Rate of Penetration Prediction Utilizing Hydromechanical Specific Energy

The prediction and the optimization of the rate of penetration (ROP), an important measure of drilling performance, have increasingly generated great interest. Several empirical techniques have been explored in the literature for the prediction and the optimization of ROP. In this study, four commonly used artificial intelligence (AI) algorithms are explored for the prediction of ROP based on the hydromechanical specific energy (HMSE) ROP model parameters. The AIs explored are the artificial neural network (ANN), extreme learning machine (ELM), support vector regression (SVR), and least-square support vector regression (LS-SVR). All the algorithms provided results with accuracy within acceptable range. The utilization of HMSE in selecting drilling variables for the prediction models provided an improved and consistent methodology of predicting ROP with drilling efficiency optimization objectives. This is valuable from an operational point of view, because it provides a reference point for measuring drilling efficiency and performance of the drilling process in terms of energy input and corresponding output in terms of ROP. The real-time drilling data utilized are must-haves, easily acquired, accessible, and controllable during drilling operations

Two-stage machine learning models for bowel lesions characterisation using self-propelled capsule dynamics

This is the author accepted manuscript.Data accessibility: The data sets generated and analysed during the cur rent study are available from the corresponding author on reasonable request.To foster early bowel cancer diagnosis, a non-invasive biomechanical characterisation of bowel lesions is proposed. This method uses the dynamics of a self-propelled capsule and a two-stage machine learning procedure. As the capsule travels and encoun ters lesions in the bowel, its exhibited dynamics are envisaged to be of biomechanical significance being a highly sensitive nonlinear dynamical system. For this study, measurable capsule dynamics including accel eration and displacement have been analysed for fea tures that may be indicative of biomechanical differ ences, Young’s modulus in this case. The first stage of the machine learning involves the development of su pervised regression networks including multi-layer per ceptron (MLP) and support vector regression (SVR), that are capable of predicting Young’s moduli from dynamic signals features. The second stage involves an unsupervised categorisation of the predicted Young’s moduli into clusters of high intra-cluster similarity but low inter-cluster similarity using K-means clustering. Based on the performance metrics including coefficient of determination and normalised mean absolute error, the MLP models showed better performances on the test data compared to the SVR. For situations where both displacement and acceleration were measurable, the displacement-based models outperformed the acceleration-based models. These results thus make capsule displacement and MLP network the first-line choices for the proposed bowel lesion characterisation and early bowel cancer diagnosis.Engineering and Physical Sciences Research Council (EPSRC

Noise-Insensitive Prognostic Evaluation of Historic Masonry Structures

In recent years, a significant amount of research has been directed towards the development of prognostic methodologies to forecast the future health state of an engineering system assisting condition based maintenance. These prognostic methods, having furthered the maintenance practices for mechanical systems, have yet to be applied to historic masonry structures, many of which stand in an aged and degraded state. Implementation of prognostic methodologies to historic masonry structures can advance the planning of successful conservation and restoration efforts, ultimately prolonging the life of these heritage structures. This thesis presents a review of prognostic concepts and techniques available in the literature as applied to various engineering disciplines, and evaluates the well-established prognostic techniques for their applicability to historic masonry structures. Challenges of adapting the existing prognostic techniques to historic masonry are discussed, and the future direction in research, development, and application of prognostic methods to masonry structures is highlighted. One particular prognostic technique, known as support vector regression, has had successful applications due to its ability to compromise between fitting accuracy and generalizability (i.e. flatness) in the training of prediction models. Optimal tradeoff between these two aspects depends on the amount of extraneous noise in the measurements, which in civil engineering applications, is typically caused by loading conditions unaccounted for in the development of the prediction model. Such extraneous loading, often variable with time affects the optimal tradeoff. This thesis presents an approach for optimally weighing fitting accuracy and flatness of a support vector regression model in an iterative manner as new measurements become available. The proposed approach is demonstrated in prognostic evaluation of the structural condition of a historic masonry coastal fortification, Fort Sumter located in Charleston, SC. A finite element model is used to simulate responses of a casemate within the fort considering differential settlement of supports. Within the case study, the adaptive optimal weighting approach proved to have increased prediction accuracy over the non-weighted option

Advanced Composite Materials and Structures

Composite materials are used to produce multi-objective structures such as fluid reservoirs, transmission pipes, heat exchangers, pressure vessels due to high strength and stiffness to density ratios and improved corrosion resistance. The mathematical concepts can be used to simulate and analyze the generated mechanical and thermal properties of composite materials regarding to the desired performances in actual working conditions.  To solve and obtain the exact solution of the developed nonlinear differential equations in the composite materials, analytical methods can be applied. Mechanical and thermal analysis of complex composite structures can be numerically analyzed using the Finite Element Method (FEM) to increase performances of composite structures in different working conditions. To decrease failure rate and increase performances of composite structures under complex loading system, thermal stress and effects of static and dynamic loads on the designed shapes of composite structures can be analytically investigated. The stresses and deformation of the composite materials under the complex applied loads can be calculated by using the FEM method in order to be used in terms of safety enhancement of composite structures. To increase the safety level as well as performances of the composite structures in different working conditions, crack development in elastic composites can be simulated and analyzed. To develop and optimize the process of composite deigning in terms of mechanical as well as thermal properties under different mechanical and thermal loading conditions, the advanced machine learning systems can be applied. A review in recent development of composite materials and structures is presented in the study and future research works are also suggested. Thus, to increase performances of composite materials and structures under complex loading systems, advanced methodology of composite designing and modification procedures can be provided by reviewing and assessing recent achievements in the published papers

Advanced Composite Materials and Structures

Composite materials are used to produce multi-objective structures such as fluid reservoirs, transmission pipes, heat exchangers, pressure vessels due to high strength and stiffness to density ratios and improved corrosion resistance. The mathematical concepts can be used to simulate and analyze the generated mechanical and thermal properties of composite materials regarding to the desired performances in actual working conditions.  To solve and obtain the exact solution of the developed nonlinear differential equations in the composite materials, analytical methods can be applied. Mechanical and thermal analysis of complex composite structures can be numerically analyzed using the Finite Element Method (FEM) to increase performances of composite structures in different working conditions. To decrease failure rate and increase performances of composite structures under complex loading system, thermal stress and effects of static and dynamic loads on the designed shapes of composite structures can be analytically investigated. The stresses and deformation of the composite materials under the complex applied loads can be calculated by using the FEM method in order to be used in terms of safety enhancement of composite structures. To increase the safety level as well as performances of the composite structures in different working conditions, crack development in elastic composites can be simulated and analyzed. To develop and optimize the process of composite deigning in terms of mechanical as well as thermal properties under different mechanical and thermal loading conditions, the advanced machine learning systems can be applied. A review in recent development of composite materials and structures is presented in the study and future research works are also suggested. Thus, to increase performances of composite materials and structures under complex loading systems, advanced methodology of composite designing and modification procedures can be provided by reviewing and assessing recent achievements in the published papers

Stuck pipe prediction in deviated wellbores: a numerical and statistical analysis.

Due to the significant non-productive times and recovery costs associated with stuck pipe events in oil and gas drilling operations, there is value in being able to predict an impending stuck pipe event. To achieve this, the use of numerical cuttings transport (hole cleaning) models and statistical analysis of real-time drilling data is proposed by this research. Current cuttings transport models are based on unhindered, free settling in the wellbore and do not adequately account for the effect of vortices created as the drill string rotates about its axis. This thesis addresses both shortcomings, and presents improved cutting transport models that consider hindered centrifugal settling of drilled cuttings, effect of Taylor vortices and Van der Waals forces. The implication is that the resulting cuttings settling velocity used to estimate critical transport velocities and flow rates are more representative. The transport ratio, a measure of the hole cleaning efficiency, is consequently more realistically predicted. Although several proprietary automated stuck pipe prediction tools exist in the industry, this research found that they broadly fall into five main groups. It is also apparent that current capabilities do not simultaneously and continuously combine real-time data, offset wells data and well design analytical models in a single approach. On that basis, this thesis presents an integrated stuck pipe prediction concept that utilizes all three data streams, called the "ROW" approach. The concept presented in this thesis was then coded into a tool called the stuck pipe index (SPI). The SPI tool risk assessment is determined in real-time and is referenced by a traffic light alert system (green – amber – red), to warn the user of an impending potential stuck pipe situation. The numerical models developed in this research estimate critical velocities to within 10 – 15% and show strong agreement with published empirical data. Combined with the cuttings transport numerical models developed in this research and other publicly available well design models (such as hydraulics, and torque and drag), the SPI tool has been tested with several case histories and proven to detect stuck pipe events with warning alerts significantly ahead of the event. The tool has equally been deployed in real-time with >90% success rate and without spurious alerts recorded. The results thus confirm that the developed numerical models and the "ROW" approach are robust, and offer an improvement to current industry capabilities in terms of accuracy and sensitivity to changing downhole wellbore conditions

Advanced Composite Materials and Structures

Composite materials are used to produce multi-objective structures such as fluid reservoirs, transmission pipes, heat exchangers, pressure vessels due to high strength and stiffness to density ratios and improved corrosion resistance. The mathematical concepts can be used to simulate and analyze the generated mechanical and thermal properties of composite materials regarding to the desired performances in actual working conditions.  To solve and obtain the exact solution of the developed nonlinear differential equations in the composite materials, analytical methods can be applied. Mechanical and thermal analysis of complex composite structures can be numerically analyzed using the Finite Element Method (FEM) to increase performances of composite structures in different working conditions. To decrease failure rate and increase performances of composite structures under complex loading system, thermal stress and effects of static and dynamic loads on the designed shapes of composite structures can be analytically investigated. The stresses and deformation of the composite materials under the complex applied loads can be calculated by using the FEM method in order to be used in terms of safety enhancement of composite structures. To increase the safety level as well as performances of the composite structures in different working conditions, crack development in elastic composites can be simulated and analyzed. To develop and optimize the process of composite deigning in terms of mechanical as well as thermal properties under different mechanical and thermal loading conditions, the advanced machine learning systems can be applied. A review in recent development of composite materials and structures is presented in the study and future research works are also suggested. Thus, to increase performances of composite materials and structures under complex loading systems, advanced methodology of composite designing and modification procedures can be provided by reviewing and assessing recent achievements in the published papers

Advanced Underground Space Technology

The recent development of underground space technology makes underground space a potential and feasible solution to climate change, energy shortages, the growing population, and the demands on urban space. Advances in material science, information technology, and computer science incorporating traditional geotechnical engineering have been extensively applied to sustainable and resilient underground space applications. The aim of this Special Issue, entitled “Advanced Underground Space Technology”, is to gather original fundamental and applied research related to the design, construction, and maintenance of underground space