Machine Learning Solution for Predicting Vibrations while Drilling the Curve Section.

The downhole vibration is one of the most crucial factors that affect downhole equipment performance and failure, besides wellbore instability. Downhole tool failure, hole problems, mechanical energy loss, and ineffective drilling performance are commonly associated with drillstring high vibration levels. The high vibration level will lead to more complications while drilling that might cause nonproductive time and extra cost. Meanwhile, the downhole sensors for detecting the drillstring vibrations add more cost to the operation. Consequently, the new solutions based on technology capabilities provide a powerful tool to integrate and interpret the drilling data for the best use of @@the data for operation performance enhancement. This study provides a successful application for utilizing the surface drilling data to automate drillstring vibration detection during the drilling curve section employing machine learning (ML) techniques. The axial, torsional, and lateral vibration modes are detected through testing four ML techniques named the @@adaptive neuro-fuzzy inference system (ANFIS), radial basis function (RBF), functional networks (FN), and support vector machines (SVMs) with real field data. The models' development was achieved by comprehensive study starting from data gathering, wrangling, statistical analysis, developing the ML models, evaluating the model prediction accuracy, and reporting the high accuracy results. The developed models were evaluated, and results showed that ANFIS and SVM models provided the highest accuracy with a coefficient of correlation (R) ranging from 0.9 to 0.99 followed by the RBF and FN models through model training and testing (R ranging from 0.82 to 0.96). Validating the models over unseen data confirmed the high accuracy prediction for the three vibration modes. Generally, the developed models provided technically accepted accuracy with R higher than 0.93 and AAPE less than 2.8% for SVM and ANFIS models while FN and RBF showed R between 0.82 and 0.95 and AAPE less than 5.7% between actual readings and predictions. Based on these results, the developed ML algorithm might be utilized as an intelligent solution to autodetect downhole vibration while drilling from surface sensor data only, which will save the downhole tool cost.

Detecting downhole vibrations through drilling horizontal sections: machine learning study.

During the drilling operations and because of the harsh downhole drilling environment, the drill string suffered from downhole vibrations that affect the drilling operation and equipment. This problem is greatly affecting the downhole tools (wear and tear), hole problems (wash-out), mechanical energy loss, and ineffective drilling performance. Extra non-productive time to address these complications during the operation, and hence, extra cost. Detecting the drillstring vibrations during drilling through the downhole sensors is costly due to the extra service and downhole sensors. Currently, the new-technology-based solutions are providing huge capabilities to deal intelligently with the data, and machine learning applications provide high computational competencies to learn and correlate the parameters for technical complex problems. This research presented a successful case study for developing machine learning models through a comprehensive methodology process for vibration detection using surface rig data through data collection, preprocessing, analytics, training and optimizing the models' parameters, and evaluating the performance to have the best prediction results. Evaluating the models' performance showed that obtained predictions have a great match with actual measurements for the different stages of training, testing, and even during models' validation with unseen well data. Real-field horizontal drilling data was utilized to feed and train the models through different tools named radial basis function (RBF), support vector machines (SVMs), adaptive neuro-fuzzy inference system (ANFIS), and functional networks (FN) to auto-detect the three types of downhole vibrations (axial, torsional, and lateral). The study results showed a high correlation coefficient (higher than 0.9) and technically accepted average absolute percentage error (below 7.5%) between actual readings and predictions of the developed ML models. The study outcomes will add to the automation process of drilling operations to avoid many tools failure by comparing predicted vibrations versus downhole tools limits such as red zone and continuing drilling without interruption to the well total depth especially while drilling horizontal sections.

Role of Rock Saturation Condition on Rock-Mud Interaction: Sandstone Geomechanics Study.

The rock saturation condition affects the rock elastic and strength characteristics due to the role of fluid-rock interaction. However, the role of this parameter has not been studied well for mud-rock exposure during drilling operations. Hence, this study targets to assess the role of different rock saturation conditions on the rock geomechanics changes during the rock exposure for the drilling fluids. During the drilling operation, the mud filtrate invades the drilled formation pore system and replaces the saturating fluid and consequent alterations occur for the rock elastic moduli and failure properties. The current study employed Berea Buff sandstone rock type with different saturation conditions (brine-saturated, dry, and oil-saturated) to interact with drilling mud (water-based) through the filtration test to mimic the downhole rock-mud exposure under pressure, temperature, and time conditions. Extensive laboratory analysis was accomplished that covered the scratching test to get the strength of rock samples, acoustic data determination, elastic moduli evaluation, and scanning electron microscopy to assess the internal alterations of the rock pore system. The obtained results showed that the oil-saturated sample showed the least filtration characteristics for the rock-mud exposure and the best condition to maintain the rock strength from deterioration compared to the dry and brine-saturated samples. The rock strength showed a weakening behavior for the brine-saturated and dry samples by 5 and 18% respectively, while the oil-saturated sample showed only a 2% strength reduction after the mud exposure. Poisson's ratio showed a 21% increase for the brine-saturated sample and the dry sample showed a small increase from 0.2 to 0.22, while the oil-saturated sample maintains a stable Poisson's ratio at 0.24. Young's modulus showed an increase for the dry and brine-saturated rock samples by 10 and 7%, respectively, while a 25% reduction for the oil-saturated. The spectrometry analysis results showed the internal changes in the rock samples' pore system for the brine-saturated and dry samples, while the oil-saturated sample showed no internal changes that maintain the rock structure and strength after the mud exposure.

Machine Learning Model for Monitoring Rheological Properties of Synthetic Oil-Based Mud.

The drilling fluid rheology is a critical parameter during the oil and gas drilling operation to achieve optimum drilling performance without nonproductive time or extra remedial operation cost. The close monitoring for rheological properties will help the drilling fluid crew to take quick actions to maintain the designed profiles for the drilling fluid rheology, especially when it comes to the flat rheology drilling fluid system, which is a new generation for harsh and specific drilling conditions that require flat profiles for the mud rheology regarding the temperature condition changes. The current study introduces a machine learning application toward predicting the rheology of synthetic oil-based mud (flat rheology type) for the full automation system of monitoring the mud rheological properties. Four models are developed, for the first time, to determine the rheological characteristics of flat rheology synthetic oil-based system using artificial neural networks. The developed models are capable of predicting the plastic and apparent viscosities, yield point, and flow behavior index from only the mud density and Marsh funnel as model inputs. The proposed models were trained and optimized from a real field dataset (369 measurements) with further testing the models using an unseen dataset of 153 data points. The predicted rheological properties achieved a high degree of accuracy versus the actual measurements and showed a coefficient of correlation range from 0.91 to 0.97 with an average absolute percentage error of less than 9.66% during the training and testing phases. Besides, machine learning-based correlations are proposed for estimating the rheological properties on the rig site without running the machine learning system for easy field applications.

The role of overbalance pressure on mud induced alteration of sandstone rock pore system.

Overbalance pressure is a very critical parameter in drilling operations. It has a great impact on formation damage, depending on other downhole parameters such as temperature, time, type and composition of mud, and rock mineralogical content. The objective of this study is to determine the degree of the impact of overbalance pressure on mud-rock interaction and the resultant effects on the rock pore system. This research presents an experimental study for the interaction of a Berea Buff sandstone and barite water-based under different overbalance pressure (300, 700, and 1000 psi) under the same temperature and interaction time. The experiments involved the use of the scanning electron microscope and nuclear magnetic resonance relaxation measurements to monitor changes in the pore system of the rock samples. A modified filtration cell was used to accommodate the rock samples and mud at different overbalance pressures. The obtained results showed that the filtration properties, rock flow characteristics (rock permeability, pore throat radius, and pore system scale type) are all affected by increasing the overbalance pressure. The filtration properties increased in terms of mud cake thickness and filtrate volume by 111% and 36% respectively when the overbalance pressure was increased from 300 to 1000 psi. The total rock porosity showed a decrease from 21.6% (pre-mud interaction) to 17.6, 15.2, and 14.2% under 300, 700, and 1000 psi, respectively. The rock permeability decreased by 75% under 1000 psi overbalance pressure while pore throat radius decreased by 45%. However, the rock pore type remains on the same scale (Macro) after interaction with the mud. Statistical analysis showed that the rock porosity and permeability decreased with the overbalance pressure increase through a polynomial relationship with a high determination coefficient of 0.99. Analysis of the internal pore system by the scanning electron microscope showed that the formation damage is mainly attributed to the precipitations of mud solids as overbalance pressure is increased.

Machine Learning Models for Equivalent Circulating Density Prediction from Drilling Data.

Equivalent circulating density (ECD) is considered a critical parameter during the drilling operation, as it could lead to severe problems related to the well control such as fracturing the drilled formation and circulation loss. The conventional way to determine the ECD is either by carrying out the downhole tool measurements or by using mathematical models. The downhole measurement is costly and has some limitations with the practical operations, while the mathematical models do not provide a high level of accuracy. Determination of the ECD should have a high level of accuracy, and therefore, the objective of this study is to employ machine learning techniques such as artificial neural networks (ANNs) and adaptive network-based fuzzy inference systems (ANFISs) to predict the ECD from only the drilling data with a high accuracy level. The study utilized drilling data from a horizontal drilling section that includes drilling parameters (penetration rate, rotating speed, torque, weight on bit, pumping rate, and pressure of standpipe). The models were built and tested from a data set that has 3570 data points, and another data set of 1130 measurements was employed for validating the models. The accuracy of the models was determined by key performance indices, which are the coefficient of correlation (R) and the average absolute percentage error (AAPE). The results showed the strong prediction capability for ECD from the two models through training, testing, and validation processes with R greater than 0.98 and a very low error of 0.3% for the ANN model, while ANFIS recorded R of 0.96 and AAPE of 0.7, and hence, the two models showed great performance for ECD estimation application. Also, the study introduces a newly developed equation for ECD determination from drilling data in real time.

The Role of Drilled Formation in Filter Cake Properties Utilizing Different Weighting Materials.

The filter cake formed during a filtration process plays a vital role in the success of a drilling operation. There are several factors affecting the filter cake build-up such as drilled formation, drilling fluid properties, and well pressure and temperature. The collective impact of these two factors (i.e., formation and the drilling fluid) on the filter cake build-up needs to be fully investigated. In this study, two types of formations represented as limestone and sandstone were used with different weighting materials to assess and compare their impact on the filter cake properties, filtration behavior, and solid invasion. The used weighting materials are manganese tetroxide, ilmenite, barite, and hematite. The filter cake was formed under a temperature of 200 °F and differential pressure of 300 psi. Nuclear magnetic resonance spectroscopy was employed to explore the pore structure of the used core samples. The results showed that the properties (i.e., shape and dimensions) of the different weighting materials are the dominant factors compared to the formation characteristics in most of the investigated filter cake properties. Nevertheless, the formation properties, namely, the permeability and pore structure, have a somehow higher contribution when it comes to the filter cake porosity and thickness. For solid invasion, there were no clear results about the main factor contributing to this issue.

Intelligent Prediction for Rock Porosity While Drilling Complex Lithology in Real Time.

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 (R 2), 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 R 2 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 R 2 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 R 2 of 0.88 and AAPE of 8.58%, while RF has R 2 of 0.92 and AAPE of 6.5%.

Effect of Different Weighting Agents on Drilling Fluids and Filter Cake Properties in Sandstone Formations.

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.

Real-time prediction of Poisson's ratio from drilling parameters using machine learning tools.

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.

Investigating the Alteration of Sandstone Pore System and Rock Features by Role of Weighting Materials.

Drilled formations are commonly invaded by drilling fluids during the drilling operations, and as a result, the rock pore system will have alterations that consequently alter the rock properties. The objective of this study is to investigate the impact of the most commonly used weighting materials in water-based mud (WBM) on the Berea Buff sandstone pore system and rock characteristics. Rock-mud interaction was imposed by using a customized high-pressure high-temperature filtration test cell under 300 psi differential pressure and 200 °F temperature to simulate downhole conditions during drilling that affect the rock-mud interaction. Extensive lab analysis was accomplished to investigate the rock characteristic alterations in terms of rock porosity, permeability, pore size distribution, flow characteristics, resistivity, and acoustic properties. Ilmenite-WBM showed the maximum values (8.3 cm3 filtrate volume and 7.6 mm cake thickness), while barite recorded the lowest filtrate volume (5.3 cm3) and thickness (3 mm). Nuclear magnetic resonance profiles illustrated the changes in the rock pore system due to the dominant precipitation or dissolution effects. A general porosity reduction was recorded with all mud types that ranged from 4.2 to 9.9% for ilmenite and Micromax, respectively. The rock permeability showed severe damage after mud exposure and a reduction in the pore throat radius. After mud invasion, the rock electrical resistivity showed alterations based on the mineralogical composition of the weighting materials that replaced the saturated brine from the rock pores. Compressional wave velocities (V p) showed an increasing trend as V p of Micromax-WBM increased by 4.5%, while hematite- and ilmenite-WBMs recorded the minimum increase of 1.8%. A general reduction was found for shear wave velocities (V s); Micromax-WBM showed the highest V s reduction by 6.6%, while ilmenite-WBM recorded the minimum reduction of 1.8%. The pore system alterations are the main reason behind V p increase, where the rock lithology alterations controlled the V s changes. The study findings will add more for the rock logging interpretation and rock properties alterations after the mud exposure.

Development of a Unique Organic Acid Solution for Removing Composite Field Scales.

Removal of oil field scales commonly requires low pH acid, which may cause many issues under downhole conditions. Because of the deposition of different scale types and the economic effect, there is a need to develop a remedial descaling fluid that can be effectively used to remove different types of scales at a different position in the well. This paper provides a new scale dissolver that is noncorrosive and has high scale dissolution performance for composite scales. This study shows a series of comprehensive experimental lab tests as scale characterization, equilibrium brine compositional analysis, fluid compatibility and stability, solubility test, precipitation tendency for the dissolved solids, corrosion test, and core flooding. The scale samples contain magnetite, kaolinite, calcium carbonate, and sulfate scales. The results showed that the dissolution rate was higher than 74% for composite field scale samples after 6 h at 70 °C, while the new dissolver completely dissolved the two samples at 100 °C after 5 h. The new dissolver outperformed the common commercial dissolver used in the oil and gas industry. The new dissolver has a pH of 9 and showed safe use regarding the precipitation of dissolved solids that can be produced during the scale treatment and a low corrosion rate of 0.063 kg/m2 at 6.9 MPa and 100 °C for 6 h. Also, the new dissolver was tested through core flooding for Indiana limestone and showed core permeability enhancement; the treatment with the new dissolver enhanced the core permeability from an initial value of 0.67 milliDarcy (mD) to record 1.29 mD.

Effect of the Filtrate Fluid of Water-Based Mud on Sandstone Rock Strength and Elastic Moduli.

During drilling operations, the filtrate fluid of the drilling mud invades the drilled rock. The invading filtrate fluid will interact with the rock and therefore alter the rock internal topography, pore system, elastic moduli, and rock strength. The objective of this study is to evaluate the effect of the mud filtrate of barite-weighted water-based mud on the geomechanical properties of four types of sandstone rocks (Berea Buff, Berea Spider, Bandera Brown, and Parker). The mud filtrate was collected to provide mud filtrate-rock exposure at a pressure of 300 psi and 200 °F temperature for 10 days. The study assessed the alteration in the rock geomechanics employing an integrated laboratory analysis of X-ray diffraction (XRD), scanning electron microscopy (SEM), nuclear magnetic resonance (NMR), and scratch testing. The ultrasonic results showed changes after exposure to the mud filtrate and an obvious reduction trend in the shear wave velocities due to the dissolution and mineralogical modifications in rock samples. The obtained results displayed a general strength reduction for the four sandstone types with different levels. The strength reduction ranged from 6% reduction for Berea Spider to a record 23% reduction for Parker. For all sandstone types, Young's modulus showed a general reduction ranging from 11 to 40%, while Poisson's ratio recorded an increase by 62-155% after the filtrate interaction. The study illustrated the role of pore-system alteration in controlling the rock strength and dynamic moduli.

Exposure Time Impact on the Geomechanical Characteristics of Sandstone Formation during Horizontal Drilling.

The rock geomechanical properties are the key parameters for designing the drilling and fracturing operations and for programing the geomechanical earth models. During drilling, the horizontal-section drilling fluids interact with the reservoir rocks in different exposure time, and to date, there is no comprehensive work performed to study the effect of the exposure time on the changes in sandstone geomechanical properties. The objective of this paper is to address the exposure time effect on sandstone failure parameters such as unconfined compressive strength, tensile strength, acoustic properties, and dynamic elastic moduli while drilling horizontal sections using barite-weighted water-based drilling fluid. To simulate the reservoir conditions, Buff Berea sandstone core samples were exposed to the drilling fluid (using filter press) under 300 psi differential pressure and 200 °F temperature for different exposure times (up to 5 days). The rock characterization and geomechanical parameters were evaluated as a function of the exposure time. Scratch test was implemented to evaluate rock strength, while ultrasonic pulse velocity was used to obtain the sonic data to estimate dynamic elastic moduli. The rock characterization was accomplished by X-ray diffraction, nuclear magnetic resonance, and scanning electron microscope. The study findings showed that the rock compression and tensile strengths reduced as a function of exposure time (18% and 19% reduction for tensile strength and unconfined compression strength, respectively, after 5 days), while the formation damage displayed an increasing trend with time. The sonic results demonstrated an increase in the compressional and shear wave velocities with increasing exposure time. All the dynamic elastic moduli showed an increasing trend when extending the exposure time except Poisson's ratio which presented a constant behavior after 1 day. Nuclear magnetic resonance results showed 41% porosity reduction during the five days of mud interaction. Scanning electron microscope images showed that the rock internal surface topography and internal integrity changed with exposure time, which supported the observed strength reduction and sonic variation. A new set of empirical correlations were developed to estimate the dynamic elastic moduli and failure parameters as a function of the exposure time and the porosity with high accuracy.

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

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 » 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.

Exposure to radioactive rocks from the Egyptian eastern desert attenuates the efficiency of the immune organs and induces apoptosis of blood lymphocytes in rat model.

Exposure to ionizing radiation emitted from natural sources induces many health hazards. The response to ionizing radiation involves a number of mediators including inflammatory cytokines and free radicals which mediate immunosuppression. The present study aimed to monitor the impact of exposure to natural radioactive rocks from the Egyptian eastern desert on the primary immune organs. Therefore, three experimental groups (15 rats per group) were used: group I included the control non-irradiated rats; group II included rats that were exposed for 28 consecutive days to natural radioactive rocks from the Egyptian eastern desert (IR/R group); and group III (positive control group) included rats that were exposed to high dose of γ-rays (4 Gy/14 days for 28 days) (IR/γR group). We found that rats of both the IR/R and IR/γR groups exhibited pathological alterations in the architecture of the primary immune organs (bone marrow and thymus). Additionally, the levels of C-reactive protein (CRP), proinflammatory cytokines (IL-1ß, IL-6 and TNF-α), and reactive oxygen species (ROS) were significantly increased in the IR/R and IR/γR groups compared to the control group. Furthermore, rats from the IR/R and IR/γR groups exhibited significant increase in the activity of caspase-3 and caspase-9 and subsequently exhibited a significant increase in the apoptosis of PBMCs compared with the control group. Most importantly, apoptosis induction in the PBMCs was associated with increased expression of cyclin B1 and decreased expression of cyclin D1 and survivin compared with the control non-irradiated group. Taken together, our data demonstrated that consecutive exposure to natural radioactive rocks from the Egyptian eastern desert could dampen the immune response through damaging the architectures of the immune system and mediating serious health problems to the population inhabiting this region.

Considerable radioactivity levels in the granitic rocks of the central areas of the Eastern Desert, Egypt.

The distribution of the natural radioisotopes 238U, 226Ra, 232Th, and 40K in addition to their radiological parameters in granitic rock samples from five different localities (Gebel El-Missikat, Gebel El-Gidamy, Gebel Ria El-Garra, Gebel El-Aradiya, and Gebel Kab Amira) in the central area of the Eastern Desert, Egypt, was measured using high purity germanium (HPGe) detector-based γ-spectrometry. The average activity concentrations of 238U, 226Ra, 232Th, and 40K in all five studied areas are higher than the corresponding global average values. The highest average activity concentrations of 238U and 226Ra were observed in Gebel El-Missikat, whereas the highest average value of 232Th activity concentration was found at Gebel El-Gidamy, and the highest concentration of 40K was obtained at Gebel El-Aradiya. The radiological hazard parameters radium equivalent (Raeq), external hazard index (Hex), internal hazard index (Hin), absorbed dose rate (ADR), annual effective dose rate (AEDR) outdoors, annual gonadal dose equivalent (AGDE), and excess lifetime cancer risks (ELCR) were calculated to assess the radiation hazards associated with the rock samples. The average values of these parameters are higher than the recommended reference levels. The obtained data provide a valuable future database for estimating the impact of radioactive contamination in the studied area and in the places where the rocks are used.

Familial history of hypertension as a predictor of increased arterial stiffness in normotensive offspring.

BACKGROUND: Increased arterial stiffness can be used as a prognostic marker of arterial hypertension. The relationship between arterial stiffness and arterial hypertension seems to be reciprocal. OBJECTIVE: Evaluation of changes of the arterial elastic prosperities in normotensive subjects, with and without parental history of hypertension. SUBJECTS AND METHODS: One hundred and ten normotensive individuals, aged 20-30 years, were divided into two groups: group-A (n = 57) and group-B (n = 53) subjects with positive and negative parental history of hypertension, respectively. Systolic, diastolic and pulse pressures were measured using mercury sphygmomanometer. The elastic properties of the ascending aorta and the common carotid arteries were assessed using M-mode echo and B-mode imaging, respectively. Stiffness index of the digital volume pulse (SIDVP) was measured in the right index finger using photoplethysmography. RESULTS: Group A subjects showed higher aortic stiffness index (p = 0.002), carotid stiffness index (p = 0.001), carotid pulse wave velocity (p â©½ 0.001) and stiffness index of digital volume pulse (p = 0.001). Group A subjects showed lower aortic distensibility (p = 0.001), aortic strain (p = 0.004), changes in aortic diameter (p = 0.022), carotid distension (p = 0.026), carotid distensibility coefficient (p â©½ 0.001) and carotid compliance coefficient (p = 0.002). CONCLUSION: The aortic and carotid stiffness parameters and SIDVP were higher in normotensive offspring of hypertensive parents. This finding could direct the attention towards the increased cardiovascular risk in this group and thus prompt earlier and tighter prevention of cardiovascular risk factors.

Assessment of natural radioactivity levels in soil samples from some areas in Assiut, Egypt.

The natural radioactivity of soil samples from Assiut city, Egypt, was studied. The activity concentrations of 28 samples were measured with a NaI(Tl) detector. The radioactivity concentrations of (226)Ra, (232)Th, and (40)K showed large variations, so the results were classified into two groups (A and B) to facilitate the interpretation of the results. Group A represents samples collected from different locations in Assiut and characterized by low activity concentrations with average values of 46.15 ± 9.69, 30.57 ± 4.90, and 553.14 ± 23.19 for (226)Ra, (232)Th, and (40)K, respectively. Group B represents samples mainly collected from the area around Assiut Thermal Power Plant and characterized by very high activity concentrations with average values of 3,803 ± 145, 1,782 ± 98, and 1,377 ± 78 for (226)Ra, (232)Th, and (40)K, respectively. In order to evaluate the radiological hazard of the natural radioactivity, the radium equivalent activity (Raeq), the absorbed dose rate (D), the annual effective dose rate (E), the external hazard index (H ex), and the annual gonadal dose equivalent (AGDE) have been calculated and compared with the internationally approved values. For group A, the calculated averages of these parameters are in good agreement with the international recommended values except for the absorbed dose rate and the AGDE values which are slightly higher than the international recommended values. However, for group B, all obtained averages of these parameters are much higher by several orders of magnitude than the international recommended values. The present work provides a background of radioactivity concentrations in the soil of Assiut.

Considerable hazards produced by heavy fuel oil in operating thermal power plant in Assiut, Egypt.

Heavy fuel oil and ash samples were collected from the Assiut thermal power plant in Egypt and subjected to gamma spectrometry analysis for natural radioactivity contents. Considerable results were observed where the ash contains nearly 1,000 times natural radionuclides more than raw oil. The results were confirmed by measuring the samples via using different devices in different institutions. All ash samples had radium equivalent activities and external hazard index values more than 370 Bq/kg and unity respectively. The mean absorbed dose rate was10,650 nGy/h which is nearly 190 times higher than the global average value of 55 nGy/h. The corresponding annual external effective dose is estimated to be 13 mSv/year, which is nearly 30 times higher than that in areas of natural background radiation (0.46 mSv/year).