Machine Learning Based Approach to Predict Fuel Consumption on Mobile Offshore Drilling Units

Master's thesis in Mechanical EngineeringThe use of machine learning models for optimization and improved decision-making has a great potential in the drilling industry. This thesis demonstrates a model for predicting fuel consumption on the Mobile Offshore Drilling Unit (MODU) Deepsea Atlantic, which is a semi-submersible drilling rig currently operating on the Fram field in the North Sea. A Multi-layer Perceptron (MLP) artificial neural network is proposed as a tool for setting fuel consumption related performance goals for offshore personnel on the MODU. A dashboard layout for presenting fuel related performance goals to offshore personnel based on the predictive model is also proposed in this thesis. This method for presenting performance goals is inspired by Equinor’s ”Perfect well” and Shell’s ”Drilling the Limit” performance philosophies. Implementing performance goals for offshore personnel has the potential to develop a pursuit of operational excellence through a collaborative and competitive mindset, and as a result lead to a significant improvement in fuel efficiency. Operational modes, environmental and positional data have been used as input variables for the MLP model with a dataset split into an 80 % training set and a 20% testset for performance validation. The best results came with three hidden layers in the neural network architecture with 38 neurons in each hidden layer. The Adam solver performs better than the Stochastic Gradient Descent (SGD) solver for weight optimization, and the best α parameter for the L2 regularization term is 0.0001 with the Adam solver. The MLP regression model predicts the fuel consumption for the test set with a Root Mean Square Error (RMSE) of 0.0770. Results indicate that an artificial neural network and the MLP regressor is a suitable algorithm for predictive modelling of fuel consumption on a MODU

An Artificial Intelligence Approach to Tumor Volume Delineation

Postponed access: the file will be accessible after 2023-11-14Masteroppgave for radiograf/bioingeniørRABD395MAMD-HELS

Smart Surfaces: Design, Structuring and Characterization

NANO399MAMN-NAN

Adaptive training with cloud-based simulators in maritime education

Maritime cloud-based simulation is an emerging technological development that creates a new condition for decentralized interaction where it\u27s content and functionality mirrors traditional on-site-simulator software. This paper uses a quasi-experimental study to examine a training design that is adaptive to the trainee. The training goal is to deliver traditional learning outcomes of comprehension and familiarity with the operation of steering gear systems. The simulator training was administered through novel cloud-based simulator technology to a sample comprising of first year students in nautical sciences (n=12) and marine engineering (n=6) at the college and university level in Norway who had no previous education or operational level experience with steering gear systems in their respective programmes. All participants (N=18) were first subjected to a knowledge acquisition phase of video conference lectures before conducting a simulator training scenario of a standardized pre-departure procedure. Data was collected from 3 sources: (1) a multiple-choice knowledge test, (2) programmed simulator performance indicators, and (3) the Self-Efficacy for Learning and Performance scale. Initial results show that the level of student\u27s self-efficacy predicts the final training performance, and the level of knowledge prior to training is not significant for the outcome

Can Survey Measures Predict Key Performance Indicators of Safety? Confirmatory and Exploratory Analyses of the Association Between Self-Report and Safety Outcomes in the Maritime Industry

Safety management may be improved if managers implement measures based on reliable empirical knowledge about how psychological factors cause or prevent accidents. While such factors are often investigated with self-report measures, few studies in the maritime industry have investigated whether self-report measures predict objectively registered accidents. The current pre-registered study used structural equation modelling to test whether “Safety attitude,” “Situation awareness,” “Reporting attitude” and “Safe behaviour” predicted “Number of reports” and “Number of safety events” in the following year. The study was conducted among crew on chemical tanker vessels operating in Arctic and Baltic waters. The pre-registered model of expected associations between self-reported safety factors and recorded safety outcomes was not supported. However, an exploratory model based on the pre-registered hypotheses supported an association between self-reported “Safe behaviour” and the overall number of recorded safety outcomes. While much safety research in the maritime industry builds on the assumption that self-reported behaviour, attitude or cognitions are causally related to actual accidents, the current study shows that such a relationship can be difficult to confirm. Until more conclusive studies are performed, the assumed causal relationship between self-reported psychological factors and safety outcomes should be treated with caution.publishedVersio

Assessing the precision of frequency distributions estimated from trawl-survey samples

In trawl surveys a cluster of fish are caught at each station, and fish caught together tend to have more similar characteristics, such as length, age, stomach contents etc., than those in the entire population. When this is the case, the effective sample size for estimates of the frequency distribution of a population characteristic can, therefore, be much smaller than the number of fish sampled during a survey. As examples, it is shown that the effective sample size for estimates of length-frequency distributions generated by trawl surveys conducted in the Barents Sea, off Namibia, and off South Africa is on average approximately one fish per tow. Thus many more fish than necessary are measured at each station (location). One way to increase the effective sample size for these surveys and, hence, increase the precision of the length-frequency estimates, is to reduce tow duration and use the time saved to collect samples at more stations

Analysis of Norwegian Offshore Wind Power Production : Ranking wind farm locations using a composite index method

This thesis studies wind power production within the Norwegian Economic Zone, and analyzes the potential production of wind power farm locations outlined by the Norwegian Water Resource and Energy Directorate. Offshore wind farms have great potential as an energy source, but have high initial investment and maintenance costs, and finding the optimal locations for production is therefore essential. We use estimation data on offshore wind production, Norwegian energy consumption, and the filling degree of Norwegian hydropower plants. We perform a descriptive analysis, exploring how seasonal variations in wind power production relate to Norwegian electricity consumption, and how offshore wind can benefit Norwegian hydropower reservoirs. We find that in an average year, wind power production and electricity consumption will follow a similar seasonal cycle. The output potential of offshore wind power peaks during months with high electricity demand, which suggests that offshore wind is suited to Norwegian energy needs. Additionally, we find that wind power production and water levels in Norwegian reservoirs do not follow the same pattern. Water levels are at their lowest point during the spring, a period when the wind power output is still substantial. Therefore, we argue that offshore wind is a good complementary energy source for hydropower. To analyze the potential of the suggested locations we use three indicators that reflect the capability of the locations in a composite index. The index ranks the locations based on power output, stability, and correlation with Norway's electricity consumption. The three locations scoring the highest are "Sørlige Nordsjø 2", "Sørlige Nordsjø l " , and "Nordøyan - Ytre Vikna", all located in the southern half of Norway.nhhma

Correcting for avoidance in acoustic abundance estimates for herring using a generalized linear model

When a research vessel passes over a herring school or layer, the herring may avoid the vessel by swimming downwards and horizontally. The fish may also change its orientation, which may alter its mean target strength. Consequently, the echo abundance measured by the relatively narrow echo sounder beam does not always reflect the true density of the school. The fish reaction is strongest in the upper parts of the water column. This avoidance behaviour has been quantified in several experiments where a stationary, submerged transducer has been used to measure the changes in echo abundance during the passage of a survey vessel. In this paper two approaches for correcting the echo abundance for avoidance are investigated. The first approach is to correct the echo abundance in each depth layer separately; the second is to correct the total echo abundance, letting the correction depend on the mean depth of the fish at passing. In both approaches generalized linear models are fitted to the experimental data. Since the parameters are estimated with uncertainty, this uncertainty can be taken into account when the fitted models are used for correcting standard survey data

Possible opioid-saving effect of cannabis-based medicine using individual-based data from the Norwegian Prescription Database

Some ecological studies have shown that areas with higher use of cannabis may have lower opioid use and fewer opioid-related problems. Newer studies are questioning this finding. Few individually based studies have been performed. Using data from the Norwegian Prescription Database, this study investigated the individual level effect of prescribed cannabis extract (Sativex®) in prescription opioid users on their opioid use in the following year. Looking at all those filling a prescription for Sativex®, opioid use was only marginally lowered in the follow-up period. Some Sativex® users, however, filled more prescriptions for Sativex® and were able to reduce their opioid use substantially. Further studies are needed to elucidate more details on these patients, so as to know who can benefit from such cannabis-based extracts in reducing their opioid use