Managed Pressure Drilling and Cementing and Optimizing with Digital Solutions

This manuscript provides a comprehensive examination of cutting-edge drilling technologies and their implications for the energy industry. Managed Pressure Drilling (MPD) and Controlled Mud Level (CML) are meticulously dissected, uncovering their techniques, equipment, and operational intricacies. Additionally, Managed Pressure Cementing (MPC) is explored, showcasing the ingenious application of MPD during cementing operations. The chapter then ventures into the realm of digitalization and automation, highlighting the role of Wired Drill Pipe as a conduit for high-speed data transmission, transforming drilling through real-time monitoring and decision-making. The convergence of digitalization and automation with MPD and CML systems is unveiled, elucidating how these technologies can create semi or fully automated systems that optimize drilling processes, enhance accuracy, and reduce nonproductive time (NPT). This manuscript invites readers on a journey into the frontier of drilling technology, where innovation knows no bounds, and the future of energy exploration and production is being reshaped

Machine learning algorithm for prediction of stuck pipe incidents using statistical data: case study in middle east oil fields

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 divided 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

Application of Digitalization in Real-Time Analysis of Drilling Dynamics Using Along-String Measurement (ASM) Data along Wired Pipes

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

Review of the Leak-off Tests with a Focus on Automation and Digitalization

The drilling and research communities are leading the way toward more digitally-controlled operations to ensure that the drilling process takes place as safely and gently as possible with the lowest possible carbon footprint. Today’s cutting-edge operations are run on large high-performance drilling installations where operations are largely run remotely from the driller’s operating station. Digitalization of the drilling process is the goal for performing drilling operations remotely from onshore. Leak-off test (LOT) or extended leak-off test (XLOT) plays a critical role in the petroleum industry. Therefore, recognizing all affecting parameters on LOT/XLOT and Formation integrity test (FIT) performance is vital. Because, in some cases, it is not possible to fully understand what happened during the test, having a deep insight into the LOT procedure is very important. One of the current study's main objectives is to thoroughly explain all stages of these tests and assemble all the significant parameters. Thus, many scientific papers on these tests were deeply reviewed and were classified into four main groups focusing on the application of LOT/XLOT (i) in stress estimation and geomechanical studies, (ii) concerning hydraulic fracturing, (iii) concerning wellbore stability, and (iv) numerical modeling, and then, the corresponding discussions were conducted. It was found that in-situ stress estimation is the most common application of the leak-off test. Moreover, considering the importance of LOT and the desire to digitize operations in the oil and gas industry, it was found that the automatic LOT/XLOT is a fully required approach. The primary purpose of this study, which is hence considered its main contribution, is to prepare a LOT flowchart that would set off the further code development tasks of the field. The fundamental code of the present study was written and checked using a real dataset in a Python environment. The results were satisfying and indicated a successful start, which lays a foundation for future automated LOT/XLOT tests