Identification of the Thief Zone Using a Support Vector Machine Method

Fu, Chen; Guo, Tianyue; Liu, Chong-jiang; Wang, Ying; Huang, Bin

doi:10.3390/pr7060373

Cited by 3 publications

(3 citation statements)

References 19 publications

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“…Generally, there is a strong heterogeneity in carbonate reservoirs due to the comprehensive impacts of tectonic movement and the leaching effect (Ghafoori et al, 2009;He et al, 2014;Al-Ali et al, 2019). During oil and gas recovery, the zones with relatively high permeability will form low-resistivity seepage channels of fluids, that is, the so-called thief zones (Fu et al, 2019). Thief zones will have high oil recovery rates at the early stage of the exploitation, while they will result in an early water breakthrough and, thereby, lead to a low sweep efficiency at the later stage of water flooding development (Li et al, 2015;Kong et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

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Identification method of thief zones in carbonate reservoirs based on the combination of static and dynamic data: A case study from the Cretaceous Mishrif Formation in the H oilfield, Iraq

Deng

et al. 2023

Front. Energy Res.

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Carbonate reservoirs usually have a strong heterogeneity. The zones with relatively high permeability will form a channel through which fluids can easily flow. These channels are called thief zones. Thief zones have notable effects on oil or gas production, for example, high oil recovery rates at the early stage of the exploitation or an early water breakthrough during the later stage of water flooding development. Therefore, it is essential to have a precise identification of thief zones in carbonate reservoirs. In this research study, a simple approach to identify thief zones based on reservoir permeability gathered from well logging is developed. The thief zones are first identified at wells based on the lower limit value of the thief-zone permeability. This value is determined based on the dynamic production data, indicating that the thief zones identified by applying this criterion can reflect the product characteristics. Then, a zonal inter-well recognition method is adopted to identify the connectivity and distributions of thief zones in the regions far away from the well. This method is applied to identify thief zones for the Cretaceous Mishrif Formation in the H oilfield, Iraq. The reliability of the identification results is tested by the well-group injection test. The distributions of thief zones in the study region are discussed. In the study region, 12 members developed thief zones, while two members (i.e., MC1-3 and MC2-2) did not develop thief zones. Specifically, there are five members having a high level of thief-zone development. They are MB1-2C, MB2-1, MB2-2, MC2-3, and MC3-2. Comparing the distribution of thief zones with that of sedimentary microfacies, it is concluded that the thief-zone development is mainly controlled by the sedimentary microfacies and tends to occur in high-energy shoals.

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Section: Introductionmentioning

confidence: 99%

“…Wei et al (2019) proposed an index to describe the relative contribution of a certain layer based on the data gathered from PLT. One disadvantage of this kind of method is that the data collected from well logging can only describe the thief zones near well regions (Fu et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Identification method of thief zones in carbonate reservoirs based on the combination of static and dynamic data: A case study from the Cretaceous Mishrif Formation in the H oilfield, Iraq

Deng

et al. 2023

Front. Energy Res.

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“…Mechanisms and data-driven models are playing more and more important roles in the field of engineering [1]. Traditional machine learning algorithms can only be trained by labeled datasets [2]. A lot of manpower and material resources are consumed when experts mark unlabeled samples, but unlabeled data is easily obtained in the production equipment.…”

Section: Introductionmentioning

confidence: 99%

Semi-Supervised Ensemble Classification Method Based on Near Neighbor and Its Application

Xie

Chen

2020

Processes

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Semi-supervised learning can be used to solve the problem of insufficient labeled samples in the process industry. However, in an actual scenario, traditional semi-supervised learning methods usually do not achieve satisfactory performance when the small number of labeled samples is subjective and inaccurate and some do not consider how to develop a strategy to expand the training set. In this paper, a new algorithm is proposed to alleviate the above two problems, and consequently, the information contained in unlabeled samples can be fully mined. First, the multivariate adaptive regression splines (MARS) and adaptive boosting (Adaboost) algorithms are adopted for co-training to make the most of the deep connection between samples and features. In addition, the strategies, pseudo-labeled dataset selection algorithm based on near neighbor degree (DSSA) and pseudo-labeled sample detection algorithm based on near neighbor degree selection (SPDA) are adopted to enlarge the dataset of labeled samples. When we select the samples from the pseudo-labeled data to join the training set, the confidence degree and the spatial relationship with labeled samples are considered, which are able to improve classifier accuracy. The results of tests on multiple University of California Irvine (UCI) datasets and an actual dataset in the aluminum electrolysis industry demonstrate the effectiveness of the proposed algorithm.

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Fast Identification of Adverse Drug Reactions (ADRs) of Digestive and Nervous Systems of Organic Drugs by In Silico Models

et al. 2021

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This study aimed to discover concurrences of adverse drug reactions (ADRs) and derive models of the most frequent items of ADRs based on the SIDER database, which included 1430 marketed drugs and 5868 ADRs. First, common ADRs of organic drugs were manually reclassified according to side effects in the human system and followed by an association rule analysis, which found ADRs of digestive and nervous systems often occurred at the same time with a good association rule. Then, three algorithms, linear discriminant analysis (LDA), support vector machine (SVM) and deep learning, were used to derive models of ADRs of digestive and nervous systems based on 497 organic monomer drugs and to identify key structural features in defining these ADRs. The statistical results indicated that these kinds of QSAR models were good tools for screening ADRs of digestive and nervous systems, which gave the ROC AUC values of 81.5%, 98.9%, 91.5%, 69.5%, 78.4% and 78.8%, respectively. Then, these models were applied to investigate ADRs of 1536 organic compounds with four phase and zero rule-of-five (RO5) violations from the ChEMBL database. Based on the consensus ADRs’ predictions of models, 58.1% and 42.6% of compounds were predicted to cause these two ADRs, respectively, indicating the significance of initial assessment of ADRs in early drug discovery.

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Identification of the Thief Zone Using a Support Vector Machine Method

Cited by 3 publications

References 19 publications

Identification method of thief zones in carbonate reservoirs based on the combination of static and dynamic data: A case study from the Cretaceous Mishrif Formation in the H oilfield, Iraq

Identification method of thief zones in carbonate reservoirs based on the combination of static and dynamic data: A case study from the Cretaceous Mishrif Formation in the H oilfield, Iraq

Semi-Supervised Ensemble Classification Method Based on Near Neighbor and Its Application

Fast Identification of Adverse Drug Reactions (ADRs) of Digestive and Nervous Systems of Organic Drugs by In Silico Models

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