Hydraulic fracturing modelling of glutenite formations using an improved form of SPH method

Yu, Shuyang; Zhou, Yu; Yang, Jie; Chen, Weiqiang

doi:10.1016/j.geoen.2023.211842

“…Machine learning is effectively used by Man et al (2021) to boost the prediction of permeability and reduces uncertainty in reservoir modeling. Recently, a variety of conventional methods and machine learning algorithms were investigated in determining hydraulic flow units (HFUs), and the performance of each method was evaluated (Fernandes et al, 2023a;Forbes Inskip et al, 2020;Kianoush et al, 2022bKianoush et al, , 2023cKianoush et al, 2023a;Masroor et al, 2023;mohammadinia et al, 2023;Shi et al, 2023;Yu et al, 2023). Salavati et al (2023) used hydraulic flow units, multi-resolution graph-based clustering, and fuzzy c-mean clustering methods to determine rock types.…”

Section: Introductionmentioning

confidence: 99%

“…It performs cluster analysis of the mean K data by MATLAB software, then linear regression analysis. On the data and calculate the sum of the squares of the errors (Abraham-A et al, 2023;Duda et al, 2012;Yu et al, 2023;Zhang et al, 2023a;Zhang et al, 2023b;Zheng et al, 2023). The same for the number of other batches has been done, and finally, the sum of the squares of errors against the number of batches has been plotted.…”

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

Preprint

0

View full text Add to dashboard Cite

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.

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

“…Machine learning is effectively used by Man et al (2021) to boost the prediction of permeability and reduces uncertainty in reservoir modeling. Recently, a variety of conventional methods and machine learning algorithms were investigated in determining hydraulic flow units (HFUs), and the performance of each method was evaluated (Fernandes et al, 2023a;Kianoush et al, 2022bKianoush et al, , 2023cKianoush et al, 2023a;Masroor et al, 2023;mohammadinia et al, 2023;Shi et al, 2023;Yu et al, 2023). Salavati et al (2023) used hydraulic flow units, multi-resolution graphbased clustering, and fuzzy c-mean clustering methods to determine rock types.…”

Section: Introductionmentioning

confidence: 99%

“…It performs cluster analysis of the mean K data by MATLAB software, then linear regression analysis. On the data and calculate the sum of the squares of the errors (Duda et al, 2012;Yu et al, 2023;Q. Zhang et al, 2023;R.-h. Zhang et al, 2023;Zheng et al, 2023).…”

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

Preprint

0

View full text Add to dashboard Cite

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 obtained from one of the wells drilled in the Asmari reservoir located in the Mansouri field. 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. Amounts of porosity and permeability of the different reservoir rock samples have high dispersion; the relationship between these two parameters improves by using hydraulic flow unit techniques significantly. In this study, the relationship between porosity and permeability of correlation coefficient improves and increases in each hydraulic flow unit by using the flow zone index method so that in the general case for all samples increased from 0.55 to 0.81 in the first hydraulic flow unit, 0.94 in the second hydraulic flow unit, 0.85 in the third hydraulic flow unit and 0.94 in the fourth hydraulic flow unit that this is because 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.

show abstract

Hydraulic fracturing modelling of glutenite formations using an improved form of SPH method

Cited by 8 publications

References 26 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 petrophysical rock types utilizing the Fuzzy C-means Clustering technique and the concept of hydraulic flow units

Contact Info

Product

Resources

About