ANN-based estimation of pore pressure of hydrocarbon reservoirs—a case study

Kianoush, Pooria; Mohammadi, Ghodratollah; Hosseini, Seyed Aliakbar; Khah, Nasser Keshavarz Faraj; Afzal, Peyman

doi:10.1007/s12517-023-11373-6

“…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%

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

Self Cite

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

“…Compilation of core porosity and permeability are used to identify these units. Yasmaniar et al (2018) utilized Artificial Neural Network (ANN) to determine the permeability of different Rock Type Using the Hydraulic Flow Unit Concept (Ding et al, 2022;Kharrat et al, 2009;Kianoush et al, 2023c;Mahadasu and Singh, 2022;Masroor et al, 2023;Rafik and Kamel, 2017). Oliveira et al (2020) demonstrated that an inter-clustering process is recommended when selecting data points associated with representative volumes and local spots characterizing HFUs.…”

Section: Introductionmentioning

confidence: 99%

“…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%

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

Self Cite

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

“…Compilation of core porosity and permeability are used to identify these units. Yasmaniar et al (2018) utilized Artificial Neural Network (ANN) to determine the permeability of different Rock Type Using the Hydraulic Flow Unit Concept (Ding et al, 2022;Kharrat et al, 2009;Kianoush et al, 2023c;Mahadasu & Singh, 2022;Masroor et al, 2023;Rafik & Kamel, 2017). Oliveira et al (2020) demonstrated that an inter-clustering process is recommended when selecting data points associated with representative volumes and local spots characterizing HFUs.…”

Section: Introductionmentioning

confidence: 99%

“…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%

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

Self Cite

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

ANN-based estimation of pore pressure of hydrocarbon reservoirs—a case study

Cited by 31 publications

References 71 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

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

Contact Info

Product

Resources

About