A time domain induced polarization relaxation time spectrum inversion method based on a damping factor and residual correction

Relaxation time spectra （RTS） derived from time domain induced polarization data （TDIP） are helpful to assess oil reservoir pore structures. However, due to the sensitivity to the signal-to-noise ratio （SNR）, the inversion accuracy of the traditional singular value decomposition （SVD） inversion method reduces with a decrease of SNR. In order to enhance the inversion accuracy and improve robustness of the inversion method to the SNR, an improved inversion method, based on damping factor and spectrum component residual correction, is proposed in this study. The numerical inversion results show that the oscillation of the RTS derived from the SVD method increased with a decrease of SNR, which makes it impossible to get accurate inversion components. However, the SNR has little influence on inversion components of the improved method, and the RTS has high inversion accuracy and robustness. Moreover, RTS derived from core sample data is basically in accord with the pore-size distribution curve, and the RTS derived from the actual induced polarization logging data is smooth and continuous, which indicates that the improved method is practicable.

Residual Correction Procedure with Bernstein Polynomials for Solving Important Systems of Ordinary Differential Equations

One of the most attractive subjects in applied sciences is to obtain exact or approximate solutions for different types of linear and nonlinear systems.Systems of ordinary differential equations like systems of second-order boundary value problems(BVPs),Brusselator system and stiff system are significant in science and engineering.One of the most challenge problems in applied science is to construct methods to approximate solutions of such systems of differential equations which pose great challenges for numerical simulations.Bernstein polynomials method with residual correction procedure is used to treat those challenges.The aim of this paper is to present a technique to approximate solutions of such differential equations in optimal way.In it,we introduce a method called residual correction procedure,to correct some previous approximate solutions for such systems.We study the error analysis of our given method.We first introduce a new result to approximate the absolute solution by using the residual correction procedure.Second,we introduce a new result to get appropriate bound for the absolute error.The collocation method is used and the collocation points can be found by applying Chebyshev roots.Both techniques are explained briefly with illustrative examples to demonstrate the applicability,efficiency and accuracy of the techniques.By using a small number of Bernstein polynomials and correction procedure we achieve some significant results.We present some examples to show the efficiency of our method by comparing the solution of such problems obtained by our method with the solution obtained by Runge-Kutta method,continuous genetic algorithm,rational homotopy perturbation method and adomian decomposition method.

Ultrastructural and Immunohistochemical Characteristics of Corneal Lenticule Extracted during Correction of Residual Myopia in the Long-Term Period after SMILE

Purpose: To evaluate ultrastructural characteristics of lenticule surface extracted during correction of residual myopia in patients after small-incision lenticule extraction (SMILE). Methods and material: This study had a prospective, consecutive, comparative design. Sixteen patients (16 eyes) underwent additional intervention for residual myopia correction after SMILE. 16 specimens of removed lenticules underwent morphological examination. Markers and reagents were used to determine actin microfilaments, neutral fats and cell nuclei. The tissue was analyzed in layers in 2D slices form, volumetric Z-stacks, or selected areas were formed in orthogonal projections. The surface of the extracted lenticule was analyzed using scanning electron microscopy. Patients’ refractive outcomes were measured postoperatively (1 day;1 and 3 months). Results: Postoperatively uncorrected distance visual acuity (20/20 or better) was in 100% cases 3 months after surgery. Ultrastructural studies have shown the difference in surfaces of the newly formed lenticule. Structural changes of the posterior lenticule surface were characterized by ruptures of collagen fibers on its surface, degenerative changes in keratocytes with signs of colliquation necrosis, cell apoptosis and F-actin in cell cytoplasm. Conclusion: Collagen fibers are immersed in the stroma on the anterior surface of the lenticule. There is no complete structure restoration of collagen fibers explaining the lack of tight adhesion of anterior and posterior surfaces of the intrastromal space even in the long-term postoperative period. There are no degenerative changes of keratocytes on the anterior lenticule surface, that is, their changes in SMILE are reversible in most cases.

A Data-Driven Rutting Depth Short-Time Prediction Model With Metaheuristic Optimization for Asphalt Pavements Based on RIOHTrack

Rutting of asphalt pavements is a crucial design criterion in various pavement design guides. A good road transportation base can provide security for the transportation of oil and gas in road transportation. This study attempts to develop a robust artificial intelligence model to estimate different asphalt pavements’ rutting depth clips, temperature, and load axes as primary characteristics. The experiment data were obtained from19 asphalt pavements with different crude oil sources on a 2.038km long full-scale field accelerated pavement test track(Road Track Institute, RIOHTrack) in Tongzhou, Beijing. In addition,this paper also proposes to build complex networks with different pavement rutting depths through complex network methods and the Louvain algorithm for community detection. The most critical structural elements can be selected from different asphalt pavement rutting data, and similar structural elements can be found. An extreme learning machine algorithm with residual correction(RELM) is designed and optimized using an independent adaptive particle swarm algorithm. The experimental results of the proposed method are compared with several classical machine learning algorithms, with predictions of average root mean squared error(MSE), average mean absolute error(MAE), and a verage mean absolute percentage error(MAPE) for 19 asphalt pavements reaching 1.742, 1.363, and 1.94% respectively. The experiments demonstrate that the RELM algorithm has an advantage over classical machine learning methods in dealing with non-linear problems in road engineering. Notably, the method ensures the adaptation of the simulated environment to different levels of abstraction through the cognitive analysis of the production environment parameters. It is a promising alternative method that facilitates the rapid assessment of pavement conditions and could be applied in the future to production processes in the oil and gas industry.

基于自动识别系统和门控递归装置的港口流量预测模型

Water transportation today has become increasingly busy because of economic globalization.In order to solve the problem of inaccurate port traffic flow prediction,this paper proposes an algorithm based on gated recurrent units(GRUs)and Markov residual correction to pass a fixed cross-section.To analyze the traffic flow of ships,the statistical method of ship traffic flow based on the automatic identification system(AIS)is introduced.And a model is put forward for predicting the ship flow.According to the basic principle of cyclic neural networks,the law of ship traffic flow in the channel is explored in the time series.Experiments have been performed using a large number of AIS data in the waters near Xiazhimen in Zhoushan,Ningbo,and the results show that the accuracy of the GRU-Markov algorithm is higher than that of other algorithms,proving the practicability and effectiveness of this method in ship flow prediction.

Static Corrections Methods in the Processing of Deep Reflection Seismic Data

Statics are big challenges for the processing of deep reflection seismic data. In this paper several different statics solutions have been implemented in the processing of deep reflection seismic data in South China and their corresponding results have been compared in order to find proper statics solutions. Either statics solutions based on tomographic principle or combining the low-frequency components of field statics with the high-frequency ones of refraction statics can provide reasonable statics solutions for deep reflection seismic data in South China with very rugged surface topography, and the two statics solutions can correct the statics anomalies of both long spatial wavelengths and short ones. The surface-consistent residual static corrections can serve as the good compensations to the several kinds of the first statics solutions. Proper statics solutions can improve both qualities and reso- lutions of seismic sections, especially for the reflections of Moho in the upmost mantle.

Forecasting oil production in unconventional reservoirs using long short term memory network coupled support vector regression method: A case study

Production prediction is crucial for the recovery of hydrocarbon resources.However,accurate and rapid production forecasting remains challenging for unconventional reservoirs due to the complexity of the percolation process and the scarcity of available data.To address this problem,a novel model combining a long short-term memory network(LSTM)and support vector regression(SVR)was proposed to forecast tight oil production.Three variables,the tubing head pressure,nozzle size,and water rate were utilized as the inputs of the presented machine-learning workflow to account for the influence of operational parameters.The time-series response of tight oil production was the output and was predicted by the optimized LSTM model.An SVR-based residual correction model was constructed and embedded with LSTM to increase the prediction accuracy.Case studies were carried out to verify the feasibility of the proposed method using data from two wells in the Ma-18 block of the Xinjiang oilfield.Decline curve analysis(DCA)methods,LSTM and artificial neural network(ANN)models were also applied in this study and compared with the LSTM-SVR model to prove its superiority.It was demonstrated that introducing residual correction with the newly proposed LSTM-SVR model can effectively improve prediction performance.The LSTM-SVR model of Well A produced the lowest prediction root mean square error(RMSE)of 5.42,while the RMSE of Arps,PLE Duong,ANN,and LSTM were 5.84,6.65,5.85,8.16,and 7.70,respectively.The RMSE of Well B of LSTM-SVR model is 0.94,while the RMSE of ANN,and LSTM were 1.48,and 2.32.