Cumulative oil production in flow unit-crossing wells estimated by multilayer perceptron networks

Knowing the ultimate oil production in wells is a crucial point for reservoir planning and management to anticipate value for money. Commercial reservoir simulators are able to predict production curves with high confidence, but repetitive tasks in a few cases may spend a precious time of staff as well as require a large computational effort. Although artificial intelligence (AI) is providing an alternative path to the usual workflow, many commercial simulators lack robust AI algorithms. This work introduces a methodology based on a multilayer perceptron (MLP) neural network to predict the final cumulative oil production of a reservoir at vertical wells that cross hydraulic flow units (HFUs), which are volumes endowed with good flow attributes. Each well location is attached to special spots previously determined from clustering and calculation of maximum closeness centrality points (MaxCs) within a class of HFUs. The database is divided into training, validation, and testing sets organized after processing the UNISIM-I-D synthetic model, representative of the Namorado Field, Campos Basin, Brazil. The key rationale of this paper is to use the feature of MaxCs of being drivers for well placement as knowledge base to learn the production mechanisms of the oilfield. The outcomes are presented from two perspectives: an original MLP and its post-processed version. Both are compared with reservoir simulations carried out in CMG Imex$$^{\copyright }$$ © and achieve reasonable agreement. The performance is measured by root-mean-squared error (RMSE) and mean absolute scaled error (MASE) both in original and post-processed versions. We show that average RMSE and MASE values near 0.07 and 14.00, respectively, are achieved without post-processing. With post-processing, gains of up to 43% are reported for the integral oil volume.

ARTIFICIAL NEURAL NETWORKS IN MEDICAL DIAGNOSIS

Artificial neural networks are finding many uses in the medical diagnosis application. The article examines cases of renopathy in type 2 diabetes. Data are symptoms of disease. The multilayer perceptron networks (MLP) is used as a classifier to distinguish between a sick and a healthy person. The results of applying artificial neural networks for diagnose renopathy based on selected symptoms show the network's ability to recognize to recognize diseases corresponding to human symptoms. Various parameters, structures and learning algorithms of neural networks were tested in the modeling process.

A Study of Multilayer Perceptron Networks Applied to Classification of Ceramic Insulators Using Ultrasound

Interruptions in the supply of electricity cause numerous losses to consumers, whether residential or industrial and may result in fines being imposed on the regulatory agency’s concessionaire. In Brazil, the electrical transmission and distribution systems cover a large territorial area, and because they are usually outdoors, they are exposed to environmental variations. In this context, periodic inspections are carried out on the electrical networks, and ultrasound equipment is widely used, due to non-destructive analysis characteristics. Ultrasonic inspection allows the identification of defective insulators based on the signal interpreted by an operator. This task fundamentally depends on the operator’s experience in this interpretation. In this way, it is intended to test machine learning applications to interpret ultrasound signals obtained from electrical grid insulators, distribution, class 25 kV. Currently, research in the area uses several models of artificial intelligence for various types of evaluation. This paper studies Multilayer Perceptron networks’ application to the classification of the different conditions of ceramic insulators based on a restricted database of ultrasonic signals recorded in the laboratory.

Grape Drying Process Using Machine Vision Based on Multilayer Perceptron Networks

This paper proposed a grape drying machine using computer vision and Multi-layer Perceptron (MLP) method. Computer vision is for taking grapes’ image on conveyor, whereas MLP is for controlling grape drying machine and classifying its output. To evaluate the proposed, a kind of grapes are put on conveyor of the machine and their images are taken every two min. Some parameters of MLP to control the drying machine includes dried grape, temperature, grape area, motor position, and motion speed. Those parameters are to adjust an appropriate MLP’s output, including motion control and heater control. Two different temperatures are employed on the machine, including 60 and 75°C. The results showed that the grape could be dried with similar area 3800 pixel at the 770th min using temperature 60°C and at the 410th min using temperature 75°C. Comparing between them, the similar ratio could also be achieved at 0.64 with different time 360 min. Indeed, the temperature setting at 75°C resulted faster drying performance.

Mapping Ensembles of Trees to Sparse, Interpretable Multilayer Perceptron Networks

Tree-based classifiers provide easy-to-understand outputs. Artificial neural networks (ANN) commonly outperform tree-based classifiers; nevertheless, understanding their outputs requires specialized knowledge in most cases. The highly redundant architecture of ANN is typically designed through an expensive trial-and-error scheme. We aim at (1) investigating whether using ensembles of decision trees to design the architecture of low-redundant, sparse ANN provides better-performing networks, and (2) evaluating whether such trees can be used to provide human-understandable explanations for their outputs. Information about the hierarchy of the features, and how good they are at separating subsets of samples among the classes, is gathered from each branch in an ensemble of trees. This information is used to design the architecture of a sparse multilayer perceptron network. Networks built using our method are called ForestNet. Tree branches corresponding to highly activated neurons are used to provide explanations of the networks’ outputs. ForestNets are able to handle low- and high-dimensional data, as we show on an evaluation using four datasets. Our networks consistently outperformed their respective ensemble of trees and had similar performance to their fully connected counterparts with a significant reduction of connections. Furthermore, our interpretation method seems to provide support for the ForestNet outputs. While ForestNet’s architectures do not allow them yet to capture well the intrinsic variability of visual data, they exhibit very promising results by reducing more than 98% of connections for such visual tasks. Structure similarities between ForestNets and their respective tree ensemble provide means to interpret their outputs.

Service-Life Study of Polycarbonate Outdoors Using Python with Incomplete Data

The deterioration of polycarbonate (PC) depends on various environmental factors. Meanwhile, the complexity of the related weathering processes inhibits the prediction of service life based on the environmental factors. To elucidate the nonlinear correlation between PC weathering and the environmental factors, three-year-long natural weathering tests were conducted at eight experimental stations in China. The relationship between tensile-property data of PC and environmental and pollutant data is analyzed by extra-trees and multilayer perceptron networks implemented in Python. The results indicated that (1) the degradation of PC tensile properties is mainly affected by the experimental period (76.37%), whilst the effect of the environmental or pollutant factors on the degradation is less pronounced (23.63%); (2) the classification accuracy of the trained model on the training set is 91% (91/100), and on the testing set is 72.13% (44/61); and lastly, (3) it is inferred from the error analysis of the classification results that the performance change of polycarbonate in Qionghai and Wuhan is characterized by an initial reduction followed by a slight improvement. Lastly, we show that the proposed method performs well, especially in the case of areas with incomplete data available.

Machine prediction of US imports from the PRC in the context of mutual sanctions

The aim of this paper is to mechanically predict the import of the United States of America (USA) from the People's Republic of China (PRC). The trade restrictions of the USA and the PRC caused by the USA feeling of imbalance of trade between the two states have significantly influenced not only the trade between the two players, but also the overall climate of international trade. The result of this paper is the finding that multilayer perceptron networks (MLP) appear to be an excellent tool for predicting USA imports from the PRC. MLP networks can capture both the trend of the entire time series and its seasonal fluctuations. It also emerged that time series delays need to be applied. Acceptable results are shown to delay series of the order of 5 and 10 months. The mutual sanctions of both countries did not have a significant impact on the outcome of the machine learning prediction.

Machine learning forecasting of USA and PRC balance of trade in context of mutual sanctions

The USA decided to regulate the trade more by imposing tariffs on specific types of traded goods. It is therefore more interesting to find out whether the current technologies based on artificial intelligence with time series influenced by extraordinary factors such as the trade war between two powers are able to work. The objective of the contribution is to examine and subsequently equalize two time series – the USA import from the PRC and the USA export to the PRC. The dataset shows the course of the time series at monthly intervals between January 2000 and July 2019. 10,000 multilayer perceptron networks (MLP) are generated, out of which 5 with the best characteristics are retained. It has been proved that multilayer perceptron networks are a suitable tool for forecasting the development of the time series if there are no sudden fluctuations. Mutual sanctions of both states did not affect the result of machine learning forecasting.