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Trachyte reservoir characteristics and favorable reservoir identification and prediction in the J34 block, Eastern Sag

Ma Chenglong, Wu Ying, Gu Tuan, Zhang Bin, Li Yu Gang, Zhang Ying Jie, Dong Wen Bo, Cui Jie

Acta Geophysica

2022

Vol. 70, no 4

1635--1650

Influenced by tectonic activities such as the Tan-Lu Fault and mantle uplift, frequent volcanic activity in the Eastern Sag of the Liaohe Basin has resulted in the widespread development of volcanic rocks, such as basalt and trachyte, in the third member of the Shahejie Formation. Exploration and development have confirmed the presence of large oil and gas displays in trachyte reservoirs. However, previous studies have focused on the whole Eastern Sag or the volcanic rocks in the north, middle, and south sections of the Eastern Sag, while the J34 block has rarely been examined. There are challenges associated with the exploration and development of the J34 block, such as difficulties with trachyte lithology identification, reservoir space identification, quantitative logging identification, and prediction of horizontal and vertical distribution of lithologic characteristics. Therefore, this study presents an investigation and analysis of the characteristics of trachyte reservoirs, and the identification and prediction of favorable reservoirs based on core data, core sampling analysis, well logging, seismic data, and development dynamic data. The results showed that the volcanic rocks in the study area mainly include three types of lithology: trachytic breccia, trachytic lava, and basaltic lava, with trachytic breccia being the most important and favorable reservoir lithology. The trachyte reservoir space is mainly fractured and vuggy, and the secondary reservoir space is often superimposed onto the primary reservoir space, which significantly increases reservoir performance. Using the logging response characteristics of different lithologies and optimizing sensitivity curves, the quantitative identification criteria of different types of volcanic rocks were established. At the same time, the distribution characteristics of favorable reservoirs were determined by reconstructed wave impedance inversion with sensitive parameters, which showed that favorable reservoirs are mainly distributed along faults. Further analysis of the controlling factors of trachyte reservoirs can effectively guide the oil and gas development in this block and provide a reference for the exploration and development of similar blocks.

Stress and load distribution analysis in bolt connection with modified thread profile under high temperature conditions

He Liange, Zhang Bin, Guo Caidong, Shi Wenjun

Journal of Theoretical and Applied Mechanics

2021

Vol. 59 nr 3

469--480

In this paper, the load distribution of a bolt connection structure with a variable thread profile at high temperature is investigated. The parameters of time-hardening creep model of aluminum alloy at high temperature were obtained by fitting the uniaxial creep tensile test data of aluminum alloy at 250◦C. Based on ABAQUS, a two-dimensional axisymmetric model of the bolt connection structure was established, and according to the thread load distribution considering linear elasticity, plasticity and creep characteristics, modification of standard metric thread profile was carried out. The load distribution law of the thread of the modified bolt connection structure were investigted. The results show that the load- -bearing ratio of the first thread can be significantly reduced and the load-bearing distribu- tion uniformity of all threads can be improved when the modified thread is applied to the bolt connection structure.

A semi-automated annotation algorithm based on weakly supervised learning for medical images

Li Hailiang, Zhang Bin, Zhang Yu, Liu Weiwei, Mao Yijun, Huang Jiacheng, Wei Linfeng

Biocybernetics and Biomedical Engineering

2020

Vol. 40, no. 2

787--802

For medical image recognition, deep learning requires a massive training set, while anno-tation work is a tedious and time-consuming process because of the high technical thresh-old. Furthermore, it is difficult to guarantee annotation accuracy due to the knowledge, skills, and status of the annotator. In this research, we propose a semi-automated annota- tion model based on weakly supervised learning. Moreover, a target-level annotation method is proposed based on weakly supervised learning that is guided by machine learning. The machine learning method is used to screen the regions of interest (RoIs), whose semantic feature vectors are extracted by the deep learning method. Then, the machine learning method is used to cluster them, and the RoIs are finally classified and labeled by a distance comparison. Therefore, this model achieves target-level semi-auto- mated annotation by only using image-level annotations. We applied this method to ultrasound imaging of thyroid papillary carcinoma. The experiments demonstrate the potential of this new methodology to reduce the workload for pathologists and increase the objectivity of diagnoses. We find that 89.8% of papillary thyroid carcinoma regions can be detected automatically, while 82.6% of benign and normal tissue can be excluded without the use of any additional immunohistochemical markers or human intervention.