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  1. Article ; Online: Data-driven models to predict shale wettability for CO

    Ibrahim, Ahmed Farid / Elkatatny, Salaheldin

    Scientific reports

    2023  Volume 13, Issue 1, Page(s) 10151

    Abstract: The significance of ... ...

    Abstract The significance of CO
    MeSH term(s) Carbon Dioxide ; Environmental Monitoring ; Wettability ; Fuzzy Logic ; Minerals
    Chemical Substances Carbon Dioxide (142M471B3J) ; Minerals
    Language English
    Publishing date 2023-06-22
    Publishing country England
    Document type Journal Article
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-023-37327-2
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Impact of Eco-Friendly Drilling Additives on Foaming Properties for Sustainable Underbalanced Foam Drilling Applications.

    Gowida, Ahmed / Elkatatny, Salaheldin / Ibrahim, Ahmed Farid

    ACS omega

    2024  Volume 9, Issue 6, Page(s) 6719–6730

    Abstract: Underbalanced foam drilling stands out as a drilling technique acclaimed for its capacity to enhance safety and efficiency in operations. Utilizing foams as drilling fluids offers several benefits over traditional methods, including lower density, ... ...

    Abstract Underbalanced foam drilling stands out as a drilling technique acclaimed for its capacity to enhance safety and efficiency in operations. Utilizing foams as drilling fluids offers several benefits over traditional methods, including lower density, diminished formation damage, and augmented borehole stability. However, the persistent challenge of sustaining foam stability in demanding conditions, particularly amid elevated water salinity and alkaline environments, remains a critical issue. Current literature lacks comprehensive insights into foam stability under such specific circumstances, raising concerns about the practicality of numerous reported foaming agents in field applications. This study aims to fill this knowledge void to align with industry standards. With a heightened focus on sustainability due to mounting environmental considerations, the research explores the use of an eco-friendly surfactant, ammonium alcohol ether sulfate (AAES). Additionally, the investigation delves into the impact of environmentally friendly drilling additives-polyanionic cellulose (PAC), carboxymethyl cellulose (CMC), and starch-on the stability of bulk foam under mildly alkaline conditions. Employing a dynamic foam analyzer, diverse foam properties of AAES foams were assessed, encompassing stability, foamability, and bubble structure. The results demonstrated that the optimal concentrations of the tested additives, in the order of PAC > CMC > starch, significantly prolonged the half-life of the AAES foam bubbles. The introduction of PAC and CMC additives elevated the viscosity of AAES foaming solutions, enhancing the liquid retention within the foam structure. In contrast, starch addition exerted no influence on the solution viscosity and did not impede liquid drainage, although it did reduce bubble coalescence. Furthermore, the PAC- and CMC-based AAES foams manifested as considerably wetter foams with a rounded bubble structure, while the starch-based AAES foam exhibited a dry foam characterized by a distinct polyhedral bubble structure. These findings offer valuable insights into the potential application of the AAES surfactant in foam drilling, showcasing its efficacy in improving foam stability and contributing to the evolution of eco-friendly drilling practices.
    Language English
    Publishing date 2024-01-30
    Publishing country United States
    Document type Journal Article
    ISSN 2470-1343
    ISSN (online) 2470-1343
    DOI 10.1021/acsomega.3c07882
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Utilizing machine learning for flow zone indicators prediction and hydraulic flow unit classification.

    Astsauri, Tengku / Habiburrahman, Muhammad / Ibrahim, Ahmed Farid / Wang, Yuzhu

    Scientific reports

    2024  Volume 14, Issue 1, Page(s) 4223

    Abstract: Reservoir characterization, essential for understanding subsurface heterogeneity, often faces challenges due to scale-dependent variations. This study addresses this issue by utilizing hydraulic flow unit (HFU) zonation to group rocks with similar ... ...

    Abstract Reservoir characterization, essential for understanding subsurface heterogeneity, often faces challenges due to scale-dependent variations. This study addresses this issue by utilizing hydraulic flow unit (HFU) zonation to group rocks with similar petrophysical and flow characteristics. Flow Zone Indicator (FZI), a crucial measure derived from pore throat size, permeability, and porosity, serves as a key parameter, but its determination is time-consuming and expensive. The objective is to employ supervised and unsupervised machine learning to predict FZI and classify the reservoir into distinct HFUs. Unsupervised learning using K-means clustering and supervised algorithms including Random Forest (RF), Extreme Gradient Boosting (XGB), Support Vector Machines (SVM), and Artificial Neural Networks (ANN) were employed. FZI values from RCAL data formed the basis for model training and testing, then the developed models were used to predict FZI in unsampled locations. A methodical approach involves 3 k-fold cross-validation and hyper-parameter tuning, utilizing the random search cross-validation technique over 50 iterations was applied to optimize each model. The four applied algorithms indicate high performance with coefficients determination (R
    Language English
    Publishing date 2024-02-20
    Publishing country England
    Document type Journal Article
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-024-54893-1
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Real-Time Prediction of Petrophysical Properties Using Machine Learning Based on Drilling Parameters.

    Hassaan, Said / Mohamed, Abdulaziz / Ibrahim, Ahmed Farid / Elkatatny, Salaheldin

    ACS omega

    2024  Volume 9, Issue 15, Page(s) 17066–17075

    Abstract: The prediction of rock porosity and permeability is crucial for assessing reservoir productivity and economic feasibility. However, traditional methods for obtaining these properties are time-consuming and expensive, making them impractical for ... ...

    Abstract The prediction of rock porosity and permeability is crucial for assessing reservoir productivity and economic feasibility. However, traditional methods for obtaining these properties are time-consuming and expensive, making them impractical for comprehensive reservoir evaluation. This study introduces a novel approach to efficiently predict rock porosity and permeability for reservoir assessment by leveraging real-time machine learning models. Utilizing readily available drilling parameters, this approach offers a cost-effective alternative to traditional time-consuming methods to predict formation petrophysical parameters in real-time. The data set used in this study was collected from two vertical wells located in the Middle East. It encompasses drilling parameters such as the rate of penetration (ROP), gallons per minute (GPM), revolutions per minute (RPM), strokes per minute (SPP), torque, and weight on bit (WOB), along with the corresponding measurements of porosity (ϕ) and permeability (
    Language English
    Publishing date 2024-04-08
    Publishing country United States
    Document type Journal Article
    ISSN 2470-1343
    ISSN (online) 2470-1343
    DOI 10.1021/acsomega.3c08795
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Applications of Different Classification Machine Learning Techniques to Predict Formation Tops and Lithology While Drilling.

    Ibrahim, Ahmed Farid / Ahmed, Ashraf / Elkatatny, Salaheldin

    ACS omega

    2023  Volume 8, Issue 45, Page(s) 42152–42163

    Abstract: Accurate prediction of formation tops and lithology plays a critical role in optimizing drilling processes, cost reduction, and risk mitigation in hydrocarbon operations. Although several techniques like well logging, core sampling, cuttings analysis, ... ...

    Abstract Accurate prediction of formation tops and lithology plays a critical role in optimizing drilling processes, cost reduction, and risk mitigation in hydrocarbon operations. Although several techniques like well logging, core sampling, cuttings analysis, seismic surveys, and mud logging are available for identifying formation tops, they have limitations such as high costs, lower accuracy, manpower-intensive processes, and time or depth lags that impede real-time estimation. Consequently, this study aims to leverage machine learning models based on easily accessible drilling parameters to predict formation tops and lithologies, overcoming the limitations associated with traditional methods. Data from two wells (A and B) in the Middle East, encompassing drilling mechanical parameters such as rate of penetration (ROP), drill string rotation (DSR), pumping rate (
    Language English
    Publishing date 2023-10-30
    Publishing country United States
    Document type Journal Article
    ISSN 2470-1343
    ISSN (online) 2470-1343
    DOI 10.1021/acsomega.3c03725
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Data-Driven Framework for Real-time Rheological Properties Prediction of Flat Rheology Synthetic Oil-Based Drilling Fluids.

    Abdelaal, Ahmed / Ibrahim, Ahmed Farid / Elkatatny, Salaheldin

    ACS omega

    2023  Volume 8, Issue 16, Page(s) 14371–14386

    Abstract: Appropriate mud properties enhance drilling efficiency and decision quality to avoid incidents. The detailed mud properties are mainly measured in laboratories and are usually measured twice a day in the field and take a long time. This prevents real- ... ...

    Abstract Appropriate mud properties enhance drilling efficiency and decision quality to avoid incidents. The detailed mud properties are mainly measured in laboratories and are usually measured twice a day in the field and take a long time. This prevents real-time mud performance optimization and adversely affects proactive actions. As a result, it is critical to evaluate mud properties while drilling to capture mud flow dynamics. Unlike other mud properties, mud density (MD) and Marsh funnel viscosity (MFV) are frequently evaluated every 15-20 min in the field. The goal of this study is to predict the rheological properties of flat rheology synthetic oil-based mud (SOBM) in real time using machine learning (ML) techniques such as random forest (RF) and decision tree (DT). A proposed approach is followed to first predict the viscometer readings at 300 and 600 RPM (
    Language English
    Publishing date 2023-04-13
    Publishing country United States
    Document type Journal Article
    ISSN 2470-1343
    ISSN (online) 2470-1343
    DOI 10.1021/acsomega.2c06656
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Estimation of rocks' failure parameters from drilling data by using artificial neural network.

    Siddig, Osama / Ibrahim, Ahmed Farid / Elkatatny, Salaheldin

    Scientific reports

    2023  Volume 13, Issue 1, Page(s) 3146

    Abstract: Comprehensive and precise knowledge about rocks' mechanical properties facilitate the drilling performance optimization, and hydraulic fracturing design and reduces the risk of wellbore-related problems. This paper is concerned with the failure ... ...

    Abstract Comprehensive and precise knowledge about rocks' mechanical properties facilitate the drilling performance optimization, and hydraulic fracturing design and reduces the risk of wellbore-related problems. This paper is concerned with the failure parameters, namely, cohesion and friction angle which are conventionally estimated using Mohr's cycles that are drawn using compressional tests on rock samples. The availability, continuity and representability, and cost of acquiring those samples are major concerns. The objective of this paper is to investigate an alternative technique to estimate these parameters from the drilling data. In this work, more than 2200 data points were used to develop and test the correlations built by the artificial neural network. Each data point comprises the failure parameters and five drilling records that are available instantaneously in drilling rigs such as rate of penetration, weight on bit, and torque. The data were grouped into three datasets, training, testing, and validation with a corresponding percentage of 60/20/20, the former two sets were utilized in the models' building while the last one was hidden as a final check afterward. The models were optimized and evaluated using the correlation coefficient (R) and average absolute percentage error (AAPE). In general, the two models yielded good fits with the actual values. The friction angle model yielded R values around 0.86 and AAPE values around 4% for the three datasets. While the model for cohesion resulted in R values around 0.89 and APPE values around 6%. The equation and the parameters of those models are reported in the paper. These results show the ability of in-situ and instantaneous rock mechanical properties estimation with good reliability and at no additional costs.
    Language English
    Publishing date 2023-02-23
    Publishing country England
    Document type Journal Article
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-023-30092-2
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: Impact of Pressure and Temperature on Foam Behavior for Enhanced Underbalanced Drilling Operations.

    Gowida, Ahmed / Elkatatny, Salaheldin / Ibrahim, Ahmed Farid / Kamal, Muhammad Shahzad

    ACS omega

    2023  Volume 9, Issue 1, Page(s) 1042–1055

    Abstract: Foam, a versatile underbalanced drilling fluid, shows potential for improving the drilling efficiency and reducing formation damage. However, the existing literature lacks insight into foam behavior under high-pH drilling conditions. This study ... ...

    Abstract Foam, a versatile underbalanced drilling fluid, shows potential for improving the drilling efficiency and reducing formation damage. However, the existing literature lacks insight into foam behavior under high-pH drilling conditions. This study introduces a novel approach using synthesized seawater, replacing the conventional use of freshwater on-site for the foaming system's liquid base. This approach is in line with sustainability objectives and offers novel perspectives on foam stability under high-pH conditions. Experiments, conducted with a high-pressure, high-temperature (HPHT) foam analyzer, investigate how pressure and temperature affect foam properties. The biodegradable foaming agent ammonium alcohol ether sulfate (AAES) is employed. Results demonstrate that the pressure significantly impacts foam stability. Increasing pressure enhances stability, reducing decay rates and promoting uniform bubble sizes, especially at lower temperatures. This highlights foam's capacity to withstand high-pressure conditions. Conversely, the temperature plays a substantial role in foam decay, particularly at elevated temperatures (75 and 90 °C). Decreased liquid viscosity accelerates the liquid drainage and foam decay. While pressure mainly influences the AAES foam stability at temperatures up to 50 °C, temperature becomes the dominant factor at higher temperatures. Temperature's impact on foamability is minimal under constant pressure, maintaining consistent gas volume for maximum foam height. However, foam stability is sensitive to temperature variations, with increasing temperature leading to a more significant bubble size increase gradient. These findings stress the importance of considering temperature effects in foam drilling, particularly in deep and high-temperature environments. AAES foam exhibits stability at lower temperatures, making it suitable for surface and intermediate drilling. Understanding temperature-induced changes in foam structure and bubble size is essential for optimizing performance in high-temperature and deep drilling scenarios.
    Language English
    Publishing date 2023-12-29
    Publishing country United States
    Document type Journal Article
    ISSN 2470-1343
    ISSN (online) 2470-1343
    DOI 10.1021/acsomega.3c07263
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: Prediction of Water Saturation in Tight Gas Sandstone Formation Using Artificial Intelligence.

    Ibrahim, Ahmed Farid / Elkatatny, Salaheldin / Al Ramadan, Mustafa

    ACS omega

    2022  Volume 7, Issue 1, Page(s) 215–222

    Abstract: Water saturation ( ...

    Abstract Water saturation (
    Language English
    Publishing date 2022-01-03
    Publishing country United States
    Document type Journal Article
    ISSN 2470-1343
    ISSN (online) 2470-1343
    DOI 10.1021/acsomega.1c04416
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: A hybrid data-driven solution to facilitate safe mud window prediction.

    Gowida, Ahmed / Ibrahim, Ahmed Farid / Elkatatny, Salaheldin

    Scientific reports

    2022  Volume 12, Issue 1, Page(s) 15773

    Abstract: Safe mud window (SMW) defines the allowable limits of the mud weights that can be used while drilling O&G wells. Controlling the mud weight within the SMW limits would help avoid many serious problems such as wellbore instability issues, loss of ... ...

    Abstract Safe mud window (SMW) defines the allowable limits of the mud weights that can be used while drilling O&G wells. Controlling the mud weight within the SMW limits would help avoid many serious problems such as wellbore instability issues, loss of circulation, etc. SMW can be defined by the minimum mud weight below which shear failure (breakout) may occur (MW
    Language English
    Publishing date 2022-09-21
    Publishing country England
    Document type Journal Article
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-022-20195-7
    Database MEDical Literature Analysis and Retrieval System OnLINE

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