Data Mining Well Logs for Optimum Well Placement

2020 ◽  
Author(s):  
Anuroop Pandey ◽  
Mohammed F. Al Dushaishi ◽  
Espen Hoel ◽  
Svein Hellvik ◽  
Runar Nygaard
Keyword(s):  
Data Mining ◽  
Gamma Ray ◽  
Principal Component ◽  
Decision Criterion ◽  
Classification Model ◽  
Well Logs ◽  
Well Placement ◽  
Reservoir Rocks ◽  
Data Mining Approach ◽  
Gamma Ray Log

Abstract Well placement with geosteering can get very complex in reservoirs with formation change not simply addressed by changes in the gamma ray log response. This paper uses data mining to characterize complex reservoirs for optimum well placement. The objective of this paper is to develop a data mining process to evaluate non-trivial geologic settings for geosteering reservoir well placement. The well logs’ data was collected from multiple wells in a Norwegian North Sea field, where the reservoir rocks are characterized with high heterogeneities. Principal component analysis was used to recognize data pattern and extract underlying features. The extracted features are then into distinct groups using Hierarchical clustering (HC) analysis. A classification model, that is based on the deviance analysis, was constructed to build a criterion to identify each cluster within a set of well log data. The results show that the data mining approach sufficiently identified highly heterogeneous formations and can be used for geosteering applications. Classification trees defined quantitative decision criterion for the identified clusters. The approach is capable of distinguishing between potential and non-potential steering clusters, as the identified clusters have distinct decision criteria and effectively explain the variations within a section, as verified with the lithology described from core analysis.

Email Worm Detection Using Data Mining

2011 ◽  
pp. 20-34
Author(s):  
Mohammad M. Masud ◽  
Latifur Khan ◽  
Bhavani Thuraisingham
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Principal Component ◽  
Classification Model ◽  
Support Vector ◽  
Two Phase ◽  
Feature Selection Technique ◽  
Worm Detection ◽  
Phase Selection ◽  
Using Data

This chapter applies data mining techniques to detect email worms. Email messages contain a number of different features such as the total number of words in message body/subject, presence/absence of binary attachments, type of attachments, and so on. The goal is to obtain an efficient classification model based on these features. The solution consists of several steps. First, the number of features is reduced using two different approaches: feature-selection and dimension-reduction. This step is necessary to reduce noise and redundancy from the data. The feature-selection technique is called Two-phase Selection (TPS), which is a novel combination of decision tree and greedy selection algorithm. The dimensionreduction is performed by Principal Component Analysis. Second, the reduced data is used to train a classifier. Different classification techniques have been used, such as Support Vector Machine (SVM), Naïve Bayes and their combination. Finally, the trained classifiers are tested on a dataset containing both known and unknown types of worms. These results have been compared with published results. It is found that the proposed TPS selection along with SVM classification achieves the best accuracy in detecting both known and unknown types of worms.

scholarly journals Depositional Environment and facies analysis of Useni- 1, Niger Delta Basin, using well logs, core data

2019 ◽  
Vol 7 (1) ◽  
pp. 58
Author(s):  
G. O. Aigbadon ◽  
E. O. Akpunonu ◽  
S. O. Agunloye ◽  
A. Ocheli ◽  
O. O .Akakaru
Keyword(s):  
Gamma Ray ◽  
Water Saturation ◽  
Oil Field ◽  
Well Logs ◽  
Depth Range ◽  
Depositional Facies ◽  
Core Data ◽  
Delta Plain ◽  
Niger Delta Basin ◽  
Gamma Ray Log

This study was carried out integrating well logs and core to build reservoir model for the Useni-1 oil field. Core data and well logs were used to evaluate the petrophysical characteristics of the reservoirs. The paleodepositional environment was deduce from the wells and cores data. The depositional facies model showed highly permeable channels where the wells where positioned. The environments identified that the fluvial channel facies with highly permeable zones constituted the reservoirs. Four reservoirs were mapped at depth range of 8000ft to 8400ft with thicknesses varying from 20ft to 400ft. Petrophysical results showed that porosity of the reservoirs varied from 12% to 28 %; permeability from 145.70 md to 454.70md; water saturation from 21.65% to 54.50% and hydrocarbon saturation from 45.50% to 78.50 %. Core data and the gamma ray log trends with right boxcar trend indicate fluvial point bar and tidal channel fills in the lower delta plain setting. By-passed hydrocarbons were identified in low resistivity pay sands D1, D2 at depth of 7800 – 78100ft in the field.  

Proximate Breast Cancer Factors Using Data Mining Classification Techniques

2019 ◽  
Vol 4 (1) ◽  
pp. 47-56
Author(s):  
Alice Constance Mensah ◽  
Isaac Ofori Asare
Keyword(s):  
Breast Cancer ◽  
Data Mining ◽  
Random Forest ◽  
Cancer Patients ◽  
Classification Model ◽  
Tree Model ◽  
Cancer Data ◽  
Data Mining Approach ◽  
Learning Techniques ◽  
Using Data

Breast cancer is the most common of all cancers and is the leading cause of cancer deaths in women worldwide. The classification of breast cancer data can be useful to predict the outcome of some diseases or discover the genetic behavior of tumors. Data mining technology helps in classifying cancer patients and this technique helps to identify potential cancer patients by simply analyzing the data. This study examines the determinant factors of breast cancer and measures the breast cancer patient data to build a useful classification model using a data mining approach. In this study of 2397 women, 1022 (42.64%) were diagnosed with breast cancer. Among the four main learning techniques such as: Random Forest, Naive Bayes, Classification and Regression Model (CART), and Boosted Tree model were used for the study. The Random Forest technique had the better accuracy value of 0.9892(95%CI,0.9832 -0.9935) and a sensitivity value of about 92%. This means that the Random Forest learning model is the best model to classify and predict breast cancer based on associated factors.

The study of lateral variability of Kockatea Shale across the Perth Basin: an integration of electrofacies and lithofacies characteristics

2014 ◽  
Vol 54 (1) ◽  
pp. 241
Author(s):  
Hanieh Jafary Dargahi ◽  
Reza Rezaee
Keyword(s):  
Shale Gas ◽  
Gamma Ray ◽  
Well Logs ◽  
Well Log ◽  
Gas Reservoirs ◽  
Rock Types ◽  
Shale Gas Reservoirs ◽  
Gamma Ray Log ◽  
Core Description

The recognition of distinct rock types through log responses, referred to as electrofacies, is a fundamental role in mapping stratigraphic units that do not have any specific geological description. Lateral variability within adjoining intervals is differentiated by studying lithological characteristics such as petrography and mineralogy acquired from visual core description. In non-cored wells electrofacies analysis, therefore, is the most reliable way in determining reservoir zonations. The electrofacies’ accuracy is critically important in defining potentially desirable rock types for shale gas reservoirs in non-cored intervals, which can be obtained through an analogy of well log responses in identical lithofacies within cored wells. Considering the complexity of making a final prediction due to the unavailability of different well logs covering the whole area, only the gamma-ray log is used in determining electrofacies patterns within the studied shale gas intervals. The electrofacies patterns within identified lithofacies have been studied for the Kockatea Shale, which presented analogous patterns for identical lithological facies. The similarity has allowed for the correlation of lithofacies in cored and non-cored wells, and the evaluation of lithofacies variability and development within various wells. The correlation of the defined electrofacies indicates facies changes across the basin in association with thickening of some lithofacies. The thickest part of the electrofacies is shown at the Dandaragan Trough and the Beagle Ridge. Some electrofacies, however, have disappeared in some parts of these areas, such as lithofacies E in the Beagle Ridge, which is partially replaced by electrofacies C.

CLASSIFICATION MODEL FOR HOTSPOT SEQUENCES AS INDICATOR FOR PEATLAND FIRES USING DATA MINING APPROACH

2017 ◽  
Vol 3 (2) ◽  
pp. 588-597
Author(s):  
Muhammad Murtadha Ramadhan ◽  
◽  
Imas Sukaesih Sitanggang ◽  
Larasati Puji Anzani ◽  
◽  
...  
Keyword(s):  
Data Mining ◽  
Classification Model ◽  
Data Mining Approach ◽  
Using Data

scholarly journals Precise and Accurate Job Cycle Time Forecasting in a Wafer Fabrication Factory with a Fuzzy Data Mining Approach

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
Toly Chen ◽  
Richard Romanowski
Keyword(s):  
Data Mining ◽  
Cycle Time ◽  
Principal Component ◽  
Wafer Fabrication ◽  
Fuzzy Data ◽  
Lower And Upper Bounds ◽  
Forecasting Accuracy ◽  
Data Mining Approach ◽  
Backpropagation Network ◽  
Better Than

Many data mining methods have been proposed to improve the precision and accuracy of job cycle time forecasts for wafer fabrication factories. This study presents a fuzzy data mining approach based on an innovative fuzzy backpropagation network (FBPN) that determines the lower and upper bounds of the job cycle time. Forecasting accuracy is also significantly improved by a combination of principal component analysis (PCA), fuzzy c-means (FCM), and FBPN. An applied case that uses data collected from a wafer fabrication factory illustrates this fuzzy data mining approach. For this applied case, the proposed methodology performs better than six existing data mining approaches.

Petrophysical Analysis of Well Logs for the Estimation of Oil Reserves in Southern Niger Delta

2018 ◽  
Vol 6 (1) ◽  
pp. 145
Author(s):  
Paul S S ◽  
Okwueze . ◽  
E E ◽  
Udo K I
Keyword(s):  
Niger Delta ◽  
Gamma Ray ◽  
Water Saturation ◽  
Well Logs ◽  
Petrophysical Properties ◽  
Oil Reserves ◽  
Hydrocarbon Saturation ◽  
Resistivity Log ◽  
Gamma Ray Log ◽  
Sand Bodies

Gamma Ray log, Resistivity log, Density log, Micro-spherical focus log (MSFL), Deep Induction log (ILD) , Medium Induction log(ILM) and Spontaneous Potential (SP) log were collected for 2 wells in onshore Niger Delta. These insitu well logs were analyzed and interpreted. Porosity, permeability, water saturation, reservoir thickness and Shale volume were estimated for each hydrocarbon bearing zone delineated for each well. The parameters obtained were further analyzed and interpreted quantitatively to estimate the hydrocarbon potentials of each well. Twelve reservoir zones of interest (sand bodies) were delineated, correlated across the field and were ranked using average results of petrophysical parameters. In well one, Reservoirs E and F were identified as the thickest with 41ft each while reservoir A is the smallest in thickness (30ft). Petrophysical properties of hydrocarbon bearing zones delineated in well one ranged from 17.81% to 23.20% for porosity, 1292.09mD to 2018.17mD for permeability and 56.40% to 68.40% for hydrocarbon saturation compared to well 2 with 14.67% to 19.52% for porosity, 1211.61mD to1843.11mD for permeability and 51.80% to 66.40% for hydrocarbon saturation. The estimated averages of petrophysical properties for well one are 20.14% porosity, 1643.65mD permeability, 63.20% hydrocarbon saturation compared to well 2 with 15.55% porosity, 1582.58mD permeability and 61.93% hydrocarbon saturation. Results show 148.45MMBB and 145.91MMBB as oil reserve (Recoverable) for the field. From the results obtained, well one is likely to be more productive than well [2] and the field has exploitable oil in place.  

Data Mining Well Logs for Optimum Well Placement

2021 ◽  
Author(s):  
Anuroop Pandey ◽  
Mohammed Al Dushaishi ◽  
Espen Hoel ◽  
Svein Hellvik ◽  
Runar Nygaard
Keyword(s):  
Data Mining ◽  
Well Logs ◽  
Well Placement
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