The stuck pipe is one of the most important problems that it is costly and time consuming in drilling. Studies show that the parameters such as torque and the structure of the ground during drill operation are effective in the stuck pipe. Experience has shown that the torque suddenly rises in the moment of stuck, and drill bit rotates (rotation per minute RPM) get more slowly. Because this approach has many weaknesses with regard to the possibility to human mistakes and lack sufficient precision, thus, by detecting torque and rotation per minute spikes, the stuck moment can be detected. Various methods have been introduced to spike detection in the proposed method. In addition, smooth nonlinear energy operator is used. In this method, signal is detected positive and negative changes. In this study, a method for the detection of positive spike for torque and negative spike for RPM has been introduced. Moreover, it has been shown that the suggested method needs less time for analyzing a signal; therefore, it can be used in the immediately systems.

Stuck pipe is one of the most serious drilling problems, estimated to cost the petroleum industry hundreds of millions of dollars annually. One way to avoid stuck pipe risks is to predict the stuck pipe with the available drilling parameters which can be employed to modify drilling variables. In this work, Artificial Neural Network (ANN) was used for stuck pipe prediction according to the fact that this method is applicable when relationships of parameters are too complicated. Based on the drilling fluid condition from one of the Iranian oil fields, stuck pipe instances were divided into static and dynamic types. The results of this study show more than 90% accuracy for stuck pipe prediction in the investigated oilfield. The methodology presented in this paper enables the Iranian drilling industry to estimate the risk of stuck pipe occurrence during the well planning procedure.

Research Subject:Drilling operations frequently encounter numerous challenges that can lead to significant financial, human, and environmental losses. Therefore, predicting potential problems before they occur and implementing necessary preventive measures is crucial to minimizing risks. In this context, this study investigates the impact of employing artificial intelligence (AI) algorithms to forecast drilling complications using real-time mud logging data collected from existing wells in an Iranian oilfield. Research approach: A hybrid architecture combining Long Short-Term Memory (LSTM) and Fully Connected neural networks was developed for the identification and detection of anomalies such as kicks and stuck pipe. Given the scarcity of these anomalies in the dataset, which could adversely affect model accuracy and performance, the Synthetic Minority Oversampling Technique (SMOTE) was applied to balance class distribution and enhance the overall effectiveness of the network. Furthermore, the influence of varying hyperparameters on reducing network error was systematically analyzed. Main Results: Various network architectures and structures were examined. The experimental results indicated that the optimal model achieved an accuracy of 94.45% on the testing dataset with the following hyperparameters: a lookback of 7, a learning rate of 0.001, a dropout rate of 0.2, a batch size of 32, and a four-layer network architecture with 512, 256, and 256 units in the first, second, and third hidden layers, respectively. This configuration yielded higher accuracy and fewer false alarms in anomaly detection compared to other tested models. Based on the obtained results, this approach demonstrates significant potential for real-time anomaly detection in drilling operations.

The traditional interscalene block has evolved over time to lower, lateral and posterior approaches, coinciding with the advent of Peripheral Nerve Stimulator and Ultrasonography. In limited infrastructure settings, such technology remains elusive and the classical paresthesia technique comes to the rescue. This approach is however very difficult to elicit in pediatric patients. It is here that the grassroot approach of a fascial “, click”,or “, pop”,emerges as the savior to administer a satisfactory block. We describe a case of a young boy with successful conduct of anaesthesia for K wire fixation of supracondylar fracture of humerus under lower interscalene block with loss of resistance technique. In light of recent research showing about 40-50% variation in the brachial plexus anatomy around the subclavian artery, use of technology in anaesthesia is the future. However, till such technology is made universally affordable, it is pertinent to remember the roots of interscalene block!

The aim of the present research is to study the lived experience of in stuck–hearted phenomenon. The method was qualitative and phenomenological. Data collection was conducted through semi-structured interviews. Purposeful sampling and interviews re used and continued until data saturation. The number of participants was 20. Thematic analysis was used for data analysis. By examining people's experience, the following common elements emerged as themes reflecting the stuck-hearted experience: The most important experience is the feeling of reluctance. Other feelings were discomfort, pressure, and to feel being is stuck. To overcome these feelings, these thoughts flow, falls way: focus on the opposite side of the relationship, doubtful thoughts, thoughts that focus on values, furious thought, and self-focused thoughts. All of these thoughts cause a person to engage in conduct that feature, alert, stereotypical, hard and excruciating, dishonest, insincere and passive behavior. Immediately after that, the feelings of regret, helplessness and weakness, anger and other emotions such as hatred and being under dominance in the relationship and consequently, blame inquisitive, ambitious and furiously thoughts is experienced. In fact, according to the characteristics of intimate relationships that is self-disclosure and trust, in stuck-hearted relationships is the opposite of intimate relationships.

Introduction: The aim of this study is evaluation of efficacy of lacrimal sac localization with lacrimal probe without using light pipe. The study was based on nonrandomized prospective clinical trial with one year follow up period.Methods: A group of 40 patients operated since march 2001 to December 2004 The operation was Endoscopic Dacryocystorhynostomy and localization of lacrimal sac, which was performed with a lacrimal probe inserted in lacrimal sac. With pushing the probe inferomedialy pierces lacrimal sac lacrimal bone, and nasal musca over the bone. Follow up includes symptom evaluation such as epiphora and endoscopy. Success was defined as relief of symptoms & duct patancy with irrigation for a 1 year period.Results: 29 patients were male (72.5 %) and 11 were female (27.5 %). Mean age was 37.5 (with SD±12.26) in 20 patients right eye involved, left eye involved in 16 cases and in 4 cases both eyes involved. For 3 patients septoplasty was done besides EDCR. success rate was 82.5% and no complication was seen.Discussion: the use of lacrimal probe for lacrimal sac localization is a safe and easy method and don't have light pipe limitations then can replace light pipe.

Double-pipe heat exchangers are commonly used in various industries, including power plants, solar cells, refineries, and automotive. The double-pipe heat exchanger is one of the most used simple exchangers in the industry. In this study, we employ computational fluid dynamics to investigate the flow characteristics of nanofluids within a double-pipe heat exchanger featuring a square inner tube and a circular outer tube (SC). The simulations are conducted under constant heat flux conditions, exploring laminar and turbulent flow regimes. Numerical results for water flow under forced convection are compared with reference results for validation. The results indicate that as the Reynolds number increases, particularly in turbulent flow regimes, the Nusselt number in nanofluid flow increases more than in water flow. For instance, in the case of aluminum oxide nanofluid flow at a Reynolds number of 500, the Nusselt number demonstrates a nearly 5% enhancement over water flow. In contrast, at a Reynolds number of 20000, this enhancement escalates to approximately 20%. Three types of nanoparticles are considered to investigate the effect of nanoparticle type on heat transfer and pressure drop. The results show that the use of nanoparticles has a slight effect on the friction factor while significantly enhancing heat transfer.