Similarity-based laboratory study of CO_(2) huff-n-puff in tight conglomerate cores

Tight conglomerate reservoirs are featured with extremely low permeability,strong heterogeneity and poor water injectivity.CO_(2) huff-n-puff has been considered a promising candidate to enhance oil recovery in tight reservoirs,owing to its advantages in reducing oil viscosity,improving mobility ratio,quickly replenishing formation pressure,and potentially achieving a miscible state.However,reliable inhouse laboratory evaluation of CO_(2) huff-n-puff in natural conglomerate cores is challenging due to the inherent high formation pressure.In this study,we put forward an equivalent method based on the similarity of the miscibility index and Grashof number to acquire a lab-controllable pressure that features the flow characteristics of CO_(2) injection in a tight conglomerate reservoir.The impacts of depletion degree,pore volume injection of CO_(2) and soaking time on ultimate oil recovery in tight cores from the Mahu conglomerate reservoir were successfully tested at an equivalent pressure.Our results showed that oil recovery decreased with increased depletion degree while exhibiting a non-monotonic tendency(first increased and then decreased)with increased CO_(2) injection volume and soaking time.The lower oil recoveries under excess CO_(2) injection and soaking time were attributed to limited CO_(2) dissolution and asphaltene precipitation.This work guides secure and reliable laboratory design of CO_(2) huff-n-puff in tight reservoirs with high formation pressure.

Mapping Soil Salinity Using a Similarity-based Prediction Approach:A Case Study in Huanghe River Delta,China

Spatial distribution of soil salinity can be estimated based on its environmental factors because soil salinity is strongly affected and indicated by environmental factors. Different with other properties such as soil texture, soil salinity varies with short-term time. Thus, how to choose powerful environmental predictors is especially important for soil salinity. This paper presents a similarity-based prediction approach to map soil salinity and detects powerful environmental predictors for the Huanghe(Yellow) River Delta area in China. The similarity-based approach predicts the soil salinities of unsampled locations based on the environmental similarity between unsampled and sampled locations. A dataset of 92 points with salt data at depth of 30–40 cm was divided into two subsets for prediction and validation. Topographical parameters, soil textures, distances to irrigation channels and to the coastline, land surface temperature from Moderate Resolution Imaging Spectroradiometer(MODIS), Normalized Difference Vegetation Indices(NDVIs) and land surface reflectance data from Landsat Thematic Mapper(TM) imagery were generated. The similarity-based prediction approach was applied on several combinations of different environmental factors. Based on three evaluation indices including the correlation coefficient(CC) between observed and predicted values, the mean absolute error and the root mean squared error we found that elevation, distance to irrigation channels, soil texture, night land surface temperature, NDVI, and land surface reflectance Band 5 are the optimal combination for mapping soil salinity at the 30–40 cm depth in the study area(with a CC value of 0.69 and a root mean squared error value of 0.38). Our results indicated that the similarity-based prediction approach could be a vital alternative to other methods for mapping soil salinity, especially for area with limited observation data and could be used to monitor soil salinity distributions in the future.

Soil polygon disaggregation through similarity-based prediction with legacy pedons

Conventional soil maps generally contain one or more soil types within a single soil polygon.But their geographic locations within the polygon are not specified.This restricts current applications of the maps in site-specific agricultural management and environmental modelling.We examined the utility of legacy pedon data for disaggregating soil polygons and the effectiveness of similarity-based prediction for making use of the under-or over-sampled legacy pedon data for the disaggregation.The method consisted of three steps.First,environmental similarities between the pedon sites and each location were computed based on soil formative environmental factors.Second,according to soil types of the pedon sites,the similarities were aggregated to derive similarity distribution for each soil type.Third,a hardening process was performed on the maps to allocate candidate soil types within the polygons.The study was conducted at the soil subgroup level in a semi-arid area situated in Manitoba,Canada.Based on 186 independent pedon sites,the evaluation of the disaggregated map of soil subgroups showed an overall accuracy of 67% and a Kappa statistic of 0.62.The map represented a better spatial pattern of soil subgroups in both detail and accuracy compared to a dominant soil subgroup map,which was commonly used in practice.Incorrect predictions mainly occurred in the agricultural plain area and the soil subgroups that are very similar in taxonomy,indicating that new environmental covariates need to be developed.We concluded that the combination of legacy pedon data with similarity-based prediction is an effective solution for soil polygon disaggregation.

Similarity-based emergency event detection in social media

Emergency events need early detection,quick response,and accuracy recover.In the era of big data,the use of social media platforms is being popularized.Social media users can be seen as social sensors to monitor real time emergency events.In this paper,a similarity-based method is proposed to early detect all kinds of emergency events in social media,including natural disasters,accidents,public health events and social security events.The method focuses on clustering social media texts based on the 3 W attribute information(What,When,and Where)of events.First,with the two-step classification,emergency related messages are detected and divided into different types from the massive and irrelevant data.Second,the time and location information are respectively extracted with the regular expression matching and the BiLSTM model.Finally,the text similarity is calculated using the type,time and location information,based on which social media texts are clustered into different events.The experiments on Sina Weibo data demonstrate the superiority of the proposed framework.Case studies on some real emergency events show the proposed framework has good performance and high timeliness.As the attribute information of events is extracted during the algorithm flow,it can be described what emergency,and when and where it happened.

Similarity Intelligence:Similarity Based Reasoning,Computing,and Analytics

Similarity has been playing an important role in computer science,artificial intelligence(AI)and data science.However,similarity intelligence has been ignored in these disciplines.Similarity intelligence is a process of discovering intelligence through similarity.This article will explore similarity intelligence,similarity-based reasoning,similarity computing and analytics.More specifically,this article looks at the similarity as an intelligence and its impact on a few areas in the real world.It explores similarity intelligence accompanying experience-based intelligence,knowledge-based intelligence,and data-based intelligence to play an important role in computer science,AI,and data science.This article explores similarity-based reasoning(SBR)and proposes three similarity-based inference rules.It then examines similarity computing and analytics,and a multiagent SBR system.The main contributions of this article are:1)Similarity intelligence is discovered from experience-based intelligence consisting of data-based intelligence and knowledge-based intelligence.2)Similarity-based reasoning,computing and analytics can be used to create similarity intelligence.The proposed approach will facilitate research and development of similarity intelligence,similarity computing and analytics,machine learning and case-based reasoning.

Knowledge modeling based on interval-valued fuzzy rough set and similarity inference: prediction of welding distortion

Knowledge-based modeling is a trend in complex system modeling technology. To extract the process knowledge from an information system, an approach of knowledge modeling based on interval-valued fuzzy rough set is presented in this paper, in which attribute reduction is a key to obtain the simplified knowledge model. Through defining dependency and inclusion functions, algorithms for attribute reduction and rule extraction are obtained. The approximation inference plays an important role in the development of the fuzzy system. To improve the inference mechanism, we provide a method of similaritybased inference in an interval-valued fuzzy environment. Combining the conventional compositional rule of inference with similarity based approximate reasoning, an inference result is deduced via rule translation, similarity matching, relation modification, and projection operation. This approach is applied to the problem of predicting welding distortion in marine structures, and the experimental results validate the effectiveness of the proposed methods of knowledge modeling and similarity-based inference.