... by visual inspection of the shale shakers, is to evaluate the solids return in the shale shakers. Hole cleaning and wellbore instability issues, among other events can be detected. ( Grossi, 2020 ). upstream oil & gas machine learning neural network experiment accuracy deep learning...

... neural network artificial intelligence anomaly perturbation drilling fluid management & disposal health statistics contribution monitoring sensor machine learning operation diagnosis detection interface statistics viscosity application software real-time anomaly detection...

.... The same process is repeated for each of the wells as they are in turn defined as a subject well. Results show that the framework can infer and generate logs such as GR logs in real time. neural network drilling operation deep learning norway government well logging drilling measurement...

... to infer if a reservoir formation is well consolidated or not, as a support to the selection of sand control strategies. This work proposes a statistical classification model and the usage of a memory based neural network, known as LSTM (long short-term memory) network. This model explores time series...

...Model Deep convolutional neural networks are used to develop the algorithm. The neural network architecture is based on an inception module with dimension reduction which consists of convolutional layers of different sizes followed by max pooling layers, dropout layer, fully connection layer...

... drilling operation europe government scenario machine learning neural network personnel competence trainer competency driller competency scenario management system drilling rig simulator dysfunction drilling equipment artificial intelligence simulator training trainee Background...

... equipment drilling fluid management & disposal threshold united states government neural network maintenance ae sensor accelerometer suction module drilling operation artificial intelligence sensor discharge module asia government automated alarm system selection detection spectral...

... signs of poor hole cleaning. These techniques are not limited to downlinking or heave detection, and can be applied more generally to scenarios with complex periodic signals. deep learning machine learning neural network united states government artificial intelligence international drilling...

... Abstract There is considerable value in automatically quantifying cutter damage from drill bit pictures. Current approaches do not classify cutter damage by type, i.e., broken, chipped, lost, etc. We, therefore, present a computer vision model using deep learning neural networks to automate...

... by the operator. united states government acceptance criteria neural network machine learning upstream oil & gas optimization problem real time system rop international drilling conference objective prediction drilling operation artificial intelligence vibration driller downhole vibration...

... for preventing hole cleaning problems that may lead to a stuck pipe, and well pressure management more generally. In this work, we demonstrate a Machine Learning approach to estimating downhole ECD in real-time using a deep neural network. Surface measurements that are widely available from most rigs are used...

.... The measured indicators are skin temperature, specific face movements, heart rate, and sweat. The model uses machine vision to identify key physiological parameters and a convolutional neural network to interpret them. Finally, a third algorithm correlates the stress index to specific operations. The system...

... parameters under real differential fluid pressure. machine learning optimization problem bit selection neural network artificial intelligence upstream oil & gas rock drillability modeling axial force presented cutter back rake angle real time system bit design regression correlation...

... learning generator network identification drilling operation information pipe neural network category pipe lifting drilling measurement rig job mud logger rig operation active state artificial intelligence upstream oil & gas tool active state rig pipe drill string sequence attention...

... of the design process was intently developed, considering requirements for both functionalities of the system. Neural network detection algorithms, 3D localization, and drilling data signal processing all combined to interpret rig state and use the appropriate computer vision algorithms at the correct time...

... to output the highest modeling performance and the output parameter recommendations are then uploaded on a dashboard for real time guidance. neural network drilling process drilling operation accuracy machine learning deep learning drilling parameter rop different drilling environment...

... associated with stick/slip classes and their probabilities. This paper presents a method that uses a physics-guided neural network (PGNN). The network leverages the physics models of drillstring systems and reinforces it with machine-learning models, such as fully connected neural networks, recurrent neural...

.... The first step of the model performs time series predictions using a Recurrent Neural Network (RNN) with Walk Forward Validation. The second part of the model uses a Random Forest Classifier to classify the predictions from the previous step and determine if a stuck situation is likely. The classification...

... stringer stringer position trip risk log neural network upstream oil & gas variation representation well path Introduction There is an upcoming large operational expenditure with thousands of drilling projects over the next decades regardless the scope of such: oil and gas, geothermal...

.... reservoir characterization logging while drilling drilling data acquisition drilling measurement deep learning accuracy neural network muddy micaceous sandstone upstream oil & gas drilling parameter virtual log model lwd structural geology micaceous sandstone stringer machine learning...