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Dhafer A. Al Shehri
Department of Petroleum Engineering, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia

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Journal article
Published: 09 June 2021 in Journal of Energy Resources Technology
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Natural gas is one of the main fossil energy resources, and its density is an effective thermodynamic property, which is required in almost every pressure–volume–temperature (PVT) calculation. Conventionally, the density of natural gas is determined from the gas deviation (Z-) factor using an equation of states (EOS). Several models have been developed to estimate the Z-factor utilizing the EOS approach, however, most of these models involve complex calculations and require many input parameters. In this study, an improved natural gas density prediction model is presented using robust machine learning techniques such as artificial neural networks and functional networks. A total of 3800 data points were collected from different published sources covering a wide range of input parameters. Moreover, explicit empirical correlations are also derived that can be used explicitly without the need for any machine learning-based software. The proposed correlations are a function of molecular weight (Mw) of natural gas, pseudo-reduced pressure (Ppr), and pseudo-reduced temperature (Tpr). The proposed correlations can be applied for the gases having Mw between 16 and 129.7 g, Ppr range of 0.02–29.3, and Tpr range 0.of 5–2.7. The prediction of the new correlation was compared against the most common methods for determining the natural gas density. The developed correlation showed better estimation than the common prediction models. The estimation error was reduced by 2% on average using the new correlations, and the coefficient of determination (R2) was 0.98 using the developed correlation.

ACS Style

Zeeshan Tariq; Amjed Hassan; Umair Bin Waheed; Mohamed Mahmoud; Dhafer Al-Shehri; Abdulazeez Abdulraheem; Esmail M. A. Mokheimer. A Data-Driven Machine Learning Approach to Predict the Natural Gas Density of Pure and Mixed Hydrocarbons. Journal of Energy Resources Technology 2021, 143, 1 -27.

AMA Style

Zeeshan Tariq, Amjed Hassan, Umair Bin Waheed, Mohamed Mahmoud, Dhafer Al-Shehri, Abdulazeez Abdulraheem, Esmail M. A. Mokheimer. A Data-Driven Machine Learning Approach to Predict the Natural Gas Density of Pure and Mixed Hydrocarbons. Journal of Energy Resources Technology. 2021; 143 (9):1-27.

Chicago/Turabian Style

Zeeshan Tariq; Amjed Hassan; Umair Bin Waheed; Mohamed Mahmoud; Dhafer Al-Shehri; Abdulazeez Abdulraheem; Esmail M. A. Mokheimer. 2021. "A Data-Driven Machine Learning Approach to Predict the Natural Gas Density of Pure and Mixed Hydrocarbons." Journal of Energy Resources Technology 143, no. 9: 1-27.

Research article
Published: 06 May 2021 in ACS Omega
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Reservoir rock wettability has been linked to the adsorption of crude fractions on the rock, with much attention often paid to the bulk mineralogy rather than contacting minerals. Crude oil is contacted by different minerals that contribute to rock wettability. The clay mineral effect on wettability alterations is examined using the mineral surface charge. Also, the pH change effect due to well operations was investigated. Clay mineral surface charge was examined using zeta potential computed from the particle electrophoretic mobility. Clay minerals considered in this study include kaolinite, montmorillonite, illite, and chlorite. Results reveal that the clay mineral charge development is controlled by adsorption of ionic species and double layer collapse. Also, clay mineral surface charge considered in this study shows that their surfaces become more conducive for the adsorption of hydrocarbon components due to the presence of salts. The salt effect is greater in the following order: NaHCO3 < Na2SO4 < NaCl < MgCl2 < CaCl2. Furthermore, different well operations induce pH environments that change the clay mineral surface charge. This change results in adsorption prone surfaces and with reservoir rock made up of different minerals, and the effect of contacting minerals is critical as shown in our findings. This is due to the contacting mineral control wettability rather than the bulk mineralogy.

ACS Style

Isah Mohammed; Dhafer Al Shehri; Mohamed Mahmoud; Muhammad Shahzad Kamal; Olalekan Saheed Alade. A Surface Charge Approach to Investigating the Influence of Oil Contacting Clay Minerals on Wettability Alteration. ACS Omega 2021, 6, 12841 -12852.

AMA Style

Isah Mohammed, Dhafer Al Shehri, Mohamed Mahmoud, Muhammad Shahzad Kamal, Olalekan Saheed Alade. A Surface Charge Approach to Investigating the Influence of Oil Contacting Clay Minerals on Wettability Alteration. ACS Omega. 2021; 6 (19):12841-12852.

Chicago/Turabian Style

Isah Mohammed; Dhafer Al Shehri; Mohamed Mahmoud; Muhammad Shahzad Kamal; Olalekan Saheed Alade. 2021. "A Surface Charge Approach to Investigating the Influence of Oil Contacting Clay Minerals on Wettability Alteration." ACS Omega 6, no. 19: 12841-12852.

Research article
Published: 18 February 2021 in ACS Omega
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Determination of emulsion stability has important applications in crude oil production, separation, and transportation. The turbidimetry method offers advantage of rapid determination of stability at a relatively low cost with good accuracy. In this study, the stability of an oil-in-water (O/W) emulsion prepared by dispersing heavy oil particles in the aqueous solution containing poly(vinyl alcohol) (PVA) has been determined using turbidity measurements. The turbidimetry theory of emulsion stability has been validated using experimental data of turbidity at different wavelengths (350–800 nm) and storage times (0–300 min). The artificial neural network (ANN) has been found to give good predictive performance of the turbidity data. The characteristic change in turbidity has been supported using particle size and distribution analyses performed using optical/video microscopy. The results obtained from the turbidimetry correlation show that the emulsion destabilization rate constant (κ′, min–1) is in the range of 0.01–0.04 min–1 (at wavelengths between 350 and 800 nm, respectively). The rate constant remains unchanged (κ′ = 0.02 min–1) between the wavelength of 375 and 650 nm. In addition, the demulsification rate constant (κ′ = 0.015 min–1) obtained from kinetic modeling using the bottle test is in close agreement with this value. The overall findings ultimately revealed that the turbidimetry method could be used to determine stability of typical O/W emulsions with an acceptable level of accuracy.

ACS Style

Olalekan S. Alade; Mohamed Mahmoud; Dhafer A. Al Shehri; Abdullah S. Sultan. Rapid Determination of Emulsion Stability Using Turbidity Measurement Incorporating Artificial Neural Network (ANN): Experimental Validation Using Video/Optical Microscopy and Kinetic Modeling. ACS Omega 2021, 6, 5910 -5920.

AMA Style

Olalekan S. Alade, Mohamed Mahmoud, Dhafer A. Al Shehri, Abdullah S. Sultan. Rapid Determination of Emulsion Stability Using Turbidity Measurement Incorporating Artificial Neural Network (ANN): Experimental Validation Using Video/Optical Microscopy and Kinetic Modeling. ACS Omega. 2021; 6 (8):5910-5920.

Chicago/Turabian Style

Olalekan S. Alade; Mohamed Mahmoud; Dhafer A. Al Shehri; Abdullah S. Sultan. 2021. "Rapid Determination of Emulsion Stability Using Turbidity Measurement Incorporating Artificial Neural Network (ANN): Experimental Validation Using Video/Optical Microscopy and Kinetic Modeling." ACS Omega 6, no. 8: 5910-5920.

Research article
Published: 27 January 2021 in ACS Omega
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Asphaltene adsorption and deposition onto rock surfaces are predominantly the cause of wettability and permeability alterations which result in well productivity losses. These alterations can be induced by rock–fluid interactions which are affected by well operations such as acidizing, stimulation, gas injections, and so forth. Iron minerals are found abundantly in sandstone reservoir formations and pose a problem by precipitation and adsorption of polar crude components. This is due to rock–fluid interactions, which are dependent on reservoir pH; thus, this research work studied the surface charge development of pyrite, magnetite, and hematite. To ascertain conditions that will result in iron mineral precipitation and adsorption of asphaltene on iron mineral surfaces, zeta potential measurement was carried out. This is to determine the charge and colloidal stability of the iron mineral samples across wide pH values. Experimental results show that the charge development of iron minerals is controlled by mineral dissolution, the formation of complexes, adsorption of ions on the mineral surface, and the collapse of the double layer. The findings provide insights into the implications of iron mineral contacting crude oil in reservoir formations and how they contribute to wettability alterations due to different well operations.

ACS Style

Isah Mohammed; Dhafer Al Shehri; Mohamed Mahmoud; Muhammad Shahzad Kamal; Olalekan Saheed Alade. Impact of Iron Minerals in Promoting Wettability Alterations in Reservoir Formations. ACS Omega 2021, 6, 4022 -4033.

AMA Style

Isah Mohammed, Dhafer Al Shehri, Mohamed Mahmoud, Muhammad Shahzad Kamal, Olalekan Saheed Alade. Impact of Iron Minerals in Promoting Wettability Alterations in Reservoir Formations. ACS Omega. 2021; 6 (5):4022-4033.

Chicago/Turabian Style

Isah Mohammed; Dhafer Al Shehri; Mohamed Mahmoud; Muhammad Shahzad Kamal; Olalekan Saheed Alade. 2021. "Impact of Iron Minerals in Promoting Wettability Alterations in Reservoir Formations." ACS Omega 6, no. 5: 4022-4033.

Review
Published: 21 October 2020 in Polymers
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Several publications by authors in the field of petrochemical engineering have examined the use of chemically enhanced oil recovery (CEOR) technology, with a specific interest in polymer flooding. Most observations thus far in this field have been based on the application of certain chemicals and/or physical properties within this technique regarding the production of 50–60% trapped (residual) oil in a reservoir. However, there is limited information within the literature about the combined effects of this process on whole properties (physical and chemical). Accordingly, in this work, we present a clear distinction between the use of xanthan gum (XG) and hydrolyzed polyacrylamide (HPAM) as a polymer flood, serving as a background for future studies. XG and HPAM have been chosen for this study because of their wide acceptance in relation to EOR processes. To this degree, the combined effect of a polymer’s rheological properties, retention, inaccessible pore volume (PV), permeability reduction, polymer mobility, the effects of salinity and temperature, and costs are all investigated in this study. Further, the generic screening and design criteria for a polymer flood with emphasis on XG and HPAM are explained. Finally, a comparative study on the conditions for laboratory (experimental), pilot-scale, and field-scale application is presented.

ACS Style

Nasiru Salahu Muhammed; Bashirul Haq; Dhafer Al-Shehri; Mohammad Mizanur Rahaman; Alireza Keshavarz; S. M. Zakir Hossain. Comparative Study of Green and Synthetic Polymers for Enhanced Oil Recovery. Polymers 2020, 12, 2429 .

AMA Style

Nasiru Salahu Muhammed, Bashirul Haq, Dhafer Al-Shehri, Mohammad Mizanur Rahaman, Alireza Keshavarz, S. M. Zakir Hossain. Comparative Study of Green and Synthetic Polymers for Enhanced Oil Recovery. Polymers. 2020; 12 (10):2429.

Chicago/Turabian Style

Nasiru Salahu Muhammed; Bashirul Haq; Dhafer Al-Shehri; Mohammad Mizanur Rahaman; Alireza Keshavarz; S. M. Zakir Hossain. 2020. "Comparative Study of Green and Synthetic Polymers for Enhanced Oil Recovery." Polymers 12, no. 10: 2429.

Journal article
Published: 26 July 2020 in Molecules
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Laboratory measurements of capillary pressure (Pc) and the electrical resistivity index (RI) of reservoir rocks are used to calibrate well logging tools and to determine reservoir fluid distribution. Significant studies on the methods and factors affecting these measurements in rocks containing oil, gas, and water are adequately reported in the literature. However, with the advent of chemical enhanced oil recovery (EOR) methods, surfactants are mixed with injection fluids to generate foam to enhance the gas injection process. Foam is a complex and non-Newtonian fluid whose behavior in porous media is different from conventional reservoir fluids. As a result, the effect of foam on Pc and the reliability of using known rock models such as the Archie equation to fit experimental resistivity data in rocks containing foam are yet to be ascertained. In this study, we investigated the effect of foam on the behavior of both Pc and RI curves in sandstone and carbonate rocks using both porous plate and two-pole resistivity methods at ambient temperature. Our results consistently showed that for a given water saturation (Sw), the RI of a rock increases in the presence of foam than without foam. We found that, below a critical Sw, the resistivity of a rock containing foam continues to rise rapidly. We argue, based on knowledge of foam behavior in porous media, that this critical Sw represents the regime where the foam texture begins to become finer, and it is dependent on the properties of the rock and the foam. Nonetheless, the Archie model fits the experimental data of the rocks but with resulting saturation exponents that are higher than conventional gas–water rock systems. The degree of variation in the saturation exponents between the two fluid systems also depends on the rock and fluid properties. A theory is presented to explain this phenomenon. We also found that foam affects the saturation exponent in a similar way as oil-wet rocks in the sense that they decrease the cross-sectional area of water available in the pores for current flow. Foam appears to have competing and opposite effects caused by the presence of clay, micropores, and conducting minerals, which tend to lower the saturation exponent at low Sw. Finally, the Pc curve is consistently lower in foam than without foam for the same Sw.

ACS Style

Abdulrauf R. Adebayo; Abubakar Isah; Mohammed Mahmoud; Dhafer Al-Shehri. Effects of Foam Microbubbles on Electrical Resistivity and Capillary Pressure of Partially Saturated Porous Media. Molecules 2020, 25, 3385 .

AMA Style

Abdulrauf R. Adebayo, Abubakar Isah, Mohammed Mahmoud, Dhafer Al-Shehri. Effects of Foam Microbubbles on Electrical Resistivity and Capillary Pressure of Partially Saturated Porous Media. Molecules. 2020; 25 (15):3385.

Chicago/Turabian Style

Abdulrauf R. Adebayo; Abubakar Isah; Mohammed Mahmoud; Dhafer Al-Shehri. 2020. "Effects of Foam Microbubbles on Electrical Resistivity and Capillary Pressure of Partially Saturated Porous Media." Molecules 25, no. 15: 3385.

Journal article
Published: 20 March 2020 in Sustainability
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The oil and gas production operations suffer from scale depositions. The scale precipitations have a damaging impact on the reservoir pores, perforations, downhole and completion equipment, pipeline network, wellhead chokes, and surface facilities. Hydrocarbon production possibly decreased because of the scale accumulation in the well tubular, leading to a well plugging, this requires wells to be shut-in in severe cases to perform a clean-out job. Therefore, scale deposition is badly affecting petroleum economics. This research aims to design a scale dissolver with low cost, non-damaging for the well equipment and has a high performance at the field operating conditions. This paper presents a novel non-corrosive dissolver for sulfate and sulfide composite scale in alkaline pH and works at low-temperature conditions. The scale samples were collected from a production platform from different locations. A complete description of the scale samples was performed as X-ray diffraction (XRD) and X-ray fluorescence (XRF). The new scale dissolver was prepared in different concentrations to examine its dissolution efficiency for the scale with time at low temperatures. The experimental design studied the solid to fluid ratio, temperature, solubility time, and dissolution efficiency in order to achieve the optimum and most economic performance of solubility in terms of high dissolution efficiency with the smallest possible amount of scale dissolver. A solubility comparison was performed with other commercial-scale-dissolvers and the corrosion rate was tested. The experimental work results demonstrated the superior performance of the new scale dissolver. The new scale dissolver showed a solubility efficiency of 91.8% at a low temperature of 45 °C and 79% at 35 °C. The new scale dissolver showed a higher solubility ratio for the scale sample than the ethylenediaminetetraacetic acid (EDTA) (20 wt. %), diethylenetriamine pentaacetic acid (DTPA) (20 wt. %), and HCl (10 wt. %). The corrosion rate for the new non-corrosive dissolver was 0.01357 kg/m2 (0.00278 lb./ft²) which was considered a very low rate and non-damaging for the equipment. The low corrosive effect of the new dissolver will save the extra cost of adding the corrosion inhibitors and save the equipment from the damaging effect of the corrosive acids.

ACS Style

Hany Gamal; Salaheldin Elkatatny; Dhafer Al Shehri; Mohamed Bahgat. A Novel Low-Temperature Non-Corrosive Sulfate/Sulfide Scale Dissolver. Sustainability 2020, 12, 2455 .

AMA Style

Hany Gamal, Salaheldin Elkatatny, Dhafer Al Shehri, Mohamed Bahgat. A Novel Low-Temperature Non-Corrosive Sulfate/Sulfide Scale Dissolver. Sustainability. 2020; 12 (6):2455.

Chicago/Turabian Style

Hany Gamal; Salaheldin Elkatatny; Dhafer Al Shehri; Mohamed Bahgat. 2020. "A Novel Low-Temperature Non-Corrosive Sulfate/Sulfide Scale Dissolver." Sustainability 12, no. 6: 2455.

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: 18 November 2019 in Journal of Petroleum Science and Engineering
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Commercial production of heavy oil has heavily relied upon steam injection method due to its effectiveness to reduce the viscosity and improved mobility. However, due to uncertainty of oil price, high energy cost, water requirement, heat loss, and environmental issues, attention is being given to new thermal EOR technologies such as insitu steam generation using thermochemical fluid (TCF) injection. This technology essentially involves downhole generation of heat and pressure from exothermic chemical reaction. In this investigation, thermochemical reactants were injected, at different temperatures (100, 50, and 30 °C) and constant rate of 0.5 ml/mln, into Berea sandstone core samples saturated with heavy oil (18 %wt./wt. asphaltene content and 11° API) and brine. It was observed that the pressure generated at the inlet of the core due to 100, 50, and 30 °C injection temperatures of the thermochemical fluids rose to 1600, 1200, and 280 psi, respectively; while the recovery factor was 83%, 66% and 54% OOIP, respectively. In comparison, from the injection of steam generated at 250 °C, the pressure at the inlet of the core was 212 psi and the recovery factor was 71% OOIP. These results therefore confirm increase in interest in the application of the TCF injection technology in heavy oil production.

ACS Style

Olalekan S. Alade; Mohamed Hamdy; Mohamed Mahmoud; Dhafer A. Al Shehri; Esmail Mokheimer; Shirish Patil; Ayman Al-Nakhli. A preliminary assessment of thermochemical fluid for heavy oil recovery. Journal of Petroleum Science and Engineering 2019, 186, 106702 .

AMA Style

Olalekan S. Alade, Mohamed Hamdy, Mohamed Mahmoud, Dhafer A. Al Shehri, Esmail Mokheimer, Shirish Patil, Ayman Al-Nakhli. A preliminary assessment of thermochemical fluid for heavy oil recovery. Journal of Petroleum Science and Engineering. 2019; 186 ():106702.

Chicago/Turabian Style

Olalekan S. Alade; Mohamed Hamdy; Mohamed Mahmoud; Dhafer A. Al Shehri; Esmail Mokheimer; Shirish Patil; Ayman Al-Nakhli. 2019. "A preliminary assessment of thermochemical fluid for heavy oil recovery." Journal of Petroleum Science and Engineering 186, no. : 106702.

Journal article
Published: 14 November 2019 in Journal of Energy Resources Technology
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Unconventional hydrocarbon resources mostly found in highly stressed, overpressured, and deep formations, where the rock strength and integrity are very high. When fracturing these kinds of rocks, the hydraulic fracturing operation becomes much more challenging and difficult and in some cases reaches to the maximum pumping capacity limits without generating any fracture. This reduces the operational gap to optimally place the hydraulic fractures. Current stimulation methods to reduce the fracture pressures involvement with adverse environmental effects and high costs due to the entailment of water mixed with huge volumes of chemicals. In this study, a new environment friendly approach to reduce the breakdown pressure of the unconventional rock is presented. The new method incorporates the injection of chemical-free fracturing fluid in a series of cycles with a progressive increase of the pressurization rate in each cycle. This study is carried out on different cement blocks with varying petrophysical and mechanical properties to simulate real rock types. The results showed that the new method of cyclic fracturing can reduce the breakdown pressure to 24.6% in ultra-tight rocks, 19% in tight rocks, and 14.8% in medium- to low-permeability rocks. This reduction in breakdown pressure helped to overcome the operational challenges in the field and makes the fracturing operation much greener.

ACS Style

Zeeshan Tariq; Mohamed Mahmoud; Abdulazeez Abdulraheem; Dhafer Al-Shehri; Mobeen Murtaza. An Environment Friendly Approach to Reduce the Breakdown Pressure of High Strength Unconventional Rocks by Cyclic Hydraulic Fracturing. Journal of Energy Resources Technology 2019, 142, 1 -26.

AMA Style

Zeeshan Tariq, Mohamed Mahmoud, Abdulazeez Abdulraheem, Dhafer Al-Shehri, Mobeen Murtaza. An Environment Friendly Approach to Reduce the Breakdown Pressure of High Strength Unconventional Rocks by Cyclic Hydraulic Fracturing. Journal of Energy Resources Technology. 2019; 142 (4):1-26.

Chicago/Turabian Style

Zeeshan Tariq; Mohamed Mahmoud; Abdulazeez Abdulraheem; Dhafer Al-Shehri; Mobeen Murtaza. 2019. "An Environment Friendly Approach to Reduce the Breakdown Pressure of High Strength Unconventional Rocks by Cyclic Hydraulic Fracturing." Journal of Energy Resources Technology 142, no. 4: 1-26.

Journal article
Published: 21 June 2019 in Energies
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The viscosity data of two heavy oil samples X and Y, with asphaltene contents 24.8% w/w and 18.5% w/w, respectively, were correlated with temperature and pressure using empirical models and the artificial neural network (ANN) approach. The viscosities of the samples were measured over a range of temperatures between 70 °C and 150 °C; and from atmospheric pressure to 7 MPa. It was found that the viscosity of sample X, at 85 °C and atmospheric pressure (0.1 MPa), was 1894 cP and that it increased to 2787 cP at 7 MPa. At 150 °C, the viscosity increased from 28 cP (at 0.1 MPa) to 33 cP at 7 MPa. For sample Y, the viscosity at 70 °C and 0.1 MPa increased from 2260 cP to 3022 cP at 7 MPa. At 120 °C, the viscosity increased from 65 cP (0.1 MPa) to 71 cP at 7 MPa. Notably, using the three-parameter empirical models (Mehrotra and Svrcek, 1986 and 1987), the correlation constants obtained in this study are very close to those that were previously obtained for the Canadian heavy oil samples. Moreover, compared to other empirical models, statistical analysis shows that the ANN model has a better predictive accuracy (R2 ≈ 1) for the viscosity data of the heavy oil samples used in this study.

ACS Style

Olalekan Alade; Dhafer Al Shehri; Mohamed Mahmoud; Kyuro Sasaki. Viscosity–Temperature–Pressure Relationship of Extra-Heavy Oil (Bitumen): Empirical Modelling versus Artificial Neural Network (ANN). Energies 2019, 12, 2390 .

AMA Style

Olalekan Alade, Dhafer Al Shehri, Mohamed Mahmoud, Kyuro Sasaki. Viscosity–Temperature–Pressure Relationship of Extra-Heavy Oil (Bitumen): Empirical Modelling versus Artificial Neural Network (ANN). Energies. 2019; 12 (12):2390.

Chicago/Turabian Style

Olalekan Alade; Dhafer Al Shehri; Mohamed Mahmoud; Kyuro Sasaki. 2019. "Viscosity–Temperature–Pressure Relationship of Extra-Heavy Oil (Bitumen): Empirical Modelling versus Artificial Neural Network (ANN)." Energies 12, no. 12: 2390.

Journal article
Published: 20 June 2019 in Journal of Energy Resources Technology
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This paper introduces a novel approach to generate downhole steam using thermochemical reactions to overcome the challenges associated with heavy oil resources. The procedure developed in this paper is applied to a heavy oil reservoir, which contains heavy oil (12–23 API) with an estimated range of original oil in place (OOIP) of 13–25 billion barrels while its several technical challenges are limiting its commercial development. One of these challenges is the overlying 1800–2000-ft thick permafrost layer, which causes significant heat losses when steam is injected from the surface facilities. The objective of this research is to conduct a feasibility study on the application of the new approach in which the steam is generated downhole using the thermochemical reaction (SGT) combined with steam-assisted gravity drainage (SAGD) to recover heavy oil from the reservoir. A numerical simulation model for a heavy oil reservoir is built using a CMG-STARS simulator, which is then integrated with a matlab framework to study different recovery strategies on the project profitability. The design and operational parameters studied and optimized in this paper involve (1) well configurations and locations and (2) steam injection rate and quality as well as a steam trap in SAGD wells. The results show that the in situ SGT is a successful approach to recover heavy oil from the reservoir, and it yields high-project profitability. The main reason for this outperformance is the ability of SGT to avoid the significant heat losses and associated costs associated with the surface steam injection.

ACS Style

Tamer Moussa; Mohamed Mahmoud; Esmail M. A. Mokheimer; Dhafer Al-Shehri; Shirish Patil; Tamer Mousa. Heavy Oil Recovery Using In Situ Steam Generated by Thermochemicals: A Numerical Simulation Study. Journal of Energy Resources Technology 2019, 141, 122903 -17.

AMA Style

Tamer Moussa, Mohamed Mahmoud, Esmail M. A. Mokheimer, Dhafer Al-Shehri, Shirish Patil, Tamer Mousa. Heavy Oil Recovery Using In Situ Steam Generated by Thermochemicals: A Numerical Simulation Study. Journal of Energy Resources Technology. 2019; 141 (12):122903-17.

Chicago/Turabian Style

Tamer Moussa; Mohamed Mahmoud; Esmail M. A. Mokheimer; Dhafer Al-Shehri; Shirish Patil; Tamer Mousa. 2019. "Heavy Oil Recovery Using In Situ Steam Generated by Thermochemicals: A Numerical Simulation Study." Journal of Energy Resources Technology 141, no. 12: 122903-17.

Original paper
Published: 04 June 2019 in Petroleum Science
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The effect of silica nanoparticles on the rheological characteristics of water-in-heavy oil emulsions has been investigated. Enhanced oil recovery methods for heavy oil production (most especially, thermal fluid injection) usually result in the formation of water-in-oil (W/O) emulsion. In reality, the emulsion produced also contains some fine solid mineral particles such as silica, which, depending on its quantity, may alter the viscosity and/or rheological properties of the fluid. A series of binary-component emulsions were separately prepared by dispersing silica nanoparticles [phase fraction, βs, = 0.5%–5.75% (wt/v)] in heavy oil (S/O suspension) and by dispersing water [water cut, θw = 10%–53% (v/v)] in heavy oil (W/O emulsion). Ternary-component emulsions comprising heavy oil, water droplets and suspended silica nanoparticles (S/W/O) were also prepared with similar ranges of θw and βs. The viscosity was measured at different shear rates (5.1–1021.4 s−1) and temperatures (30–70 °C). Both binary-component and ternary-component emulsion systems were observed to exhibit non-Newtonian shear thinning behaviour. The viscosity of the heavy oil and W/O emulsions increased in the presence of silica nanoparticles. The effect was, however, less significant below βs = 2% (wt/v). Moreover, a generalized correlation has been proposed to predict the viscosity of both binary-component and ternary-component emulsions.

ACS Style

O. S. Alade; D. A. Al Shehri; M. Mahmoud. Investigation into the effect of silica nanoparticles on the rheological characteristics of water-in-heavy oil emulsions. Petroleum Science 2019, 16, 1374 -1386.

AMA Style

O. S. Alade, D. A. Al Shehri, M. Mahmoud. Investigation into the effect of silica nanoparticles on the rheological characteristics of water-in-heavy oil emulsions. Petroleum Science. 2019; 16 (6):1374-1386.

Chicago/Turabian Style

O. S. Alade; D. A. Al Shehri; M. Mahmoud. 2019. "Investigation into the effect of silica nanoparticles on the rheological characteristics of water-in-heavy oil emulsions." Petroleum Science 16, no. 6: 1374-1386.

Journal article
Published: 18 May 2019 in Sustainability
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Unconventional reservoirs have shown tremendous potential for energy supply for long-term applications. However, great challenges are associated with hydrocarbon production from these reservoirs. Recently, injection of thermochemical fluids has been introduced as a new environmentally friendly and cost-effective chemical for improving hydrocarbon production. This research aims to improve gas production from gas condensate reservoirs using environmentally friendly chemicals. Further, the impact of thermochemical treatment on changing the pore size distribution is studied. Several experiments were conducted, including chemical injection, routine core analysis, and nuclear magnetic resonance (NMR) measurements. The impact of thermochemical treatment in sustaining gas production from a tight gas reservoir was quantified. This study demonstrates that thermochemical treatment can create different types of fractures (single or multistaged fractures) based on the injection method. Thermochemical treatment can increase absolute permeability up to 500%, reduce capillary pressure by 57%, remove the accumulated liquids, and improve gas relative permeability by a factor of 1.2. The findings of this study can help to design a better thermochemical treatment for improving gas recovery. This study showed that thermochemical treatment is an effective method for sustaining gas production from tight gas reservoirs.

ACS Style

Amjed M. Hassan; Mohamed A. Mahmoud; Abdulaziz A. Al-Majed; Dhafer Al-Shehri; Ayman R. Al-Nakhli; Mohammed A. Bataweel. Gas Production from Gas Condensate Reservoirs Using Sustainable Environmentally Friendly Chemicals. Sustainability 2019, 11, 2838 .

AMA Style

Amjed M. Hassan, Mohamed A. Mahmoud, Abdulaziz A. Al-Majed, Dhafer Al-Shehri, Ayman R. Al-Nakhli, Mohammed A. Bataweel. Gas Production from Gas Condensate Reservoirs Using Sustainable Environmentally Friendly Chemicals. Sustainability. 2019; 11 (10):2838.

Chicago/Turabian Style

Amjed M. Hassan; Mohamed A. Mahmoud; Abdulaziz A. Al-Majed; Dhafer Al-Shehri; Ayman R. Al-Nakhli; Mohammed A. Bataweel. 2019. "Gas Production from Gas Condensate Reservoirs Using Sustainable Environmentally Friendly Chemicals." Sustainability 11, no. 10: 2838.

Journal article
Published: 04 February 2019 in Sustainability
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Wellbore integrity management for oil and gas wells plays a vital role throughout the typical lifespan of a well. Downhole casing leaks in oil- and gas-producing wells significantly affect their shallow water horizon, the environment, and fresh water resources. Additionally, downhole casing leaks may cause seepage of toxic gases to fresh water zones and the surface, through the casing annuli. Forecasting of such leaks and proactive measures of prevention will help eliminate their consequences and, in turn, better protect the environment. The objective of this study is to formulate an effective, robust, and accurate model for predicting the corrosion rate of metal casing string using artificial intelligence (AI) techniques. The input parameters used to train AI models include casing leaks, the percentage of metal loss, casing age, and average remaining barrier ratio (ARBR). The target parameter is the corrosion rate of the metal casing string. The dataset from which the AI models were trained was comprised of 250 data points collected from 218 wells in a giant carbonate reservoir that covered a wide range of practically reasonable values. Two AI tools were used: artificial neural networks (ANNs) and adaptive network-based fuzzy inference systems (ANFISs). A prediction comparison was made between these two tools. Based on the minimum average absolute percentage error (AAPE) and the highest coefficient of determination (R2) between the measured and predicted corrosion rate values, the ANN model proposed here was determined to be best for predicting the corrosion rate. An ANN-based empirical model is also presented in this study. The proposed model is based on the associated weights and biases. After evaluating the new ANN equation using an unseen validation dataset, it was concluded that the ANN equation was able to make predictions with a significantly lower AAPE and higher R2. Use of the proposed new equation is very cost-effective in terms of reducing the number of sequential surveys and experiments conducted. The proposed equation can be utilized without an AI engine. The developed model and empirical correlation are very promising and can serve as a handy tool for corrosion engineers seeking to determine the corrosion rate without training an AI model.

ACS Style

Dhafer A. Al-Shehri. Oil and Gas Wells: Enhanced Wellbore Casing Integrity Management through Corrosion Rate Prediction Using an Augmented Intelligent Approach. Sustainability 2019, 11, 818 .

AMA Style

Dhafer A. Al-Shehri. Oil and Gas Wells: Enhanced Wellbore Casing Integrity Management through Corrosion Rate Prediction Using an Augmented Intelligent Approach. Sustainability. 2019; 11 (3):818.

Chicago/Turabian Style

Dhafer A. Al-Shehri. 2019. "Oil and Gas Wells: Enhanced Wellbore Casing Integrity Management through Corrosion Rate Prediction Using an Augmented Intelligent Approach." Sustainability 11, no. 3: 818.

Conference paper
Published: 10 December 2018 in All Days
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For a well production optimization and cost per barrel of oil reduction, it is very essential to have an accurate measurement of well flowing bottom-hole pressure (FBHP) available all the time during the life of the well. It is a common practice follow in the oil and gas industry to run bottom-hole pressure gauges to record FBHP. However, interfering a producing well is an expensive and time-consuming task, involved with production disruptions and safety risks. To address these concerns, numerous mechanistic and empirical models were formulated to predict FBHP. Among these a large number of models were developed under small laboratory scale conditions and are, ultimately, inaccurate when up-scaled to field conditions. This study presents an intelligent solution by the development of an empirical model to quantify FBHP for a vertical well. The new model is based on the surface production data such as tubing perforation depth, flow rate of oil, flow rate of gas, flow rate of water, API gravity of oil, tubing string internal diameter, well bottom-hole temperature, wellhead surface temperature, and wellhead pressure. The data used to train the proposed empirical model collected from the published sources, which covered practically reasonable values. The proposed model is also validated by testing against new dataset, and the results were then compared statistically with the other methods used in petroleum industry. The results show that the proposed empirical model considerably outperforms reviewed models and delivers the prediction of FBHP with high accuracy. The novelty of the proposed empirical model is that it depends only on the surface production which makes the prediction of FBHP in a real time. The proposed model is accurate and can serve as a handy tool for the production engineers to forecast the FBHP in a real time.

ACS Style

Zeeshan Tariq; Mohamed Mahmoud; Abdulazeez Abdulraheem; Dhafer Al-Shehri; Muhammad Rasheed Khan; Aneeq Nasir Janjua. An Intelligent Solution to Forecast Pressure Drop in a Vertical Well Having Multiphase Flow Using Functional Network Technique. All Days 2018, 1 .

AMA Style

Zeeshan Tariq, Mohamed Mahmoud, Abdulazeez Abdulraheem, Dhafer Al-Shehri, Muhammad Rasheed Khan, Aneeq Nasir Janjua. An Intelligent Solution to Forecast Pressure Drop in a Vertical Well Having Multiphase Flow Using Functional Network Technique. All Days. 2018; ():1.

Chicago/Turabian Style

Zeeshan Tariq; Mohamed Mahmoud; Abdulazeez Abdulraheem; Dhafer Al-Shehri; Muhammad Rasheed Khan; Aneeq Nasir Janjua. 2018. "An Intelligent Solution to Forecast Pressure Drop in a Vertical Well Having Multiphase Flow Using Functional Network Technique." All Days , no. : 1.

Journal article
Published: 02 July 2018 in Journal of Energy Resources Technology
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Downhole casing leaks in oil and gas wells will highly impact the shallow water horizons and this will affect the environment and fresh water resources. Proactive measures and forecasting of this leak will help eliminate the consequences of downhole casing leaks and, in turn, will protect the environment. Additionally, downhole casing leaks may also cause seepage of toxic gases to the fresh water zones and to the surface through the casing annuli. In this paper, we introduced a risk-based methodology to predict the downhole casing leaks in oil and gas wells using advanced casing corrosion logs such as electromagnetic logs. Downhole casing corrosion was observed to assess the remaining well life. Electromagnetic (EM) corrosion logs are the current practice for monitoring the casing corrosion. The corrosion assessment from EM logs is insufficient because these logs cannot read in multiple casings in the well. EM tool gives average reading for the corrosion in the casing at a specific depth and it does not indicate the orientation of the corrosion. EM log does not assess the 360 deg corrosion profile in the casing and it only provides average value and this may lead to wrong decision. All of this makes EM logs uncertain tools to assess the corrosion in the downhole casing. A unified criterion to assess the corrosion in the casing and to decide workover operations or not has been identified to minimize the field challenges related to this issue. A new approach was introduced in this paper to enhance the EM logs to detect the downhole casing corrosion. Corrosion data were collected from different fields (around 500 data points) to build a probabilistic approach to assess the casing failure based on the average metal loss from the EM corrosion log. The failure model was used to set the ranges for the casing failure and the probability of casing failure for different casings. The prediction of probability of failure (PF) will act as proactive maintenance which will help prevent further or future casing leaks.

ACS Style

Mohammed D. Al-Ajmi; Dhafer Al-Shehri; Mohamed Mahmoud; Nasser M. Al-Hajri. Risk-Based Approach to Evaluate Casing Integrity in Upstream Wells. Journal of Energy Resources Technology 2018, 140, 122901 .

AMA Style

Mohammed D. Al-Ajmi, Dhafer Al-Shehri, Mohamed Mahmoud, Nasser M. Al-Hajri. Risk-Based Approach to Evaluate Casing Integrity in Upstream Wells. Journal of Energy Resources Technology. 2018; 140 (12):122901.

Chicago/Turabian Style

Mohammed D. Al-Ajmi; Dhafer Al-Shehri; Mohamed Mahmoud; Nasser M. Al-Hajri. 2018. "Risk-Based Approach to Evaluate Casing Integrity in Upstream Wells." Journal of Energy Resources Technology 140, no. 12: 122901.

Conference paper
Published: 23 April 2018 in All Days
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Continuous monitoring of the rheological properties of the drilling mud is essential so that any drilling operation can be completed more effectively and efficiently with the least problems. Mud rheological properties play a vital role in the in the efficiency of the drilling fluid to lift the cuttings from the wellbore. The mud rheological properties include the plastic viscosity, apparent viscosity, and the yield point. However, these properties are not measured continuously during the drilling process and they are only measured once or twice a day while other mud properties, such as the mud weight, the Marsh funnel viscosity, and solid content, are measured regularly and continuously. Therefore, it is valuable to come up with a relation that relates the mud rheological properties to these parameters. Many researchers tried to introduce models that allow for the prediction of the apparent viscosity from the Marsh funnel viscosity. However, these models have the deficiency that the prediction is with high errors. For the first time, the solid percent was used to predict the rheological properties of the oil-based drilling fluid based on the artificial neural network using actual field measurements. The purpose of this study is to use the Artificial Neural Networks (ANN) Technique to develop a model that allows the prediction of the mud rheological properties such as the plastic viscosity, apparent viscosity, the rheometer readings at 600 and 300 rpm and the flow behavior index for oil-based mud from the mud weight, the Marsh funnel viscosity and solid content. The study is based on 400 data points collected from the field measurements of actual drilling fluid samples. The obtained results showed that the five developed models using ANN technique can be used to predict the rheological properties of oil- based drilling fluid with a high accuracy; the average absolute error was less than 5% and the correlation coefficient was higher than 90%. The developed technique is inexpensive with no additional required equipment. It will help the drilling engineers to calculate the equivalent circulation density, surge and swab pressures, and hole cleaning which are strong functions of the rheological parameters in a real time. The method and approach used in this paper to predict and determine the unknown drilling fluid properties and trend out of accurately defined parameters is futuristic and progressive. The method is one step forward toward automating the drilling fluid system which is another step forward toward fully automating the drilling process overall.

ACS Style

Khaled H. Al-Azani; Salaheldin Elkatatny; Abdulaziz Abdulraheem; Mohamed Mahmoud; Dhafer Al-Shehri. Real Time Prediction of the Rheological Properties of Oil-Based Drilling Fluids Using Artificial Neural Networks. All Days 2018, 1 .

AMA Style

Khaled H. Al-Azani, Salaheldin Elkatatny, Abdulaziz Abdulraheem, Mohamed Mahmoud, Dhafer Al-Shehri. Real Time Prediction of the Rheological Properties of Oil-Based Drilling Fluids Using Artificial Neural Networks. All Days. 2018; ():1.

Chicago/Turabian Style

Khaled H. Al-Azani; Salaheldin Elkatatny; Abdulaziz Abdulraheem; Mohamed Mahmoud; Dhafer Al-Shehri. 2018. "Real Time Prediction of the Rheological Properties of Oil-Based Drilling Fluids Using Artificial Neural Networks." All Days , no. : 1.

Conference paper
Published: 23 April 2018 in All Days
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In heterogeneous reservoir formations, water tends to have early breakthrough due to the overriding and viscous fingering during secondary recovery. The overall hydrocarbon recovery efficiency remains very low in gas and water flooding projects because of less viscosity and higher mobility of water and gas. Therefore, there is an underlying need for improving recovery through a suitable chemical enhanced oil recovery (EOR) method. After investigating the feasibility of alkaline, polymer, surfactant, surfactant-polymer, alkaline-polymer and alkaline-surfactant-polymer (ASP) flood, ASP was selected as a chemical EOR method in low permeability heterogeneous reservoirs. However, the performance of the ASP flooding was highly dependent on operational parameters. Thus, it was important to select these parameters with extensive care to increase the recovery along with the profitability. The relationship between the ASP flooding operational parameters and profitability (NPV) has not been yet understood fully. In this research, the new stochastic optimization approach to optimize the ASP flooding operational parameters has been proposed. To gain the objective of this research, a numerical simulation study was carried out and Particle Swarm Optimization (PSO) was implemented as an optimization algorithm. The net present value (NPV) served as the objective function that has to be maximized among the compared flooding processes. The used operational parameters were location of production and injection well, number of injection cycles, oil production rate, ASP injection time, ASP injection rate, alkaline-surfactant and polymer concentrations, surfactant and polymer viscosities. Sensitivity study of these parameters shows significant impact on net present value and ultimate oil recovery. Results also confirm that NPV is increased significantly after the optimization of all flooding parameters by using Particle Swarm Optimizer. The new optimized model was developed for designing the ASP as a chemical EOR method in low permeability heterogeneous reservoir. It can be served as a handy tool for reservoir engineer to select the best ASP flood parameters to achieve maximum NPV.

ACS Style

Zeeshan Tariq; Mohamed Mahmoud; Dhafer Al-Shehri; Najmudeen Sibaweihi; Ahmed Sadeed; M. Enamul Hossain. A Stochastic Optimization Approach for Profit Maximization Using Alkaline-Surfactant-Polymer Flooding in Complex Reservoirs. All Days 2018, 1 .

AMA Style

Zeeshan Tariq, Mohamed Mahmoud, Dhafer Al-Shehri, Najmudeen Sibaweihi, Ahmed Sadeed, M. Enamul Hossain. A Stochastic Optimization Approach for Profit Maximization Using Alkaline-Surfactant-Polymer Flooding in Complex Reservoirs. All Days. 2018; ():1.

Chicago/Turabian Style

Zeeshan Tariq; Mohamed Mahmoud; Dhafer Al-Shehri; Najmudeen Sibaweihi; Ahmed Sadeed; M. Enamul Hossain. 2018. "A Stochastic Optimization Approach for Profit Maximization Using Alkaline-Surfactant-Polymer Flooding in Complex Reservoirs." All Days , no. : 1.