scholarly journals Reservoir Parameters Estimation with Some Log Curves of Qiongdongnan Basin Based on LS-SVM Method

2021 ◽  
Vol 2092 (1) ◽  
pp. 012024
Author(s):  
Tangwei Liu ◽  
Hehua Xu ◽  
Xiaobin Shi ◽  
Xuelin Qiu ◽  
Zhen Sun
Keyword(s):  
Prediction Models ◽  
Parameters Estimation ◽  
Qiongdongnan Basin ◽  
Support Vector ◽  
Well Log ◽  
Statistical Regression ◽  
Log Data ◽  
Core Data ◽  
Permeability Prediction ◽  
Porosity And Permeability

Abstract Reservoir porosity and permeability are considered as very important parameters in characterizing oil and gas reservoirs. Traditional methods for porosity and permeability prediction are well log and core data analysis to get some regression empirical formulas. However, because of strong non-linear relationship between well log data and core data such as porosity and permeability, usual statistical regression methods are not completely able to provide meaningful estimate results. It is very difficult to measure fine scale porosity and permeability parameters of the reservoir. In this paper, the least square support vector machine (LS-SVM) method is applied to the parameters estimation with well log and core data of Qiongdongnan basin reservoirs. With the log and core exploration data of Qiongdongnan basin, the approach and prediction models of porosity and permeability are constructed and applied. There are several type of log data for the determination of porosity and permeability. These parameters are related with the selected log data. However, a precise analysis and determine of parameters require a combinatorial selection method for different type data. Some curves such as RHOB,CALI,POTA,THOR,GR are selected from all obtained logging curves of a Qiongdongnan basin well to predict porosity. At last we give some permeability prediction results based on LS-SVM method. High precision practice results illustrate the efficiency of LS-SVM method for practical reservoir parameter estimation problems.

scholarly journals Comparison of Rock Type Determination Based on Permeability Estimation and FZI Value in Upper Cibulakan Shaly Sand Formation, ASR Field

2020 ◽  
Vol 2 (3) ◽  
Author(s):  
Anditya Sapta Rahesthi ◽  
Ratnayu Sitaresmi ◽  
Sigit Rahmawan
Keyword(s):  
Rock Type ◽  
Well Log ◽  
Rock Permeability ◽  
Log Data ◽  
Core Data ◽  
Permeability Prediction ◽  
Rock Types ◽  
Fluid Production ◽  
Shaly Sand ◽  
High Uncertainty

<em>Rock permeability is an important rock characteristic because it can help determine the rate of fluid production. Permeability can only be determined by direct measurement of core samples in the laboratory. Even though coring gives good results, the disadvantage is that it takes a lot of time and costs so it is not possible to do coring at all intervals. So that the well log is required to predict the level of permeability indirectly. However, the calculation of permeability prediction using well log data has a high uncertainty value, so rock typing is required so that the calculation of permeability prediction becomes more detailed. This research was conducted in an effort to determine the Hydraulic Flow Unit (HFU) of the reservoir in the well that has core data using the Flow Zone Indicator (FZI) parameter and FZI value propagation on wells that do not have core data so that the type of rock and permeability value are obtained from every well interval. From the results of the study, the reservoirs on the ASR field can be grouped into six rock types. The six rock types each have permeability as a function of validated porosity by applying it at all intervals. After FZI is calculated from log data and validated with core data, it can be seen that the results of the method produce a fairly good correlation (R<sup>2</sup> = 0.92). Furthermore, from the permeability equation values for each different rock type, the predicted permeability results are also quite good (R<sup>2 </sup>= 0.81).</em>

scholarly journals Hydraulic Flow Unit Classification and Prediction Using Machine Learning Techniques: A Case Study from the Nam Con Son Basin, Offshore Vietnam

Energies ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7714
Author(s):  
Ha Quang Man ◽  
Doan Huy Hien ◽  
Kieu Duy Thong ◽  
Bui Viet Dung ◽  
Nguyen Minh Hoa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Algorithms ◽  
Flow Unit ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Learning Methods ◽  
Log Data ◽  
Hydraulic Flow ◽  
Core Data ◽  
Machine Learning Methods

The test study area is the Miocene reservoir of Nam Con Son Basin, offshore Vietnam. In the study we used unsupervised learning to automatically cluster hydraulic flow units (HU) based on flow zone indicators (FZI) in a core plug dataset. Then we applied supervised learning to predict HU by combining core and well log data. We tested several machine learning algorithms. In the first phase, we derived hydraulic flow unit clustering of porosity and permeability of core data using unsupervised machine learning methods such as Ward’s, K mean, Self-Organize Map (SOM) and Fuzzy C mean (FCM). Then we applied supervised machine learning methods including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Boosted Tree (BT) and Random Forest (RF). We combined both core and log data to predict HU logs for the full well section of the wells without core data. We used four wells with six logs (GR, DT, NPHI, LLD, LSS and RHOB) and 578 cores from the Miocene reservoir to train, validate and test the data. Our goal was to show that the correct combination of cores and well logs data would provide reservoir engineers with a tool for HU classification and estimation of permeability in a continuous geological profile. Our research showed that machine learning effectively boosts the prediction of permeability, reduces uncertainty in reservoir modeling, and improves project economics.

Enhanced group method of data handling (GMDH) for permeability prediction based on the modified Levenberg Marquardt technique from well log data

Energy ◽  
2022 ◽  
Vol 239 ◽  
pp. 121915
Author(s):  
Alvin K. Mulashani ◽  
Chuanbo Shen ◽  
Baraka M. Nkurlu ◽  
Christopher N. Mkono ◽  
Martin Kawamala
Keyword(s):  
Group Method ◽  
Well Log ◽  
Data Handling ◽  
Log Data ◽  
Permeability Prediction ◽  
Levenberg Marquardt
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