed and been compared using different point of views, mathematicalwise, petroleum engineeringwise, and geologicalwise. Ultimately, an equation to correlate between the results was developed graphically and using logistic regression techniques.
While many factors influence the success of a given well, the permeability of the surrounding formation is one of the most important properties to understand the nature of any reservoir and to be utilized for effective oil and gas drilling. Gathering data from well logs for different wells can be highly expensive and time-consuming. The goal of this work is to find the best artificial intelligent model which can predict the permeability values with minimum error while saving time and money. Therefore, accurately estimating is highly beneficial to use such a model for further field and engineering applications. In this project, a trial was accomplished through a Machine Learning (ML) approach using several modules of Artificial Intelligent including ANFIS and ANN to examine and build a permeability prediction model based on nine (9) well-logging parameters taken from well-logging data measured at a borehole in carbonate rock. The permeability was predicted from well-log data using Artificial Intelligent (AI) technique. Field data were recorded at one borehole, where all logs are correlated together. After obtaining results, the prediction model can be considered successful, it is highly recommended to utilize ANFIS- Genfis2 as it gives outstanding results as the correlation coefficient training was 1.0 and testing was 0.9347 compared with ANFIS-Genfis1 which was not satisfying with training correlation coefficient of 1.0 and testing 0.4073, including a significant reduction in the percentage error of 14.3% compared of 301%, and utilize ANN with a double layer not single, as the result of single layer showed a correlation coefficient of 0.9337 in training and 0.9924 in testing. In addition, single layer method showed higher error compared with double layer. Conclusively, it is recommended to apply the model with other data obtained from the same reservoir, to minimize the number of unneeded data, enhance the measurement performance by avoiding human errors, and develop other relationships between a set of parameters that can result in a better and most effective prediction model. In novelty, utilizing and studying the output of this trial application of the machine learning approach will summarize the best models and techniques for predicting many important reservoir properties such as Permeability. The number of well logging parameters is high and has been statically analyzed to increase the resolution of the input data. Building this prediction model will increase the recovered amount from the subsurface and will lead to significant cost savings in drilling and exploration operational
Formation damage phenomenon constitutes serious operational and economic problems to the petroleum production. Oil production in certain reservoirs is inadvertently impaired by precipitation and deposition of the high molecular weight components such as paraffin wax. A facile applicability of synergistic effects of thermochemical reaction and ultrasonication to mitigate wax deposition has been presented in this article. Thermochemical heat source has to do with exothermic heat generation from certain chemical reactions. On the other hand, ultrasonication causes cavitation and implosion of bubbles, which is trasimmted as energy in the medium and assit in detaching contaminants from the surface. Series of imbibition experiments were conducted at different ultrasound frequencies (low 28kHz, and high 40kHz), exposure times (20, 40, and 60 mins), and different molarities (M1, M2, and M3) of the thermochemical fluids (TCF), for removing wax deposit from tight Scioto Sandstone core samples. The performance was followed through permeability and porosity tests, as well as Scanning Electron Microscopy with Energy-Dispersive X-ray (SEM-EDX) analyses. Ultimately, the results revealed promising potentials for the proposed technology for efficient paraffin wax removal from a tight rock sample up to 70% within the experimental limits investigated.
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