Well production optimization using streamline features-based objective function and Bayesian adaptive direct search algorithm

Well production optimization is a complex and time-consuming task in the oilfield development.The combination of reservoir numerical simulator with optimization algorithms is usually used to optimize well production.This method spends most of computing time in objective function evaluation by reservoir numerical simulator which limits its optimization efficiency.To improve optimization efficiency,a well production optimization method using streamline features-based objective function and Bayesian adaptive direct search optimization(BADS)algorithm is established.This new objective function,which represents the water flooding potential,is extracted from streamline features.It only needs to call the streamline simulator to run one time step,instead of calling the simulator to calculate the target value at the end of development,which greatly reduces the running time of the simulator.Then the well production optimization model is established and solved by the BADS algorithm.The feasibility of the new objective function and the efficiency of this optimization method are verified by three examples.Results demonstrate that the new objective function is positively correlated with the cumulative oil production.And the BADS algorithm is superior to other common algorithms in convergence speed,solution stability and optimization accuracy.Besides,this method can significantly accelerate the speed of well production optimization process compared with the objective function calculated by other conventional methods.It can provide a more effective basis for determining the optimal well production for actual oilfield development.

Inference of Median Subjective Threshold in Psychophysical Experiments

We consider the response of a test subject upon a skin area being heated with an electromagnetic wave or a contact surface. When the specifications of the electromagnetic beam are fixed, the stimulus is solely described by the heating duration. The binary response of a subject, escape or no escape, is determined by the stimulus and a subjective threshold that varies among test realizations. We study four methods for inferring the median subjective threshold in psychophysical experiments: 1) sample median, 2) maximum likelihood estimation (MLE) with 2 variables, 3) MLE with 1 variable, and 4) adaptive Bayesian method. While methods 1 - 3 require samples of time to escape measured in the method of limits, method 4 utilizes binary outcomes observed in the method of constant stimuli. We find that a) the adaptive Bayesian method converges and is as efficient as the sample median even when the assumed model distribution is incorrect;b) this robust convergence is lost if we infer the mean instead of the median;c) for the optimal performance in an uncertain situation, it is best to use a wide model distribution;d) the predicted error from the posterior standard deviation is unreliable, dominated by the assumed model distribution.

High-quality quasi-monochromatic near-field radiative heat transfer designed by adaptive hybrid Bayesian optimization

The increasing demand for versatile and high-quality near-field radiative heat transfer(NFRHT) has created a critical need for a design approach that can handle numerous candidate structures. In this work, we employ and develop an adaptive hybrid Bayesian optimization(AHBO) algorithm to design the high-quality quasi-monochromatic NFRHT. The candidate materials include hexagonal boron nitride, silicon carbide, and doped silicon. The high-quality quasi-monochromatic NFRHT is optimized over 1.0 × 10^(8) candidate structures to maximize the evaluation factor. It is worth noting that only 2.6% of the candidate structures needed to be calculated to identify the optimal structure. The optimal structure of quasi-monochromatic NFRHT is an aperiodic multilayer metamaterial that differs from conventional periodic multilayer structures. Moreover, we investigate the robustness and mechanisms of the optimal quasi-monochromatic NFRHT with respect to the vacuum gap distance and the temperature difference between the emitter and receiver. In addition, the high-quality multi-peak NFRHT is designed using the AHBO algorithm by improving the definition of the evaluation factor. The results demonstrate that the AHBO algorithm is efficient in designing high-quality quasi-monochromatic and multi-peak NFRHT, and it can be further expanded to other structural designs in the field of energy conversion.

Enhanced elastic beam model with BADS integrated for settlement assessment of immersed tunnels

Excessive settlement may induce structural damage and water leakage in immersed tunnels,seriously threatening the tunnels’safety.However,making accurate assessment of the settlement in immersed tunnels is difficult due to the incomplete knowledge of the geotechnical parameters and the inadequacy of the model itself.This paper proposes an effective method to accurately assess the settlement in immersed tunnels.An enhanced beam on elastic foundation model(E-BEFM)is developed for the settlement assessment,with the Bayesian adaptive direct search algorithm adopted to estimate unknown model parameters based on previous observations.The proposed method is applied to a field case of the Hong Kong–Zhuhai–Macao immersed tunnel.The original BEFM is used for comparison to highlight the better assessment performance of E-BEFM,particularly for joints’differential settlement.Results show that the proposed method can provide accurate predictions of the total settlement,angular distortion(a representation of tubes’relatively differential settlement),and joints’differential settlement,which consequently supports the associated maintenance decision-making and potential risk prevention for immersed tunnels in service.

Strip width spread prediction in rough rolling process based on mechanism modeling and optimization

Aiming at the problems of low accuracy and poor robustness that existed in the current hot rolling strip width spread model,an improved strip spread prediction model based on a material forming mechanism and Bayesian optimized adaptive differential evolution algorithm(BADE)was proposed.At first,we improved the original spread mechanism model by adding the weight and bias term to enhance the model robustness based on rolling temperature.Then,the BADE algorithm was proposed to optimize the improved spread mechanism model.The optimization algorithm is based on a novel adaptive differential evolution algorithm,which can effectively achieve the global optimal solution.Finally,the prediction performances of five machine learning algorithms were compared in experiments.The results show that the prediction accuracy of the improved spread model is obviously better than that of the machine learning algorithms,which proves the effectiveness of the proposed method.