.... By developing tools of deep learning, it was possible to analyze the fractures and compare the results with visual interpretation. In both cases, the results indicate the predominance of fractures along roughly N-S and E-W trends. The outcomes allow the comparison and correlation to the offshore geological...

... Abstract Different parameters of a fully convolutional network (FCN) are experimented to evaluate which combination predicts sound velocity models from a single configuration of seismic modeling. The evaluation is made considering some fixed parameters of the deep learning model, such as number...

..., a common fact in all is the need for a great number of computationally expensive reservoir simulations, hindering extensive optimizations. This paper proposes the use of deep learning algorithms in proxy models, in order to accurately replicate the behavior of the simulator by forecasting production based...

... life extension machine learning fatigue damage subsea structure predictive maintenance schedule deep learning fe model neural network platform assessment ml model offshore structure API , Recommended Practice for Planning , Designing, and Constructing Fixed Offshore Platforms...

... with the aim of supporting 3D geological modeling. The results are compared and validated with geological and petrophysical interpretation. Image-based facies recognition is challenging when applying Deep Learning techniques: 1/ the volume of released labeled data constrains the abilities to build a robust...

...-based history matching. This work reports our initial results towards the development of a robust parameterization based on Deep Learning (DL) for proper history matching of facies models with ensemble methods. The process begins with a set of prior facies realizations, which are used for training a DL...