Hany Gamal

Bio: Hany Gamal is an academic researcher from King Fahd University of Petroleum and Minerals. The author has contributed to research in topics: Drilling fluid & Drilling. The author has an hindex of 7, co-authored 36 publications receiving 164 citations.

Effect of pH on Rheological and Filtration Properties of Water-Based Drilling Fluid Based on Bentonite

Abstract: The design of drilling fluids is very important for the drilling operation success. The rheological properties play a key role in the performance of the drilling fluid. Therefore, studying the mud rheological properties of the water-based drilling fluid based on bentonite is essential. The main objectives of this study are to address the effect of pH changes on the rheological and filtration properties of the water-based drilling fluid based on bentonite and to provide a recommended pH range for this drilling fluid for a safe and high-performance drilling operation. Different samples of the water-based drilling fluid based on bentonite with different pH values were prepared, and the rheological properties such as plastic viscosity, yield point, and gel strength were measured. After that, the filtration test was performed under 300 psi differential pressure and 200 °F. The pH for the water-based drilling fluid based on bentonite significantly affects the mud rheology. The shear stress and shear rate relation were varying with the change in the pH. Increasing the pH from 8 to 12 resulted in decreasing the plastic viscosity by 53% and the yield point by 82%, respectively. The ratio of yield point / plastic viscosity was 1.4 for pH of 8 while it decreased to 0.5 for a pH of 11 and 12. There was a significant decrease in the gel strength readings by increasing the pH. The filtrate volume and filter cake thickness increased by increasing pH. The filtration volume increased from 9.5 cm3 to 12.6 cm3 by increasing the pH from 9 to 12. The filter cake thickness was 2 mm at 9 pH, while it was increased to 3.6 mm for 12 pH. It is recommended from the results to keep the pH of water-based drilling fluid based on bentonite in the range of 9 to 10 as it provides the optimum mud rheological and filtration properties. The findings of this study illustrated that keeping the pH in the range of 9 to 10 will reduce the plastic viscosity that will help in increasing the rate of penetration and reducing the required pump pressure to circulate the mud to the surface which will help to sustain the drilling operation. In addition, reducing the filtrate volume will produce a thin filter cake which will help in avoiding the pipe sticking and protect the environment. In general, optimizing the pH of the water-based drilling fluid based on bentonite in the range of 9 to 10 will improve the drilling operation and minimize the total cost.

Real-Time Prediction of Rheological Properties of Invert Emulsion Mud Using Adaptive Neuro-Fuzzy Inference System.

Abstract: Tracking the rheological properties of the drilling fluid is a key factor for the success of the drilling operation. The main objective of this paper is to relate the most frequent mud measurements (every 15 to 20 min) as mud weight (MWT) and Marsh funnel viscosity (MFV) to the less frequent mud rheological measurements (twice a day) as plastic viscosity (PV), yield point (YP), behavior index (n), and apparent viscosity (AV) for fully automating the process of retrieving rheological properties. The adaptive neuro-fuzzy inference system (ANFIS) was used to develop new models to determine the mud rheological properties using real field measurements of 741 data points. The data were collected from 99 different wells during drilling operations of 12 ¼ inches section. The ANFIS clustering technique was optimized by using training to a testing ratio of 80% to 20% as 591 data points for training and 150 points, cluster radius value of 0.1, and 200 epochs. The results of the prediction models showed a correlation coefficient (R) that exceeded 0.9 between the actual and predicted values with an average absolute percentage error (AAPE) below 5.7% for the training and testing data sets. ANFIS models will help to track in real-time the rheological properties for invert emulsion mud that allows better control for the drilling operation problems.

Unconfined compressive strength (UCS) prediction in real-time while drilling using artificial intelligence tools

Abstract: Unconfined compressive strength (UCS) is a major mechanical parameter of the rock which has an essential role in developing geomechanical models. It can be estimated directly by lab testing of retrieved core samples or from well log data. These methods are very expensive and require huge efforts and time. Therefore, there is a need to develop a new technique for predicting UCS values in real-time. In this study, three artificial intelligence (AI) models were developed using artificial intelligence tools; artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS), and support vector machine (SVM) to predict UCS of the downhole formations while drilling based on real-time recording of the drilling mechanical parameters. These parameters include rate of penetration (ROP), mud pumping rate (GPM), stand-pipe pressure (SPP), rotary speed in revolution per minute (RPM), torque (T), and weight on bit (WOB). A dataset of 1771 points from a Middle Eastern field was used to build the developed models: for training and testing processes. A new UCS correlation was developed based on the optimized AI model. Another set of data (2175 data points unseen by the model) was used to validate the model and the developed UCS correlation. The developed ANN-model outperformed the ANFIS- and SVM-models with a correlation coefficient (R-value) of 0.99 and an average absolute percentage error (AAPE) of 3.48% between the predicted and actual UCS values. The new UCS correlation outperformed the available correlations for UCS prediction and it was able to predict the UCS with AAPE of 4.2% compared to the actual UCS values.

Rock Strength Prediction in Real-Time While Drilling Employing Random Forest and Functional Network Techniques

Abstract: The rock unconfined compressive strength (UCS) is one of the key parameters for geomechanical and reservoir modeling in the petroleum industry. Obtaining the UCS by conventional methods such as experimental work or empirical correlation from logging data are time consuming and highly cost. To overcome these drawbacks, this paper utilized the help of artificial intelligence (AI) to predict (in a real-time) the rock strength from the drilling parameters using two AI tools. Random forest (RF) based on principal component analysis (PCA), and functional network (FN) techniques were employed to build two UCS prediction models based on the drilling data such as weight on bit (WOB), drill string rotating speed (RS), drilling torque (T), stand-pipe pressure (SPP), mud pumping rate (Q), and the rate of penetration (ROP). The models were built using 2333 data points from well (A) with 70:30 training to testing ratio. The models were validated using unseen dataset (1300 data points) of well (B) which is located in the same field and drilled across the same complex lithology. The results of the PCA-based RF model outperformed the FN in terms of correlation coefficient (R) and average absolute percentage error (AAPE). The overall accuracy for PCA-based RF was R of 0.99 and AAPE of 4.3%, and for FN yielded R of 0.97 and AAPE of 8.5%. The validation results showed that R was 0.99 for RF and 0.96 for FN, while the AAPE was 4% and 7.9% for RF and FN models, respectively. The developed PCA-based RF and FN models provide an accurate UCS estimation in real-time from the drilling data, saving time and cost, and enhancing the well stability by generating UCS log from the rig drilling data.

Application of artificial neural network to predict the rate of penetration for S-shape well profile

Abstract: The rate of penetration (ROP) is defined as the required speed to break the drilled rock by the bit action. The existing established models for estimating the rate of penetration include the basic mathematical correlation that have many limitations. The objective of this paper is to implement an artificial neural network (ANN) technique to predict the rate of penetration for the S-shape well profile from the surface drilling data. The data used to build the ANN model is based on real field data of more than 7900 data points obtained from two wells. The data from well A and B was used to train and test an ANN model, while 4000 unseen data points from well C were used for validation. More than 30 sensitivity analyses were performed and the results showed that ANN-ROP model has a high performance with an average correlation coefficient of around 0.93 and a root mean square error (RMSE) of 6.2%. The best ANN parameter combination was with 1 layer, 29 neurons, tan-sigmoid as the transfer function, and trainlm as the training function. The model was then validated by the data from well C which was unseen by the model during the training and testing stage with a correlation coefficient of 0.92 and an RMSE of 6.7%. To enable ROP prediction in real time, an empirical correlation was developed based on the optimized ANN model weights and biases.

Fundamentals of drilling engineering

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Environmental Friendliness and High Performance of Multifunctional Tween 80/ZnO-Nanoparticles-Added Water-Based Drilling Fluid: An Experimental Approach

Abstract: The energy industry is exploring sustainable chemistry and engineering solutions for exploitation of shale reservoirs. Smectite-rich shale is challenging to drill with traditional water-based drill...

Natural Clay-Based Materials for Energy Storage and Conversion Applications.

Abstract: Among various energy storage and conversion materials, functionalized natural clays display significant potentials as electrodes, electrolytes, separators, and nanofillers in energy storage and conversion devices. Natural clays have porous structures, tunable specific surface areas, remarkable thermal and mechanical stabilities, abundant reserves, and cost-effectiveness. In addition, natural clays deliver the advantages of high ionic conductivity and hydrophilicity, which are beneficial properties for solid-state electrolytes. This review article provides an overview toward the recent advancements in natural clay-based energy materials. First, it comprehensively summarizes the structure, classification, and chemical modification methods of natural clays to make them suitable in energy storage and conversion devices. Then, the particular attention is focused on the application of clays in the fields of lithium-ion batteries, lithium-sulfur batteries, zinc-ion batteries, chloride-ion batteries, supercapacitors, solar cells, and fuel cells. Finally, the possible future research directions are provided for natural clays as energy materials. This review aims at facilitating the rapid developments of natural clay-based energy materials through a fruitful discussion from inorganic and materials chemistry aspects, and also promotes the broad sphere of clay-based materials for other utilization, such as effluent treatment, heavy metal removal, and environmental remediation.

A review on the applications of nuclear magnetic resonance (NMR) in the oil and gas industry: laboratory and field-scale measurements

Abstract: Abstract This review presents the latest update, applications, techniques of the NMR tools in both laboratory and field scales in the oil and gas upstream industry. The applications of NMR in the laboratory scale were thoroughly reviewed and summarized such as porosity, pores size distribution, permeability, saturations, capillary pressure, and wettability. NMR is an emerging tool to evaluate the improved oil recovery techniques, and it was found to be better than the current techniques used for screening, evaluation, and assessment. For example, NMR can define the recovery of oil/gas from the different pore systems in the rocks compared to other macroscopic techniques that only assess the bulk recovery. This manuscript included different applications for the NMR in enhanced oil recovery research. Also, NMR can be used to evaluate the damage potential of drilling, completion, and production fluids laboratory and field scales. Currently, NMR is used to evaluate the emulsion droplet size and its behavior in the pore space in different applications such as enhanced oil recovery, drilling, completion, etc. NMR tools in the laboratory and field scales can be used to assess the unconventional gas resources and NMR showed a very good potential for exploration and production advancement in unconventional gas fields compared to other tools. Field applications of NMR during exploration and drilling such as logging while drilling, geosteering, etc., were reviewed as well. Finally, the future and potential research directions of NMR tools were introduced which include the application of multi-dimensional NMR and the enhancement of the signal-to-noise ratio of the collected data during the logging while drilling operations.