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Ahmed Abdulhamid Mahmoud
College of Petroleum Engineering & Geosciences, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia

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Journal article
Published: 29 April 2021 in Journal of Energy Resources Technology
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The evaluation of the quality of unconventional hydrocarbon resources becomes a critical stage toward characterizing these resources, and this evaluation requires the evaluation of the total organic carbon (TOC). Generally, TOC is determined from laboratory experiments; however, it is hard to obtain a continuous profile for the TOC along the drilled formations using these experiments. Another way to evaluate the TOC is through the use of empirical correlation, and the currently available correlations lack the accuracy especially when used in formations other than the ones used to develop these correlations. This study introduces an empirical equation for the evaluation of the TOC in Devonian Duvernay shale from only gamma-ray and spectral gamma-ray logs of uranium, thorium, and potassium as well as a newly developed term that accounts for the TOC from the linear regression analysis. This new correlation was developed based on the artificial neural networks (ANNs) algorithm which was learned on 750 datasets from Well-A. The developed correlation was tested and validated on 226 and 73 datasets from Well-B and Well-C, respectively. The results of this study indicated that for the training data, the TOC was predicted by the ANN with an AAPE of only 8.5%. Using the developed equation, the TOC was predicted with an AAPE of only 11.5% for the testing data. For the validation data, the developed equation overperformed the previous models in estimating the TOC with an AAPE of only 11.9%.

ACS Style

Ahmed Abdulhamid Mahmoud; Salaheldin Elkatatny. Novel Empirical Correlation for Estimation of the Total Organic Carbon in Devonian Shale From the Spectral Gamma-Ray and Based on the Artificial Neural Networks. Journal of Energy Resources Technology 2021, 143, 1 -28.

AMA Style

Ahmed Abdulhamid Mahmoud, Salaheldin Elkatatny. Novel Empirical Correlation for Estimation of the Total Organic Carbon in Devonian Shale From the Spectral Gamma-Ray and Based on the Artificial Neural Networks. Journal of Energy Resources Technology. 2021; 143 (9):1-28.

Chicago/Turabian Style

Ahmed Abdulhamid Mahmoud; Salaheldin Elkatatny. 2021. "Novel Empirical Correlation for Estimation of the Total Organic Carbon in Devonian Shale From the Spectral Gamma-Ray and Based on the Artificial Neural Networks." Journal of Energy Resources Technology 143, no. 9: 1-28.

Journal article
Published: 29 April 2021 in Journal of Energy Resources Technology
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Predicting the rate of penetration (ROP) is challenging especially during horizontal drilling. This is because there are many factors affecting ROP. Machine learning techniques are very promising in identifying the structural relationships existing between the inputs and target variables; these techniques were recently successfully applied to estimate the ROP in different wellbore shapes and through various formation lithologies. This study is aimed to introduce a random forest (RF) regression model for ROP prediction based on many factors such as the drilling mechanical parameters (torque, pipe speed, and weight on bit), hole cleaning parameters (the drilling fluid flowrate and pump pressure), and formation properties (formation bulk density and formation resistivity). In addition to its superiority in providing accurate results, RF has the advantage of providing interpretable rules. These rules help in understanding the relationships between the regressors and the target variable. Actual field measurements collected during horizontally drilling carbonate formation were used for training and testing the RF model. Unseen data collected from another well were used for validating the optimized model. Using the K-fold validation method, the proposed RF model has proven its superior performance when compared to artificial neural networks and support vector regression models. An illustrative example on a sample of real drilling data is presented to explain how the RF regression model is applied to the drilling data. In addition, developing interpretable regression rules through merging RF results is explained. These rules can guide drilling practitioners in accomplishing drilling projects at minimum time and cost.

ACS Style

Hany Osman; Abdulwahab Ali; Ahmed Abdulhamid Mahmoud; Salaheldin Elkatatny. Estimation of the Rate of Penetration While Horizontally Drilling Carbonate Formation Using Random Forest. Journal of Energy Resources Technology 2021, 143, 1 -34.

AMA Style

Hany Osman, Abdulwahab Ali, Ahmed Abdulhamid Mahmoud, Salaheldin Elkatatny. Estimation of the Rate of Penetration While Horizontally Drilling Carbonate Formation Using Random Forest. Journal of Energy Resources Technology. 2021; 143 (9):1-34.

Chicago/Turabian Style

Hany Osman; Abdulwahab Ali; Ahmed Abdulhamid Mahmoud; Salaheldin Elkatatny. 2021. "Estimation of the Rate of Penetration While Horizontally Drilling Carbonate Formation Using Random Forest." Journal of Energy Resources Technology 143, no. 9: 1-34.

Review
Published: 29 April 2020 in Journal of Petroleum Science and Engineering
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During the drilling operations, drilling fluids invade the drilled formations which could cause a plugging or permeability alteration in the reservoir zone and consequently additional cost to the drilling operations. Buildup of a thin impermeable layer toward a well's wall called the filter cake reduces this invasion. However, this layer must be removed afterward for a better cementing job and to eliminate any restrictions on hydrocarbon flow at the production stage. Filter cake layer composed mainly of particles of the weighting material, in this paper we highlighted the main challenges for water-based filter cake removal which contains barite, ilmenite, manganese tetroxide, and calcium carbonate as a weighting material. Many of the reported filter cake removal processes are multi-stage events because of the incompatibility of the polymer breaker with the removal solution, therefore, more researches need to be conducted to come up with an enzyme compatible with the removal fluids. We also recommend conducting more researches to investigate the effect of the presence of the carbonate or sandstone particles inside the filter cake formation on the removal efficiency and to study the removal performance at high temperatures.

ACS Style

Osama Siddig; Ahmed Abdulhamid Mahmoud; Salaheldin Elkatatny. A review of different approaches for water-based drilling fluid filter cake removal. Journal of Petroleum Science and Engineering 2020, 192, 107346 .

AMA Style

Osama Siddig, Ahmed Abdulhamid Mahmoud, Salaheldin Elkatatny. A review of different approaches for water-based drilling fluid filter cake removal. Journal of Petroleum Science and Engineering. 2020; 192 ():107346.

Chicago/Turabian Style

Osama Siddig; Ahmed Abdulhamid Mahmoud; Salaheldin Elkatatny. 2020. "A review of different approaches for water-based drilling fluid filter cake removal." Journal of Petroleum Science and Engineering 192, no. : 107346.

Journal article
Published: 02 March 2020 in Sustainability
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Prediction of the mechanical characteristics of the reservoir formations, such as static Young’s modulus (Estatic), is very important for the evaluation of the wellbore stability and development of the earth geomechanical model. Estatic considerably varies with the change in the lithology. Therefore, a robust model for Estatic prediction is needed. In this study, the predictability of Estatic for sandstone formation using four machine learning models was evaluated. The design parameters of the machine learning models were optimized to improve their predictability. The machine learning models were trained to estimate Estatic based on bulk formation density, compressional transit time, and shear transit time. The machine learning models were trained and tested using 592 well log data points and their corresponding core-derived Estatic values collected from one sandstone formation in well-A and then validated on 38 data points collected from a sandstone formation in well-B. Among the machine learning models developed in this work, Mamdani fuzzy interference system was the highly accurate model to predict Estatic for the validation data with an average absolute percentage error of only 1.56% and R of 0.999. The developed static Young’s modulus prediction models could help the new generation to characterize the formation rock with less cost and safe operation.

ACS Style

Ahmed Abdulhamid Mahmoud; Salaheldin Elkatatny; Dhafer Al Shehri. Application of Machine Learning in Evaluation of the Static Young’s Modulus for Sandstone Formations. Sustainability 2020, 12, 1880 .

AMA Style

Ahmed Abdulhamid Mahmoud, Salaheldin Elkatatny, Dhafer Al Shehri. Application of Machine Learning in Evaluation of the Static Young’s Modulus for Sandstone Formations. Sustainability. 2020; 12 (5):1880.

Chicago/Turabian Style

Ahmed Abdulhamid Mahmoud; Salaheldin Elkatatny; Dhafer Al Shehri. 2020. "Application of Machine Learning in Evaluation of the Static Young’s Modulus for Sandstone Formations." Sustainability 12, no. 5: 1880.

Journal article
Published: 13 February 2020 in Sustainability
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Rate of penetration (ROP) is one of the most important drilling parameters for optimizing the cost of drilling hydrocarbon wells. In this study, a new empirical correlation based on an optimized artificial neural network (ANN) model was developed to predict ROP alongside horizontal drilling of carbonate reservoirs as a function of drilling parameters, such as rotation speed, torque, and weight-on-bit, combined with conventional well logs, including gamma-ray, deep resistivity, and formation bulk density. The ANN model was trained using 3000 data points collected from Well-A and optimized using the self-adaptive differential evolution (SaDE) algorithm. The optimized ANN model predicted ROP for the training dataset with an average absolute percentage error (AAPE) of 5.12% and a correlation coefficient (R) of 0.960. A new empirical correlation for ROP was developed based on the weights and biases of the optimized ANN model. The developed correlation was tested on another dataset collected from Well-A, where it predicted ROP with AAPE and R values of 5.80% and 0.951, respectively. The developed correlation was then validated using unseen data collected from Well-B, where it predicted ROP with an AAPE of 5.29% and a high R of 0.956. The ANN-based correlation outperformed all previous correlations of ROP estimation that were developed based on linear regression, including a recent model developed by Osgouei that predicted the ROP for the validation data with a high AAPE of 14.60% and a low R of 0.629.

ACS Style

Ahmad Al-AbdulJabbar; Salaheldin Elkatatny; Ahmed Abdulhamid Mahmoud; Tamer Moussa; Dhafer Al-Shehri; Mahmoud Abughaban; Abdullah Al-Yami. Prediction of the Rate of Penetration while Drilling Horizontal Carbonate Reservoirs Using the Self-Adaptive Artificial Neural Networks Technique. Sustainability 2020, 12, 1376 .

AMA Style

Ahmad Al-AbdulJabbar, Salaheldin Elkatatny, Ahmed Abdulhamid Mahmoud, Tamer Moussa, Dhafer Al-Shehri, Mahmoud Abughaban, Abdullah Al-Yami. Prediction of the Rate of Penetration while Drilling Horizontal Carbonate Reservoirs Using the Self-Adaptive Artificial Neural Networks Technique. Sustainability. 2020; 12 (4):1376.

Chicago/Turabian Style

Ahmad Al-AbdulJabbar; Salaheldin Elkatatny; Ahmed Abdulhamid Mahmoud; Tamer Moussa; Dhafer Al-Shehri; Mahmoud Abughaban; Abdullah Al-Yami. 2020. "Prediction of the Rate of Penetration while Drilling Horizontal Carbonate Reservoirs Using the Self-Adaptive Artificial Neural Networks Technique." Sustainability 12, no. 4: 1376.

Journal article
Published: 28 January 2020 in Journal of Natural Gas Science and Engineering
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The hydrated products of Class G cement drastically change after exposure to CO2-saturated brine, compromising the cement physical properties, especially, the compressive and tensile strengths. In this study, the effect of different concentrations of the synthetic polypropylene fiber (PPF) on the cement matrix strength retrogression resistance under the carbonation process was evaluated. Four cement slurries with 0%, 0.125%, 0.25%, and 0.375% by weight of cement (BWOC) of the PPF were prepared and tested for the change in their compressive and tensile strengths and permeability after reacting with CO2-saturated 0.5 M NaCl brine at 130 °C and 10 MPa for 10 and 20 days. The results of this study revealed that incorporating 0.125% BWOC of the PPF decreased the portlandite concentration as well as considerably decreased the cement permeability, therefore, enhanced the cement carbonation resistance as indicated by the reduction in the carbonation depth and carbonation rate. Incorporation of 0.125% BWOC of PPF enhanced the strength retrogression resistance of the cement against CO2-saturated brine. After 20 days of carbonation, the compressive and tensile strengths of the cement samples incorporating 0.125% BWOC of PPF are 50.6% and 27.4% greater than the base cement, respectively. The carbonation rate inside the samples with 0.125% BWOC of the PPF is 31.0% less than that inside the base cement. Cement sample with PPF of 0.125% BWOC has the lowest permeability among all evaluated samples.

ACS Style

Ahmed Abdulhamid Mahmoud; Salaheldin Elkatatny. Improving class G cement carbonation resistance for applications of geologic carbon sequestration using synthetic polypropylene fiber. Journal of Natural Gas Science and Engineering 2020, 76, 103184 .

AMA Style

Ahmed Abdulhamid Mahmoud, Salaheldin Elkatatny. Improving class G cement carbonation resistance for applications of geologic carbon sequestration using synthetic polypropylene fiber. Journal of Natural Gas Science and Engineering. 2020; 76 ():103184.

Chicago/Turabian Style

Ahmed Abdulhamid Mahmoud; Salaheldin Elkatatny. 2020. "Improving class G cement carbonation resistance for applications of geologic carbon sequestration using synthetic polypropylene fiber." Journal of Natural Gas Science and Engineering 76, no. : 103184.

Journal article
Published: 29 November 2019 in Sustainability
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In deep hydrocarbon development wells, cement slurry with high density is required to effectively balance the high-pressure formations. The increase in the slurry density could be achieved by adding different heavy materials. In this study, the effect of the weighting materials (barite, hematite, and ilmenite) on the properties of Saudi Class G cement matrix of vertical homogeneity, compressive strength, porosity, and permeability was evaluated. Three cement slurries were weighted with barite, hematite, and ilmenite, and cured at 294 °F and 3000 psi for 24 h. All slurries have the same concentration of the different additives except the weighting material. The amount of weighting material used in every slurry was determined based on the targeted density of 18 lbm/gal. The results of this study revealed that the most vertically homogenous cement matrix was the ilmenite-weighted sample with a vertical variation of 17.6% compared to 20.2 and 24.8% for hematite- and barite-weighted cement, respectively. This is attributed to the small particle size of the ilmenite. The medical computerized tomography (CT) scan confirmed that the ilmenite-weighted sample is the most homogeneous, with a narrow range of density variation vertically along the sample. Hematite-weighted cement showed the highest compressive strength of 55.3 MPa, and the barite- and ilmenite-weighted cement compressive strengths are each 18.4 and 36.7% less than the compressive strength of the hematite-weighted cement, respectively. Barite-weighted cement has the lowest porosity and permeability of 6.1% and 18.9 mD, respectively. The maximum particle size of ilmenite used in this study is less than 42 μm to ensure no abrasion effect on the drilling system, and it minimized the solids segregation while maintaining a compressive strength that is higher than the minimum acceptable strength, which is the recommended weighting material for Saudi Class G cement.

ACS Style

Abdulmalek Ahmed; Ahmed Abdulhamid Mahmoud; Salaheldin Elkatatny; Weiqing Chen. The Effect of Weighting Materials on Oil-Well Cement Properties While Drilling Deep Wells. Sustainability 2019, 11, 6776 .

AMA Style

Abdulmalek Ahmed, Ahmed Abdulhamid Mahmoud, Salaheldin Elkatatny, Weiqing Chen. The Effect of Weighting Materials on Oil-Well Cement Properties While Drilling Deep Wells. Sustainability. 2019; 11 (23):6776.

Chicago/Turabian Style

Abdulmalek Ahmed; Ahmed Abdulhamid Mahmoud; Salaheldin Elkatatny; Weiqing Chen. 2019. "The Effect of Weighting Materials on Oil-Well Cement Properties While Drilling Deep Wells." Sustainability 11, no. 23: 6776.

Journal article
Published: 13 October 2019 in Sustainability
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Total organic carbon (TOC) is an essential parameter used in unconventional shale resources evaluation. Current methods that are used for TOC estimation are based, either on conducting time-consuming laboratory experiments, or on using empirical correlations developed for specific formations. In this study, four artificial intelligence (AI) models were developed to estimate the TOC using conventional well logs of deep resistivity, gamma-ray, sonic transit time, and bulk density. These models were developed based on the Takagi-Sugeno-Kang fuzzy interference system (TSK-FIS), Mamdani fuzzy interference system (M-FIS), functional neural network (FNN), and support vector machine (SVM). Over 800 data points of the conventional well logs and core data collected from Barnett shale were used to train and test the AI models. The optimized AI models were validated using unseen data from Devonian shale. The developed AI models showed accurate predictability of TOC in both Barnett and Devonian shale. FNN model overperformed others in estimating TOC for the validation data with average absolute percentage error (AAPE) and correlation coefficient (R) of 12.02%, and 0.879, respectively, followed by M-FIS and SVM, while TSK-FIS model showed the lowest predictability of TOC, with AAPE of 15.62% and R of 0.832. All AI models overperformed Wang models, which have recently developed to evaluate the TOC for Devonian formation.

ACS Style

Ahmed Abdulhamid Mahmoud; Salaheldin Elkatatny; Abdulwahab Z. Ali; Mohamed Abouelresh; Abdulazeez Abdulraheem. Evaluation of the Total Organic Carbon (TOC) Using Different Artificial Intelligence Techniques. Sustainability 2019, 11, 5643 .

AMA Style

Ahmed Abdulhamid Mahmoud, Salaheldin Elkatatny, Abdulwahab Z. Ali, Mohamed Abouelresh, Abdulazeez Abdulraheem. Evaluation of the Total Organic Carbon (TOC) Using Different Artificial Intelligence Techniques. Sustainability. 2019; 11 (20):5643.

Chicago/Turabian Style

Ahmed Abdulhamid Mahmoud; Salaheldin Elkatatny; Abdulwahab Z. Ali; Mohamed Abouelresh; Abdulazeez Abdulraheem. 2019. "Evaluation of the Total Organic Carbon (TOC) Using Different Artificial Intelligence Techniques." Sustainability 11, no. 20: 5643.

Journal article
Published: 25 September 2019 in Energies
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Hydrocarbon reserve evaluation is the major concern for all oil and gas operating companies. Nowadays, the estimation of oil recovery factor (RF) could be achieved through several techniques. The accuracy of these techniques depends on data availability, which is strongly dependent on the reservoir age. In this study, 10 parameters accessible in the early reservoir life are considered for RF estimation using four artificial intelligence (AI) techniques. These parameters are the net pay (effective reservoir thickness), stock-tank oil initially in place, original reservoir pressure, asset area (reservoir area), porosity, Lorenz coefficient, effective permeability, API gravity, oil viscosity, and initial water saturation. The AI techniques used are the artificial neural networks (ANNs), radial basis neuron networks, adaptive neuro-fuzzy inference system with subtractive clustering, and support vector machines. AI models were trained using data collected from 130 water drive sandstone reservoirs; then, an empirical correlation for RF estimation was developed based on the trained ANN model’s weights and biases. Data collected from another 38 reservoirs were used to test the predictability of the suggested AI models and the ANNs-based correlation; then, performance of the ANNs-based correlation was compared with three of the currently available empirical equations for RF estimation. The developed ANNs-based equation outperformed the available equations in terms of all the measures of error evaluation considered in this study, and also has the highest coefficient of determination of 0.94 compared to only 0.55 obtained from Gulstad correlation, which is one of the most accurate correlations currently available.

ACS Style

Ahmed Mahmoud; Salaheldin Elkatatny; Weiqing Chen; Abdulazeez Abdulraheem. Estimation of Oil Recovery Factor for Water Drive Sandy Reservoirs through Applications of Artificial Intelligence. Energies 2019, 12, 3671 .

AMA Style

Ahmed Mahmoud, Salaheldin Elkatatny, Weiqing Chen, Abdulazeez Abdulraheem. Estimation of Oil Recovery Factor for Water Drive Sandy Reservoirs through Applications of Artificial Intelligence. Energies. 2019; 12 (19):3671.

Chicago/Turabian Style

Ahmed Mahmoud; Salaheldin Elkatatny; Weiqing Chen; Abdulazeez Abdulraheem. 2019. "Estimation of Oil Recovery Factor for Water Drive Sandy Reservoirs through Applications of Artificial Intelligence." Energies 12, no. 19: 3671.

Journal article
Published: 05 June 2019 in Journal of Natural Gas Science and Engineering
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The reaction of the cement with CO2 saturated brine leads to cement carbonation which results in numerous chemo-mechanical changes in the cement matrix. In this study, the mechanism of nanoclay (NC) to enhance the carbonation resistance of class G oil well cement (OWC) is addressed. Two cement slurries with 0% and 1% of the modified montmorillonite NC were prepared. The samples were characterized with the powder X-ray diffraction (PXRD) technique which confirmed the decrease in the portlandite (CH) content and the increase in the most thermodynamically stable calcium silicate hydrates (CSH) for the sample with 1% NC. The samples then immersed into 0.5 M NaCl solution at 95ºC and 10 MPa for 30 days and the changes in the compressive and tensile strengths, permeability, and pore structure were evaluated with the carbonation time. The results revealed that incorporating NC into OWC paste increased the concentration of the CSH, decrease the reduction in the compressive and tensile strengths to 8.56% and 9.82%, respectively, compared with the compressive and tensile strengths retrogressions of 45.67% and 27.39% for the base cement. The final permeability of the NC-based sample is 51.67% less than the base sample permeability. The decrease in the cement permeability and CH content and the increase in the CSH are the main mechanisms which enhanced the NC-based cement resistance to carbonation.

ACS Style

Ahmed Abdulhamid Mahmoud; Salaheldin Elkatatny. Mitigating CO2 reaction with hydrated oil well cement under geologic carbon sequestration using nanoclay particles. Journal of Natural Gas Science and Engineering 2019, 68, 102902 .

AMA Style

Ahmed Abdulhamid Mahmoud, Salaheldin Elkatatny. Mitigating CO2 reaction with hydrated oil well cement under geologic carbon sequestration using nanoclay particles. Journal of Natural Gas Science and Engineering. 2019; 68 ():102902.

Chicago/Turabian Style

Ahmed Abdulhamid Mahmoud; Salaheldin Elkatatny. 2019. "Mitigating CO2 reaction with hydrated oil well cement under geologic carbon sequestration using nanoclay particles." Journal of Natural Gas Science and Engineering 68, no. : 102902.

Journal article
Published: 03 June 2019 in Energies
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In this study, we used artificial neural networks (ANN) to estimate static Young’s modulus (Estatic) for sandstone formation from conventional well logs. ANN design parameters were optimized using the self-adaptive differential evolution optimization algorithm. The ANN model was trained to predict Estatic from conventional well logs of the bulk density, compressional time, and shear time. The ANN model was trained on 409 data points from one well. The extracted weights and biases of the optimized ANN model was used to develop an empirical relationship for Estatic estimation based on well logs. This empirical correlation was tested on 183 unseen data points from the same training well and validated using data from three different wells. The optimized ANN model estimated Estatic for the training dataset with a very low average absolute percentage error (AAPE) of 0.98%, a very high correlation coefficient (R) of 0.999 and a coefficient of determination (R2) of 0.9978. The developed ANN-based correlation estimated Estatic for the testing dataset with a very high accuracy as indicated by the low AAPE of 1.46% and a very high R and R2 of 0.998 and 0.9951, respectively. In addition, the visual comparison of the core-tested and predicted Estatic of the validation dataset confirmed the high accuracy of the developed ANN-based empirical correlation. The ANN-based correlation overperformed four of the previously developed Estatic correlations in estimating Estatic for the validation data, Estatic for the validation data was predicted with an AAPE of 3.8% by using the ANN-based correlation compared to AAPE’s of more than 36.0% for the previously developed correlations.

ACS Style

Ahmed Abdulhamid Mahmoud; Salaheldin Elkatatny; Abdulwahab Ali; Tamer Moussa. Estimation of Static Young’s Modulus for Sandstone Formation Using Artificial Neural Networks. Energies 2019, 12, 2125 .

AMA Style

Ahmed Abdulhamid Mahmoud, Salaheldin Elkatatny, Abdulwahab Ali, Tamer Moussa. Estimation of Static Young’s Modulus for Sandstone Formation Using Artificial Neural Networks. Energies. 2019; 12 (11):2125.

Chicago/Turabian Style

Ahmed Abdulhamid Mahmoud; Salaheldin Elkatatny; Abdulwahab Ali; Tamer Moussa. 2019. "Estimation of Static Young’s Modulus for Sandstone Formation Using Artificial Neural Networks." Energies 12, no. 11: 2125.

Journal article
Published: 05 May 2019 in Materials
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High-temperature conditions drastically compromise the physical properties of cement, especially, its strengths. In this work, the influence of adding nanoclay (NC) particles to Saudi class G oil well cement (OWC) strength retrogression resistance under high-temperature condition (300 °C) is evaluated. Six cement slurries with different concentrations of silica flour (SF) and NC were prepared and tested under conditions of 38 °C and 300 °C for different time periods (7 and 28 days) of curing. The changes in the cement matrix compressive and tensile strengths, permeability, loss in the absorbed water, and the cement slurry rheology were evaluated as a function of NC content and temperature, the changes in the structure of the cement surfaces were investigated through the optical microscope. The results revealed that the use of NC (up to 3% by weight of cement (BWOC)) can prevent the OWC deterioration under extremely high-temperature conditions. Incorporating more than 3% of NC severely damaged the cement matrix microstructure due to the agglomeration of the nanoparticles. Incorporation of NC particles increased all the cement slurry rheological properties.

ACS Style

Ahmed Abdulhamid Mahmoud; Salaheldin Elkatatny; Abdulmalek Ahmed; Rahul Gajbhiye. Influence of Nanoclay Content on Cement Matrix for Oil Wells Subjected to Cyclic Steam Injection. Materials 2019, 12, 1452 .

AMA Style

Ahmed Abdulhamid Mahmoud, Salaheldin Elkatatny, Abdulmalek Ahmed, Rahul Gajbhiye. Influence of Nanoclay Content on Cement Matrix for Oil Wells Subjected to Cyclic Steam Injection. Materials. 2019; 12 (9):1452.

Chicago/Turabian Style

Ahmed Abdulhamid Mahmoud; Salaheldin Elkatatny; Abdulmalek Ahmed; Rahul Gajbhiye. 2019. "Influence of Nanoclay Content on Cement Matrix for Oil Wells Subjected to Cyclic Steam Injection." Materials 12, no. 9: 1452.

Journal article
Published: 02 March 2019 in Journal of Petroleum Science and Engineering
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Understanding the interactions between the injected enhanced oil recovery (EOR) fluids and sandstone reservoir rock minerals during the flooding processes requires special attention due to the presence of several types of highly reactive clays and non-clay minerals with different concentrations which interact with the injected non-native solutions and affect the effeciency of the oil recovery process. In this study, four coreflood experiments were carried out using two different sandstone core samples namely Gray Berea and Gray Bandera sandstone having clay contents of 8 wt% and 12 wt%, respectively, to evaluate the integrity of the clayey sandstone rocks flooded with 5 wt% and 10 wt% EDTA solutions with pH of 12. To study the presence and distribution of the clay and non-clay minerals into these sandstone rocks, x-ray diffraction (XRD), scanning electron microscope (SEM), and nuclear magnetic resonance (NMR) were used to characterize the core samples before the coreflooding tests. After the coreflooding experiments, XRD analysis was used to identify the precipitated minerals, and the alteration in the flooded rocks integrity was investigated through application of NMR, computed tomography (CT) scan analyses, and permeability measurements. The exchangeability of the chelated iron from the sandstone rocks to the EDTA solutions was studied by analyzing the produced effluents through inductively coupled plasma (ICP) analyses. The pressure drop observed during the coreflooding experiments revealed the compatibility of the injected solutions and the core samples. The results of this study dominates that Gray Berea sandstone is compatible with the 5 wt% and 10 wt% of the EDTA solutions as confirmed by the permeability measurements, CT scan analysis, NMR results, and the pressure drop during the flooding tests; which indicates that there was no fine migration, clay minerals swelling, and/or non-clay minerals dissolution caused by the interaction of Gray Berea with the EDTA solutions. On the other hand, Gray Bandera core samples were found to exhibit incompatibility with a solution of more than 5 wt% of EDTA as indicated by the CT scan and the high permeability enhancement because of the interaction of EDTA with ankerite mineral.

ACS Style

Ahmed Abdulhamid Mahmoud; Hasan Al-Hashim. Evaluating the integrity of clayey sandstone rocks flooded with high pH EDTA chelating agent solutions. Journal of Petroleum Science and Engineering 2019, 177, 614 -623.

AMA Style

Ahmed Abdulhamid Mahmoud, Hasan Al-Hashim. Evaluating the integrity of clayey sandstone rocks flooded with high pH EDTA chelating agent solutions. Journal of Petroleum Science and Engineering. 2019; 177 ():614-623.

Chicago/Turabian Style

Ahmed Abdulhamid Mahmoud; Hasan Al-Hashim. 2019. "Evaluating the integrity of clayey sandstone rocks flooded with high pH EDTA chelating agent solutions." Journal of Petroleum Science and Engineering 177, no. : 614-623.