ORCID Profile
0000-0002-4867-8860
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Publisher: Springer Science and Business Media LLC
Date: 19-01-2023
DOI: 10.1007/S13202-023-01607-4
Abstract: The problem of lost circulation occurred long during the drilling operation. Through induced and natural fractures, huge drilling fluid losses lead to higher operating expenses during the drilling. Historically, this problem was addressed with the help of the Lost Circulation Materials (LCMs). These materials are added to the drilling fluid to seal the fractures and increase fracture initiation or propagation pressure. Therefore, understanding the mechanisms of fracture sealing and the performance of the lost circulation materials is critical if the problem of lost circulation is to be mitigated effectively. Despite extensive advances in the last couple of decades, lost circulation materials used today still have disadvantages, such as damaging production zones, failing to seal large fractures, or plugging drilling tools. Here, we propose a new blend of smart expandable lost circulation material (LCM) to remotely control the expanding force and functionality of the injected LCM. This paper aimed to assess the performance of the selected LCMs (Mica, Wheat Straw, Oak Shell, and Sugarcane Bagasse Fiber or Canes) in water-based drilling fluids. The particle bridging of LCMs was investigated using particle bridging experiments in the laboratory. Moreover, we determined the particle size distribution of D50. The cell utilized in the sealing experiments had 1000- and 3000 micron fractures to mimic different size fractures in the formation. Fracture widths are predicted based on well-log data and adaptation of existing models in the desired oil field. The concentrations of LCMs in Mica, Wheat Straw, Oak Shell, and Sugarcane Bagasse Fiber (Canes) were (25, 50, and 80 ppb), (1.5, 2, 2.5 ppb), (3, 6, and 10 ppb), and (1.5, 2, 2.5 ppb), respectively. The results indicate that a combination of LCMs outperforms in idual LCMs. When used in idually, Oak Shells performed the highest, followed by Mica and Sugarcane Bagasse Fiber mixtures. Also, the Wheat Straw blend served the weakest lost circulation treatments. Finally, the combination applied in this investigation successfully sealed fractures up to 3 mm in diameter in the targeted oil field, which traditional LCM would be unable to do. Due to the abundance and low cost of these materials in the study area, they can be used to ensure successful plugging. Graphical Abstract
Publisher: MDPI AG
Date: 25-11-2022
DOI: 10.3390/EN15238930
Abstract: An automated drilling system requires a real-time evaluation of the drilling bit during drilling to optimize operation and determine when to stop drilling and switch bits. Furthermore, in the dynamic modeling of drill strings, it is necessary to take into account the interactions between drilling bits and rock. To address this challenge, a hybrid approach that combines physics-based models with data analytics has been developed to handle downhole drilling measurements in real time. First, experimental findings were used to formulate mathematical models of cutter–rock interaction in accordance with their geometrical characteristics, rock properties, and drilling parameters. Specifically, these models represent the normal and contact forces of polycrystalline diamond compact cutters (PDCs). Experimental data are analyzed utilizing deep learning, nonlinear regression, and genetic algorithms to fit nonlinear equations to data points. Following this, the recursive least square was implemented as a data analytic method to integrate real-time drilling data, drilling bit models, and mathematical models. Drilling data captured by the along-string measurement system (ASM) is implemented to estimate cutting and normal forces, torque, and specific energy at the bit. The unique aspect of this research is our approach in developing a detailed cutter–rock interaction model that takes all design and operation parameters into account. In addition, the applicability of the algorithm is demonstrated by real-time assessments of drilling dynamics, utilizing downhole digital data, that enable the prediction of drilling events and problems related to drilling bits.
Publisher: Elsevier BV
Date: 05-2023
Publisher: Springer Science and Business Media LLC
Date: 07-01-2022
DOI: 10.1007/S13202-021-01436-3
Abstract: One of the most troublesome issues in the drilling industry is stuck drill pipes. Drilling activities will be costly and time-consuming due to stuck pipe issues. As a result, predicting a stuck pipe can be more useful. This study aims to use an artificial intelligence technology called hybrid particle swarm optimization neural network (PSO-based ANN) to predict the probability of a stuck pipe in a Middle East oil field. In this field, a total of 85 wells were investigated. Therefore, to predict this problem, we must examine and determine the role of drilling parameters by creating an appropriate model. In this case, an artificial neural network is used to solve and model the problem. In this way, by processing the parameters of wells with and without being stuck in this field, the stuck or non-stuck of drilling pipes in future wells is predicted. To create a PSO-based ANN model database, mud characteristics, geometry, hydraulic, and drilling parameters were gathered from well daily drilling reports. In addition, two databases for directional and vertical wells were established. There are two types of datasets used for each database: stuck and non-stuck. It was discovered that the PSO-based ANN model could predict the incidence of a stuck pipe with an accuracy of over 80% for both directional and vertical wells. This study ided data from several cases into four sections: 17 ½″, 12 ¼″, 8 ½″, and 6 1/8″. The key reasons for sticking and the mechanics have been thoroughly investigated for each section. The methodology presented in this paper enables the Middle East drilling industry to estimate the risk of stuck pipe occurrence during the well planning procedure.
Publisher: Springer Science and Business Media LLC
Date: 30-08-2023
DOI: 10.1007/S13202-023-01691-6
Abstract: Efficient and safe drilling operations require real-time identification and mitigation of downhole vibrations like stick-slip, which can significantly diminish performance, reliability, and efficiency. This pioneering research introduces a robust machine learning approach combining model-agnostic regression techniques with Bayesian Optimized Extra Trees (BO_ET) to accurately predict stick-slip events in real-time using downhole sensor data. The model is rigorously tested and validated on a substantial offshore dataset comprising over 78,000 data points from a Norwegian continental shelf (NCS) oil field. The key input features encompassing real-time downhole and surface drilling parameters are carefully selected, including critical variables such as collar rotational speed, shock risks, annular pressure, torque, mud flow rate, drill string vibration severity, and other relevant measurements. These parameters offer significant insights into the occurrence of harmful stick-slip vibrations. Among several sophisticated machine learning models, the Extra Trees (ET) algorithm demonstrates superior performance with the lowest errors of 5.5056 revolutions per minute (r/min) Mean Absolute Error (MAE) and 9.9672 r/min Root Mean Square Error (RMSE) on out-of-s le test data. Further hyperparameter tuning of the ET algorithm via Bayesian Optimization dramatically reduces errors down to 0.002156 MAE and 0.024495 RMSE, underscoring the significant innovation and advantages of the proposed approach. By seamlessly incorporating real-time downhole sensor data and drill string mechanics, the model enables reliable identification of stick-slip events as they occur downhole. This grants opportunities to optimize critical drilling parameters including revolutions per minute (RPM), weight-on-bit (WOB), mud flow rates, and more to effectively mitigate stick-slip severity and improve the rate of penetration (ROP). Integrating the approach into automatic driller systems on offshore rigs offers immense benefits for drilling operations through substantially increased efficiency, fewer premature failures, lower costs, and significantly improved productivity and safety. Overall, this research strongly emphasizes the immense transformative potential of advanced data analytics and machine learning in enabling more efficient, economical, and sustainable drilling practices. The proposed model demonstrates clear superiority over existing methods and establishes a robust and reliable platform for real-time stick-slip prediction and mitigation, maximizing drilling performance. Graphical abstract
Location: Iran (Islamic Republic of)
No related grants have been discovered for Behzad Elahifar.