... intelligence deconvolution offshore pipeline proceedings robotic fish neural network machine learning computer vision ambient light detection ieee transaction algorithm application underwater image offshore technology conference underwater environment recognition restoration platform...

... detection speed and high false detection rate still exists. While many recent works of literature have attempted to solve the influx detection problem with machine learning algorithms, only a few of them have considered the time series information in real-time drilling data. Since there may be lags...

.... In such domains, the integration of sonic log-derived information into the joint inversion problem is critical as the scale is fundamentally different. Our algorithm respects the resolution of sonic measurement, while it calibrates the wavelength where microseismic data have sensitivity: the number of unknown...

... vary significantly, thus it is of paramount importance to accurately detect lithology changes and formation tops while drilling. In order to do so, geologic data and logs are often utilized by experts and operators to identify lithological variations. Machine learning algorithms and random forest have...

... & gas deep learning slug catcher asia government flowrate uae government technology conference correlation neural network israel government machine learning algorithm field measurement united states government artificial intelligence forecasting accuracy information optimization phase...

... floating production storage and offloading floating production storage & offloading consequence equipment item upstream oil & gas safeguard causer algorithm hazop query information recommendation database flow direction instrument deviation floating production system fpso node...

.... This paper demonstrates how fully validated signal data from geothermal facility sensors optimize the algorithms dependent on this data. Individual validations of signals are shown and compared to other data streams related to this process. We hypothesize that while some unqualified data may indicate future...

... line failure detection by authors of this paper have validated various methods incorporating environmental conditions – wind, wave and current – into predictions of vessel position or response. Without the failure detection algorithm having an awareness of the prevailing environmental conditions...

... & gas formation tester onepetro classification variance correlation machine learning formation testing station depth database different well wft response algorithm offshore technology conference good point recommendation latent space application petrophysical parameter dataset wft...

... witnessed impressive growth since the ML technology is sufficiently precise, quick, reliable, and cost-effective to model various linear and non-linear problems. In this study, three robust ML algorithms comprising the Decision Tree Regression (DTR), Random Forest Regression (RFR), and Extra Tree...

... computational cost. Machine Learning systems are based on learning algorithms, which find relationships between sensor data and output variables in a training dataset. The approach requires a good understanding of the learning algorithms and statistics. Small datasets or changes in operational conditions limit...

... perspective. artificial intelligence background detection machine learning industrial facility variation application algorithm optimization problem accuracy productivity turnaround health & medicine dataset inspection monitoring safety milestone automation offshore technology...

... metric assumption total distance travelled scenario reduction distance travelled material cargo flexibility offshore technology conference time window algorithm delivery Introduction This paper explores two case studies indicating how artificial intelligence can be used to automate...

...Comparison After finalizing optimized versions of the computational intelligence models, the next phase is to constitute a comparative analysis of all developed models. This will aid in identifying the most effective intelligent algorithm for the case of ROP prediction for this dataset...

... & gas metals & mining neural network recall score tsim efficiency condenser system sensor fouling state precision score subscale condenser system bundle exchanger condenser accuracy prediction algorithm fouling rapid decarbonization flow rate anti-fouling treatment offshore...

... neural network upstream oil & gas adsorption isotherm prediction cortés scale inhibition adsorption franco algorithm error magnitude asphaltene remediation remediation of hydrates accuracy asphaltene adsorption nanoparticle asphaltene inhibition concentration ai agent asphaltene...

... spontaneous leak detection algorithm sensor negative pressure wave npw-based sensing leak offshore technology conference leak detection application offshore riser Introduction For oil and gas operators, unplanned releases of product from spontaneous pipeline leaks are a serious risk...

... offshore technology conference transparency wellbore design assurance database algorithm franklin Introduction Recent market trends have increased focus on efficient management of well barriers. Continuing technology progression and development is critical to reducing costs while ensuring...

... reservoir simulation stosag algorithm constraint npv perturbation npv value mnpv function waterflooding algorithm reservoir life unconstrained rc stosag cost function mnpv stosag Introduction In the field of the reservoir engineering, optimization problems are well discussed, due...

... based on its performance, agility, and functionality. XGBoost is a decision-tree-based ensemble Machine Learning algorithm that uses a gradient boosting framework. XGBoost is built upon 4 core concepts: supervised machine learning, decision trees, ensemble learning, and gradient boosting. Abstract...