Joint prediction of time series data in inventory management

Abstract(#br)The problem of time series prediction has been well explored in the community of data mining. However, little research attention has been paid to the case of predicting the movement of a collection of related time series data. In this work, we study the problem of simultaneously predicting multiple time series data using joint predictive models. We observe that in real-world applications, strong relationships between different time-sensitive variables are often held, either explicitly predefined or implicitly covered in nature of the application. Such relationships indicate that the prediction on the trajectory of one given time series could be improved by incorporating the properties of other related time series data into predictive models. The key challenge is to capture..

Influence of consumer preference and government subsidy on prefabricated building developer’s decision-making: a three-stage game model

Consumer preference and government subsidies are two of the key influencing factors in the decision-making of building developers, which plays a leading role in the development of prefabricated building market. However, the majority of the existing efforts only used empirical research methods to identify the barriers of prefabricated construction, and failed to quantitatively study the interaction mechanism, process, and trends among the influencing factors. To address this knowledge gap, this study aims to analyze and quantify the dynamic and interactive relationships among the three major stakeholders in the prefabricated building industry – the government, building developers, and consumers. A three-stage game model was developed, and an analysis of two numerical simulations was conducted. The results provided equilibrium solutions for the optimal selling price and optimal assembly rate for the building developers, as well as the optimal minimum assembly rate for government subsidy. This study provides a better understanding of the interactive behaviors among the major stakeholders, and offers meaningful insights for policy design and strategic planning for promoting the development of prefabricated buildings

Identification of Diagenetic Facies Logging of Tight Oil Reservoirs Based on Deep Learning—A Case Study in the Permian Lucaogou Formation of the Jimsar Sag, Junggar Basin

As a typical tight oil reservoir in a lake basin, the Permian Lucaogou Formation of the Jimsar Sag in the Junggar Basin has great potential for exploration and development. However, at present, there are few studies on the identification of the diagenetic facies of tight oil reservoir logging in the study area, and the control effect of diagenesis on tight oil reservoirs is not clear. The present work investigates the diagenesis and diagenetic facies logging of the study area, making full use of core data, thin sections, and logs, among other data, in order to understand the reservoir characteristics of the Permian Lucaogou Formation in the Jimsar Sag. The results show that the Lucaogou Formation has undergone diagenetic activity such as compaction, carbonate cementation, quartz cementation, and clay mineral infilling and dissolution. The diagenetic facies are classified according to mineral and diagenetic type, namely, tightly compacted facies, carbonate-cemented facies, clay mineral-filling facies, quartz-cemented facies, and dissolution facies. The GR, RT, AC, DEN, and CNL logging curves were selected, among others, and the convolutional neural network was introduced to construct a diagenetic facies logging recognition model. The diagenetic facies of a single well was divided and identified, and the predicted diagenetic facies types were compared with thin sections and SEM images of the corresponding depths. Prediction results had a high coincidence rate, which indicates that the model is of a certain significance to accurately identify the diagenetic facies of tight oil reservoirs. Assessing the physical properties of the studied reservoirs, dissolution facies are the dominant diagenetic facies in the study area and are also the preferred sequence for exploration—to find dominant reservoirs in the following stage

Identification of Diagenetic Facies Logging of Tight Oil Reservoirs Based on Deep Learning—A Case Study in the Permian Lucaogou Formation of the Jimsar Sag, Junggar Basin

As a typical tight oil reservoir in a lake basin, the Permian Lucaogou Formation of the Jimsar Sag in the Junggar Basin has great potential for exploration and development. However, at present, there are few studies on the identification of the diagenetic facies of tight oil reservoir logging in the study area, and the control effect of diagenesis on tight oil reservoirs is not clear. The present work investigates the diagenesis and diagenetic facies logging of the study area, making full use of core data, thin sections, and logs, among other data, in order to understand the reservoir characteristics of the Permian Lucaogou Formation in the Jimsar Sag. The results show that the Lucaogou Formation has undergone diagenetic activity such as compaction, carbonate cementation, quartz cementation, and clay mineral infilling and dissolution. The diagenetic facies are classified according to mineral and diagenetic type, namely, tightly compacted facies, carbonate-cemented facies, clay mineral-filling facies, quartz-cemented facies, and dissolution facies. The GR, RT, AC, DEN, and CNL logging curves were selected, among others, and the convolutional neural network was introduced to construct a diagenetic facies logging recognition model. The diagenetic facies of a single well was divided and identified, and the predicted diagenetic facies types were compared with thin sections and SEM images of the corresponding depths. Prediction results had a high coincidence rate, which indicates that the model is of a certain significance to accurately identify the diagenetic facies of tight oil reservoirs. Assessing the physical properties of the studied reservoirs, dissolution facies are the dominant diagenetic facies in the study area and are also the preferred sequence for exploration—to find dominant reservoirs in the following stage