Uncertainty Analysis of Reservoir Static Modelling: A Case Study of KMJ Oil Field

Dedy Kristanto; Hariyadi; Emanuel Jiwandono Saputro

doi:doi:10.11648/j.ogce.20231101.12

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Uncertainty Analysis of Reservoir Static Modelling: A Case Study of KMJ Oil Field

Dedy Kristanto, Hariyadi, Emanuel Jiwandono Saputro

Published in International Journal of Oil, Gas and Coal Engineering (Volume 11, Issue 1)

Received: 15 February 2023 Accepted: 9 March 2023 Published: 20 March 2023

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Abstract

This case study explains the uncertainty of Original Oil in Place (OOIP) calculations in reservoir static modeling of KMJ Oil Field. This field consists of 4 (four) wells in an area of ± 600 acres with high heterogeneity, so in building a 3D Model, it is necessary to analyze the sensitivity and uncertainty of geological concepts, calculations of petrophysical properties, and fluid contact. The OOIP calculation uses a probabilistic method and determines reserves related to field development. The uncertainty analysis study begins by identifying the parameters with the most significant influence (Sensitivity Analysis) in calculating OOIP in the static reservoir model. To determine the ranking of reservoir uncertainty parameters, several geological, geophysical, and petrophysical factors in building a static model must be tested according to the method used in each parameter. The OOIP calculation in the static model is calculated into three scenario categories, namely low estimate (P10), base estimate (P50), and high estimate (P90). The combination of determining facies (shale volume) porosity, fluid contact, and the cut-off is a variable/parameter that is very influential in volumetric multi-scenario calculations (probabilistic method) in the KMJ Oil Field. The results of the uncertainty analysis of the KMJ Oil Field have a low OOIP estimate (P10) of 10.86 MMSTB, a base estimate (P50) of 11.49 MMSTB, and a high estimate (P90) of 12.01 MMSTB. Furthermore, the static model used for reservoir simulation (dynamic model) in the KMJ Oil Field is the base estimate model (P50) of 11.49 MMSTB.

Published in	International Journal of Oil, Gas and Coal Engineering (Volume 11, Issue 1)
DOI	10.11648/j.ogce.20231101.12
Page(s)	9-16
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2023. Published by Science Publishing Group

Keywords

Uncertainty Analysis, Sensitivity Analysis, Original Oil in Place, Low Estimate (P10), Base Estimate (P50), High Estimate (P90)

References

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[2]	Fanha, A. B., Filho, J. S. A. C, Reyes-Perez, Y. A., Uncertainty Analysis of an Integrating Two Structural 3D Sthochastic Geological Models of a Siliciclastic Reservoir; Potiguar Basin, Northeast of Brazil, SPE EUROPEC/EAGE Annual Conference and Exhibition held in Barcelona, Spain, 2010.
[3]	Perrin, M., Zhu, B., Rainaud, J., and Schneider, S., Knowledge-driven Applications for Geological Modelling, Journal of Petroleum Science and Engineering, 2005.
[4]	Lazar, R., The Concept of Right Modelling, Toward a More Effective Usage of the Static Modelling Tool, SPE Paper Presentation at the Abu Dhabi International Exhibition & Conference held in Abu Dhabi, UAE, 2018.
[5]	Gomes, J., Parra, H., and Ghosh, D., Quality Control of 3D GeoCellular Models: Examples from UAE Carbonate Reservoirs, SPE Paper Presentation at Abu Dhabi International Exhibition & Conference held in Abu Dhabi, UAE, 2018.
[6]	Ellison, S. L. R. Rosslein, M., and Williams., A Quantifying Uncertainty in Analytical Measurement, Eurachem, CITAC Guide, UK, 2000.
[7]	Kamanli, S. T., An Integrated 3D Geological Modeling Study of Heavy Oil Field in Southeast Turkey, SPE Paper Presentation at the SPE Annual Caspian Technical Conference held in Baku, Azerbaijan, 2019.
[8]	Chongrueanglap, P., Siriwattanakajorn, W., Kamal, M., Poret, K. L. G., Soontornnateepat, T., Mahamat, S., Wongpaet, K., and Cheong, Y. P., Challenges on Building Representative 3D under Subsurface Uncertainties for a Giant Carbonat Field in Central Luconia, Offshore Sarawak, Paper Presentation at the Offshore Technology Conference Asia held in Kuala Lumpur, Malaysia, 2022.
[9]	Bueno, Juliana F., et. al. Constraining Uncertainty in Static Reservoir Modeling: A Case Study from Namorado Field, Brazil, AAPG Annual Convention and Exhibition, Houston, Texas, USA, 2011.
[10]	Al-Otaibi, A. M., Integration of 3D Seismic Data in reservoir Modeling and Assessing Uncertainty in Lithology Distributions in The Nuayyim Field of Central Saudi Arabia, SPE Paper Presentation at the 1997 Middle East Oil Show, Bahrain, 1997.
[11]	Aguilar, E. G., Gonzalez, L. M., & Ruiz, V. G., Initial Characterization of an Extra Heavy Oil Carbonate Exploratory Field, SPE Paper Persentation at the SPE Latin American and Caribbean Petroleum Engineering Conference held in Mexico City, Mexico, 2012.
[12]	Torres, K. M., Al-Hashmi, N. F., Al-Hosani, I. A., and Al-Rawahi, A. S., Reducing the Uncertainty of Static Reservoir Model in a Carbonate Platform, Through the Implementation of an Integrated Workflow: Case A-Field, Abu Dhabi, UAE, SPE Paper Presentation at the Abu Dhabi International Exhibition & Conference held in Abu Dhabi, UAE, 2016.
[13]	Orellana, Ney, Cavero, J, et. al. Influence of Variograms in 3D Reservoir-Modeling Outcomes: An Example, The Society of Exploration Geophysicists, 2014.
[14]	Bangsal, R. S., Vargas-Guzman, J. A., Uncertainty Quantification of Top Structures in 3D Geocellular Models, SPE Reservoir Characterisation and Simulation and Exhibition, Abu Dhabi, UAE, 2015.
[15]	Rukmana, D., Kristanto, D., and Cahyoko Aji, D., Reservoir Engineering: Theory and Application, Revision Edition, Pohon Cahaya Publishing Co., Yogyakarta, 2018, Chapter 11, pp. 367-462.
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APA Style

Dedy Kristanto, Hariyadi, Emanuel Jiwandono Saputro. (2023). Uncertainty Analysis of Reservoir Static Modelling: A Case Study of KMJ Oil Field. International Journal of Oil, Gas and Coal Engineering, 11(1), 9-16. https://doi.org/10.11648/j.ogce.20231101.12

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ACS Style

Dedy Kristanto; Hariyadi; Emanuel Jiwandono Saputro. Uncertainty Analysis of Reservoir Static Modelling: A Case Study of KMJ Oil Field. Int. J. Oil Gas Coal Eng. 2023, 11(1), 9-16. doi: 10.11648/j.ogce.20231101.12

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AMA Style

Dedy Kristanto, Hariyadi, Emanuel Jiwandono Saputro. Uncertainty Analysis of Reservoir Static Modelling: A Case Study of KMJ Oil Field. Int J Oil Gas Coal Eng. 2023;11(1):9-16. doi: 10.11648/j.ogce.20231101.12

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@article{10.11648/j.ogce.20231101.12,
  author = {Dedy Kristanto and Hariyadi and Emanuel Jiwandono Saputro},
  title = {Uncertainty Analysis of Reservoir Static Modelling: A Case Study of KMJ Oil Field},
  journal = {International Journal of Oil, Gas and Coal Engineering},
  volume = {11},
  number = {1},
  pages = {9-16},
  doi = {10.11648/j.ogce.20231101.12},
  url = {https://doi.org/10.11648/j.ogce.20231101.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ogce.20231101.12},
  abstract = {This case study explains the uncertainty of Original Oil in Place (OOIP) calculations in reservoir static modeling of KMJ Oil Field. This field consists of 4 (four) wells in an area of ± 600 acres with high heterogeneity, so in building a 3D Model, it is necessary to analyze the sensitivity and uncertainty of geological concepts, calculations of petrophysical properties, and fluid contact. The OOIP calculation uses a probabilistic method and determines reserves related to field development. The uncertainty analysis study begins by identifying the parameters with the most significant influence (Sensitivity Analysis) in calculating OOIP in the static reservoir model. To determine the ranking of reservoir uncertainty parameters, several geological, geophysical, and petrophysical factors in building a static model must be tested according to the method used in each parameter. The OOIP calculation in the static model is calculated into three scenario categories, namely low estimate (P10), base estimate (P50), and high estimate (P90). The combination of determining facies (shale volume) porosity, fluid contact, and the cut-off is a variable/parameter that is very influential in volumetric multi-scenario calculations (probabilistic method) in the KMJ Oil Field. The results of the uncertainty analysis of the KMJ Oil Field have a low OOIP estimate (P10) of 10.86 MMSTB, a base estimate (P50) of 11.49 MMSTB, and a high estimate (P90) of 12.01 MMSTB. Furthermore, the static model used for reservoir simulation (dynamic model) in the KMJ Oil Field is the base estimate model (P50) of 11.49 MMSTB.},
 year = {2023}
}

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TY  - JOUR
T1  - Uncertainty Analysis of Reservoir Static Modelling: A Case Study of KMJ Oil Field
AU  - Dedy Kristanto
AU  - Hariyadi
AU  - Emanuel Jiwandono Saputro
Y1  - 2023/03/20
PY  - 2023
N1  - https://doi.org/10.11648/j.ogce.20231101.12
DO  - 10.11648/j.ogce.20231101.12
T2  - International Journal of Oil, Gas and Coal Engineering
JF  - International Journal of Oil, Gas and Coal Engineering
JO  - International Journal of Oil, Gas and Coal Engineering
SP  - 9
EP  - 16
PB  - Science Publishing Group
SN  - 2376-7677
UR  - https://doi.org/10.11648/j.ogce.20231101.12
AB  - This case study explains the uncertainty of Original Oil in Place (OOIP) calculations in reservoir static modeling of KMJ Oil Field. This field consists of 4 (four) wells in an area of ± 600 acres with high heterogeneity, so in building a 3D Model, it is necessary to analyze the sensitivity and uncertainty of geological concepts, calculations of petrophysical properties, and fluid contact. The OOIP calculation uses a probabilistic method and determines reserves related to field development. The uncertainty analysis study begins by identifying the parameters with the most significant influence (Sensitivity Analysis) in calculating OOIP in the static reservoir model. To determine the ranking of reservoir uncertainty parameters, several geological, geophysical, and petrophysical factors in building a static model must be tested according to the method used in each parameter. The OOIP calculation in the static model is calculated into three scenario categories, namely low estimate (P10), base estimate (P50), and high estimate (P90). The combination of determining facies (shale volume) porosity, fluid contact, and the cut-off is a variable/parameter that is very influential in volumetric multi-scenario calculations (probabilistic method) in the KMJ Oil Field. The results of the uncertainty analysis of the KMJ Oil Field have a low OOIP estimate (P10) of 10.86 MMSTB, a base estimate (P50) of 11.49 MMSTB, and a high estimate (P90) of 12.01 MMSTB. Furthermore, the static model used for reservoir simulation (dynamic model) in the KMJ Oil Field is the base estimate model (P50) of 11.49 MMSTB.
VL  - 11
IS  - 1
ER  -

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Author Information

Dedy Kristanto

Petroleum Engineering Department, Universitas Pembangunan Nasional “Veteran” Yogyakarta, Yogyakarta, Indonesia
Hariyadi

Petroleum Engineering Department, Universitas Pembangunan Nasional “Veteran” Yogyakarta, Yogyakarta, Indonesia
Emanuel Jiwandono Saputro

Petroleum Engineering Department, Universitas Pembangunan Nasional “Veteran” Yogyakarta, Yogyakarta, Indonesia

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APA Style

Dedy Kristanto, Hariyadi, Emanuel Jiwandono Saputro. (2023). Uncertainty Analysis of Reservoir Static Modelling: A Case Study of KMJ Oil Field. International Journal of Oil, Gas and Coal Engineering, 11(1), 9-16. https://doi.org/10.11648/j.ogce.20231101.12

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ACS Style

Dedy Kristanto; Hariyadi; Emanuel Jiwandono Saputro. Uncertainty Analysis of Reservoir Static Modelling: A Case Study of KMJ Oil Field. Int. J. Oil Gas Coal Eng. 2023, 11(1), 9-16. doi: 10.11648/j.ogce.20231101.12

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AMA Style

Dedy Kristanto, Hariyadi, Emanuel Jiwandono Saputro. Uncertainty Analysis of Reservoir Static Modelling: A Case Study of KMJ Oil Field. Int J Oil Gas Coal Eng. 2023;11(1):9-16. doi: 10.11648/j.ogce.20231101.12

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@article{10.11648/j.ogce.20231101.12,
  author = {Dedy Kristanto and Hariyadi and Emanuel Jiwandono Saputro},
  title = {Uncertainty Analysis of Reservoir Static Modelling: A Case Study of KMJ Oil Field},
  journal = {International Journal of Oil, Gas and Coal Engineering},
  volume = {11},
  number = {1},
  pages = {9-16},
  doi = {10.11648/j.ogce.20231101.12},
  url = {https://doi.org/10.11648/j.ogce.20231101.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ogce.20231101.12},
  abstract = {This case study explains the uncertainty of Original Oil in Place (OOIP) calculations in reservoir static modeling of KMJ Oil Field. This field consists of 4 (four) wells in an area of ± 600 acres with high heterogeneity, so in building a 3D Model, it is necessary to analyze the sensitivity and uncertainty of geological concepts, calculations of petrophysical properties, and fluid contact. The OOIP calculation uses a probabilistic method and determines reserves related to field development. The uncertainty analysis study begins by identifying the parameters with the most significant influence (Sensitivity Analysis) in calculating OOIP in the static reservoir model. To determine the ranking of reservoir uncertainty parameters, several geological, geophysical, and petrophysical factors in building a static model must be tested according to the method used in each parameter. The OOIP calculation in the static model is calculated into three scenario categories, namely low estimate (P10), base estimate (P50), and high estimate (P90). The combination of determining facies (shale volume) porosity, fluid contact, and the cut-off is a variable/parameter that is very influential in volumetric multi-scenario calculations (probabilistic method) in the KMJ Oil Field. The results of the uncertainty analysis of the KMJ Oil Field have a low OOIP estimate (P10) of 10.86 MMSTB, a base estimate (P50) of 11.49 MMSTB, and a high estimate (P90) of 12.01 MMSTB. Furthermore, the static model used for reservoir simulation (dynamic model) in the KMJ Oil Field is the base estimate model (P50) of 11.49 MMSTB.},
 year = {2023}
}

Copy | Download

TY  - JOUR
T1  - Uncertainty Analysis of Reservoir Static Modelling: A Case Study of KMJ Oil Field
AU  - Dedy Kristanto
AU  - Hariyadi
AU  - Emanuel Jiwandono Saputro
Y1  - 2023/03/20
PY  - 2023
N1  - https://doi.org/10.11648/j.ogce.20231101.12
DO  - 10.11648/j.ogce.20231101.12
T2  - International Journal of Oil, Gas and Coal Engineering
JF  - International Journal of Oil, Gas and Coal Engineering
JO  - International Journal of Oil, Gas and Coal Engineering
SP  - 9
EP  - 16
PB  - Science Publishing Group
SN  - 2376-7677
UR  - https://doi.org/10.11648/j.ogce.20231101.12
AB  - This case study explains the uncertainty of Original Oil in Place (OOIP) calculations in reservoir static modeling of KMJ Oil Field. This field consists of 4 (four) wells in an area of ± 600 acres with high heterogeneity, so in building a 3D Model, it is necessary to analyze the sensitivity and uncertainty of geological concepts, calculations of petrophysical properties, and fluid contact. The OOIP calculation uses a probabilistic method and determines reserves related to field development. The uncertainty analysis study begins by identifying the parameters with the most significant influence (Sensitivity Analysis) in calculating OOIP in the static reservoir model. To determine the ranking of reservoir uncertainty parameters, several geological, geophysical, and petrophysical factors in building a static model must be tested according to the method used in each parameter. The OOIP calculation in the static model is calculated into three scenario categories, namely low estimate (P10), base estimate (P50), and high estimate (P90). The combination of determining facies (shale volume) porosity, fluid contact, and the cut-off is a variable/parameter that is very influential in volumetric multi-scenario calculations (probabilistic method) in the KMJ Oil Field. The results of the uncertainty analysis of the KMJ Oil Field have a low OOIP estimate (P10) of 10.86 MMSTB, a base estimate (P50) of 11.49 MMSTB, and a high estimate (P90) of 12.01 MMSTB. Furthermore, the static model used for reservoir simulation (dynamic model) in the KMJ Oil Field is the base estimate model (P50) of 11.49 MMSTB.
VL  - 11
IS  - 1
ER  -

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