ORCID Profile
0000-0002-6974-6236
Current Organisations
Cairo University
,
Elsevier Foundation
,
Universitat de Barcelona
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Publisher: IPTC
Date: 21-02-2022
Abstract: Over the years, various cementitious materials have been investigated as a substitute for conventional cement. One ex le of these materials is geopolymer, a binder developed when an alkaline solution is used to activate materials containing alumina and silica. The use of this material is well established in the construction industry. In oil-well cementing, its feasibility is currently being investigated. An extensive survey on the various geopolymer studies has been conducted. The goal is to present a manuscript containing a summary of these studies. This will help researchers merge the knowledge acquired going forward. The study showed that the application of geopolymer in acidic and saline conditions, and in well plugging and abandonment operations. Additionally, geopolymer-mud compatibility and the impact of temperature on geopolymer systems have also been studied. In general, geopolymer systems show better performance, overcoming the limitations of the OPC systems. For instance, the geopolymer is more suited for CO2 sequestrations wells as it does not undergo a carbonation reaction which would result in degradation. Furthermore, geopolymers have superior performance in highly saline conditions and besides their compatibility with mud, a geopolymer-mud combination produces cementitious systems with enhanced properties.
Publisher: Elsevier BV
Date: 08-2020
Publisher: American Chemical Society (ACS)
Date: 29-04-2022
Publisher: ASME International
Date: 18-01-2022
DOI: 10.1115/1.4053248
Abstract: Water saturation (Sw) is a vital factor for the hydrocarbon in-place calculations. Sw is usually calculated using different equations however, its values have been inconsistent with the experimental results due to often incorrectness of their underlying assumptions. Moreover, the main hindrance remains in these approaches due to their strong reliance on experimental analysis which are expensive and time-consuming. This study introduces the application of different machine learning (ML) methods to predict Sw from the conventional well logs. Function networks (FNs), support vector machine (SVM), and random forests (RFs) were implemented to calculate the Sw using gamma-ray log, neutron porosity log, and resistivity (Rt) log. A dataset of 782 points from two wells (well-1 and well-2) in tight gas sandstone formation was used to build and then validate the different ML models. The dataset from well-1 was applied for the ML models training and testing, then the unseen data from well-2 were used to validate the developed models. The results from FN, SVM, and RF models showed their capability of accurately predicting the Sw from the conventional well logging data. The correlation coefficient (R) values between actual and estimated Sw from the FN model were found to be 0.85 and 0.83 compared to 0.98 and 0.95 from the RF model in the case of training and testing sets, respectively. SVM model shows an R-value of 0.95 and 0.85 in the different datasets. The average absolute percentage error (AAPE) was less than 8% in the three ML models. The ML models outperform the empirical correlations that have AAPE greater than 19%. This study provides ML applications to accurately forecast the water saturation using the readily available conventional well logs without additional core analysis or well site interventions.
Publisher: Routledge
Date: 22-05-2015
Publisher: IEEE
Date: 12-2008
Publisher: Elsevier BV
Date: 09-2021
Publisher: Egypts Presidential Specialized Council for Education and Scientific Research
Date: 29-03-2022
Publisher: Elsevier BV
Date: 2018
Publisher: Elsevier BV
Date: 2022
Location: Bolivia (Plurinational State of)
No related grants have been discovered for Dr. Yasmin Abdelraouf.