Browsing by Author "Shaame, Mbarouk"

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    Application of machine learning in the prediction of compressive, and shear bond strengths from the experimental data in oil well cement at 80 °C. Ensemble trees boosting approach
    (Elsevier, 2022) Nyakilla, Edwin E.; Jun, Gu; Kasimu, Naswibu A.; Robert, Edwin F.; Innocent, Ndikubwimana; Mohamedy, Thamudi; Shaame, Mbarouk; Ngata, Mbega Ramadhani; Mabeyo, Petro E.
    The current study aimed at predicting shear bond strength (SBS) and compressive strength (CS) using ensemble techniques of gradient boosting regression tree (GBRT) from the experimental data. Experimental data were obtained from CS and SBS studies using class F fly ash as supplementary cementitious materials at different proportions. The experimental results showed that the application of class F fly ash increases both CS and SBS with curing time due to the pozzolanic action of the fly ash. The SBS and CS for 15% replacement after 28 days were 0.353 and 41.9 MPa, respectively compared to 0.324 and 39.5 Mpa for 30% fly ash. This means higher fly ash content decreases both CS and SBS. Cement, OWC, water, fly ash, curing time, and dispersant were set as input data for machine learning (ML) while experimental SBS and CS as output. ML results showed that GBRT overperformed Artificial neural network (ANN), support vector machine (SVM), and Gaussian process regression (GPR)models since it gave the greatest R2 = 0.995 for CS, 0.989 for SBS and the least loss functions (MSE = 0.160 , MAE = 0.174), and (MSE = 0.0005 , MAE = 0.0031) for CS and SBS, respectively.. The comparative findings of both experimental and estimation, therefore affirm that for the long life of oil and gas wells, GBRT can be implemented as an improved approach for cement hydration prediction.
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    Identification of karst cavities from 2D seismic wave impedance images based on Gradient-Boosting Decision Trees Algorithms (GBDT): Case of Ordovician fracture-vuggy carbonate reservoir, Tahe oilfield, Tarim basin, China
    (MDPI, 2023) Kouassi, Allou Koffi Franck; Pan, Lin; Wang, Xiao; Wang, Zhangheng; Mulashani, Alvin K; James, Faulo; Shaame, Mbarouk; Hussain, Altaf; Hussain, Hadi; Nyakilla, Edwin E
    Theprecisecharacterizationofgeologicalbodiesinfracture-vuggycarbonatesischallenging due to their high complexity and heterogeneous distribution. This study aims to present the hybrid of Visual Geometry Group 16 (VGG-16) pre-trained by Gradient-Boosting Decision Tree (GBDT) models as a novel approach for predicting and generating karst cavities with high accuracy on various scales based on uncertainty assessment from a small dataset. Seismic wave impedance images were used as input data. Their manual interpretation was used to build GBDT classifiers for Light Gradient-Boosting Machine (LightGBM) and Unbiased Boosting with Categorical Features (CatBoost) for predicting the karst cavities and unconformities. The results show that the LightGBM was the best GBDT classifier, which performed excellently in karst cavity interpretation, giving an F1-score between 0.87 and 0.94 and a micro-G-Mean ranging from 0.92 to 0.96. Furthermore, the LightGBM performed better in cave prediction than Linear Regression (LR) and Multilayer Perceptron (MLP). The prediction of karst cavities according to the LightGBM model was performed well according to the uncertainty quantification. Therefore, the hybrid VGG16 and GBDT algorithms can be implemented as an improved approach for efficiently identifying geological features within similar reservoirs worldwide
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    Review of developments in nanotechnology application for formation damage control
    (American Chemical Society, 2022) Ngata, Mbega Ramadhani; Yang, Baolin; Aminu, Mohammed Dahiru; Iddphonce, Raphael; Omari, Athumani; Shaame, Mbarouk; Nyakilla, Edwin E.; Mwakateba, Imani Asukile; Mwakipunda, Grant Charles; Yanyi-Akofur, David
    Formation damage has the potential to impair and weaken reservoir productivity and injectivity, causing substantial economic losses. Oil and gas wells can be damaged by various mechanisms, such as solid invasion, rock–fluid incompatibilities, fluid–fluid incompatibilities, and phase trapping/blocking, which can reduce natural permeability of oil and gas near the wellbore zone. These can happen during most field operations, including drilling operations, completion, production, stimulation, and enhanced oil recovery (EOR). Numerous studies have been undertaken in recent years on the application of nanotechnology to aid the control of formation damage. This review has found that nanotechnology is more successful than traditional materials in controlling formation damages in different phases of oil and gas development. This is facilitated by their small size and high surface area/volume ratio, which increase reactivity and interactivity to the adjacent materials/surfaces. Furthermore, adding hydrophilic nanoparticles (0.05wt %) to surfactants during EOR alters their wettability from 15 to 33%. Wettability alteration capabilities of nanoparticles are also exemplified by carbonate rock from oil-wet to water-wet after the concentration of 4 g/L silica nanoparticles is added. In addition, mixing nanoparticles to the drilling fluid reduced 70% of fluid loss. However, the mechanisms of impairment of conductivity in shale/tight formations are not consistent and can differ from one formation to another as a result of a high level of subsurface heterogeneity. Thus, the reactivity and interaction of nanoparticles in these shale/tight formations have not been clearly explained, and a recommendation is made for further investigations. We also recommend more nanotechnology field trials for future research because deductions from current studies are insufficient. This review provides more insights on the applications of nanoparticles in different stages of oil and gas development, increasing our understanding on the measures to control formation damage