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This study aims to generate and also validate an automatic detection algorithm for pharyngeal airway on CBCT data using an AI software (Diagnocat) which will procure a measurement method. The second aim is to validate the newly developed artificial intelligence system in comparison to commercially available software for 3D CBCT evaluation. A Convolutional Neural Network-based machine learning algorithm was used for the segmentation of the pharyngeal airways in OSA and non-OSA patients. Radiologists used semi-automatic software to manually determine the airway and their measurements were compared with the AI. OSA patients were classified as minimal, mild, moderate, and severe groups, and the mean airway volumes of the groups were compared. The narrowest points of the airway (mm), the field of the airway (mm2), and volume of the airway (cc) of both OSA and non-OSA patients were also compared. There was no statistically significant difference between the manual technique and Diagnocat measurements in all groups (p > 0.05). Inter-class correlation coefficients were 0.954 for manual and automatic segmentation, 0.956 for Diagnocat and automatic segmentation, 0.972 for Diagnocat and manual segmentation. Although there was no statistically significant difference in total airway volume measurements between the manual measurements, automatic measurements, and DC measurements in non-OSA and OSA patients, we evaluated the output images to understand why the mean value for the total airway was higher in DC measurement. It was seen that the DC algorithm also measures the epiglottis volume and the posterior nasal aperture volume due to the low soft-tissue contrast in CBCT images and that leads to higher values in airway volume measurement.

Cone-beam computed tomography (CBCT) in dental practice is becoming increasingly popular. However, the correct teeth identification, positioning and diagnosis based on CBCT can be tedious and challenging for the untrained eye. This is due to additional training, specific knowledge and time required for analysis and diagnosis. When compared to conventional dental imaging methods. In this study, we introduce a novel artificial intelligence (AI) system that facilitates analysis and diagnosis. This system is based on deep learning approaches that can localize teeth and define pathologies within three-dimensional CBCT scans. The study showed that the diagnostic performance of AI system image interpretation reaches and sometimes exceeds in comparison to clinician’s expertise. In this randomized cross-over trial we demonstrated a significant improvement of aided diagnostic accuracy for various dental diseases in comparison to a group of radiologists that made unaided decisions. AI can be used for both stand-alone CBCT interpretation and as a decision support system to improve quality of diagnostics and time efficiency.

In this work we present a methodology for optimal management of brownfields that is illustrated on a real field. The approach does not depend on the particular reservoir flow simulator used although streamline-derived information is leveraged to accelerate the optimization. The method allows one to include (nonlinear) constraints (e.g., recovery factor larger than a given baseline value), which are very often challenging to address with optimization tools. We rely on robust (derivative-free) optimization combined with the filter method for nonlinear constraints. It should be noted that the approach yields not only a feasible optimized solution but also a set of alternative infeasible solutions that could be considered in case the constraints can be relaxed. The whole procedure is accelerated using streamline-derived information. Performance in terms of wall-clock time can be improved further if distributed-computing resources are available (the method is amenable to parallel implementation). The methodology is showcased using a real field in West Siberia where net present value (NPV) is maximized subject to a constraint for the recovery factor (RF). The optimization variables represent a discrete time series for well bottomhole pressure over a fraction of the production time frame. An increase in NPV of 7.9% is obtained with respect to an existing baseline. The optimization methods studied include local optimization algorithms (e.g., Generalized Pattern Search) and global search procedures (e.g., Particle Swarm Optimization). We provide solutions with different levels of approximation and computational efficiency. Without the acceleration achieved through streamline-derived information, the method, while effective, could be prohibitive in many practical scenarios. It is worthwhile noting that part of the solution determined in this work has been tested out on the real field. Optimal management of brownfields is typically addressed using bottomhole pressure values or rates as well control variables. Well controls given as bottomhole pressure values, although not directly implementable in the real field, are often much easier to put into practice than if they are given as rates. However, optimization algorithms that deal with well rates as control variables can be in many cases computationally faster than methods based on bottomhole pressure values. In this work we combine the two aforementioned desirable features for the optimal management of mature fields: well controls are given as bottomhole pressure values for a more practical implementation, and these values are also determined efficiently using concepts borrowed from optimization via well rates.

Summary A lot of anomalies can occur and lead to failures during drilling process. It is crucial to detect these deviations from normal process as soon as possible, so engineers can analyse and decide what activities to take in order to prevent potential NPT. In this work we propose a new machine learning based approach for detection abnormal drilling behaviour in an online manner. The idea is to cluster drilling data, which is preprocessed in a very special way. Our aproach allows using all available data for training as it does not need any labeled data and incorporates both raw drilling parameters and expert knowledge, thus enhancing prediction results.

This paper is devoted to the testing of of automatic well logs interpretation testing, developed on the basis of machine learning methods. The basis of the method presented in the paper is recurrent artificial neural networks. For their training and adjustment, log curves and their corresponding interpretation of different years are used. The set of well data is divided into training, validation and test samples. The resulting tool is set up on training and validation samples, and then used on a test sample of wells for which the interpretatoin was hidden, in order to automatically predict net pays and compare the results with the interpretation performed by an expert petrophysicist. For the test sample, traditional machine learning metrics metrics and special geological were calculated to assess the quality of the algorithm. During the work a number of experiments were carried out, in which the dependence of the forecast quality was estimated not only on the different architecture and settings of the artificial neural network, but also on the amount of input data. The iterative approach in the research allowed to determine the best parameters for the solution of the task. For each well of the test set, a forecast of reservoir intervals distribution is made. The resulting interpretation shows high accuracy, both in terms of different mathematical metrics, and the results of analysis and evaluation of the expert petrophysics. Also, during the experiments, an important conclusion was made about the generalizing ability of the proposed methodology. The use of several variants of interpretation of well log data, performed by different specialists at different times and on the basis of different petrophysical models, allows to generalize and use all the accumulated experience of well logs interpretation, thereby improving the quality of the forecast. The main conclusion of the study can be considered a statement about the efficient applicability of machine learning algorithms for automatic well logs interpretation.

In this paper we present an enhanced and new optimization algorithm based on Particle Swarm Optimization (PSO) that can be used in reservoir characterization applications to determine multiple solutions that reproduce measurements within satisfactory accuracy. The method is illustrated on a history-matching problem where significant speed-up is obtained with respect to a standard PSO implementation. PSO is a global-search iterative technique where the solution space is explored using a swarm of particles. In every PSO iteration each particle is individually attracted to: the best point (in terms of objective function) in the swarm during last iteration, the best point visited by that particle in all iterations, and the best point found by any particle in all iterations. In the PSO variant proposed here, rather than single points, collections of points carefully selected are used as attractors. The algorithm ensures by a series of mechanisms that the number of attractors is adequate and that the solutions progressively determined are sufficiently different from one another. A series of experiments were performed with a synthetic model based on a real oil field for which nine years of historical data were generated. Both standard PSO and the PSO variant were tested to obtain several models that reproduced well rates within acceptable accuracy. In order to determine multiple solutions via standard PSO (which usually returns only one solution), a number of independent runs with different initial conditions were considered (i.e., standard PSO was run in multi-start fashion). In our experiments, which had a budget of 3,072 simulations in the two cases, the number of solutions found on average by standard PSO and by our PSO variant was 8 and 837, respectively. That is, multi-start standard PSO may require, on average, around 100 times more simulations than our PSO variant to compute a comparable number of models (possibly) distributed in similar manner in the solution space. These results may be explained by noticing that the search for multiple solutions may be more efficient when all information is considered simultaneously than separately by means of a number of independent runs. Standard PSO, when applied to many uncertainty quantification problems, may provide only one solution because the particles in the swarm generally tend to converge to essentially a single point. The Ensemble Kalman Filter (EnKF), which has lately become a very popular history-matching algorithm, presents a similar problem because the ensemble sometimes collapses into one solution. The PSO variant introduced in this work can be an efficient alternative to these algorithms in reservoir characterization since it has been designed to provide multiple and different solutions in a single run.

In the study there are introduced several control methods that maximize average velocity of the vibratory robot subject to several constraints. The robot is presented by a rigid box with a pendulum inside it. It can move forwards and backwards and there is a Coulomb friction between the box and the surface. In the paper it is not only shown the difference and advantages of proposed control laws but there is also done a comparison between efforts done by the motor that provides rotation of the pendulum for different control methods.

The subject of the study is control synthesis for a vibratory robot, which is presented by a rigid box with a pendulum inside it. The robot can move forward and backward and there is a Coulomb friction between the box and the surface. There are considered certain constraints of motion of the robot and limitations of control. In this paper, there is shown that the proposed control not only provides motion within the constraints and limitations but also maximizes average velocity of the robot. This control contains two phases on each period: resting phase where the box is standing still and gliding phase where the robot is moving forward. To show the advantages of the proposed solution there is provided numerical comparison between different types of control. Numerical experiments also show that the considered control appears to be energetically efficient.