Well-log data analysis

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Enhanced Prospect Definition Using Well and 4D Seismic Data in a Niger Delta Field

Enhanced Prospect Definition Using Well and 4D Seismic Data in a Niger Delta Field

Subsequently, the seismic data from the two vintages were independently inverted into acoustic impedance volumes through a model based inversion scheme. These inverted impedances were used to generate the Lamb- da-mu-rho attributes for saturated media [5] [8] [10], to quantitatively characterize the reservoir in the field based on the results of well log cross plot analysis. The generation of Lambda-mu-rho attributes was done using a multi-attribute neural network algorithm. This algorithm works by identifying all possible linear and non- linear relationships that may exist between the desired log attribute and the several available seismic attributes at the well location. Based on these relationships, networks are trained to predict the log attribute over the seismic volume away from the well position in a least-squares sense, after validation.
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RESERVOIR STUDIES USING SEISMIC ATTRIBUTES AND WELL LOG AND WELL LOG ANALYSIS OF Y FIELD, NIGER DELTA

RESERVOIR STUDIES USING SEISMIC ATTRIBUTES AND WELL LOG AND WELL LOG ANALYSIS OF Y FIELD, NIGER DELTA

Root Mean Square Amplitude extraction was carried out on reservoir A and B level where the reservoir can be confidently identified on the seismic data from the well-synthetic tie. Amplitude anomalies exist at all levels where it was possible, both within the field itself and the surrounding areas. These anomalies are of two types: those that conform to structure, and those that appear to be appraisal i.e. doesn‟t conform to any structure. Amplitude being a seismic attribute was superimposed on the time structure map of horizon A and B to check its conformity to structure. Amongst the different types of amplitude which can be extracted (maximum positive and negative, average positive and negative e.t.c.), root mean square amplitude was chosen because of its unique characteristics as a good indicator of the presence of hydrocarbon (Mangal et al. 2004). R.M.S amplitude is obtained by summing all the square of all the amplitudes of the reflection and calculating the roots of the cumulative; high amplitudes with respect to amplitude distribution are direct hydrocarbon indicators (Mangal et al. 2004).
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MAPPING OF OVERPRESSURED ZONES IN COASTAL SWAMP DEPOBELT OF NIGER DELTA NIGERIA, USING WELL-LOG AND SEISMIC DATA

MAPPING OF OVERPRESSURED ZONES IN COASTAL SWAMP DEPOBELT OF NIGER DELTA NIGERIA, USING WELL-LOG AND SEISMIC DATA

If the deg ee of f eedo is 3 , at 5 eve of significance (α) the c itica va ue 2 42 Results of the EKON and IDUMA pressure analysis established a coefficient of correlation of 0.9954 and coefficient of determination as 0.9976. While the test of the hypothesis was estimated at 132.34. If the critical value was estimated at 2.042, hence, the decision rule states that for the EKON and IDUMA fields, the overpressure distribution will have no correlation if the test of hypothesis were within the critical values of plus 2.042 and minus 2.042. However since the test of hypothesis conformed to the rejection region, the overpressure distribution within the two fields were said to be correlatable, (Figure 21).
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A REVIEW ON TEXT DATA MINING OF CARE LIFE LOG USING KEY GRAPH

A REVIEW ON TEXT DATA MINING OF CARE LIFE LOG USING KEY GRAPH

Data Mining is a method that requires analyzing and exploring large blocks of data to glean meaningful trends and patterns. In today’s period, every person on earth relies on allopathic treatments and medicines. Data mining techniques can be applied to medical databases that have a vast scope of opportunity for textual as well as visual data. Care Life Log is used to integrate and analyze the level of care required. There are five levels of care, with Level 1 vocabulary including recreation, toilet, morning, afternoon, etc. The level of care gradually increases from Level 1 to Level 5, which has vocabulary that includes tube, danger, treatment, removal, and discovery. The higher the level, the worse the health condition and therefore the greater care required. These levels allow for a clear analysis of a patient’s condition. This analysis has led to an improvement in Quality of Life as well as a decrease in mismatches between the level of care required for patients and the level of care given by care takers. The qualitative analysis result of in-patient nursing records used a text data mining technique to achieve the initial goal: a visual record of such information. The analysis discovered vocabularies relating to proper treatment methods and concisely summarized their extracts from in-patient nursing records. Important vocabularies that characterize each nursing record were also revealed. The results of this research will contribute to nursing work evaluation and education. This research used a text data mining technique to extract useful information from nursing records within Electronic Medical Records.
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Well Log Petrophysics of the Cretaceous Pay Zones in Zubair Field, Basrah, South Iraq

Well Log Petrophysics of the Cretaceous Pay Zones in Zubair Field, Basrah, South Iraq

Discovered in 1949 with a rate of (195,000) bbl/day from pay zones in Mishrif and Zubair Formation, the expected production of Zubair field is anticipated to be 1125 million bbl/day. Despite this production history, there is a major deficiency in detailed petrophysical analysis of the producing zones. In the present study well log data of 7 wells, selected from numerous wells, are inves- tigated in details to examine the reservoir properties and characterize the re- servoir architecture. The petrophysical analysis of Mishrif Formation indi- cated two or three pay zones. Lithologically, all zones of Mishrif Formation are dominantly clean limestone to dolomitic limestone with zone 2 and 3 re- porting higher dolomitic content (20% to 40%) compared to zone 1 (6% to 13%). Mishrif pay zones indicated a relatively good porosity (18% - 24%) with zone 2 predominant in secondary porosity associating dolomitization processes. In Zubair Formation one pay zone is identified but locally could separate into two zones. The clay content is generally low with average con- tent between 2% and 3% while the average porosity showed slightly better values in zone 1 (~0.20) compared to average porosity of zone 2 (0.17) that is rich in silt content associating deposition at a relatively deeper parts of the shelf. The average water saturation shows distinct lower values that vary be- tween 15% and 18.7%. The petrophysical results are statistically analyzed and property histograms and crossplots are constructed to investigate mutual rela- tionships. Such analysis is essential for understanding the reservoir architec- ture and calculations of reservoir capacity for future development.
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Predicting Zones of Overpressure in Coastal Swamp Depobelt of Niger Delta Nigeria, Using Well-Log and Seismic Data

Predicting Zones of Overpressure in Coastal Swamp Depobelt of Niger Delta Nigeria, Using Well-Log and Seismic Data

The methods adopted in this study include identification of key lithologic and reservoir units from gamma ray (GR), Resistivity (ILD) and spontaneous potential (SP) logs, well to seismic tie using check shot sonic (DT) and density (RHOB) logs. Faults were mapped on seismic as breaks in the continuity of reflections, while the horizions mapped were used in generating time map. Checkshots were inputed to the velocity equations to enable the conversion of time structural maps to depth. Petrophysical/reservoir quality analysis was carried out with well log data. Over pressured zones were predicted in wells by generating compaction curves obtained from the Eaton sonic transit time model while interval velocities and acoustic impedance were the basis of the seismic prediction. Overpressure mechanisms were deducted from the result of the study this enabled inferences of prospective localities.
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Pre-Processing: Procedure on Web Log File for Web Usage Mining

Pre-Processing: Procedure on Web Log File for Web Usage Mining

With the continued growth and proliferation of e- commerce, Web services, and Web-based information systems, the volumes of clickstream and user data collected by Web-based organizations in their daily operations has reached astronomical proportions. Analyzing such data can help these organizations determine the life-time value of clients, design cross-marketing strategies across products and services, evaluate the effectiveness of pro-motional campaigns, optimize the functionality of Web-based applications, provide more personalized content to visitors, and find the most effective logical structure for their Web space. This type of analysis involves the automatic discovery of meaningful patterns and relationships from a large collection of primarily semi-structured data, often stored in Web and applications server access logs, as well as in related operational data sources.
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Analysis of Log File Data to Understand  Mobile Service Context and Usage Patterns

Analysis of Log File Data to Understand Mobile Service Context and Usage Patterns

These six dimensions focus on realistic service usage. This emphasises the valuation of a service by the way how end- users apply services to solve given problems. Such behaviour patterns have the potential to tell us about underlying reasons why specific service fail or become well accepted. Recording such behaviourally relevant data also allow the emulation of service usage in respect to given user´s context. Both aspects are important for developers to continuously improve the service. According to the BSC approach, the intention is to find a few aggregated indicators that quantify a given dimension. The indicator must meet the requirements of reasonability and measurability. A general problem of social surveys is to translate the indicators into precise measures. The abstract classes of measurement types, correspond hereby with different event and error logging data types. To achieve comparability between different numerical scales of measurements e.g. an event/error frequency scale, a function has to be defined which maps selected scale areas on specific quality rating values. Since humans perceive the influence of various indicators for a given dimension differently, weight coefficients are used to balance the influence of individual indicators. Both mapping function properties and weight coefficients can be obtained through a profiling questionnaire prior to the field trials.
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Petrophysical evaluation and reservoir characterization of the “X” Field, Niger Delta using well log data

Petrophysical evaluation and reservoir characterization of the “X” Field, Niger Delta using well log data

The wireline log data were studied to characterize the porosity, water saturation, volume of shale of the “X-field” reservoirs. The results of the analysis revealed a total number of 23 reservoir (sand) units in well one, 7 reservoir (sand) units were delineated in well Two. And in Well three, 19 reservoir (sand) units were characterized. The thickness of each sand unit varied between 5.0m and 35.0m in well one, 12.0m and 32.0m in well two and between 1.0m and 27.0m in well three. The porosity values range between 15% and 39%. The volume of shale is not within the limits that could affect the water saturation (between 0.08 v/v decimal to 0.082 v/v decimal) and well above the limit in some reservoirs(>10%). The water saturation (Sw) values of the reservoirs in the study area range from 0.053% - 0.95% which invariably are indication that the hydrocarbon saturation are high in some reservoirs (i.e S h ranges between 67% - 95%). The reservoirs in well one are more
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ENHANCING THE ACCESSIBILITY AND USABILITY OF WEBSITE USING WEB LOG ANALYSIS

ENHANCING THE ACCESSIBILITY AND USABILITY OF WEBSITE USING WEB LOG ANALYSIS

The internet during the past few years, the World Wide Web (WWW) has become most famous and drastic platform to store, propagate and retrieve information as well as mine useful knowledge. It is a way of communication and information dissemination and it server as a platform for exchanging several types of information. Web usage mining also known as web log mining is the application of data mining techniques of discover interesting usage patterns from web data to understand and better serve the need of web based applications. Usage data hold the identify or filiation of web users along with their browsing conduct at a web site. Web Usage Mining consists of four process, first web server log, second preprocessing, third pattern discovery and fourth pattern analysis. After the consummation of these four process, the user can find the required usage pattern and user this information for the specific need [9] in a variety of way such as improvement of the application, recognizing the visitor’s conduct, customer attraction, customer tenure etc. Web usage mining is the third category in web mining. This types of web mining permits for the accumulation of web access information for web pages. Web usage data provides the paths leading to accessed web page. The user logs are collected by the web server. Typical data includes IP address page reference and access time.
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Analyzing log in big data environment: A review

Analyzing log in big data environment: A review

Log Analysis is a crucial process in most system and network activities where log data is used for various reasons such as for performance monitoring, security auditing or even for reporting and profiling. However, as years passed by, the volume of log data increases along with the size of the system as well as the number of users involved. Traditional or existing log analyzer tools are not able to handle the massive amount of data. Therefore, Big Data is the solution to overcome this issue. The main purpose of this paper is to present a review of log file analysis in Big Data environment based on previous research works. This paper also highlights the characteristics of Big Data as well as Hadoop Framework that has been widely used as Big Data application. Results from the papers reviewed shows that majority researchers applied MapReduce as the main component of Hadoop for analyzing the log files and HDFS as the data storage. Previous researchers have also used other tools and algorithms together with the Hadoop Framework for analysis purposes. The findings of this paper will provide a comprehensible review of Hadoop usage performance in analyzing different types of log files and recommend understandable results for end users to use in future work.
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Query log analysis with LangLog

Query log analysis with LangLog

(SLA). According to Jansen (2008) both TLA and SLA have three stages: data collection, data preparation and data analysis. In the data collec- tion stage one collects data describing the user interaction with the system. Data preparation is the process of loading the collected data in a re- lational database. The data loaded in the database gives a transaction log representation independent of the particular log syntax. In the final stage the data prepared at the previous step is analyzed. One may notice that the traditional three levels log analyses give a syntactic view of the infor- mation in the logs. Counting terms, measuring the logical complexity of queries or the simple procedures that associate queries with the ses- sions in no way accesses the semantics of queries. LangLog system addreses the semantic problem performing clustering and classification for real query logs. Clustering the queries in the logs al- lows the identification of meaningful groups of queries. Classifying the queries according to a relevant list of categories permits the assessment of how well the searching engine meets the user needs. In addition the LangLog system address problems like automatic language identification, Name Entity Recognition, and automatic query translation. The rest of the paper is organized as follows: the next section briefly reviews some systems performing SLA. Then we present the data sources the architecture and the analysis pro- cess of the LangLog system. The conclusion sec- tion concludes the article summarizing the work and presenting some new possible enhancements of the LangLog.
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Static Reservoir Modeling Using Well Log and 3 D Seismic Data in a KN Field, Offshore Niger Delta, Nigeria

Static Reservoir Modeling Using Well Log and 3 D Seismic Data in a KN Field, Offshore Niger Delta, Nigeria

This study focuses on the application of 3D static model using 3-D seismic and well log data for proper optimiza- tion and development of hydrocarbon potential in KN field of Niger Delta Province. 3D Seismic data were used to generate the input interpreted horizon grids and fault polygons. The horizon which cut across the six wells was used for the analysis and detailed petrophysical analysis was carried out. Structural and property modeling (net to gross, porosity, permeability, water saturation and facies) were distributed stochastically within the con- structed 3D grid using Sequential Gaussian Simulation and Sequential Indicator Simulation algorithms. The reservoir structural model show system of different oriented growth faults F1 to F6. Faults 1 and Fault 4 are the major growth faults, dipping towards south-west and are quite extensive. A rollover anticline formed as a result of deformation of the sediments deposited on the downthrown block of fault F1. The other faults (2, 3, 5 and 6) are minor fault (synthetic and antithetic). The trapping mechanism is a fault assisted anticlinal closure. Results from well log analysis and petrophysical models classified sand 9 reservoir as a moderate to good reservoir in terms of facies, with good porosity, permeability, moderate net to gross and low water saturation. The volume- tric calculation of modeled sand 9 horizon reveals that the (STOIIP) value at the Downthrown and Ramp seg- ment are 15.7 MMbbl and 3.8 MMbbl respectively. This implies that the mapped horizon indicates hydrocarbon accumulation in economic quantity. This study has also demonstrated the effectiveness of 3-D static modeling technique as a tool for better understanding of spatial distribution of discrete and continuous reservoir proper- ties, hence, has provided a framework for future prediction of reservoir performance and production behavior of sand 9 reservoir. However, more horizontal wells should be drilled to enhance optimization of the reservoir.
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Characteristics of the Sedimentary Microfacies of Fuyu Reservoir in Yushulin Oilfield, Songliao Basin

Characteristics of the Sedimentary Microfacies of Fuyu Reservoir in Yushulin Oilfield, Songliao Basin

According to the regional sedimentary data , the observation of coring well core and the analysis of large amounts of log data, we recognized the sand microfacies types of group development in the study area are underwater distributary channel, crevasse splay, sand sheet and excessive bank sand, etc. By lithology, electrical and physical properties, oil content analysis, we determined the underwater distributary channel sand body is the main reservoir (Fig.4).

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Linear Maximum Likelihood Regression Analysis for Untransformed Log Normally Distributed Data

Linear Maximum Likelihood Regression Analysis for Untransformed Log Normally Distributed Data

There is a need for methods that handle log-normally distributed data in linear regression models, based on moderate sample sizes. In order to estimate the linear association (and the absolute effect), but still take into account the log-normal distribution with a non-constant variance, we propose a maximum likelihood (ML) based method for regression analysis. In this paper we have evaluated this new method using large scale simulations, which allowed us to analyze the bias, variance and dis- tribution of the regression coefficients resulting from the new method, as well as comparing it to LS- and weighted- least-squares (WLS) regression analysis. A data set on personal exposure to 1,3-butadiene in five Swedish cities was used to illustrate the three methods.
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Evaluation of Petrophysical   Properties of a Reservoir in Kolo Creek Field, Niger Delta, Using Well Log Analysis.

Evaluation of Petrophysical Properties of a Reservoir in Kolo Creek Field, Niger Delta, Using Well Log Analysis.

The main petrophysical parameters needed for the evaluation of a reservoir are porosity, permeability, hydrocarbon saturation, thickness and area. Other parameters such as the reservoir geometry, formation temperature & pressure and Lithology, can play vital roles in the evaluation, completion and production of the reservoir. In the study area which is within the Kolo Creek field in the Niger Delta, the method adopted for the evaluation utilized gamma ray logs to differentiate potentially porous and permeable reservoir rock (sands, sandstones and siltstones) from non-permeable clays and shales. Also, neutron and density log combination, were used principally to delineate porous formations, determine the porosity as well as differentiate gas and oil zones. Resistivity logs were used to assess water and hydrocarbon saturations. The quantitative analytical data show average porosity values in the three wells (X, Y and Z) as 16.44%, 16.38% and 17.48%, respectively. Also observable results indicate that wells
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Joint interpretation of magnetotelluric, seismic, and well-log data in Hontomín (Spain)

Joint interpretation of magnetotelluric, seismic, and well-log data in Hontomín (Spain)

ological structures, and the analysis of the data allows infer- ring information on the subsurface velocity field. However, seismic data is unable to provide information on pore fluid nature and is not very sensitive to fluid saturation (e.g. Eid et al., 2015). On the other hand, electromagnetics, for example the magnetotelluric method (MT), are particularly suitable for fluid characterization purposes, thanks to their sensitivity to changes in the electrical conductivity of the pore space di- rectly linked to changes in fluid saturation (Bedrosian, 2007; Nakatsuka et al., 2010; MacGregor, 2012). Joint inversion of magnetotelluric and seismic data is a promising practice to resolve different aspects of the same structure (e.g. Gallardo and Meju, 2003), but its application is computationally ex- pensive and the algorithms used are still under development (Moorkamp et al., 2011). Joint interpretation of seismic and electromagnetic data have proven successful in subsurface characterization (e.g. Harris and MacGregor, 2006; Juanatey et al., 2013; Solon et al., 2015). Hence, a compelling reser- voir characterization will benefit from the integration of the structural interpretation, provided by the seismic image, with the electrical properties derived from the MT method (e.g. Eberhart-Phillips et al., 1995; Muñoz et al., 2010).
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Real Time Web Log Analysis and Hadoop for Data Analytics on Large Web Logs

Real Time Web Log Analysis and Hadoop for Data Analytics on Large Web Logs

The much needed robustness and scalability option to a distributed system is provided by Hadoop, which also provides inexpensive as well as reliable storage. Tools for analysing structured and unstructured data are also provided by Hadoop. So, Map Reduce and HDFS of Hadoop use simple & robust techniques for delivering very high availability of data and for analysis enormous amounts of information quickly. It may not be possible to convert all the sequential algorithms into the parallel form, which in turn could be converted to the map/reduce format. There could emerge circumstance that the calculations may not be viably actualized in the guide/lessen organization, or usage of calculations could require more prominent overheads and not adjust for the points of interest Hadoop gives, along these lines making execution on Hadoop a terrible decision. Affiliation guideline mining is a sort of information mining process. Affiliation standard mining is done to separate fascinating connections, designs, relationship among things in the exchange database or other information archives. For instance an affiliation guideline natural product => milk created from the exchange database of a supermarket can help in planning promoting system around the standard. Affiliation standards are generally utilized as a part of different ranges, for example, telecom systems, promoting and hazard administration, and stock control and so on. Numerous organizations and firms keep huge amounts of their everyday exchange information. These information could be dissected to take in the buying pattern of the client. Such important understanding can be utilized to bolster assortment of business-related applications, for example, advertising and advancement of the items, stock administration and so on.
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Well-organized Data Mining Techniques for Clustering of Users on Web Log Data

Well-organized Data Mining Techniques for Clustering of Users on Web Log Data

In the similar manner simple K-means operates over the generated MID obtained from web log data. J. Xiao et al. [14] developed a cluster analysis method to mine the web, this method clusters the web users based on their profiles. User profile clustering algorithm consists of three modules. First it finds the similarity measure based on Belief function and applies greedy clustering using this function then creates the common user profiles. In greedy clustering, it first randomly selects the users into common profile set and for each user calculate similarity measure, based on this choose best representative and update similarity of each point to the closest representative finally it gives the unique clusters. To generate the clusters using session representatives V. Sujatha and Punitha Valli [15] adopted a method called
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Use of Log Data for Predictive Analytics through Data Mining
                 

Use of Log Data for Predictive Analytics through Data Mining  

Abstract — The availability of the data of web accessed is in human readable form generated by computer referred to as web log, provided by online sources, it make that data into day to day life of individual as well as for business operations for more dynamism and bring it closer to real time for the web administrator about what is happening with the web. With the help of such web log data helping the business organization before having to wait a week, or even a month for data through which those people will able to mine data and perform predictive analysis from multiple access made daily as well as in regular manner from users around the world. Data Mining is used for finding expected patterns from that large set of log data using Web Mining. When used together, predictive analytics and data mining can make the future prediction more efficiently with respect to web access.
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