... 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...

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... 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...

... 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...

.... 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...

... metocean condition modeling & simulation environmental condition position data machine learning mooring system accuracy fpso api rp 2mim offshore technology conference operational data proceedings algorithm prediction detection algorithm Introduction The importance of mooring...

.... real time system artificial intelligence well logging log analysis upstream oil & gas formation tester onepetro classification variance correlation machine learning formation testing station depth database different well wft response algorithm offshore technology conference good...

... 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...

... 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 the suitability of this approach. Hybrid Models combine Physics...

... 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...

... to significant advances in test validation algorithms efficiency and robustness beyond industry standard validation criteria. Utilizing advanced algorithms to verify BOP integrity provides the highest levels of well barrier assurance. Linking rig-site BOP testing and control system monitoring through a cloud...

... 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...

... learning well logging reservoir geomechanics upstream oil & gas wellbore log compaction trend calculation log analysis accuracy prediction drilling event algorithm machine learning application decision tree depth-dependent normal compaction trendline pore pressure prediction hazardous...

... distributions (based on core data) are comparatively modeled with well-log data using two tree-based ensemble algorithms: extreme gradient boosting (XGBoost) and adaptive boosting (AdaBoost) configured as classifiers. The predicted lithofacies are then combined with recorded well-log data for analysis...

.... 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 conference scenario...

... 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...

.... Consequently, optimizing and predicting ROP is a complex challenge faced by the industry. This study proposes a novel computational intelligence-based (CI) model to predict ROP as a function of multiple readily available mud- and petrophysical- logs. Furthermore, multiple algorithms are utilized...

... & 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...