... 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...

... 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...

... of the calibration dataset with good accuracy. upstream oil & gas flow metering production monitoring deep learning artificial intelligence well aa neural network reservoir surveillance prediction united states government well test production control equation machine learning well test data...

... be improved by 7% using motion limit criteria and only 2% of historical data provided a rigid no-go (all limits reached). brazil government machine learning artificial intelligence upstream oil & gas top tension united states government riser assessment duncan marotta rodrigue correlation...

... false alarms and improve interpretability compared to previous methods. The framework is highly extensible, and further performance improvements can be expected in the future. asia government united states government upstream oil & gas neural network machine learning detection drilling...

... outputs the fatigue damage or remaining fatigue life, which is an essential part of the decision-making process in the digital twin framework. Machine learning-based algorithms: Extreme Gradient Boosting (XGBoost) is used to estimate the SIF through a surrogate model. The result from surrogate model...

... Abstract Machine learning (ML) models offer intriguing alternatives for multiphase pipe flow simulations. Certain subsets of ML algorithms are computationally robust and may outperform physics-based models when applied within the training range. However, they tend to deteriorate...

... inhibition machine learning division 2 histogram inspection h2s management complex reservoir artificial intelligence fatigue life crack size api 17tr8 fcgr curve fatigue analysis requirement geometry fatigue failure frequency fatigue screening test data load histogram offshore technology...

... to build a geomechanical model is to measure in the lab or calculate it from the dipole sonic log. However, it cannot be practically done routinely due to the high cost of logging and processing the dipole sonic logs. With the training of a machine learning model using conventional logging data and dipole...

... & 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...

... design model for the fabrication of USA based and produced substations. The application of this standard could shorten lead times and production times significantly. north america government sustainable development oss substation united states government sub-sea system machine learning...

... Abstract The objective of this study is to develop a data-driven machine learning based tool to estimate the FPSO topsides weight. The data were collected from public sources including IHS, news and magazines, covering world-wide active FPSO geographic locations, topsides weights...

... for their operations and business models, reduce greenhouse gas emissions, and achieve the Paris Agreement and Glasgow Climate Pact targets. A solution is integrating machine learning and geothermal energy to optimise field development to reduce CO 2 emissions while meeting energy demands. The study area...

... united states government artificial intelligence north america government wind energy wind speed machine learning wind resource model result assessment social responsibility sustainable development lidar buoy lease numerical model variability wrf model result simulation offshore...

... and challenges of turning strategy into delivery through 3-4 cases and examples. operation climate change upstream oil & gas unattended facility artificial intelligence platform europe government machine learning intervention complexity installation journey autonomous operation maturity...

... the methodology was applied. optimization problem sub-sea system upstream oil & gas risk management risk and uncertainty assessment inspection plan united states government artificial intelligence machine learning inspection maximum risk index optimization manifold information subsea...

... 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...

... Abstract A method is proposed to calculate pore pressure at the bit while drilling using all data typically available in a modern drilling operation. This method utilizes a machine learning approach that can estimate pore pressures at the same or lesser range of uncertainty as traditional...

... of the TLP, and some of these systems have been in operation for over a decade. However, the degradation of the load cells over time due to harsh environments, wear and tear, reduces the reliability of the tension measurement system. This study investigates the use of machine learning (ML) to model tendon...

... applications. Finally, we discuss operations to date using this critical exploration-enabling asset. sustainability upstream oil & gas social impact assessment united states government north america government machine learning cook islands artificial intelligence implementation marine...