Authors :
Dr. Djoï Noukpo André
Volume/Issue :
Volume 9 - 2024, Issue 10 - October
Google Scholar :
https://tinyurl.com/4z8mye3d
Scribd :
https://tinyurl.com/3rb4f9nt
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24OCT1152
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
The reservoir formation porosity is one of the
main reservoirs petrophysical properties required for
fields characterization. The study aims to verify whether
the core porosity of Benin’s offshore petroleum block 1
reservoir formations depends significantly upon the
nature of reservoir formations and to determine the
porosity ranges, the average porosities and the porosity
percentiles (P10, P50 and P90) of these formations. The
results have shown that Benin’s Petroleum block 1
reservoir formations core porosities depend significantly
on the horizons and the nature of formations. Moreover,
the core porosities range from 2.1 to 27.8 percent with
averages between 12.31 and 18.95 percent. H9 Albian
sand has the highest porosity and H8 Albian sand the
lowest one. Abeokuta reservoir formations porosities are
respectively 16.95 and 17.77 percent for H6 and H6.5
horizons. They have 50 and 90 percent of chance to be
respectively greater than 12 and 5.84 percent no matter
the formation. Abeokuta formation core porosity has
high chance to be more than 17.3 percent.
Keywords :
Statistical Analysis; Reservoir Formations; Core Porosity; Benin’s Offshore; Coastal Sedimentary Basin.
References :
- N. A. Djoï, I. J. Nwosu, S. S. Ikiensikimama. (2022). Hydrocarbon Reservoir Petrophysical Characterization with Statistical Simulations: A Case Study from the Gulf of Guinea. International Journal of Scientific Research and Engineering Development, 5(2), 298-308. Available:
- www.ijsred.com/volume5/issue2/IJSRED-V5I2P33.pdf
- O. Toraeter and M. Abtahi (2000). Experimental Reservoir Engineering Laboratory Work Book. Department of Petroleum engineering and Applied Geophysics, Norwegian University of Science and Technology.
- Conventional Core Analysis Study Reports for Wells S2, S4, S5, S9, S11, SC1, SC2 and SC3: SEM Study: Petrographic Study. Petroleum Project Seme-Ashland. Redwood Corex (Services) LTD, Aberdeen Laboratory: Unit B1, 2, 3, Dyce, Aberdeen AB2 OGJ, 1984-1990.
- N. A. Djoï, I. J. Nwosu, S. S. Ikiensikimama. (2023). Stochastic Activity-Based Time and Cost (S ATC) Model for Oil Wells: Case Study of Niger Delta Onshore, Nigeria. International Journal of Engineering Trends and Technology, 71(5), 49-69. doi: https://doi.org/10.14445/22315381/IJETT V71I5P206.
- N. A. Djoï. (2024). Well Log Data Statistical Processing for Unbiased Qualitative and Quantitative Analyses: Case Study from the Gulf of Guinea. International Journal of Geoscience, Engineering and Technology, Volume 9(1), 1-19. Available: http://www.geovales.com/index.php/Journal/article/view/170/111
- K. S. Anoop. (2017). F-TEST and Analysis of Variance (ANOVA). Lecture Note. Department of Applied Economics, University of Lucknow.
- N. A. Djoï. (2024). Stochastic Approach to Economic Evaluation of Oil Wells in Niger Delta, Nigeria. Thesis of PhD research. World Bank African Center of Excellence for Oil Field Chemical Research (ACE-CEFOR), University of Port Harcourt, Nigeria.
- D. M. Mukta. (2015). ANOVA One way and Two-way classified data. Lecture Note. Department of Statistics.
- N. A. Djoï, I. J. Nwosu, S. S. Ikiensikimama. (2022). Monte Carlo Simulation to Autoregressive Integrated Moving Average (MS-ARIMA) Model for Time Series Modelling and Forecasting. International Journal of Scientific Research and Engineering Development, (5)1, 771-782. Available: www.ijsred.com/volume5/issue1/IJSRED-V5I1P86.pdf
- N. A. Djoï, I. J. Nwosu, S. S. Ikiensikimama. (2023). Central Limit Theorem-Based Stochastic Economic Evaluation (CLT-SEE) Model for Evaluating Oil Wells. European Journal of Engineering and Technology Research (EJ-ENG), (8) 3, 8-16. Available: https://ej-eng.org/index.php/ejeng/article/view/3033/1397
- S. K. Chong and R. Choo. (2011). Introduction to Bootstrap. Proceedings of Singapore Healthcare, 20(3), 236-240.
- B. Efron. (2020). Estimation, Accuracy and Boostrap. Lecture Material, Department of Statistics Stanford University. [Online], Available: [email protected]
The reservoir formation porosity is one of the
main reservoirs petrophysical properties required for
fields characterization. The study aims to verify whether
the core porosity of Benin’s offshore petroleum block 1
reservoir formations depends significantly upon the
nature of reservoir formations and to determine the
porosity ranges, the average porosities and the porosity
percentiles (P10, P50 and P90) of these formations. The
results have shown that Benin’s Petroleum block 1
reservoir formations core porosities depend significantly
on the horizons and the nature of formations. Moreover,
the core porosities range from 2.1 to 27.8 percent with
averages between 12.31 and 18.95 percent. H9 Albian
sand has the highest porosity and H8 Albian sand the
lowest one. Abeokuta reservoir formations porosities are
respectively 16.95 and 17.77 percent for H6 and H6.5
horizons. They have 50 and 90 percent of chance to be
respectively greater than 12 and 5.84 percent no matter
the formation. Abeokuta formation core porosity has
high chance to be more than 17.3 percent.
Keywords :
Statistical Analysis; Reservoir Formations; Core Porosity; Benin’s Offshore; Coastal Sedimentary Basin.