scholarly journals Overpressure detection from geophysical, drilling and well testing data for petroleum exploration wells in the Beaufort-Mackenzie Basin, Yukon and Northwest Territories

2021 ◽  
Author(s):  
K Hu ◽  
D R Issler ◽  
Z Chen
Keyword(s):  
Northwest Territories ◽  
Petroleum Exploration ◽  
Well Testing ◽  
Mackenzie Basin ◽  
Testing Data

scholarly journals Vibration-based Processing and Classification Method for Oil Well-testing Data from Downhole Pressure Gauges

2020 ◽  
Vol 206 ◽  
pp. 01024
Author(s):  
Feng Xin ◽  
Li Shaohui ◽  
Feng Qiang ◽  
Liu Shugui
Keyword(s):  
Classification Performance ◽  
Petroleum Exploration ◽  
Classification Method ◽  
Support Vector ◽  
High Signal ◽  
Oil Well ◽  
Well Testing ◽  
Identification Rate ◽  
Time Frequency ◽  
Testing Data

During petroleum exploration and exploitation, the oil well-testing data collected by pressure gauges are used for monitoring the well condition and recording the reservoir performance. However, due to the large number of the collected data, the classification of this large volume of data requires a previous processing for the removal of noise and outliers. It is impractical to partition and process these data manually. Vibration-based features reflect geological properties and offer a promising option to fulfil such requirements. Based on the 75 on-site measured samples, the time-frequency-domain features are extracted and the classification performance of three classical classifiers are investigated. Then the downhole data processing and classification method is present by analysing the cross interaction of different types of data features and different classification mechanism. Several feature combinations are tested to establish a processing flow that can efficiently remove the noise and preserve the shape of curves, high signal to noise ratio rates, with minimum absolute errors. The results show that optimal multi-feature combination can achieve the highest working stage identification rate of 72%, the parameters optimized support vector machine can achieve the better classification performance than other listed classifiers. This paper provides a theoretical study for the data denoising and processing to enhance the working stage classification accuracy.

Research on Types of Oil Reservoirs and Character of Oil-Water Distribution in Heidimiao Layer

2013 ◽  
Vol 772 ◽  
pp. 789-794
Author(s):  
Gui You Lv
Keyword(s):  
Water Distribution ◽  
Oil And Gas ◽  
Dominant Role ◽  
Three Dimensional ◽  
Physical Property ◽  
Research Area ◽  
Oil Reservoir ◽  
Well Testing ◽  
Testing Data ◽  
Oil Water

This paper takes Yingtai area which is located in the south of Qijia-Gulong sag and part of central sag area in the north of the Songliao Basin as the research area. Then combining all information of core, logging, three-dimensional seism and well testing data, it studies the reservoir type and oil-water distribution characteristics of Heidimiao by analyzing the comparison charts of sandstone, profile map of reservoir, T07 structure diagram, well testing data, stratum thickness, sandstone thickness, ratio of sandstone thickness to stratum thickness, porosity values, permeability contour maps. The reservoir lithology of Heidimiao oil layer is siltstone-oriented with poor physical property. The main controlling factor of oil-water distribution is the lithology, followed by the structure. Heidimiao oil layer mainly includes three types, lithological oil reservoir, lithological - structural oil reservoir and structural oil reservoir, among which lithological reservoir plays a dominant role. Its oil-water distribution is characterized by the pattern of upper-water and bottom-oil; when the fault acts as the pathway for the longitudinal migration of oil and gas, the pattern changes to the upper-oil and bottom-water. This research could provide reliable geological basis for the research of old well re-examination, favorable area evaluation and horizontal well drilling design.

Analysis of Well-Testing Data From a Restricted-Entry Well

1989 ◽  
Author(s):  
W. Ding ◽  
M. Onur ◽  
A.C. Reynolds
Keyword(s):  
Well Testing ◽  
Testing Data
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