김성일 교수

Previous ordinal classification methods implicitly assume that the class distribution within a dataset is balanced, which is often not the case for real-world datasets. If the dataset is imbalanced, the previous methods tend to be biased toward the majority class. The authors propose a new method for ordinal classification that attempts to mitigate the impact of imbalanced datasets. They propose a modified version of the weighted k-nearest neighbors method that determines the class membership us

This study conducts saturation modeling in a gas hydrate (GH) sand sample with X-ray CT images using the following machine learning algorithms: random forest (RF), convolutional neural network (CNN), and support vector machine (SVM). The RF yields the best prediction performance for water, gas, and GH saturation in the samples among the three methods. The CNN and SVM also exhibit sufficient performances under the restricted conditions, but require improvements to their reliability and overall pr

This study proposes a reliable evaluation method for three-phase saturation (water, gas hydrate (GH), and gas) evaluation during the GH dissociation core experiment using deep learning. A convolutional neural network (CNN) takes computed tomography (CT) images obtained during the GH core experiment as an input and provides three-phase saturation as an output. Although machine/deep learning methods have been applied to the saturation evaluation from CT images in previous research, they were not r

It is necessary to monitor, acquire, preprocess, and classify microseismic data to understand active faults or other causes of earthquakes, thereby facilitating the preparation of early-warning earthquake systems. Accordingly, this study proposes the application of machine learning for signal–noise classification of microseismic data from Pohang, South Korea. For the first time, unique microseismic data were obtained from the monitoring system of the borehole station PHBS8 located in Yongcheon-r

This study reviews 254 papers on artificial intelligence (AI) applications in the geoenergy sector, categorized into conventional and future-oriented technologies. Conventional geoenergy includes reservoir, production, and drilling, while future-oriented technologies cover geological CO2 storage (GCS), gas hydrates (GH), and underground hydrogen storage (UHS). The 254 papers were analyzed systematically based on authorship, publication year, key findings, input-output data relationships, applied

This study investigates the applicability of ensemble machine learning to predict cumulative gas production for the first 36 months of shale gas reservoirs in the Wolfcamp A and B, Delaware basin. The machine learning application was conducted from three notable perspectives: (1) Well log data generation based on stratigraphy interpretation, manual well correlations, and pre-process of category type data to utilize it for machine learning training. (2) General applicability of machine learning u

In this study, the severity of slugging is assessed by predicting maximum slug lengths (MSL) quickly using the random forest (RF) algorithm based on the geometric features of well trajectories for a shale gas field. Severe slugging is one of the critical issues production engineering-wise because it causes operation shut-down. Thus it should be predicted for proactive measurements. A total of 5033 well trajectories were acquired from the northeastern area of British Columbia, Canada. The well tr

This study proposes three-phase saturation identification using X-ray computerized tomography (CT) images of gas hydrate (GH) experiments considering critical GH saturation (SGH,C) based on the machine-learning method of random forest. Eight GH samples were categorized into three low and five high GH saturation (SGH) groups. Mean square error of test results in the low and the high groups showed decreases of 37% and 33%, respectively, compared to that of the total eight. Additionally, a universa

Liquid surge refers to an excessive liquid inflow to a slug catcher or a separator and is one of the main issues in flow assurance. The wellhead choke valves of gas wells must be adjusted to maintain the target flow rate as the reservoir pressure drops. The wellhead choke opening can be determined by conducting multiphase pipeline transient flow simulations to achieve the target flow rate and avoid liquid surges. However, it is not financially and computationally practical to conduct many multip

This study aimed to validate the synergistic enhancement of the machine learning model random forest (RF) to predict the oil and gas estimated ultimate recovery (EUR) by integrating well data from basins. The study used data from six shale basins of the USA: Delaware, Marcellus, Barnett, Eagle Ford, Haynesville, and Midland. The input parameters of RF models are composed of fundamental well data such as well location and hydraulic fracturing, which are attainable and feasible to predict EUR. Thr

This study provides an interpretation of the three-phase saturation (water, gas, gas hydrate (GH); SW, SG, SGH) in the GH cores during GH formation and depressurization experiments. The saturations are predicted in real-time based on X-ray computed tomography (CT) images using the verified deep learning model reported earlier (Kim et al., 2022), using the convolutional neural network (CNN) with data augmentation. The interpretation explains the saturation behaviors spatiotemporally: depressuriza

코로나19 팬데믹 속에서 주거생활은 급격히 변하고 있다. 본 연구는 코로나19 팬데믹 속에서 주거 욕구별 변화가 어떻게 이뤄졌고 그것이 주거생활에서 시사하는 바가 무엇인지 규명하고자 한다. 이에 본 연구는 주거의 사회적 욕구 변화를 올인홈 구축에서, 주거의 자아실현 욕구 변화를 홈 루덴스를 통해, 주거의 생존 욕구 변화를 생활 방역을 통해 살펴보려 한다. 연구 결과, 집에서 일과 학습, 문화 활동을 원활히 수행하고자 생긴 새로운 주거 욕구들은 디지털 생활양식을 새롭게 만들어 가고 있다. 또한 환기, 채광, 주거 밀도와 관련해 생활 방역 강화를 위한 주거환경 개선이 전개되고 있다. 취약계층의 경우, 국가 차원의 주거 안정 정책을 시급히 추진해야 한다. 이상의 논의를 통해 본 연구는 스마트시티 구축에서 생활과 방역 공간으로 재탄생한 집이 어떤 의미를 갖는지에 관한 통찰력을 제공할 것이다. 그런데 포스트코로나와 관련해 위의 주거생활 변화가 뉴노멀로 정착될 것이지는 살펴보지 못했다. 이는