Bayesian Neural Networks for Virtual Flow Metering: An Empirical Study

Recent works have presented promising results from the application of machine learning (ML) to the modeling of flow rates in oil and gas wells. Encouraging results and advantageous properties of ML models, such as computationally cheap evaluation and ease of calibration to new data, have sparked optimism for the development of data-driven virtual flow meters (VFMs). Data-driven VFMs are developed in the small data regime, where it is important to question the uncertainty and robustness of models. The modeling of uncertainty may help to build trust in models, which is a prerequisite for industrial applications. The contribution of this paper is the introduction of a probabilistic VFM based on Bayesian neural networks. Uncertainty in the model and measurements is described, and the paper shows how to perform approximate Bayesian inference using variational inference. The method is studied by modeling on a large and heterogeneous dataset, consisting of 60 wells across five different oil and gas assets. The predictive performance is analyzed on historical and future test data, where an average error of 4-6% and 8-13% is achieved for the 50% best performing models, respectively. Variational inference appears to provide more robust predictions than the reference approach on future data. Prediction performance and uncertainty calibration is explored in detail and discussed in light of four data challenges. The findings motivate the development of alternative strategies to improve the robustness of data-driven VFMs.Comment: 34 pages, 11 figure

Mutations in \u3ci\u3eDMRT3\u3c/i\u3e Affect Locomotion in Horses and Spinal Circuit Function in Mice

Locomotion in mammals relies on a central pattern-generating circuitry of spinal interneurons established during development that coordinates limb movement. These networks produce left–right alternation of limbs as well as coordinated activation of flexor and extensor muscles. Here we show that a premature stop codon in the DMRT3 gene has a major effect on the pattern of locomotion in horses. The mutation is permissive for the ability to perform alternate gaits and has a favorable effect on harness racing performance. Examination of wild-type and Dmrt3-null mice demonstrates that Dmrt3 is expressed in the dI6 subdivision of spinal cord neurons, takes part in neuronal specification within this subdivision, and is critical for the normal development of a coordinated locomotor network controlling limb movements. Our discovery positions Dmrt3 in a pivotal role for configuring the spinal circuits controlling stride in vertebrates. The DMRT3 mutation has had a major effect on the diversification of the domestic horse, as the altered gait characteristics of a number of breeds apparently require this mutation

UAV Path Planning using MILP with Experiments

In this paper, we look at the problem of tracking icebergs using multiple Unmanned Aerial Vehicles (UAVs). Our solutions use combinatorial optimization for UAV path planning by formulating a mixed integer linear programing (MILP) optimization problem. To demonstrate the approach, we present both a simulation and a practical experiment. The simulation demonstrates the possibilities of the MILP algorithm by constructing a case where three UAVs help a boat make a safe passage through an area with icebergs. Furthermore, we compare the performance of three against a single UAV. In the practical experiment, we take the first step towards full-scale experiments. We run the algorithm on a ground station and use it to set the path for a UAV tracking five simulated icebergs

Embedded Model Predictive Control on a PLC Using a Primal-DualFirst-Order Method for a Subsea Separation Process

The results of a PLC implementation of embedded Model Predictive Control (MPC) for an industrial problem are presented in this paper. The embedded MPC developed is based on the linear MPC module in SEPTIC (Statoil Estimation and Prediction Tool for Identification and Control), and it combines custom ANSI C code generation with problem size reduction methods, embedded real-time considerations, and a primal-dual first-order method that provides a fast and light QP solver obtained from the FiOrdOs code generator toolbox. Since the primal-dual first-order method proposed in this paper is new in the control community, an extensive comparison study with other state-of-the-art first-order methods is conducted to underline its potential. The embedded MPC was implemented on the ABB AC500 PLC, and its performance was tested using hardware-in-the-loop simulation of Statoil's newly patented subsea compact separation process. A warm-start variant of the proposed first-order method outperforms a tailored interior-point method by a factor of 4 while occupying 40% less memory

Mutations in DMRT3 affect locomotion in horses and spinal circuit function in mice

Locomotion in mammals relies on a central pattern-generating circuitry of spinal interneurons established during development that coordinates limb movement(1). These networks produce left–right alternation of limbs as well as coordinated activation of flexor and extensor muscles(2). Here we show that a premature stop codon in the DMRT3 gene has a major effect on the pattern of locomotion in horses. The mutation is permissive for the ability to perform alternate gaits and has a favourable effect on harness racing performance. Examination of wild-type and Dmrt3-null mice demonstrates that Dmrt3 is expressed in the dI6 subdivision of spinal cord neurons, takes part in neuronal specification within this subdivision, and is critical for the normal development of a coordinated locomotor network controlling limb movements. Our discovery positions Dmrt3 in a pivotal role for configuring the spinal circuits controlling stride in vertebrates. The DMRT3 mutation has had a major effect on the diversification of the domestic horse, as the altered gait characteristics of a number of breeds apparently require this mutation