Bayesian Based Approach for Hydraulic Flow Unit Identification and Permeability Prediction: A Field Case Application in a Tight Carbonate Reservoir

2018 ◽  
Author(s):  
Adolfo D'Windt ◽  
Edwin Quint ◽  
Anwar Al-Saleh ◽  
Qasem Dashti
Keyword(s):  
Carbonate Reservoir ◽  
Flow Unit ◽  
Field Case ◽  
Hydraulic Flow ◽  
Permeability Prediction ◽  
Tight Carbonate ◽  
Case Application

scholarly journals Comparative Permeability Estimation Method and Identification of Rock Types using Cluster Analysis from Well Logs and Core Analysis Data in Tertiary Carbonate Reservoir-Khabaz Oil Field

2019 ◽  
Vol 25 (12) ◽  
pp. 49-61
Author(s):  
Adnan Ajam Abed ◽  
Sammer Mohammed Hamd-Allah
Keyword(s):  
Cluster Analysis ◽  
Carbonate Reservoir ◽  
Flow Unit ◽  
Well Logs ◽  
Analysis Approach ◽  
Flow Zone ◽  
Hydraulic Flow ◽  
Rock Types ◽  
Flow Units ◽  
Reservoir Zonation

Characterization of the heterogonous reservoir is complex representation and evaluation of petrophysical properties and application of the relationships between porosity-permeability within the framework of hydraulic flow units is used to estimate permeability in un-cored wells. Techniques of flow unit or hydraulic flow unit (HFU) divided the reservoir into zones laterally and vertically which can be managed and control fluid flow within flow unit and considerably is entirely different with other flow units through reservoir. Each flow unit can be distinguished by applying the relationships of flow zone indicator (FZI) method. Supporting the relationship between porosity and permeability by using flow zone indictor is carried out for evaluating the reservoir quality and identification of flow unit used in reservoir zonation.  In this study, flow zone indicator has been used to identify five layers belonging to Tertiary reservoirs. Consequently, the porosity-permeability cross plot has been done depending on FZI values as groups and for each group denoted to reservoir rock types. On the other hand, extending rock type identification in un-cored wells should apply a cluster analysis approach by using well logs data. Reservoir zonation has been achieved by cluster analysis approach and for each group known as cluster which variation and different with others. Five clusters generated in this study and permeability estimated depend on these groups in un-cored wells by using well log data that gives good results compared with different empirical methods.

Permeability Prediction Using Rock-Typing, Flow Zone Indicator and Machine Learning Techniques in a Brownfield Offshore Malaysia

2021 ◽  
Author(s):  
Budi Priyatna Kantaatmadja ◽  
Fadzlin H. Kasim ◽  
W. Nur Zainudin ◽  
Emad Elsebakhi ◽  
Ernest A. Jones Jr ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forests ◽  
Total Porosity ◽  
Flow Unit ◽  
Machine Learning Techniques ◽  
Pore Geometry ◽  
Flow Zone ◽  
Hydraulic Flow ◽  
Permeability Prediction ◽  
Learning Techniques

Abstract Predicting permeability in low-medium quality reservoirs (> 10 md to <100mD) is important in brownfields since many of them can still produce hydrocarbons. Developing an approach relating geologic properties to permeability prediction can increase field reserves and extend producing life. The common practice of predicting permeability includes linear regressions of core-porosities vs. core-permeabilities applying different lithofacies. However, these methods discount data scattering around regression-lines. This paper describes an innovative-technique for permeability prediction that combines rock-types, flow-zone-indicator (FZI), and machine-learning techniques (ML). FZI is a reservoir-flow-unit that controls hydraulic fluid-flow and is influenced by pore-geometry resulting from diagenetic-processes. In reservoirs, pore-geometry usually is heterogenous due to mineral-composition, rock-texture, cementation, and compaction. Thus,the commonly used permeability equation of Kozeny-Carman (KC) equation still can be used but it needs to be modified for better connecting FZI to hydraulic-flow-units. The modified KC equation incorporates heterogeneous poregeometry as a non-linear-function of porosity by adding cementation-exponent (m) into the equation, where the original KC equation assumes m is equal to one. The semi-log cross-plot between Reservoir-Quality-Index (RQI) vs. PHIZ*Por(m-1) (or FZIm) from the modified KC equation can be constructed using rock-type class. The ML approach was applied to predict FZI groups using 4 standard-logs: gamma-ray, resistivity, density, and neutron-porosity. Cross-plots of RQI vs. PHIZ (conventional FZI) can be compared to RQI vs. PHIZ*Por(m-1) (modified FZI model) usingdata from 11cored wells in oil field offshore Malaysia. The modified FZI model shows less data clustering compared to the conventional FZI model, shown by higher R2 coefficient correlation accuracy. The proposed modified FZI model shows narrower permeability range at low porosity which is a good indication of more accurate hydraulic-flow-unit interpretation. When applying the original and modified FZI models, each lithofacies may occur in more than one hydraulic-flow-unit due to pore-geometry difference within the same lithofacies. Furthermore, the hydraulic-flow-unit generated by the modified FZI model is more sensitive to total porosity when comparing to original FZI model. Each generated hydraulic-flow-unit has better correlation to total porosity and with less scattered permeability at the same porosity. The permeability calculated by modified FZI model was then verified with core permeability showing an excellent overall match. On the ML technique, the "Random Forests" technique will be utilized due to recognized as one of the most recent ML algorithm(s) developed as an innovative technique based on both classifications and regression trees techniques. The Random Forests technique has shown its great accuracy on predictive exactness for these challenge permeability estimations. The prediction quality was benchmark by R2 value of > 0.9 for all crossplots (porosity, permeability, and water saturation) when comparing to routine core analysis lab measurements.

Application of hydraulic flow unit for prediction of flow zone in carbonate reservoir

2021 ◽  
Vol 14 (8) ◽  
Author(s):  
Nirlipta Priyadarshini Nayak ◽  
Harinandan Kumar ◽  
Shivani Bhist
Keyword(s):  
Carbonate Reservoir ◽  
Flow Unit ◽  
Flow Zone ◽  
Hydraulic Flow
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