NEURAL NETWORK INVERSION OF RESISTIVITY DATA FOR DETERMINATION OF DISTANCE TO A BED BOUNDARY

Accurate real-time estimation of a distance to the nearest bed boundary simplifies the steering of directional wells. For estimation of that distance, we propose an approach of pointwise inversion of resistivity data using neural networks based on two-layer resistivity formation model. The model parameters are determined from the tool responses using a cascade of neural networks. The first network calculates the resistivity of the layer containing the tool measure point. The subsequent networks take as input the tool responses and the model parameters determined with the previous networks. All networks are trained on the same synthetic database. The samples of that database consist of the pairs of model parameters and corresponding noisy tool responses. The results of the proposed approach are close to the results of the general inversion algorithm based on the method of the most-probable parameter combination. At the same time, the performance of the proposed inversion is several orders faster.

Neural Network Inversion-Based Model for Predicting an Optimal Hardware Configuration

Deciding the number of processors that can efficiently speed-up solving a computationally intensive problem while perceiving efficient power consumption constitutes a major challenge to researcher in the HPC high performance computing realm. This paper exploits machine learning techniques to propose and implement a recommender system that recommends the optimal HPC architecture given the problem size. An approach for multi-objective function optimization based on neural network (neural network inversion) is employed. The neural network inversion approach is used for forward problem optimization. The objective functions in concern are maximizing the speedup and minimizing the power consumption. The recommendations of the proposed prediction systems achieved more than 89% accuracy for both validation and testing set. The experiments were conducted on 2500 CUDA core on Tesla K20 Kepler GPU Accelerator and Intel(R) Xeon(R) CPU E5-2695 v2.