Improving the Accuracy of Active Learning Method via Noise Injection for Estimating Hydraulic Flow Units: An Example from a Heterogeneous Carbonate Reservoir

2015 ◽  
pp. 225-244 ◽  
Author(s):  
Fouad Bahrpeyma ◽  
Constantin Cranganu ◽  
Bahman Golchin
Keyword(s):  
Active Learning ◽  
Carbonate Reservoir ◽  
Learning Method ◽  
Hydraulic Flow ◽  
Flow Units ◽  
Noise Injection ◽  
Active Learning Method ◽  
Hydraulic Flow Units

scholarly journals Prediction of Hydraulic Flow in the New Zohr Carbonate Reservoir, Eastern Mediterranean Using Artificial Neural Networks

2021 ◽  
Author(s):  
Amir Lala
Keyword(s):  
Eastern Mediterranean ◽  
Pore Space ◽  
Depositional Environments ◽  
Carbonate Reservoir ◽  
Petrographic Analysis ◽  
Gas Reservoir ◽  
Hydraulic Flow ◽  
Flow Units ◽  
Carbonate Formation ◽  
Hydraulic Flow Units

Abstract A new gas reservoir includes the carbonates of upper-Cretaceous Formation in the Zohr oilfield of eastern Mediterranean Sea in Egypt. The main aim of this study is to assess the new carbonate reservoir by thin section study and estimate hydraulic flow units HFUs by smart system. This carbonate formation is now considered the most important gas reservoir in northern Egypt. In this paper five microfacies were identified based on microscope petrographic analysis. The examined rocks were formed in lagoon, shoal and open marine depositional environments. The relationships between microfacies and flow units are further evaluated in this study. The determination of such relationships have proven to be challenging due to petrographic complications arising from diagenetic processes. The correlation behind pore space percentage and permeability is important to recognize hydraulic flow in the reservoir under consideration in this study.

scholarly journals Predicting the flow zone indicator of carbonate reservoirs using NMR echo transforms, and routine open-hole log measurements: Insights from a field case study spanning extreme micro-structure properties

2021 ◽  
Author(s):  
Mabkhout Al-Dousari ◽  
◽  
Salah Almudhhi ◽  
Ali A. Garrouch ◽  
◽  
...  
Keyword(s):  
Relative Error ◽  
Carbonate Reservoir ◽  
Micro Structure ◽  
Flow Zone ◽  
Hydraulic Flow ◽  
Absolute Relative Error ◽  
Flow Units ◽  
Average Absolute Relative Error ◽  
Open Hole ◽  
Hydraulic Flow Units

Predicting the flow zone indicator is essential for identifying the hydraulic flow units of hydrocarbon reservoirs. Delineation of hydraulic flow units is crucial for mapping petrophysical and rock mechanical properties. Precise prediction of the flow zone indicator (FZI) of carbonate rocks using well log measurements in un-cored intervals is still a daunting challenge for petrophysicists. This study presents a data mining methodology for predicting the rock FZI using NMR echo transforms, and conventional open-hole log measurements. The methodology is applied on a carbonate reservoir with extreme microstructure properties, from an oil “M” field characterized by a relatively high-permeability with a median of approximately 167 mD, and a maximum of 3480 mD. The reservoir from the M field features detritic, or vuggy structure, covering a wide range of rock fabrics varying from microcrystalline mudstones to coarse-grained grainstones. Porosity has a median of approximately 22%. Dimensional analysis and regression analysis are applied for the derivation of four transforms that appear to capture approximately 80% of the FZI variance. These four transforms are formulated using the geometric mean of the transverse NMR relaxation time (T2lm), the ratio of the free fluid index (FFI) to the bulk volume irreducible (BVI), the bulk density, the sonic compressional travel time, the true resistivity, the photo-electric absorption, and the effective porosity. Non-linear regression models have been developed for predicting the FZI using the derived transforms, for the carbonate reservoir from the M field. The average relative error for the estimated FZI values is approximately 52%. The same transforms are used as input for training a developed general regression neural network (GRNN), built for the purpose of predicting rock FZI. The constructed GRNN predicts FZI with a notable precision. The average absolute relative error on FZI for the training set is approximately 3.1%. The average absolute relative error on FZI for the blind testing set is approximately 22.0 %. The data mining approach presented in this study appears to suggest that (i) the relationship between the flow zone indicator and open-hole log attributes is highly non-linear, (ii) the FZI is highly affected by parameters that reflect rock texture, rock micro-structure geometry, and diagenetic alterations, and (iii) the derived transforms provide a means for further enhancement of the flow zone indicator prediction in carbonate reservoirs.

Quality-driven Deep Active Learning Method for 3D Brain MRI Segmentation

2021 ◽  
Author(s):  
Zhenxi Zhang ◽  
Jie Li ◽  
Chunna Tian ◽  
Zhusi Zhong ◽  
Zhicheng Jiao ◽  
...  
Keyword(s):  
Active Learning ◽  
Brain Mri ◽  
Learning Method ◽  
Mri Segmentation ◽  
Active Learning Method
Sign in / Sign up

Export Citation Format

Share Document