Statistical Methods to Improve the Quality of Real-Time Drilling Data

The age of easy oil is ending, the industry started drilling in remote unconventional conditions. To help produce safer, faster, and most effective operations, the utilization of artificial intelligence and machine learning (AI/ML) has become essential. Unfortunately, due to the harsh environments of drilling and the data-transmission setup, a significant amount of the real-time data could defect. The quality and effectiveness of AI/ML models are directly related to the quality of the input data; only if the input data are good, the AI/ML generated analytical and prediction models will be good. Improving the real-time data is therefore critical to the drilling industry. The objective of this paper is to propose an automated approach using eight statistical data-quality improvement algorithms on real-time drilling data. These techniques are Kalman filtering, moving average, kernel regression, median filter, exponential smoothing, lowess, wavelet filtering, and polynomial. A dataset of +150,000 rows is fed into the algorithms, and their customizable parameters are calibrated to achieve the best improvement result. An evaluation methodology is developed based on real-time drilling data characteristics to analyze the strengths and weaknesses of each algorithm were highlighted. Based on the evaluation criteria, the best results were achieved using the exponential smoothing, median filter, and moving average. Exponential smoothing and median filter techniques improved the quality of data by removing most of the invalid data points, the moving average removed more invalid data-points but trimmed the data range.

Wave-equation diffraction imaging using pseudo dip-angle gather

Diffraction images can directly indicate local heterogeneities such as faults, fracture zones, and erosional surfaces that are of high interest in seismic interpretation and unconventional reservoir development. We propose a new tool called pseudo dip-angle gather (PDAG) for imaging diffractors using the wave equation. PDAG has significantly lower computational cost compared with the classical dip-angle gather (DAG) due to using plane-wave gathers, a fast local Radon transform algorithm, and one-side decomposition assumption. Pseudo dip angle is measured from the vertical axis to the bisector of the plane-wave surface incident angle and scattered wave-propagation angle. PDAG is generated by choosing the zero lag of the correlation of the plane-wave source wavefields and the decomposed receiver wavefields. It reveals similar diffraction and reflection patterns to DAG, i.e. diffractions spreading as a flat event and reflections focused at a spectacular angle, while they may have dissimilar coverage for diffraction and different focused locations for reflection compared with that of DAG. A windowed median filter is then applied to each PDAG for extracting the diffraction energy and suppressing the focused reflection energy. Besides, the stacked PDAG can be used to evaluate the migration accuracy by measuring the flatness of the image gathers. Numerical tests on both synthetic and field data sets demonstrate that our method can efficiently produce accurate results for diffraction images.

Machine learning denoising of high-resolution X-ray nanotomography data

High-resolution X-ray nanotomography is a quantitative tool for investigating specimens from a wide range of research areas. However, the quality of the reconstructed tomogram is often obscured by noise and therefore not suitable for automatic segmentation. Filtering methods are often required for a detailed quantitative analysis. However, most filters induce blurring in the reconstructed tomograms. Here, machine learning (ML) techniques offer a powerful alternative to conventional filtering methods. In this article, we verify that a self-supervised denoising ML technique can be used in a very efficient way for eliminating noise from nanotomography data. The technique presented is applied to high-resolution nanotomography data and compared to conventional filters, such as a median filter and a nonlocal means filter, optimized for tomographic data sets. The ML approach proves to be a very powerful tool that outperforms conventional filters by eliminating noise without blurring relevant structural features, thus enabling efficient quantitative analysis in different scientific fields.

Classification of Melanoma and Nevus for Diagnosis of Skin Cancer by Using Optimized Convolution Neural Network

Melanoma is taken into account a fatal sort of carcinoma .Differentiating melanoma from nevus is difficult task. Nevus is a common pigmented skin lesion, usually developing during adulthood, which is harmless. Since they look similar it has to be identified and reduce the risk of cancer. The death rate thanks to this disease is in particular other skin-related consolidated malignancies. In this work, we have used convolution neural networks to classify the image into melanoma and nevus. The images are pre-processed using median filter, top-bottom hat filter and are passed through layers of CNN. We have achieved an accuracy of 97.56%, sensitivity of 95.23%.The F1_socre is 97.56. Index terms: Melanoma, Nevus, True Positive, True Negative, False Negative, False Positive, Confusion Matrix, Epoch, Convolution Neural Network.

Aplikasi Filter Digital untuk Citra Mengandung Derau Salt and Pepper Menggunakan Metode Directional Weighted Minimum Deviation

Teknologi telekomunikasi berkembang begitu pesat, dari yang semula berkomunikasi menggunakan surat, berkembang komunikasi suara menggunakan telepon hingga kini telah sampai pada tahap komunikasi gambar dan video. Dalam proses pentransmisian data baik suara maupun gambar tidak terlepas dari adanya derau. Salah satu solusi dalam menjawab permasalahan tersebut adalah dengan mengembangkan teknologi Filter Digital. Dalam penelitian ini direalisasikan sebuah Filter digital dengan objek gambar menggunakan metode DWMD (Directionan Weighted Minumum Deviation) Filter dengan mendeteksi jenis derau salt and pepper. Metode DWMD Filter adalah metode pengolahan data digital berbasis arah dan standar deviasi yang memperbaiki Median Filter. Dengan membandingkan parameter PSNR maka diketahui bahwa DWMD dapat menghasilkan gambar lebih baik dari Median Filter. Metode Filter DWMD ini ditambah dengan penentuan Threshold otomatis untuk membantu proses filter menjadi lebih cepat dimana diambil selisih PSNR terbesar. Hasil eksperimen menunjukkan bahwa pada level derau 5% - 65% metode DWMD menghasilkan PSNR bernilai 21-36 dB dibandingkan dengan Median Filter sebesar 13-29 dB. Penelitian ini memiliki output berupa aplikasi desktop yang dilengkapi dengan fitur-fitur yang dapat menambahkan derau salt and pepper pada berbagai densitas dan mengakses citra yang dapat diubah ke dalam format grayscale.

New Stereo Vision Algorithm Composition Using Weighted Adaptive Histogram Equalization and Gamma Correction

This work presents the composition of a new algorithm for a stereo vision system to acquire accurate depth measurement from stereo correspondence. Stereo correspondence produced by matching is commonly affected by image noise such as illumination variation, blurry boundaries, and radiometric differences. The proposed algorithm introduces a pre-processing step based on the combination of Contrast Limited Adaptive Histogram Equalization (CLAHE) and Adaptive Gamma Correction Weighted Distribution (AGCWD) with a guided filter (GF). The cost value of the pre-processing step is determined in the matching cost step using the census transform (CT), which is followed by aggregation using the fixed-window and GF technique. A winner-takes-all (WTA) approach is employed to select the minimum disparity map value and final refinement using left-right consistency checking (LR) along with a weighted median filter (WMF) to remove outliers. The algorithm improved the accuracy 31.65% for all pixel errors and 23.35% for pixel errors in nonoccluded regions compared to several established algorithms on a Middlebury dataset.

Image recognition based on Boltzmann machine algorithm

Due to the influence of recognition parameters, image recognition has low recognition accuracy, long recognition time and large storage cost. Therefore, an automatic image recognition method based on Boltzmann machine is proposed. Based on threshold method and fuzzy set method, image malformation correction is performed. The mean filter and median filter are combined to eliminate the influence of image filtering, and the pre-processing of image is completed by using the fuzzy enhancement of image. Based on the restricted Boltzmann method, the network model is dynamically evolved, and the identification parameters of each shape and contour are obtained. Different shapes and contours are classified and recognized. Simulation results show that image recognition method based on human-computer interaction has high recognition ability, shortens the time cost and greatly reduces the space needed for node storage.