K-Mean Clustering Analysis and Logistic Boosting Regression for Rock Facies Characterization and Classification in Zubair Reservoir in Luhais Oil Field, Southern Iraq

Mohammed Albuslimi

doi:10.46717/igj.54.2b.6ms-2021-08-26

K-Mean Clustering Analysis and Logistic Boosting Regression for Rock Facies Characterization and Classification in Zubair Reservoir in Luhais Oil Field, Southern Iraq

Iraqi Geological Journal ◽

10.46717/igj.54.2b.6ms-2021-08-26 ◽

2021 ◽

Vol 54 (2B) ◽

pp. 65-75

Author(s):

Mohammed Albuslimi

Keyword(s):

Water Saturation ◽

Confusion Matrix ◽

Well Logging ◽

Oil Field ◽

Core Analysis ◽

Learning Approaches ◽

Classification Rate ◽

Lithofacies Classification ◽

Core Description ◽

Southern Iraq

Identifying rock facies from petrophysical logs is a crucial step in the evaluation and characterization of hydrocarbon reservoirs. The rock facies can be obtained either from core analysis (lithofacies) or from well logging data (electrofacies). In this research, two advanced machine learning approaches were adopted for electrofacies identification and for lithofacies classification, both given the well-logging interpretations from a well in the upper shale member in Luhais Oil Field, southern Iraq. Specifically, the K-mean partitioning analysis and Logistic Boosting (Logit Boost) were conducted for electrofacies characterization and lithofacies classification, respectively. The dataset includes the routine core analysis of core porosity, core permeability, and measured discrete lithofacies along with the well-logging interpretations include (shale volume, water saturation and effective porosity) given the entire reservoir interval. The K-Mean clustering technique demonstrated good matching between the vertical sequence of identified electrofacies and the observed lithofacies from core description as attained 89.92% total correct percent from the confusion matrix table. The Logit Boost showed excellent matching between the recognized lithofacies from the core description and the predicted lithofacies through attained 98.26% total correct classification rate index from the confusion matrix table. The high accuracy of the Logit Boost algorithm comes from taking into account the non-linearity between the lithofacies and petrophysical properties in the classification process. The high degree of lithofacies classification by Logit Boost in this research can be considered in a similar procedure at all sandstone reservoirs to improve the reservoir characterization. The complete facies identification and classification were implemented with the programming language R, the powerful open-source statistical computing language.

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Research on the Distriution Rules and Calculation Method of the Parameters of Reservoir of Putaohua Groups in Yushulin Oil Field

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.490-491.468 ◽

2014 ◽

Vol 490-491 ◽

pp. 468-472

Author(s):

Ke Zeng ◽

Zheng Zhou ◽

Mei Ling Zhang

Keyword(s):

Physical Property ◽

Analysis Data ◽

Well Logging ◽

Oil Field ◽

Core Analysis ◽

The Core ◽

Reservoir Property ◽

Porosity And Permeability ◽

The Relationship ◽

Parameters Calculation

Based on the Putaohua groups in Yushulin oil field, and through the statiscics and analyses, weve found that the reservoir property of this area is in the range of specially low permeability level. So due to the low porosity and permeability, its necessary to do some reaearch on the parameters calculation method.This papers analysed the relationships between the physical property parameters such as porosity, permeability, shale content and the well logging responses such as AC, SP, GR, then we built the distribution rules histograms of each physical property parameter. And we got the distribution situations of the parameters of the oil groups. Through the multiple regression, we built the relationship formulas between the reservoir property parameters and the well logging responses by using the core analysis data of 53 test wells. Afetr comparing the parameters of calculation and the core analysis data, we found that the deviation is small, which meets the production requires of oil field.

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Integrating Electrofacies and Well Logging Data Into Regression and Machine Learning Approaches for Improved Permeability Estimation in a Carbonate Reservoir In a Giant Southern Iraqi Oil Field

10.4043/30763-ms ◽

2020 ◽

Author(s):

Watheq J Al-Mudhafar

Keyword(s):

Machine Learning ◽

Well Logging ◽

Carbonate Reservoir ◽

Oil Field ◽

Learning Approaches ◽

Permeability Estimation

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Reservoir units of Mishrif Formation in Majnoon Oil field, Southern Iraq

Iraqi Journal of Science ◽

10.24996/ijs.2019.60.12.15 ◽

2019 ◽

pp. 2656-2663

Author(s):

Layla khudhur Abbas ◽

Thamar Abdullah Mahdi

Keyword(s):

Gamma Ray ◽

Water Saturation ◽

High Volume ◽

Effective Porosity ◽

Oil Field ◽

Reservoir Properties ◽

Marine Facies ◽

Wireline Logs ◽

Low Volume ◽

Southern Iraq

The reservoir units of Mishrif Formation in Majnoon oil field were studied by using available wireline logs (gamma ray, porosity and resistivity) and facies that derived from core and cutting samples for three wells including Mj-1, Mj-15, and Mj-20. The reservoir properties were determined and interpreted by using IP software. The results showed that unit D have the best reservoir properties due to high effective porosity, low water saturation and very low volume of shale. Furthermore, a large part of this unit was deposited in shoal environment. The other reservoir units are then graded in reservoir properties including units B, A, F & E respectively, except unit C, which is considered as a cap unit, because it consists of restricted marine facies so that; it has high volume of shale and water saturation and very low effective porosity.

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Lithofacies Classification of Carbonate Reservoirs Using Advanced Machine Learning: A Case Study from a Southern Iraqi Oil Field

10.4043/31114-ms ◽

2021 ◽

Author(s):

Mohammed A. Abbas ◽

Watheq J. Al-Mudhafar

Keyword(s):

Machine Learning ◽

Random Forest ◽

Reservoir Characterization ◽

Carbonate Reservoir ◽

Oil Field ◽

Machine Learning Techniques ◽

Classification Algorithms ◽

Classification Rate ◽

Porosity And Permeability ◽

Lithofacies Classification

Abstract Estimating rock facies from petrophysical logs in non-cored wells in complex carbonates represents a crucial task for improving reservoir characterization and field development. Thus, it most essential to identify the lithofacies that discriminate the reservoir intervals based on their flow and storage capacity. In this paper, an innovative procedure is adopted for lithofacies classification using data-driven machine learning in a well from the Mishrif carbonate reservoir in the giant Majnoon oil field, Southern Iraq. The Random Forest method was adopted for lithofacies classification using well logging data in a cored well to predict their distribution in other non-cored wells. Furthermore, three advanced statistical algorithms: Logistic Boosting Regression, Bagging Multivariate Adaptive Regression Spline, and Generalized Boosting Modeling were implemented and compared to the Random Forest approach to attain the most realistic lithofacies prediction. The dataset includes the measured discrete lithofacies distribution and the original log curves of caliper, gamma ray, neutron porosity, bulk density, sonic, deep and shallow resistivity, all available over the entire reservoir interval. Prior to applying the four classification algorithms, a random subsampling cross-validation was conducted on the dataset to produce training and testing subsets for modeling and prediction, respectively. After predicting the discrete lithofacies distribution, the Confusion Table and the Correct Classification Rate Index (CCI) were employed as further criteria to analyze and compare the effectiveness of the four classification algorithms. The results of this study revealed that Random Forest was more accurate in lithofacies classification than other techniques. It led to excellent matching between the observed and predicted discrete lithofacies through attaining 100% of CCI based on the training subset and 96.67 % of the CCI for the validating subset. Further validation of the resulting facies model was conducted by comparing each of the predicted discrete lithofacies with the available ranges of porosity and permeability obtained from the NMR log. We observed that rudist-dominated lithofacies correlates to rock with higher porosity and permeability. In contrast, the argillaceous lithofacies correlates to rocks with lower porosity and permeability. Additionally, these high-and low-ranges of permeability were later compared with the oil rate obtained from the PLT log data. It was identified that the high-and low-ranges of permeability correlate well to the high- and low-oil rate logs, respectively. In conclusion, the high quality estimation of lithofacies in non-cored intervals and wells is a crucial reservoir characterization task in order to obtain meaningful permeability-porosity relationships and capture realistic reservoir heterogeneity. The application of machine learning techniques drives down costs, provides for time-savings, and allows for uncertainty mitigation in lithofacies classification and prediction. The entire workflow was done through R, an open-source statistical computing language. It can easily be applied to other reservoirs to attain for them a similar improved overall reservoir characterization.

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Petrophysical Evaluation of Mauddud Formation in Selected Wells from Ratawi Oil Field, Southern Iraq

Iraqi Journal of Science ◽

10.24996/ijs.2021.62.9.12 ◽

2021 ◽

pp. 2956-2969

Author(s):

Humam Q. Hameed ◽

Afrah H. Saleh

Keyword(s):

Water Saturation ◽

High Water ◽

Effective Porosity ◽

Oil Field ◽

Well Logs ◽

Reservoir Properties ◽

Oil Saturation ◽

Shale Volume ◽

Southern Iraq ◽

Petrophysical Evaluation

The objective of this paper is determining the petrophysical properties of the Mauddud Formation (Albian-Early Turonian) in Ratawi Oil Field depending on the well logs data by using interactive petrophysical software IP (V4.5). We evaluated parameters of available logs that control the reservoir properties of the formation, including shale volume, effective porosity, and water saturation. Mauddud Formation is divided into five units, which are distinguished by various reservoir characteristics. These units are A, B, C, D, and E. Through analyzing results of the computer processed interpretation (CPI) of available wells, we observed that the main reservoir units are B and D, being distinguished by elevated values of effective porosity (10%-32%) and oil saturation (95%-30%) with low shale content (6%-30%). Whereas, units A, C, and E were characterized by low or non-reservoir properties, due to high water saturation and low values of effective porosity caused by increased volume shale.

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DIGITAL ROCK ANALYSIS: AN ALTERNATIVE METHOD TO PREDICT PETROPHYSICAL PROPERTIES, CASE STUDY FROM MISHRIF FORMATION

Iraqi Geological Journal ◽

10.46717/igj.53.2c.4rs-2020-09-04 ◽

2020 ◽

Vol 53 (2C) ◽

pp. 34-55

Author(s):

Yahya Tawfeeq

Keyword(s):

Image Analysis ◽

Thin Section ◽

Water Saturation ◽

Oil Field ◽

Absolute Permeability ◽

Core Analysis ◽

Petrophysical Properties ◽

Correlation Model ◽

Irreducible Water Saturation ◽

Digital Core

The digital core analysis of petrophysical properties replace the use of conventional core analysis by reducing the required time for investigation. Also, the ability to capture pore geometries and fluid behavior at the pore-scale improves the understanding of complex reservoir structures. In this work, 53 samples of 2D thin section petrographic images were used for analyses from the core plugs taken from the Buzurgan oil field. Each sample was impregnated with blue-dyed epoxy, thin sectioned and then was stained for discrimination of carbonate minerals. Each thin section has been described in detail and illustrated by photomicrographs. The studied samples include a variety of rock types. Packstone is the most common rock type observed followed by grainstone and packstone – wackestone. Floatstone and dolostone are noted rarely in the studied interval. However, the samples of thin section images are processed and digitized, utilizing MATLAB programming and image analysis software. The entire workflow of digital core analysis from image segmentation to petrophysical rock properties determination was performed. A focused has been made on determining effective and total porosity, absolute permeability, and irreducible water saturation. Absolute permeability is estimated with the Kozeny-Carman permeability correlation model and Timur-Coates permeability correlation model. Irreducible water saturation simply is derived from total and effective porosity. Also, some pore void characteristics, such as area and perimeter, were calculated. The results of Digital 2D image analysis have been compared to laboratory core measurements to investigate the reliability and restrictions of the digital image interpretation techniques.

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