Skilful Precipitation Nowcasting Using NowcastNet

Designing early warning system for precipitation requires accurate short-term forecasting system. Climate change has led to an increase in frequency of extreme weather events, and hence such systems can prevent disasters and loss of life. Managing such events remain a challenge for both public and private institutions. Precipitation nowcasting can help relevant institutions to better prepare for such events as they impact agriculture, transport, public health and safety, etc. Physics-based numerical weather prediction (NWP) is unable to perform well for nowcasting because of large computational turn-around time. Deep-learning based models on the other hand are able to give predictions within seconds. We use recently proposed NowcastNet, a physics-conditioned deep generative network, to forecast precipitation for different regions of Europe using satellite images. Both spatial and temporal transfer learning is done by forecasting for the unseen regions and year. Model makes realistic predictions and is able to outperform baseline for such a prediction task.

Precipitation Nowcasting With Spatial And Temporal Transfer Learning Using Swin-UNETR

Climate change has led to an increase in frequency of extreme weather events. Early warning systems can prevent disasters and loss of life. Managing such events remain a challenge for both public and private institutions. Precipitation nowcasting can help relevant institutions to better prepare for such events. Numerical weather prediction (NWP) has traditionally been used to make physics based forecasting, and recently deep learning based approaches have been used to reduce turn-around time for nowcasting. In this work, recently proposed Swin-UNETR (Swin UNEt TRansformer) is used for precipitation nowcasting for ten different regions of Europe. Swin-UNETR utilizes a U-shaped network within which a swin transformer-based encoder extracts multi-scale features from multiple input channels of satellite image, while CNN-based decoder makes the prediction. Trained model is capable of nowcasting not only for the regions for which data is available, but can also be used for new regions for which data is not available.

Multi-stage, multi-swarm PSO for joint optimization of well placement and control

Evolutionary optimization algorithms, including particle swarm optimization (PSO), have been successfully applied in oil industry for production planning and control. Such optimization studies are quite challenging due to large number of decision variables, production scenarios, and subsurface uncertainties. In this work, a multi-stage, multi-swarm PSO (MS2PSO) is proposed to fix certain issues with canonical PSO algorithm such as premature convergence, excessive influence of global best solution, and oscillation. Multiple experiments are conducted using Olympus benchmark to compare the efficacy of algorithms. Canonical PSO hyperparameters are first tuned to prioritize exploration in early phase and exploitation in late phase. Next, a two-stage multi-swarm PSO (2SPSO) is used where multiple-swarms of the first stage collapse into a single swarm in the second stage. Finally, MS2PSO with multiple stages and multiple swarms is used in which swarms recursively collapse after each stage. Multiple swarm strategy ensures that diversity is retained within the population and multiple modes are explored. Staging ensures that local optima found during initial stage does not lead to premature convergence. Optimization test case comprises of 90 control variables and a twenty year period of flow simulation. It is observed that different algorithm designs have their own benefits and drawbacks. Multiple swarms and stages help algorithm to move away from local optima, but at the same time they may also necessitate larger number of iterations for convergence. Both 2SPSO and MS2PSO are found to be helpful for problems with high dimensions and multiple modes where greater degree of exploration is desired.

Hybrid Evolutionary Optimization Approach for Oilfield Well Control Optimization

Oilfield production optimization is challenging due to subsurface model complexity and associated non-linearity, large number of control parameters, large number of production scenarios, and subsurface uncertainties. Optimization involves time-consuming reservoir simulation studies to compare different production scenarios and settings. This paper presents efficacy of two hybrid evolutionary optimization approaches for well control optimization of a waterflooding operation, and demonstrates their application using Olympus benchmark. A simpler, weighted sum of cumulative fluid (WCF) is used as objective function first, which is then replaced by net present value (NPV) of discounted cash-flow for comparison. Two popular evolutionary optimization algorithms, genetic algorithm (GA) and particle swarm optimization (PSO), are first used in standalone mode to solve well control optimization problem. Next, both GA and PSO methods are used with another popular optimization algorithm, covariance matrix adaptation-evolution strategy (CMA-ES), in hybrid mode. Hybrid optimization run is made by transferring the resulting population from one algorithm to the next as its starting population for further improvement. Approximately four thousand simulation runs are needed for standalone GA and PSO methods to converge, while six thousand runs are needed in case of two hybrid optimization modes (GA-CMA-ES and PSO-CMA-ES). To reduce turn-around time, commercial cloud computing is used and simulation workload is distributed using parallel programming. GA and PSO algorithms have a good balance between exploratory and exploitative properties, thus are able identify regions of interest. CMA-ES algorithm is able to further refine the solution using its excellent exploitative properties. Thus, GA or PSO with CMA-ES in hybrid mode yields better optimization result as compared to standalone GA or PSO algorithms.

Uncertainty-Aware Surrogate Model For Oilfield Reservoir Simulation

Deep neural networks have gained increased attention in machine learning, but they are limited by the fact that many such regression and classification models do not capture prediction uncertainty. Though this might be acceptable for certain non-critical applications, it is not so for oil and gas industry applications where business and economic consequences of wrong or even sub-optimal decision is quite high. In this work I discuss the application of deep neural networks as a framework for approximate Bayesian inference in oilfield reservoir simulation study. Surrogate models with different neural network architecture are proposed to speed up compute- and labor-intensive simulation workflow. Regularization tools such as dropout and batch normalization, variational autoencoder for regression, and probabilistic distribution layers are used to quantify prediction uncertainty. Monte-Carlo dropout approach is further applied to estimate uncertainty given by standard deviation values for the predictions. Probabilistic distribution layers are used to compare its efficacy in capturing the model prediction uncertainty with respect to deterministic neural layers. Deep ensemble approach is also used to train multiple surrogates which capture uncertainty. Among different models tested, VAE based regression model with multivariate-normal latent features works best for prediction uncertainty assessment. Compute time required by surrogate model for prediction is a small fraction of that for full-physics reservoir simulator. Prediction uncertainty information can be used in various simulation workflows to decide when to use surrogate model and when to further explore the solution space using reservoir simulator, thus reducing total computational cost.