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Abo Bakr A, El Kadi HH, Mostafa T. Petrographical and petrophysical rock typing for flow unit identification and permeability prediction in lower cretaceous reservoir AEB_IIIG, Western Desert, Egypt. Sci Rep 2024; 14:5656. [PMID: 38454114 PMCID: PMC10920705 DOI: 10.1038/s41598-024-56178-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 03/03/2024] [Indexed: 03/09/2024] Open
Abstract
The primary objective of this study is to identify and analyze the petrophysical properties of the newly investigated AEB_IIIG member reservoir in Meleiha West Deep (MWD) Field and to classify it into different rock types. Additionally, this research intends to develop mathematical equations that may be utilized to estimate permeability in uncored sections of the same well or in other wells where core samples are unavailable. The analysis focused on the pore hole records of ten wells that were drilled in MWD Field. The reservoir levels were identified, and their petrophysical parameters were evaluated using well logs and core data. We were able to recognize seven different types of rocks (petrophysical static rock type 1 (PSRT1) to PSRT7) using petrography data, the reservoir quality index (RQI), the flow zone index (FZI), R35, hydraulic flow units (HFUs), and stratigraphy modified Lorenz (SML) plots. The analysis of the petrophysical data shows that AEB_IIIG has unsteady net pay thicknesses over the area. It has a range of 8-25% shale volume, 12-17% effective porosity, and 72-92% hydrocarbon saturation. The RQI results show that psrt1, psrt2 and psrt3 have a good reservoir quality as indicated by high R35 and helium porosity, respectively. They contribute with more than 75% of the reservoir production. The equation derived for each rock type of AEB_IIIG reservoir can be employed to forecast the permeability value distribution inside the reservoir.
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Affiliation(s)
- Abdelraheim Abo Bakr
- Geology Department, Faculty of Science, Al-Azhar University, P.O. Box 11884, Nasr City, Cairo, Egypt
| | - Hassan H El Kadi
- Geology Department, Faculty of Science, Al-Azhar University, P.O. Box 11884, Nasr City, Cairo, Egypt
| | - Taher Mostafa
- Geology Department, Faculty of Science, Al-Azhar University, P.O. Box 11884, Nasr City, Cairo, Egypt.
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Ibraheem Shelash Al-Hawary S, Ali E, Mohammad Husein Kamona S, Hussain Saleh L, Abdulwahid AS, Al-Saidi DN, Alhassan MS, Rasen FA, Abdullah Abbas H, Alawadi A, Abbas AH, Sina M. Prediction of geomechanical bearing capacity using autoregressive deep neural network in carbon capture and storage systems. Heliyon 2023; 9:e21913. [PMID: 38034690 PMCID: PMC10685191 DOI: 10.1016/j.heliyon.2023.e21913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 10/29/2023] [Accepted: 10/31/2023] [Indexed: 12/02/2023] Open
Abstract
Carbon Capture and Storage (CCS) field is growing rapidly as a means to mitigate the accumulation of greenhouse gas emissions. However, the geomechanical stability of CCS systems, particularly related to bearing capacity, remains a critical challenge that requires accurate prediction models. In this research paper, we investigate the efficacy of employing an Autoregressive Deep Neural Network (ARDNN) algorithm to predict the geomechanical bearing capacity in CCS systems through shear wave velocity prediction as an index for bearing capacity evaluation of deep rock formations. The model utilizes a dataset consisting of 23,000 data points to train and test the ARDNN algorithm. Its scalability, use of deep learning techniques, automatic feature extraction, adaptability to changes in data, and versatility in various prediction tasks make it an attractive option for accurate predictions. The results demonstrate exceptional performance, as evidenced by an R-squared value of 0.9906 and a mean squared error of 0.0438 for the test data compared to the measured data. This research has significant practical implications for effectively predicting geomechanical stability in CCS systems, thus mitigating potential risks associated with their operation.
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Affiliation(s)
| | - Eyhab Ali
- College of Chemistry, Al-Zahraa University for Women, Karbala, Iraq
| | | | - Luma Hussain Saleh
- Department of Anesthesia Techniques, Al-Noor University College, Nineveh, Iraq
| | - Alzahraa S. Abdulwahid
- Department of Medical Laboratory Technics, Al-Hadi University College, Baghdad, 10011, Iraq
| | - Dahlia N. Al-Saidi
- Department of Medical Laboratories Technology, AL-Nisour University College, Baghdad, Iraq
| | - Muataz S. Alhassan
- Division of Advanced Nano Material Technologies, Scientific Research Center, Al-Ayen University, Thi-Qar, Iraq
| | - Fadhil A. Rasen
- Department of Medical Engineering, Al-Esraa University College, Baghdad, Iraq
| | - Hussein Abdullah Abbas
- College of Technical Engineering, National University of Science and Technology, Dhi Qar, Iraq
| | - Ahmed Alawadi
- College of Technical Engineering, The Islamic University, Najaf, Iraq
- College of Technical Engineering, The Islamic University of Al Diwaniyah, Iraq
- College of Technical Engineering, The Islamic University of Babylon, Iraq
| | - Ali Hashim Abbas
- College of Technical Engineering, Imam Ja’afar Al‐Sadiq University, Al-Muthanna, 66002, Iraq
| | - Mohammad Sina
- Department of Petroleum Engineering, Omidiyeh Branch, Islamic Azad University, Omidiyeh, Iran
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Ramskill NP, Bush I, Sederman AJ, Mantle MD, Benning M, Anger BC, Appel M, Gladden LF. Fast imaging of laboratory core floods using 3D compressed sensing RARE MRI. J Magn Reson 2016; 270:187-197. [PMID: 27500742 DOI: 10.1016/j.jmr.2016.07.017] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2016] [Revised: 07/26/2016] [Accepted: 07/27/2016] [Indexed: 06/06/2023]
Abstract
Three-dimensional (3D) imaging of the fluid distributions within the rock is essential to enable the unambiguous interpretation of core flooding data. Magnetic resonance imaging (MRI) has been widely used to image fluid saturation in rock cores; however, conventional acquisition strategies are typically too slow to capture the dynamic nature of the displacement processes that are of interest. Using Compressed Sensing (CS), it is possible to reconstruct a near-perfect image from significantly fewer measurements than was previously thought necessary, and this can result in a significant reduction in the image acquisition times. In the present study, a method using the Rapid Acquisition with Relaxation Enhancement (RARE) pulse sequence with CS to provide 3D images of the fluid saturation in rock core samples during laboratory core floods is demonstrated. An objective method using image quality metrics for the determination of the most suitable regularisation functional to be used in the CS reconstructions is reported. It is shown that for the present application, Total Variation outperforms the Haar and Daubechies3 wavelet families in terms of the agreement of their respective CS reconstructions with a fully-sampled reference image. Using the CS-RARE approach, 3D images of the fluid saturation in the rock core have been acquired in 16min. The CS-RARE technique has been applied to image the residual water saturation in the rock during a water-water displacement core flood. With a flow rate corresponding to an interstitial velocity of vi=1.89±0.03ftday(-1), 0.1 pore volumes were injected over the course of each image acquisition, a four-fold reduction when compared to a fully-sampled RARE acquisition. Finally, the 3D CS-RARE technique has been used to image the drainage of dodecane into the water-saturated rock in which the dynamics of the coalescence of discrete clusters of the non-wetting phase are clearly observed. The enhancement in the temporal resolution that has been achieved using the CS-RARE approach enables dynamic transport processes pertinent to laboratory core floods to be investigated in 3D on a time-scale and with a spatial resolution that, until now, has not been possible.
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Affiliation(s)
- N P Ramskill
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Pembroke Street, Cambridge CB2 3RA, UK.
| | - I Bush
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Pembroke Street, Cambridge CB2 3RA, UK
| | - A J Sederman
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Pembroke Street, Cambridge CB2 3RA, UK
| | - M D Mantle
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Pembroke Street, Cambridge CB2 3RA, UK
| | - M Benning
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Wilberforce Road, Cambridge CB3 0WA, UK
| | - B C Anger
- Shell Technology Centre, 3333 Highway 6 S, Houston, TX, USA
| | - M Appel
- Shell Technology Centre, 3333 Highway 6 S, Houston, TX, USA
| | - L F Gladden
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Pembroke Street, Cambridge CB2 3RA, UK
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