Performance Evaluation of Mechanistic Pore Pressure Prediction Models in Deepwater Niger Delta

References

[1] Raka S.W., Meredita Susanty and, DanHapsoro B.W. (2020). Pore Pressure Prediction Using Artificial Neural Network Based On Logging Data. Journal Migasian / E-Issn: 2615-6695(2020) , P-Issn: 2580-5258 Vol. 4, No. 1, Juni 2020

[2] Dosunmu, A. (2019). Pore pressure and fracture gradient relationship. Drilling and Well Engineering Optimization lecture notes. University of Port Harcourt, Petroleum and Gas Department.

[3] Adams, NeaI (1985). Drilling engineering. PennWell Publishing Company, Oklahoma. ISBN 0-87814-265-7

[4] Victor OM, Ude AE and Valeria AC (2017) Analysis of Hydrostatic Pressure Zones in Fabi Field, Onshore Niger Delta, Nigeria. Journal of Geology and Geophysics. 6: 275. doi:10.4172/2381-8719.1000275

[5] Irfan Yuliandri Syukri, Budi R Permana, Phil D. Heppard, Agus M. Ramdhan, And Lambok M. Hutasoit (2019). Pore Pressure Analysis In The Corridor Block, South Sumatra Basin: Distribution, Mechanism, And Prediction. Proceedings, Indonesian Petroleum Association Forty-Third Annual Convention & Exhibition, September 2019

[6] Mode A.W., Anyiam O.A., Ngala E.N. (2013). Compaction and porosity based pore pressure prediction in the “Cappe field”, coastal swamp depobelt, Niger Delta, Nigeria. Journal of Geological Sciences vol.11, 2013: 57-71

[7] Ichenwo, John Lander and Olatunji, Ayimora (2018). Pore Pressure and Fracture Pressure Forecast in Niger Delta. International Journal of Engineering Research & Technology (IJERT). Vol. 7 Issue 04, April-2018

[8] Nwankwo, Cyril Ngozi and Kalu, Stephen Onoh (2016). Integrated Approach to Pore Pressure and Fracture Pressure Prediction Using Well Logs: Case Study of Onshore Niger-Delta Sedimentary Basin. Open Journal of Geology, 2016, 6, 1279-1295

[9] Abiola O., Eyinla S. D., Adeduyite E. T. (2016). Pore Pressure Gradient Prediction Using Well Logs; A Case Study on Malcolm Field, Offshore Niger Delta, Nigeria. International Journal of Petroleum and Geoscience Engineering. Volume 04, Issue 01, Pages 58-65, 2016

[10] Ugwu G. Z. (2015). Pore Pressure Prediction Using Offset Well Logs: Insight from Onshore Niger Delta, Nigeria American Journal of Geophysics, Geochemistry and Geosystems Vol. 1, No. 3, 2015, pp. 77-86

[11] Akinsete O. Oluwatoyin, Abdulraheem Y. Toyin, Naheem B. Salawu, and Adebiyi S. Leke (2020). Integration and Interpretation of Aeromagnetic, 3D Seismic and Well Logs Data in Hydrocarbon Exploration in Niger Delta Basin. Advances in Research 21(8): 28-42, 2020; Article no.AIR.5852. ISSN: 2348-0394, NLM ID: 101666096

[12] Emujakporue Omokenu Godwin (2014). Pore-Pressure Prediction From Seimic Data In Parts Of The Onshore Niger Delta Sedimentary Basin. Physics International 4 (2): 152-159, 2013

[13] Eaton, B.A (1972). The effect of overburden stress on Geopressures prediction from well logs. Society of Petroleum Engineers 3719.JPT, Aug. 1972:929-934.

[14] Eaton, B.A (1975). The equation for Geopressure prediction from well logs. Society of Petroleum Engineers No. 5544,11p

[15] Oriji A. Boniface, Oladamola Amieyeofori (2019). Comparative Analysis of Abnormal Pore Pressure Prediction Models for Niger Delta Oil and Gas Fields Development. Advances in Petroleum Exploration and Development, 18(1), 10-18

[16] Bowers, G. L. (1995). Pore pressure estimation from velocity data: Accounting for overpressure mechanisms besides undercompaction. SPE Drilling & Completion, 10(02), 89–95.

@article{1712085,
      author = {Timi Ibuchi Olawuyi, Boniface A. Oriji, Emeka E. Okoro},
      title = {Performance Evaluation of Mechanistic Pore Pressure Prediction Models in Deepwater Niger Delta},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {9},
      number = {5},
      pages = {1518-1525},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1712085.pdf},
      abstract = {Prediction models exist through which pore pressure estimation can be made, however, there exists some discrepancies between measured values and predicted values. This study explores the performance of mechanistic pore pressure prediction models rooted in Terzaghi?s principle (Eaton, Bower, and Miller?s models) in four (4) Deepwater wells in the Niger Delta region. The study presented the predictions and quantitatively measured their performance using mean absolute error and percentage average absolute deviation. Quantitatively, Bower?s model had the lowest mean absolute error (MAE) and percentage average absolute error (PAAD) for the 4 Deepwater wells, followed by Eaton, then Millers. In one of the wells (Well3), The mean absolute error was 761.62, 1709.22, and 2759.11 for Bowers, Eaton, and Millers models respectively. For the same well, the percentage average absolute error was 17.75%, 38.14%, and 62.79% for Bowers, Eaton, and Millers models respectively. Caution should be exhibited in the application of the models with proper fine tuning, if they are to be used in predicting pore pressures in Deepwater Niger Delta. This study has significantly advanced the understanding of pore pressure prediction in Deepwater Niger Delta, by revealing in a quantified manner, the deviations of pressure predictions with these models from measured values.},
      keywords = {Deepwater, Performance Evaluation, Pore Pressure Prediction, Niger Delta},
      month = {November},
      doi = {https://doi.org/10.64388/IREV9I5-1712085}
  }