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Dr. Salaheldin Elkatatny
King Fahd University of Petroleum & Minerals

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Research Keywords & Expertise

0 Artificial Intelligence
0 Drilling Engineering
0 Geomechanics
0 Drilling Fluid Optimization
0 Oil-Well Cementing

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Artificial Intelligence
Oil-Well Cementing
Geomechanics
Drilling Fluid Optimization

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Research papers
Published: 17 August 2021 in Journal of Energy Resources Technology
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The solids sagging in high-pressure high-temperature (HP/HT) reservoirs is a common challenge associated with hematite drilling fluids. This study provides a solution to hematite sagging in invert emulsion mud for HP/HT wells which involves the combination of Micromax (Mn3O4) with hematite. The particles of both weighting agents were characterized to address their mineralogical features. A Field formulation of the mud was used over a range of Micromax/hematite ratios (0/100, 20/80, and 30/70%) in laboratory experiments to address the sag performance and determine the optimal combination ratio. Then, density, emulsion stability, rheology, viscoelasticity, and filtration performance for the formulated mud were addressed. The tests were conditioned to 500 psi and 350 °F. The acquired results of sag tests indicated that incorporation of 30% Micromax solved the hematite sagging issue and brought the sag tendency within the recommended safe range. An insignificant reduction in mud density was observed upon the inclusion of Micromax, while the emulsion stability was obviously improved from 551 to 614 volts with the 30% Micromax mixture. The recommended 30/70% combination had almost no effect on plastic viscosity and yield point since they were increased by one unit, but the gel strength was improved resulting in flat rheology and better solids suspension capacity. The filtration behavior of the formulation with 30% Micromax was enhanced compared to pure hematite as it resulted in 10 and 14% reduction of the filtrate volume and filter-cake thickness, respectively. This study contributes to improve and economize the drilling cost and time by formulating a stabilized and distinguished-performance drilling mud using combined weighting agents at HP/HT.

ACS Style

Ashraf Ahmed; Salem Basfer; Salaheldin Elkatatny. Sagging Prevention for Hematite-Based Invert Emulsion Mud. Journal of Energy Resources Technology 2021, 1 -19.

AMA Style

Ashraf Ahmed, Salem Basfer, Salaheldin Elkatatny. Sagging Prevention for Hematite-Based Invert Emulsion Mud. Journal of Energy Resources Technology. 2021; ():1-19.

Chicago/Turabian Style

Ashraf Ahmed; Salem Basfer; Salaheldin Elkatatny. 2021. "Sagging Prevention for Hematite-Based Invert Emulsion Mud." Journal of Energy Resources Technology , no. : 1-19.

Research papers
Published: 17 August 2021 in Journal of Energy Resources Technology
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Rock geomechanical properties impact wellbore stability, drilling performance, estimation of in-situ stresses, and design of hydraulic fracturing. One of these properties is Poisson's ratio which is measured from lab testing or derived from well logs, the former is costly, time-consuming and doesn't provide continuous information, and the latter may not be always available. An alternative prediction technique from drilling parameters in real-time is proposed in this paper. The novel contribution of this approach is that the drilling data is always available and obtained from the first encounter with the well. These parameters are easily obtainable from drilling rig sensors such as rate of penetration, weight on bit and torque. Three machine-learning methods were utilized, support vector machine (SVM), functional network (FN) and random forest (RF). Dataset (2905 data points) from one well were used to build the models, while a dataset from another well with 2912 data points was used to validate the constructed models. Both wells have diverse lithology consists of carbonate, shale and sandstone. To ensure optimal accuracy, sensitivity and optimization tests on various parameters in each algorithm were performed.The three machine learning tools provided good estimations, however, SVM and RF yielded close results, with correlation coefficients of 0.99 and the average absolute percentage error (AAPE) values were mostly less than 1%. While in FN the outcomes were less efficient with correlation coefficients of 0.92 and AAPE around 3.8%. Accordingly, the presented approach provides an effective tool for Poisson's ratio prediction on a real-time basis at no additional expense. In addition, the same approach could be used in other rock mechanical properties.

ACS Style

Osama Sidddig; Hany Gamal; Salaheldin Elkatatny; Abdulazeez Abdulraheem. Applying Different Artificial Intelligence Techniques in Dynamic Poisson's Ratio Prediction Using Drilling Parameters. Journal of Energy Resources Technology 2021, 1 -15.

AMA Style

Osama Sidddig, Hany Gamal, Salaheldin Elkatatny, Abdulazeez Abdulraheem. Applying Different Artificial Intelligence Techniques in Dynamic Poisson's Ratio Prediction Using Drilling Parameters. Journal of Energy Resources Technology. 2021; ():1-15.

Chicago/Turabian Style

Osama Sidddig; Hany Gamal; Salaheldin Elkatatny; Abdulazeez Abdulraheem. 2021. "Applying Different Artificial Intelligence Techniques in Dynamic Poisson's Ratio Prediction Using Drilling Parameters." Journal of Energy Resources Technology , no. : 1-15.

Research article
Published: 22 July 2021 in Computational Intelligence and Neuroscience
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Due to high oil and gas production and consumption, unconventional reservoirs attracted significant interest. Total organic carbon (TOC) is a significant measure of the quality of unconventional resources. Conventionally, TOC is measured experimentally; however, continuous information about TOC is hard to obtain due to the samples’ limitations, while the developed empirical correlations for TOC were found to have modest accuracy when applied in different datasets. In this paper, data from Devonian Duvernay shale were used to develop an optimized empirical correlation to predict TOC based on an artificial neural network (ANN). Three wells’ datasets were used to build and validate the model containing over 1250 data points, and each data point includes values for TOC, density, porosity, resistivity, gamma ray and sonic transient time, and spectral gamma ray. The three datasets were used separately for training, testing, and validation. The results of the developed correlation were compared with three available models. A sensitivity and optimization test was performed to reach the best model in terms of average absolute percentage error (AAPE) and correlation coefficient (R) between the actual and predicted TOC. The new correlation yielded an excellent match with the actual TOC values with R values above 0.93 and AAPE values lower than 14%. In the validation dataset, the correlation outperformed the other empirical correlations and resulted in less than 10% AAPE, in comparison with over 20% AAPE in other models. These results imply the applicability of this correlation; therefore, all the correlation’s parameters are reported to allow its use on different datasets.

ACS Style

Osama Siddig; Ahmed Abdulhamid Mahmoud; Salaheldin Elkatatny; Pantelis Soupios. Utilization of Artificial Neural Network in Predicting the Total Organic Carbon in Devonian Shale Using the Conventional Well Logs and the Spectral Gamma Ray. Computational Intelligence and Neuroscience 2021, 2021, 1 -12.

AMA Style

Osama Siddig, Ahmed Abdulhamid Mahmoud, Salaheldin Elkatatny, Pantelis Soupios. Utilization of Artificial Neural Network in Predicting the Total Organic Carbon in Devonian Shale Using the Conventional Well Logs and the Spectral Gamma Ray. Computational Intelligence and Neuroscience. 2021; 2021 ():1-12.

Chicago/Turabian Style

Osama Siddig; Ahmed Abdulhamid Mahmoud; Salaheldin Elkatatny; Pantelis Soupios. 2021. "Utilization of Artificial Neural Network in Predicting the Total Organic Carbon in Devonian Shale Using the Conventional Well Logs and the Spectral Gamma Ray." Computational Intelligence and Neuroscience 2021, no. : 1-12.

Research article
Published: 20 July 2021 in ACS Omega
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Measuring oil production rates of individual wells is important to evaluate a well’s performance. Multiphase flow meters (MPFMs) and test separators have been used to estimate well production rates. Due to economic and technical issues with MPFMs, especially for high gas–oil ratio (GOR) reservoirs, the use of a choke formula for estimating well production rate is still popular. The objective of this study is to implement different artificial intelligence (AI) techniques to predict the oil rate through wellhead chokes. Support-vector machine (SVM) and random forests (RF) were used to generate different models to predict the production rates for high GOR and WC wells. A set of data (548 wells) was obtained from oil fields in the Middle East. GOR varied from 1000 to 9351 scf/stb, and WC ranged from 1 to 60%. Around 300 wells were flowing under critical flow conditions, while the rest were subcritical. Hence, two cases were studied using each AI model. Seventy percent of the data was used to train both RF and SVM models, while 30% of the data was used to test and validate these models. The developed RF and SVM models were then compared against the previous empirical formulas. The RF model in both critical and subcritical flow conditions was able to perfectly match the actual oil rates. SVM was able to predict the general trend for the oil rates but missed some of the sharp changes in the oil rate trend. The average absolute percent error (AAPE) values in the subcritical flow for SVM and RF were 1.7 and 0.7%, respectively, while in the critical flow, the AAPE values were 1.4 and 0.75% for SVM and RF models, respectively. SVM and RF models outperform the published formulas by 34%. The results from this study will help to estimate the real-time oil and gas rates based on the available data from wellhead chokes without the need for field intervention.

ACS Style

Ahmed Farid Ibrahim; Redha Al-Dhaif; Salaheldin Elkatatny; Dhafer Al Shehri. Applications of Artificial Intelligence to Predict Oil Rate for High Gas–Oil Ratio and Water-Cut Wells. ACS Omega 2021, 6, 19484 -19493.

AMA Style

Ahmed Farid Ibrahim, Redha Al-Dhaif, Salaheldin Elkatatny, Dhafer Al Shehri. Applications of Artificial Intelligence to Predict Oil Rate for High Gas–Oil Ratio and Water-Cut Wells. ACS Omega. 2021; 6 (30):19484-19493.

Chicago/Turabian Style

Ahmed Farid Ibrahim; Redha Al-Dhaif; Salaheldin Elkatatny; Dhafer Al Shehri. 2021. "Applications of Artificial Intelligence to Predict Oil Rate for High Gas–Oil Ratio and Water-Cut Wells." ACS Omega 6, no. 30: 19484-19493.

Research papers
Published: 19 July 2021 in Journal of Energy Resources Technology
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Total organic carbon (TOC) is an essential parameter that indicates the quality of unconventional reservoirs. In this study, four machine learning (ML) algorithms of the adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), functional neural networks (FNN), and random forests (RFs) were optimized to evaluate the TOC. The novelty of this work is that the optimized models predict the TOC from the bulk gamma-ray (GR) and spectral GR logs of uranium, thorium, and potassium only. The ML algorithms were trained on 749 datasets from Well-1, tested on 226 datasets from Well-2, and validated on 73 data points from Well-3. The predictability of the optimized algorithms was also compared with the available equations. The results of this study indicated that the optimized ANFIS, SVR, and RF models overperformed the available empirical equations in predicting the TOC. For validation data of Well-3, the optimized ANFIS, SVR, and RF algorithms predicted the TOC with AAPEs of 10.6%, 12.0%, and 8.9%, respectively, compared with the AAPE of 21.1% when the FNN model was used. While for the same data, the TOC was assessed with AAPEs of 48.6%, 24.6%, 20.2%, and 17.8% when Schmoker model, ΔlogR method, Zhao et al. correlation, and Mahmoud et al. correlation was used, respectively. The optimized models could be applied to estimate the TOC during the drilling process if the drillstring is provided with GR and spectral GR logging tools.

ACS Style

Ahmed Abdulhamid Mahmoud; Hany Gamal; Salaheldin Elkatatny; Ahmed Alsaihati. Estimating the Total Organic Carbon for Unconventional Shale Resources During the Drilling Process: A Machine Learning Approach. Journal of Energy Resources Technology 2021, 144, 1 .

AMA Style

Ahmed Abdulhamid Mahmoud, Hany Gamal, Salaheldin Elkatatny, Ahmed Alsaihati. Estimating the Total Organic Carbon for Unconventional Shale Resources During the Drilling Process: A Machine Learning Approach. Journal of Energy Resources Technology. 2021; 144 (4):1.

Chicago/Turabian Style

Ahmed Abdulhamid Mahmoud; Hany Gamal; Salaheldin Elkatatny; Ahmed Alsaihati. 2021. "Estimating the Total Organic Carbon for Unconventional Shale Resources During the Drilling Process: A Machine Learning Approach." Journal of Energy Resources Technology 144, no. 4: 1.

Research papers
Published: 16 July 2021 in Journal of Energy Resources Technology
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The sonic data provide significant rock properties that are commonly used for designing the operational programs for drilling, rock fracturing, and development operations. The conventional methods for acquiring the rock sonic data in terms of compressional and shear slowness (ΔTc and ΔTs) are considered costly and time-consuming operations. The target of this paper is to propose machine learning models for predicting the sonic logs from the drilling data in real-time. Decision tree (DT) and random forest (RF) were employed as train-based algorithms for building the sonic prediction models for drilling complex lithology rocks that have limestone, sandstone, shale, and carbonate formations. The input data for the models include the surface drilling parameters to predict the shear and compressional slowness. The study employed data set of 2888 data points for building and testing the model, while another collected 2863 data set was utilized for further validation of the sonic models. Sensitivity investigations were performed for DT and RF models to confirm optimal accuracy. The correlation of coefficient (R) and average absolute percentage error (AAPE) were used to check the models’ accuracy between the actual values and models’ outputs, in addition to the sonic log profiles. The results indicated that the developed sonic models have a high capability for the sonic prediction from the drilling data as the DT model recorded R higher than 0.967 and AAPE less than 2.76% for ΔTc and ΔTs models, while RF showed R higher than 0.991 with AAPE less than 1.07%. The further validation process for the developed models indicated the great results for the sonic prediction and the RF model outperformed DT models as RF showed R higher than 0.986 with AAPE less than 1.12% while DT prediction recorded R greater than 0.93 with AAPE less than 1.95%. The sonic prediction through the developed models will save the cost and time for acquiring the sonic data through the conventional methods and will provide real-time estimation from the drilling parameters.

ACS Style

Hany Gamal; Ahmed Alsaihati; Salaheldin Elkatatny. Predicting the Rock Sonic Logs While Drilling by Random Forest and Decision Tree-Based Algorithms. Journal of Energy Resources Technology 2021, 144, 1 .

AMA Style

Hany Gamal, Ahmed Alsaihati, Salaheldin Elkatatny. Predicting the Rock Sonic Logs While Drilling by Random Forest and Decision Tree-Based Algorithms. Journal of Energy Resources Technology. 2021; 144 (4):1.

Chicago/Turabian Style

Hany Gamal; Ahmed Alsaihati; Salaheldin Elkatatny. 2021. "Predicting the Rock Sonic Logs While Drilling by Random Forest and Decision Tree-Based Algorithms." Journal of Energy Resources Technology 144, no. 4: 1.

Research article petroleum engineering
Published: 29 June 2021 in Arabian Journal for Science and Engineering
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The rock porosity is considered a key petrophysical property for the rock due to its great impact on the hydrocarbon reserve estimation and petroleum economics. The conventional methods for determining the rock porosity either from the logging tools, lab measurements for the cored samples, or using empirical correlations from other parameters are costly, time-consuming, or did not provide the required level of accuracy. The new horizon for implementing machine learning techniques as a new approach for predicting the rock porosity overcomes all of the above drawbacks. Therefore, the objective of this research is to develop a new model based on an artificial neural network (ANN) for predicting rock porosity from only drilling parameters that include weight on bit, torque, standpipe pressure, drill string rotation speed, rate of penetration, and pump rate. The study used two data sets for building the model (3767 data points) and the second one for validating the developed ANN model (1676 data points). ANN model was built and optimized with deep sensitivity analysis for the ANN model parameters to achieve strong prediction results. ANN model showed a correlation coefficient (R) between the predicted and actual porosity values of 0.97 and 0.92 with average absolute percentage errors (AAPE) of 6.2 and 9.3% for training and testing, respectively. The model validation enhanced the high prediction performance as ANN achieved R of 0.95 and AAPE of 8.5%. The study provides new contributions as predicting the rock porosity for complex lithology formations (sandstone, shale, and carbonate), developing an ANN porosity model with a high level of accuracy, and a newly developed ANN-based equation for estimating the porosity from only the surface drilling data.

ACS Style

Hany Gamal; Salaheldin Elkatatny. Prediction Model Based on an Artificial Neural Network for Rock Porosity. Arabian Journal for Science and Engineering 2021, 1 -11.

AMA Style

Hany Gamal, Salaheldin Elkatatny. Prediction Model Based on an Artificial Neural Network for Rock Porosity. Arabian Journal for Science and Engineering. 2021; ():1-11.

Chicago/Turabian Style

Hany Gamal; Salaheldin Elkatatny. 2021. "Prediction Model Based on an Artificial Neural Network for Rock Porosity." Arabian Journal for Science and Engineering , no. : 1-11.

Research article electrical engineering
Published: 27 June 2021 in Arabian Journal for Science and Engineering
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Gamma-ray logging (GR) is one of the most crucial measurements to evaluate oil and gas reservoirs and identify the formation lithology. Logging while drilling (LWD) offers direct downhole measurements. LWD tools being placed a considerable distance above the drill bit which might result in a measurement of already penetrated formations. In this study, two artificial intelligence (AI) techniques, including support vector machine (SVM), and random forests (RF) were applied to predict a synthetic GR log using surface drilling parameters. A total of 4609 data entries from three wells in the Middle East were used to train, test, and validate the models. The data from wells 1 and 2 were used to build the AI models. Unseen data points from well 3 were then used to validate the model. The performance of the models was assessed in terms of average absolute percentage error (AAPE) and correlation coefficient (R). Results showed that both SVM and RF-produced models were able to predict the GR log with high accuracies. SVM slightly outperforms RF in prediction GR logs with R of 0.99 and AAPE of 0.34% in the training set, and with R of 0.98 and AAPE of 1.49% in the testing set. For the validation, SVM predicted GR log with R and AAPE of 0.98, and 1.42%. The presented models assist drilling engineers to real-time predict GR log and identify the formation lithology while the bit drilling the same formation.

ACS Style

Ahmed Farid Ibrahim; Salaheldin Elkatatny. Real-Time GR logs Estimation While Drilling Using Surface Drilling Data; AI Application. Arabian Journal for Science and Engineering 2021, 1 -10.

AMA Style

Ahmed Farid Ibrahim, Salaheldin Elkatatny. Real-Time GR logs Estimation While Drilling Using Surface Drilling Data; AI Application. Arabian Journal for Science and Engineering. 2021; ():1-10.

Chicago/Turabian Style

Ahmed Farid Ibrahim; Salaheldin Elkatatny. 2021. "Real-Time GR logs Estimation While Drilling Using Surface Drilling Data; AI Application." Arabian Journal for Science and Engineering , no. : 1-10.

Journal article
Published: 22 June 2021 in Journal of Energy Resources Technology
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Fluid loss into formations is a common operational issue that is frequently encountered when drilling across naturally or induced fractured formations. This could pose significant operational risks, such as well-control, stuck pipe, and wellbore instability, which, in turn, lead to an increase of well time and cost. This research aims to use and evaluate different machine learning techniques, namely: support vector machines, random forests, and K-nearest neighbors in detecting loss circulation occurrences while drilling using solely drilling surface parameters. Actual field data of seven wells, which had suffered partial or severe loss circulation, were used to build predictive models, while Well-8 was used to compare the performance of the developed models. Different performance metrics were used to evaluate the performance of the developed models. Recall, precision, and F1-score measures were used to evaluate the ability of the developed model to detect loss circulation occurrences. The results showed the K-nearest neighbors classifier achieved a high F1-score of 0.912 in detecting loss circulation occurrence in the testing set, while the random forests was the second-best classifier with almost the same F1-score of 0.910. The support vector machines achieved an F1-score of 0.83 in predicting the loss circulation occurrence in the testing set. The K-nearest neighbors outperformed other models in detecting the loss circulation occurrences in Well-8 with an F1-score of 0.80. The main contribution of this research as compared to previous studies is that it identifies losses events based on real-time measurements of the active pit volume.

ACS Style

Ahmed Alsaihati; Mahmoud Abughaban; Salaheldin Elkatatny; Abdulazeez Abdulraheem. Detection of Loss Zones while Drilling Using Different Machine Learning Techniques. Journal of Energy Resources Technology 2021, 1 -29.

AMA Style

Ahmed Alsaihati, Mahmoud Abughaban, Salaheldin Elkatatny, Abdulazeez Abdulraheem. Detection of Loss Zones while Drilling Using Different Machine Learning Techniques. Journal of Energy Resources Technology. 2021; ():1-29.

Chicago/Turabian Style

Ahmed Alsaihati; Mahmoud Abughaban; Salaheldin Elkatatny; Abdulazeez Abdulraheem. 2021. "Detection of Loss Zones while Drilling Using Different Machine Learning Techniques." Journal of Energy Resources Technology , no. : 1-29.

Journal article
Published: 15 June 2021 in Scientific Reports
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Rock elastic properties such as Poisson’s ratio influence wellbore stability, in-situ stresses estimation, drilling performance, and hydraulic fracturing design. Conventionally, Poisson’s ratio estimation requires either laboratory experiments or derived from sonic logs, the main concerns of these methods are the data and samples availability, costs, and time-consumption. In this paper, an alternative real-time technique utilizing drilling parameters and machine learning was presented. The main added value of this approach is that the drilling parameters are more likely to be available and could be collected in real-time during drilling operation without additional cost. These parameters include weight on bit, penetration rate, pump rate, standpipe pressure, and torque. Two machine learning algorithms were used, artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). To train and test the models, 2905 data points from one well were used, while 2912 data points from a different well were used for model validation. The lithology of both wells contains carbonate, sandstone, and shale. Optimization on different tuning parameters in the algorithm was conducted to ensure the best prediction was achieved. A good match between the actual and predicted Poisson’s ratio was achieved in both methods with correlation coefficients between 0.98 and 0.99 using ANN and between 0.97 and 0.98 using ANFIS. The average absolute percentage error values were between 1 and 2% in ANN predictions and around 2% when ANFIS was used. Based on these results, the employment of drilling data and machine learning is a strong tool for real-time prediction of geomechanical properties without additional cost.

ACS Style

Osama Siddig; Hany Gamal; Salaheldin Elkatatny; Abdulazeez Abdulraheem. Real-time prediction of Poisson’s ratio from drilling parameters using machine learning tools. Scientific Reports 2021, 11, 1 -13.

AMA Style

Osama Siddig, Hany Gamal, Salaheldin Elkatatny, Abdulazeez Abdulraheem. Real-time prediction of Poisson’s ratio from drilling parameters using machine learning tools. Scientific Reports. 2021; 11 (1):1-13.

Chicago/Turabian Style

Osama Siddig; Hany Gamal; Salaheldin Elkatatny; Abdulazeez Abdulraheem. 2021. "Real-time prediction of Poisson’s ratio from drilling parameters using machine learning tools." Scientific Reports 11, no. 1: 1-13.

Journal article
Published: 14 June 2021 in Journal of Energy Resources Technology
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Allocated well production rates are crucial to evaluate the well performance. Test separators and flowmeters were replaced with choke formulas due to economic and technical issues special for high gas–oil ratio (GOR) reservoirs. This study implements Adaptive network-based fuzzy logic (ANFIS), and functional networks (FN) techniques to predict the oil rate through wellhead chokes. A set of data containing 1200 wells were obtained from actual oil fields in the Middle East. The data set included GOR, upstream and downstream pressure, choke size, and actual oil and gas rates based on the well test. GOR varied from 1000 to 9265 scf/stb, while oil rates ranged between 1156 and 7982 stb/d. Around 650 wells were flowing under critical flow conditions, while the rest were subcritical. Seventy percent of the data were used to train the artificial intelligence (AI) models, while thirty percent of the data were used to test and validate these models. The developed AI models were then compared against the previous formulas. For subcritical flow conditions, rate prediction was correlated to both upstream and downstream pressures, while at critical flow conditions, changes in the downstream pressure did not affect the prediction of the production rates. For each AI method, two models were developed for subcritical flow and critical flow conditions. The average absolute percent error (AAPE) in the case of subcritical flow for ANFIS and FN were 0.88, and 1.01%, respectively. While in the case of critical flow, the AAPE values were 1.07, and 1.3% for ANFIS and FN models, respectively. All developed AI models outperform the published formulas, where the AAPE values for published formulas were higher than 34%. The results from this study will greatly assist petroleum engineers to predict the oil and gas rates based on available data from wellhead chokes in real-time with no need for additional operational costs or field intervention.

ACS Style

Redha Al Dhaif; Ahmed Farid Ibrahim; Salaheldin Elkatatny. Prediction of Surface Oil Rates for Volatile Oil and Gas Condensate Reservoirs Using Artificial Intelligence Techniques. Journal of Energy Resources Technology 2021, 144, 1 -14.

AMA Style

Redha Al Dhaif, Ahmed Farid Ibrahim, Salaheldin Elkatatny. Prediction of Surface Oil Rates for Volatile Oil and Gas Condensate Reservoirs Using Artificial Intelligence Techniques. Journal of Energy Resources Technology. 2021; 144 (3):1-14.

Chicago/Turabian Style

Redha Al Dhaif; Ahmed Farid Ibrahim; Salaheldin Elkatatny. 2021. "Prediction of Surface Oil Rates for Volatile Oil and Gas Condensate Reservoirs Using Artificial Intelligence Techniques." Journal of Energy Resources Technology 144, no. 3: 1-14.

Research article
Published: 14 June 2021 in Computational Intelligence and Neuroscience
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Rock porosity is an important parameter for the formation evaluation, reservoir modeling, and petroleum reserve estimation. The conventional methods for determining the rock porosity are considered costly and time-consuming operations during the well drilling. This paper aims to predict the rock porosity in real time while drilling complex lithology using machine learning. In this paper, two intelligent models were developed utilizing the random forest (RF) and decision tree (DT) techniques. The drilling parameters include weight on bit, torque, standpipe pressure, drill string rotation speed, rate of penetration, and pump rate. Two datasets were employed for building the models (3767 data points) and for validating the developed models (1676 data points). Both collected datasets have complex lithology of carbonate, sandstone, and shale. Sensitivity and optimization on different parameters for each technique were conducted to ensure optimum prediction. The models’ performance was checked by four performance indices which are coefficient of determination (R2), average absolute percentage error (AAPE), variance account for (VAF), and a20 index. The results indicated the strong porosity prediction capability for the two models. DT model showed R2 of 0.94 and 0.87 between the predicted and actual porosity values with AAPE of 6.07 and 9% for training and testing, respectively. Generally, RF provided a higher level of strong prediction than DT as RF achieved R2 of 0.99 and 0.90 with AAPE of 1.5 and 7% for training and testing, respectively. The models’ validation proved a high prediction performance as DT achieved R2 of 0.88 and AAPE of 8.58%, while RF has R2 of 0.92 and AAPE of 6.5%.

ACS Style

Hany Gamal; Salaheldin Elkatatny; Ahmed Alsaihati; Abdulazeez Abdulraheem. Intelligent Prediction for Rock Porosity While Drilling Complex Lithology in Real Time. Computational Intelligence and Neuroscience 2021, 2021, 1 -12.

AMA Style

Hany Gamal, Salaheldin Elkatatny, Ahmed Alsaihati, Abdulazeez Abdulraheem. Intelligent Prediction for Rock Porosity While Drilling Complex Lithology in Real Time. Computational Intelligence and Neuroscience. 2021; 2021 ():1-12.

Chicago/Turabian Style

Hany Gamal; Salaheldin Elkatatny; Ahmed Alsaihati; Abdulazeez Abdulraheem. 2021. "Intelligent Prediction for Rock Porosity While Drilling Complex Lithology in Real Time." Computational Intelligence and Neuroscience 2021, no. : 1-12.

Research article
Published: 12 June 2021 in Geofluids
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Lightweight cement systems are used in the weak intervals of petroleum wells. Sodium bentonite is used as an extender in lightweight oil-well cement systems as it prevents excess water and sedimentation of particles, thereby ensuring the formation of homogenous and stable cement sheaths. The extending ability of sodium bentonite is enhanced when prehydrated. However, the optimum bentonite prehydration time and its effect on the stability of lightweight cement systems have not been well established. The objective of this study is to investigate the optimum sodium bentonite prehydration time and correlate it to the stability of lightweight oil-well cement systems. Bentonite suspensions were prepared by vigorous preshearing at 12000 rpm for 5 minutes, followed by aging times of 0, 30, 60, and 120 minutes. The swelling behavior of bentonite was investigated using a laser particle size analyzer. The Herschel-Bulkley model was used to determine the rheological parameters of the experimentally measured shear stress vs. shear rate data of the aged suspensions. The effect of calcium chloride salt on aged bentonite suspensions was investigated. Density measurements and pore space analysis with the nuclear magnetic resonance (NMR) technique were used to investigate the homogeneity of cement-based cores. It was observed that bentonite swells with time and, after 30 minutes, the swelling is insignificant; however, the swelling property did not have any observed impact on the properties of cement systems designed with the bentonite aged at different times. In general, all the lightweight cement slurries exhibited similar properties, in terms of rheology, stability, and homogeneity, regardless of the bentonite prehydration time. These findings indicate that aging bentonite suspension after vigorous preshearing in lightweight cement design is unnecessary and would only contribute to nonproductive time.

ACS Style

Stephen Adjei; Salaheldin Elkatatny; Abdulaziz Al-Majed. Effect of Bentonite Prehydration Time on the Stability of Lightweight Oil-Well Cement System. Geofluids 2021, 2021, 1 -8.

AMA Style

Stephen Adjei, Salaheldin Elkatatny, Abdulaziz Al-Majed. Effect of Bentonite Prehydration Time on the Stability of Lightweight Oil-Well Cement System. Geofluids. 2021; 2021 ():1-8.

Chicago/Turabian Style

Stephen Adjei; Salaheldin Elkatatny; Abdulaziz Al-Majed. 2021. "Effect of Bentonite Prehydration Time on the Stability of Lightweight Oil-Well Cement System." Geofluids 2021, no. : 1-8.

Journal article
Published: 11 June 2021 in ACS Omega
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Lab measurements for the rheological properties of mud are critical for monitoring the drilling fluid functions during the drilling operations. However, these measurements take a long time and might need more than one person to be completed. The main objectives of this research are to implement artificial intelligence for predicting the mud rheology from only Marsh funnel (μf) and measuring mud density (ρm) easily and quickly on the rig site. For the first time, an artificial neural network (ANN) was used to build different models for predicting the rheological properties of Max-bridge oil-based mud. The properties included the plastic viscosity (μp), yield point (γ), flow behavior index (η), and apparent viscosity (μa). Field measurements of 383 samples were used to build and optimize the ANN models. The obtained results showed that 32 neurons in the hidden layer and tan sigmoid function transfer function were the best parameters for all ANN models. The training and testing processes of models showed a strong prediction performance with a correlation coefficient (R) greater than 0.91 and an average absolute percentage error (AAPE) less than 5.31%. New empirical correlations were developed based on the optimized weights and biases of the ANN models. The developed empirical correlations were compared with the published correlations, and the comparison results confirmed that the ANN-developed correlations outperformed all previous work.

ACS Style

Ahmed Alsabaa; Salaheldin Elkatatny. Improved Tracking of the Rheological Properties of Max-Bridge Oil-Based Mud Using Artificial Neural Networks. ACS Omega 2021, 6, 15816 -15826.

AMA Style

Ahmed Alsabaa, Salaheldin Elkatatny. Improved Tracking of the Rheological Properties of Max-Bridge Oil-Based Mud Using Artificial Neural Networks. ACS Omega. 2021; 6 (24):15816-15826.

Chicago/Turabian Style

Ahmed Alsabaa; Salaheldin Elkatatny. 2021. "Improved Tracking of the Rheological Properties of Max-Bridge Oil-Based Mud Using Artificial Neural Networks." ACS Omega 6, no. 24: 15816-15826.

Journal article
Published: 10 June 2021 in ACS Omega
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Weighting agents such as barite, micromax, ilmenite, and hematite are commonly added to drilling fluids to produce high-density fluids that could be used to drill deep oil and gas wells. Increasing the drilling fluid density leads to highly conspicuous fluctuation in the drilling fluid characteristics. In this study, the variation in the drilling fluid’s rheological and filtration properties induced by adding different weighting agents was evaluated. For this purpose, several water-based drilling fluid samples were prepared and weighted up using the same concentration of various weighting materials including barite, micromax, ilmenite, and hematite. The characteristics of the used weighting agents’ (particle size distribution and mineralogy) were measured. Subsequently, the rheological properties of the drilling fluid were obtained using a Fann viscometer at 80 °F. The filtration test was carried out at 200 °F and 300 psi differential pressure to form a filter cake over the sandstone core samples. The properties of the formed filter cake layer such as thickness, porosity, and permeability were determined. Furthermore, the typical properties of core samples including porosity and permeability were assessed before and after the filtration test. The displayed results confirmed that the plastic viscosity (PV), yield point (YP), and filter cake sealing properties were all significantly influenced by the ratio of the large to fine particle size (D90/D10) of the weighting agents irrespective of the weighting material type. Among the examined weighting agents, barite showed novel potency to control both rheological and filter cake properties for 14 ppg drilling fluid. The results showed that D90/D10 is a key factor for the PV and YP properties as increasing the D90/D10 ratio caused PV increase and YP decrease, which indicated that the interaction among the loaded weighting materials in the drilling fluid dominated its viscosity.

ACS Style

Badr S. Bageri; Hany Gamal; Salaheldin Elkatatny; Shirish Patil. Effect of Different Weighting Agents on Drilling Fluids and Filter Cake Properties in Sandstone Formations. ACS Omega 2021, 6, 16176 -16186.

AMA Style

Badr S. Bageri, Hany Gamal, Salaheldin Elkatatny, Shirish Patil. Effect of Different Weighting Agents on Drilling Fluids and Filter Cake Properties in Sandstone Formations. ACS Omega. 2021; 6 (24):16176-16186.

Chicago/Turabian Style

Badr S. Bageri; Hany Gamal; Salaheldin Elkatatny; Shirish Patil. 2021. "Effect of Different Weighting Agents on Drilling Fluids and Filter Cake Properties in Sandstone Formations." ACS Omega 6, no. 24: 16176-16186.

Review
Published: 02 June 2021 in Journal of Petroleum Science and Engineering
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Challenges associated with drilling operations are numerous and their adverse effect could lead to severe damage or even shutting down of the drilling operations. Wellbore instability among others is but the most encountered problem by drilling engineers when water-based drilling fluids (WBDF) are applied. This is because shale formations are full of active clay minerals which can lead to hydration and swelling once encountered by the WBDFs thus destabilizing the integrity of the wellbore. To combat this, most petroleum engineers have employed different inhibitors to reduce or stop the menace posed by these active clay minerals. The purpose of this study is to highlight in detail the essential types of clays mostly encountered in drilling operations, the swelling mechanism of the active clay minerals, comprehensive characterization techniques, and the differences associated with the applied inhibitors reported in the literature for tackling clay hydration and swelling. Based on the review, we recommend extensive evaluation between smectite and vermiculite clay minerals during analysis to determine the effectiveness of some of the characterization techniques adopted for the inhibition studies. Also, a methodology blueprint for specific clay mineral types is highly recommended for future researchers to help reduce the uncertainty associated with the reported outcomes of these active clay minerals. At least five characterization techniques such as linear swelling test (LST), scanning electron microscopy (SEM), x-ray diffraction (XRD), bulk hardness test (BHT), and wettability alteration test (WAT) to be conducted for any experimental procedure to be able to understand in detail the inhibitive mechanism of the applied inhibitors. Environmentally friendly inhibitors that are cheap, pollution-free with excellent stability in harsh environments are highly encouraged since they can be sustained and have good inhibition potential.

ACS Style

Nasiru Salahu Muhammed; Teslim Olayiwola; Salaheldin Elkatatny. A review on clay chemistry, characterization and shale inhibitors for water-based drilling fluids. Journal of Petroleum Science and Engineering 2021, 206, 109043 .

AMA Style

Nasiru Salahu Muhammed, Teslim Olayiwola, Salaheldin Elkatatny. A review on clay chemistry, characterization and shale inhibitors for water-based drilling fluids. Journal of Petroleum Science and Engineering. 2021; 206 ():109043.

Chicago/Turabian Style

Nasiru Salahu Muhammed; Teslim Olayiwola; Salaheldin Elkatatny. 2021. "A review on clay chemistry, characterization and shale inhibitors for water-based drilling fluids." Journal of Petroleum Science and Engineering 206, no. : 109043.

Journal article
Published: 31 May 2021 in Journal of Energy Resources Technology
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Drilling fluid is considered the backbone of drilling operations in the oil and gas industry to unlock hydrocarbon from subterranean formations. Maintaining the drilling fluid properties, for example, flow properties such as rheology, plastic viscosity (PV), yield point (YP), gel strength (GS), and circulation loss, is the challenge for fluid/mud engineers to carry out successful drilling operations. A variety of chemicals have been added to improve the drilling fluid properties by introducing new chemicals or optimizing the existing chemicals without affecting the other essential fluid properties. The present study for the first time employs the eco-innovation concept to explore the utilization of steelmaking industry waste, i.e., silicomanganese fume (SMF), as a bridging material. The objective of this article is to design an eco-friendly framework that comprehensively explains and utilizes SMF as a bridging material in water-based fluid (WBF). The eco-innovation/eco-friendly framework includes the steps required for processing and understanding the new material and evaluating its effects on flow and the bridging properties of WBF. A scanning electron microscope (SEM), X-ray fluorescence (XRF), and particle size distribution (PSD) were used to understand the physicochemical properties of SMF. The flow properties were studied using a Fann rheometer before and after hot rolling at 120 °F. A high-pressure high-temperature (HPHT) filter press equipment was used to investigate the bridging capability of seepage losses following conditions of 190 °F and 300 psi differential pressure. Minimal cleaning and disintegration with a mortar and pestle are enough to prepare SMF to be incorporated in drilling fluid. The SEM and XRF results showed that SMF contains oxides of manganese, silicon, potassium, calcium, and magnesium, while the PSD revealed a natural bimodal distribution with an average grain size of D50 of around 29 μm. SMF showed a noticeable and measurable enhancement of flow properties and bridging capability in WBF. The SMF-based WBF showed improved rheological properties, plastic viscosity, and yield point compared with marble-based WBF. Adding SMF to WBF with and without marble showed a ten-fold superior plugging performance compared with marble-based WBF using 20-μm ceramic discs. The findings revealed the successful utilization of SMF in WBF by improving the rheology, plastic viscosity, yield point, and bridging capability.

ACS Style

Waseem Razzaq; Salaheldin Elkatatny; Hany Gamal; Ariffin Samsuri. The Utilization of Steelmaking Industrial Waste of Silicomanganese Fume as Filtration Loss Control in Drilling Fluid Application. Journal of Energy Resources Technology 2021, 144, 1 .

AMA Style

Waseem Razzaq, Salaheldin Elkatatny, Hany Gamal, Ariffin Samsuri. The Utilization of Steelmaking Industrial Waste of Silicomanganese Fume as Filtration Loss Control in Drilling Fluid Application. Journal of Energy Resources Technology. 2021; 144 (2):1.

Chicago/Turabian Style

Waseem Razzaq; Salaheldin Elkatatny; Hany Gamal; Ariffin Samsuri. 2021. "The Utilization of Steelmaking Industrial Waste of Silicomanganese Fume as Filtration Loss Control in Drilling Fluid Application." Journal of Energy Resources Technology 144, no. 2: 1.

Research article
Published: 19 May 2021 in ACS Omega
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Real-time prediction of the formation pressure gradient is critical mainly for drilling operations. It can enhance the quality of decisions taken and the economics of drilling operations. The pressure while drilling tool can be used to provide pressure data while drilling, but the tool cost and its availability limit its usage in many wells. The available models in the literature for pressure gradient prediction are based on well logging or a combination of some drilling parameters and well logging. The well-logging data are not available for all wells in all sections in most wells. The objective of this paper is to use support vector machines, functional networks, and random forest (RF) to develop three models for real-time pore pressure gradient prediction using both mechanical and hydraulic drilling parameters. The used parameters are mud flow rate (Q), standpipe pressure, rate of penetration, and rotary speed (RS). A data set of 3239 field data points was used to develop the predictive models. A different data set unseen by the model was utilized for the validation of the proposed models. The three models predicted the pore pressure gradient with a correlation coefficient (R) of 0.99 and 0.97 for training and testing, respectively. The root-mean-squared error (RMSE) ranged from 0.008 to 0.021 psi/ft for training and testing, respectively, between the predicted and the actual pore pressure data. Moreover, the average absolute percentage error (AAPE) ranged from 0.97% to 3.07% for training and testing, respectively. The RF model outperformed the other models by an R of 0.99 and RMSE of 0.01. The developed models were validated using another data set. The models predicted the pore pressure gradient for the validation data set with high accuracy (R of 0.99, RMSE around 0.01, and AAPE around 1.8%). This work shows the reliability of the developed models to predict the pressure gradient from both mechanical and hydraulic drilling parameters while drilling.

ACS Style

Ahmed Abdelaal; Salaheldin Elkatatny; Abdulazeez Abdulraheem. Data-Driven Modeling Approach for Pore Pressure Gradient Prediction while Drilling from Drilling Parameters. ACS Omega 2021, 6, 13807 -13816.

AMA Style

Ahmed Abdelaal, Salaheldin Elkatatny, Abdulazeez Abdulraheem. Data-Driven Modeling Approach for Pore Pressure Gradient Prediction while Drilling from Drilling Parameters. ACS Omega. 2021; 6 (21):13807-13816.

Chicago/Turabian Style

Ahmed Abdelaal; Salaheldin Elkatatny; Abdulazeez Abdulraheem. 2021. "Data-Driven Modeling Approach for Pore Pressure Gradient Prediction while Drilling from Drilling Parameters." ACS Omega 6, no. 21: 13807-13816.

Journal article
Published: 13 May 2021 in Journal of Energy Resources Technology
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Young's modulus is a principle geomechanical property that reflects the material stiffness. Good knowledge about rock mechanical properties significantly facilitates fracturing design and in situ stresses estimation. Conventionally, rock elastic properties are estimated either experimentally or using well log data, known as static and dynamic, respectively. Conducting experiments on core samples is costly, time consuming, and does not provide continuous information. While dynamic Young's modulus provides a complete profile, however, it needs the availability of acoustic logs, and its estimations differ from the static values. The objective of this article is to create a continuous profile of Young's modulus using the drilling rig sensors records. The presented approach relies on the fact that the drilling data such as drill pipe torque, weight on bit, and rate of penetration are available at an early stage without additional cost. Three machine learning algorithms were used to correlate the drilling data with Young's modulus: random forest, adaptive neuro-fuzzy inference system, and functional network. Two different datasets were used in this study, one construct and test the model, while the other was hidden from the algorithms and used later to validate the built models. The two datasets contain over 3900 data points and cover different types of rocks. Two out of the three methods utilized yielded a remarkable match between the given and the predicted values. The correlation coefficients ranged between 0.92 and 0.99 average absolute percentage errors were less than 13%. Supported by these results, the utilization of drilling data and artificial intelligence techniques to predict the elastic moduli is promising. This approach could be investigated for other geomechanical properties, besides the performance of other machine learning methods for the same purpose.

ACS Style

Osama Mutrif Siddig; Saad Fahaid Al-Afnan; Salaheldin Mahmoud Elkatatny; Abdulazeez Abdulraheem. Drilling Data-Based Approach to Build a Continuous Static Elastic Moduli Profile Utilizing Artificial Intelligence Techniques. Journal of Energy Resources Technology 2021, 144, 1 .

AMA Style

Osama Mutrif Siddig, Saad Fahaid Al-Afnan, Salaheldin Mahmoud Elkatatny, Abdulazeez Abdulraheem. Drilling Data-Based Approach to Build a Continuous Static Elastic Moduli Profile Utilizing Artificial Intelligence Techniques. Journal of Energy Resources Technology. 2021; 144 (2):1.

Chicago/Turabian Style

Osama Mutrif Siddig; Saad Fahaid Al-Afnan; Salaheldin Mahmoud Elkatatny; Abdulazeez Abdulraheem. 2021. "Drilling Data-Based Approach to Build a Continuous Static Elastic Moduli Profile Utilizing Artificial Intelligence Techniques." Journal of Energy Resources Technology 144, no. 2: 1.

Review
Published: 13 May 2021 in Journal of Natural Gas Science and Engineering
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Water-based drilling fluids (WBDFs), a more environment-friendly candidate than oil-based are widely used for drilling oil and gas wells. However, wellbore instability due to active clay minerals in the shales is considered a major problem faced by the WBDFs due to swelling and hydration. Several types of shale inhibitors have been proposed by researchers to help combat the menace posed by these active clay minerals during drilling operations. The purpose of this study is to investigate the application of surfactants and nanomaterials as additives to WBDFs and anti-swelling agents for drilling operations. An overview of the experimental procedures and conclusions conducted from studies to evaluate the performance of these materials ranging from types, characterization techniques, limitations, inhibition mechanisms are presented. A summary of key findings has also been reported as well as the challenges and prospects. Based on the review, we found hydrogen bond, clay surface coating, and ionic bond to be the major inhibition mechanisms. We recommend potential studies to investigate the application of vermiculate clay minerals to effectively understand the behavior of this material as shale inhibitors as compared to the commonly used montmorillonite (MMT) in the industry. Similarly, the response time of clay-bearing formation when exposed to WBDFs was suggested for future studies. Other biological extracts containing saponin molecules with fewer environmental footprints are highly recommended as shale inhibitors. Lastly, a comparative cost analysis should be encouraged for future studies interested in proposing new shale inhibitors.

ACS Style

Nasiru Salahu Muhammed; Teslim Olayiwola; Salaheldin Elkatatny; Bashirul Haq; Shirish Patil. Insights into the application of surfactants and nanomaterials as shale inhibitors for water-based drilling fluid: A review. Journal of Natural Gas Science and Engineering 2021, 92, 103987 .

AMA Style

Nasiru Salahu Muhammed, Teslim Olayiwola, Salaheldin Elkatatny, Bashirul Haq, Shirish Patil. Insights into the application of surfactants and nanomaterials as shale inhibitors for water-based drilling fluid: A review. Journal of Natural Gas Science and Engineering. 2021; 92 ():103987.

Chicago/Turabian Style

Nasiru Salahu Muhammed; Teslim Olayiwola; Salaheldin Elkatatny; Bashirul Haq; Shirish Patil. 2021. "Insights into the application of surfactants and nanomaterials as shale inhibitors for water-based drilling fluid: A review." Journal of Natural Gas Science and Engineering 92, no. : 103987.