Typical misinterpretations and scientific concepts in well-logging geologic studies

Lai, Jin; Pang, Xiaojiao; Zhao, Xianzheng; Zhao, Yidi; Wang, Guiwen; Huang, Yuyue; Li, Hongbin; Li, Yuhang

doi:10.1016/j.ngib.2023.02.003

“…Finally, a novel approach has been done for estimating pore size distribution and capillary pressure in the hydrocarbon zone through a hydraulic flow unit framework using an NMR log. The workflow establishes a robust and cost-effective methodology for NMR T2 distribution correction in hydrocarbon zone for uncored and partially cored wells (Baykin et al, 2023;Fernandes et al, 2023a;Fernandes et al, 2023b;Jehanzaib et al, 2023;Kadkhodaie, 2021;Kianoush, 2023;Lai et al, 2023;Osinowo et al, 2023;Wang and Weijermars, 2023;Zhang et al, 2023b).…”

Section: Introductionmentioning

confidence: 99%

Determining the petrophysical rock types utilizing the Fuzzy C-means Clustering technique and the concept of hydraulic flow units

Eftekhari,

Memariani,

Maleki

et al. 2023

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0

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Rock types are the reservoir's most essential properties and show special facies with a defined range of porosity and permeability. This study used the fuzzy c-means clustering technique to identify rock types in 280 core samples from one of the wells drilled in the Asmari reservoir in the Mansouri field, SW Iran. Four hydraulic flow units were determined for studied data after classifying the flow zone index with histogram analysis, normal probability analysis, and the sum of square error methods. Then the two methods of flow zone index and fuzzy c-means clustering were used to determine the rock types in given wells according to the results obtained from the implementation of these two methods in-depth, and continuity index acts, the fuzzy c-means methods with continuity number 3.12 compared to flow zone index with continuity number 2.77 shows more continuity in depth. The relationship between porosity and permeability improved using hydraulic flow unit techniques significantly. In this study, the correlation coefficient between porosity and permeability improves and increases in each hydraulic flow unit using the flow zone index method. So that in the general case, all samples increased from 0.55 to 0.81 in the first hydraulic flow unit and finally 0.94 in the fourth hydraulic flow unit. The samples were characterized by similar flow properties in a hydraulic flow unit. In comparison, the correlation coefficient is obtained less than the general case in the fuzzy c-means method in all hydraulic flow units.

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“…Hydraulic flow units (HFUs) is referred to as lateral continuity of reservoir units with consistent geological properties controlling the behavior of fluid flow in pores media (Amaefule et al, 1993;Gualda and Vlach, 2007; Finally, a novel approach has been done for estimating pore size distribution and capillary pressure in the hydrocarbon zone through a hydraulic flow unit framework using an NMR log. The workflow establishes a robust and cost-effective methodology for NMR T2 distribution correction in hydrocarbon zone for uncored and partially cored wells (Baykin et al, 2023;Fernandes et al, 2023a;Fernandes et al, 2023b;Jehanzaib et al, 2023;Kadkhodaie, 2021;Kianoush, 2023;Lai et al, 2023;Osinowo et al, 2023;Wang and Weijermars, 2023;Zhang et al, 2023b) (Abraham-A et al, 2023Duda et al, 2012;Yu et al, 2023;Zhang et al, 2023a;Zhang et al, 2023b;Zheng et al, 2023). The fuzzy c-means (FCM) method is an efficient tool for solving fuzzy clustering problems.…”

Section: Introductionmentioning

confidence: 99%

Determining the petrophysical rock types utilizing the Fuzzy C-means Clustering technique and the concept of hydraulic flow units in the Asmari Formation, SW Iran

Eftekhari,

Memariani,

Maleki

et al. 2023

Preprint

0

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Rock types are the reservoir's most essential properties and show special facies with a defined range of porosity and permeability. This study used the fuzzy c-means clustering technique to identify rock types in 280 core samples from one of the wells drilled in the Asmari reservoir in the Mansouri field, SW Iran. Four hydraulic flow units were determined for studied data after classifying the flow zone index with histogram analysis, normal probability analysis, and the sum of square error methods. Then the two methods of flow zone index and fuzzy c-means clustering were used to determine the rock types in given wells according to the results obtained from the implementation of these two methods in-depth, and continuity index acts, the fuzzy c-means methods with continuity number 3.12 compared to flow zone index with continuity number 2.77 shows more continuity in depth. The relationship between porosity and permeability improved using hydraulic flow unit techniques significantly. In this study, the correlation coefficient between porosity and permeability improves and increases in each hydraulic flow unit using the flow zone index method. So that in the general case, all samples increased from 0.55 to 0.81 in the first hydraulic flow unit and finally 0.94 in the fourth hydraulic flow unit. The samples were characterized by similar flow properties in a hydraulic flow unit. In comparison, the correlation coefficient is obtained less than the general case in the fuzzy c-means method in all hydraulic flow units.

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“…The reason for this problem is the different views of geologists and loggers. This problem creates a nonlinear solid relationship between log and lithology and causes the problem of determining any partitioning of the log data into the associated sedimentary unit 2,9,[29][30][31] . The easy determination and application of this zoning and its reliance on log data make it possible to quickly and accurately distinguish reservoir segments from non-reservoir segments.…”

Section: Introductionmentioning

confidence: 99%

Determining the electrical facies utilizing multi-resolution graph-based and artificial neural network clustering methods in an Oilfield, SW Iran

Eftekhari,

Memariani,

Maleki

et al. 2023

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0

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Asmari Formation is the most significant reservoir of the Mansouri oil field in SW Iran. This Formation is generally composed of carbonate lithologic but has sandstone layers in some zones. It is also the youngest reservoir rock in Iran. Cross-lithology diagrams determined the lithology of the Asmari Formation by applying 280 core samples from one drilled well in the studied reservoir, and a combination of lime, dolomite, anhydrite, and sandstone with shaley layers was estimated. Also, five zones were identified with different geological/reservoir conditions, with the best reservoir quality in zones 3 and 5 with sandstone/shale lithology. Furthermore, this study determines multi-resolution graph-based clustering (MRGC) and artificial neural network (ANN) facies clustering methods. For this purpose, the combined log readings of DT, RHOB, GR, Sw, NPHI, and PHIE were used. Lithology was evaluated and estimated in each sequence using corrected and edited logs and lithology cross-sections. After applying the Geolog software, an optimal model with 8 clusters (facies) with better separation of rock species was obtained. Finally, five facies with different lithological compositions and reservoir conditions were identified. The information obtained from determining electrical facies by clustering method defines the reservoir zone from non-reservoir and qualitatively (good, medium, and poor).

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Typical misinterpretations and scientific concepts in well-logging geologic studies

Cited by 12 publications

References 12 publications

Determining the petrophysical rock types utilizing the Fuzzy C-means Clustering technique and the concept of hydraulic flow units

Determining the petrophysical rock types utilizing the Fuzzy C-means Clustering technique and the concept of hydraulic flow units

Determining the petrophysical rock types utilizing the Fuzzy C-means Clustering technique and the concept of hydraulic flow units in the Asmari Formation, SW Iran

Determining the electrical facies utilizing multi-resolution graph-based and artificial neural network clustering methods in an Oilfield, SW Iran

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