Fault detection using principal component analysis of seismic attributes in the Bakken Formation, Williston Basin, North Dakota, USA

2017 ◽  
Vol 5 (3) ◽  
pp. T361-T372 ◽  
Author(s):  
Ismot Jahan ◽  
John Castagna ◽  
Michael Murphy ◽  
M. Amin Kayali
Keyword(s):  
Principal Component Analysis ◽  
Fault Detection ◽  
Principal Component ◽  
Component Analysis ◽  
Seismic Attributes ◽  
Bakken Formation ◽  
Seismic Fault ◽  
Three Forks Formation ◽  
Three Forks ◽  
Borehole Image

Seismic fault detection using principal component analysis (PCA) is an effective method for interpreting fault distribution and orientations in the Bakken Formation. The PCA fault attribute indicates significantly different, and geologically more plausible, 3D fault distributions than conventional seismic attributes, such as curvature. The PCA fault attribute has identified different fault patterns in the Upper, Middle, and Lower Bakken members and the Three Forks Formation. Two distinct fault trends in approximately 40°–50° northeast–southwest and 50°–60° northwest–southeast directions are observed in the Bakken Formation in the study area, and they are apparent on the strike and dip attributes derived from the PCA fault attribute. Fault cuts interpreted from missing well-log sections correlate well with the PCA fault attribute. Seismically derived fault orientations correlate with borehole image log data in the horizontal wells. Crossing conjugate faults observed on the fault dip attribute may result in the widening of the faulted area and localized thinning of the rock sequence where the faults intersect, and this could potentially enhance permeability along the fault strike.

scholarly journals Monitoring a Reverse Osmosis Process with Kernel Principal Component Analysis: A Preliminary Approach

2021 ◽  
Vol 11 (14) ◽  
pp. 6370
Author(s):  
Elena Quatrini ◽  
Francesco Costantino ◽  
David Mba ◽  
Xiaochuan Li ◽  
Tat-Hean Gan
Keyword(s):  
Principal Component Analysis ◽  
Fault Detection ◽  
Water Purification ◽  
Principal Component ◽  
Temporal Correlation ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Original Dataset ◽  
Monitoring Process ◽  
Machine Health

The water purification process is becoming increasingly important to ensure the continuity and quality of subsequent production processes, and it is particularly relevant in pharmaceutical contexts. However, in this context, the difficulties arising during the monitoring process are manifold. On the one hand, the monitoring process reveals various discontinuities due to different characteristics of the input water. On the other hand, the monitoring process is discontinuous and random itself, thus not guaranteeing continuity of the parameters and hindering a straightforward analysis. Consequently, further research on water purification processes is paramount to identify the most suitable techniques able to guarantee good performance. Against this background, this paper proposes an application of kernel principal component analysis for fault detection in a process with the above-mentioned characteristics. Based on the temporal variability of the process, the paper suggests the use of past and future matrices as input for fault detection as an alternative to the original dataset. In this manner, the temporal correlation between process parameters and machine health is accounted for. The proposed approach confirms the possibility of obtaining very good monitoring results in the analyzed context.

Batch process monitoring based on global enhanced multiple neighborhoods preserving embedding

2021 ◽  
pp. 014233122110447
Author(s):  
Hongjuan Yao ◽  
Xiaoqiang Zhao ◽  
Wei Li ◽  
Yongyong Hui
Keyword(s):  
Principal Component Analysis ◽  
Fault Detection ◽  
Objective Function ◽  
Principal Component ◽  
Batch Process ◽  
Component Analysis ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Order Information ◽  
Multiple Neighborhoods

Batch process generally has varying dynamic characteristic that causes low fault detection rate and high false alarm rate, and it is necessary and urgent to monitor batch process. This paper proposes a global enhanced multiple neighborhoods preserving embedding based fault detection strategy for dynamic batch process. Firstly, the angle neighbor is defined and selected to compensate for the insufficient expression for the spatial similarity of samples only by using the distance neighbor, and the time neighbor is introduced to describe the time correlations between samples. These three types of neighbors can fully characterize the similarity of the samples in time and space. Secondly, considering the minimum reconstruction error and the order information of three types of neighbors, an enhanced objective function is constructed to prevent the loss of order information when neighborhood preserving embedding (NPE) calculates the reconstruction weights. Furthermore, the enhanced objective function and a global objective function are organically combined to extract both global and local features, to describe process dynamics and visualize process data in a low-dimensional space. Finally, a monitoring index based on support vector data description is constructed to eliminate adverse effects of non-Gaussian data for monitoring performance. The advantages of the proposed method over principal component analysis, neighborhood preserving embedding, dynamic principal component analysis and time NPE are demonstrated by a numerical example and the penicillin fermentation process simulation.

Sign in / Sign up

Export Citation Format

Share Document