A New Approach in Permeability and Hydraulic Flow Unit Determination

2012 ◽  
Author(s):  
Mohammad Izadi ◽  
Ali Ghalambor
Keyword(s):  
Flow Unit ◽  
New Approach ◽  
Hydraulic Flow

scholarly journals Permeability evaluation on oil-window shale based on hydraulic flow unit: A new approach

2018 ◽  
Vol 2 (1) ◽  
pp. 1-13 ◽  
Author(s):  
Pengfei Zhang ◽  
Shuangfang Lu ◽  
Junqian Li ◽  
Jie Zhang ◽  
Haitao Xue ◽  
...  
Keyword(s):  
Flow Unit ◽  
New Approach ◽  
Hydraulic Flow ◽  
Permeability Evaluation

A New Approach in Permeability and Hydraulic-Flow-Unit Determination

2013 ◽  
Vol 16 (03) ◽  
pp. 257-264 ◽  
Author(s):  
Mohammad Izadi ◽  
Ali Ghalambor
Keyword(s):  
Experimental Data ◽  
Flow Unit ◽  
Successful Implementation ◽  
Core Analysis ◽  
Petrophysical Properties ◽  
Berea Sandstone ◽  
New Approach ◽  
New Model ◽  
Hydraulic Flow ◽  
Proposed Model

Summary Building an integrated subsurface model is one of the main goals of major oil and gas operators to guide the field-development plans. All field-data acquisitions from seismic, well logging, production, and geomechanical monitoring to enhanced-oil-recovery (EOR) operations can be affected by the accurate details incorporated in the subsurface model. Therefore, building a realistic integrated subsurface model of the field and associated operations is essential for a successful implementation of such projects. Furthermore, using a more reliable model can, in turn, provide the basis in the decision-making process for control and remediation of formation damage. One of the key identifiers of the subsurface model is accurately predicting the hydraulic-flow units (HFUs). There are several models currently used in the prediction of these units on the basis of the type of data available. The predictions that used these models differ significantly because of the assumptions made in the derivations. Most of these assumptions do not adequately reflect realistic subsurface conditions, thus increasing the need for better models. A new approach has been developed in this study for predicting the petrophysical properties and improving the reservoir characterization. The Poiseuille flow equation and Darcy equation were coupled, taking into consideration the irreducible water saturation in the pore network. The porous medium was introduced as a domain containing a bundle of tortuous capillary tubes with irreducible water lining the pore wall. A series of routine and special core analysis was performed on 17 Berea sandstone samples, and the petrophysical properties were measured and X-ray diffraction (XRD) analysis was conducted. In building the petrophysical model, it was initially necessary to assume an ideal reservoir with 17 different layers, each layer representing one Berea sample. Afterward, by the iteration and calibration of the laboratory data, the number of HFUs was determined by use of the common HFU model and the proposed model accordingly. A comparative study shows that the new model provides a better distribution of HFUs and prediction of the petrophysical properties. The new model provides a better match with the experimental data collected than the models currently used in the prediction of such parameters. The good agreement observed for the Berea sandstone experimental data and the model predictions by use of the new permeability model shows a wider range of applicability for various reservoir conditions. In addition, the model has been applied to a series of core-analysis data on low-permeability Medina sandstone, Appalachian basin, northwest Pennsylvania. The flow-unit distribution by use of the proposed model shows a better flow-zone distinction, and the permeability/ porosity relationship has a higher confidence coefficient.

scholarly journals Determination of Rock Type Using Hydraulic Flow Unit Concept to Predict Permeability with Artificial Neural Network

2018 ◽  
Vol 1 (2) ◽  
Author(s):  
Ghanima Yasmaniar ◽  
Ratnayu Sitaresmi ◽  
Suryo Prakoso
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Data Analysis ◽  
Rock Type ◽  
Flow Unit ◽  
West Java ◽  
North West ◽  
Hydraulic Flow ◽  
Core Data ◽  
The North

<em>Permeability is one of the important of reservoir characteristics, but is difficult to predict it. The accurate permeability values can be obtained from core data analysis, but it is not possible to do at all of the well intervals in the field. This study used 191 sandstone core samples from the Upper Cibulakan Formation in the North West Java Basin. The concept of HFU (Hydraulic Flow Unit) developed by Kozeny-Carman is used to generate the relationship between porosity and permeability for each rock type. Afterward, to estimate the permeability value at uncored intervals, the statistical methods of artificial neural network based on log data are used on G-19 Well, G Field which is located in the North West Java Basin. Based on core data analysis from this research, the reservoir consists of eight HFU with different equations to estimate permeability for each HFU. From this reserarch, the results of permeability calculations at uncored intervals are not much different from the core data at the same depth. Therefore the approach of permeability prediction can be used to determine the value of permeability without performing core data analysis so that it can save the company expenses.</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.

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