Quantifying Uncertainty in Pore Pressure Estimation Using Bayesian Networks, with Application to Use of an Offset Well

Oughton, Rachel H.; Wooff, David; Hobbs, Richard; O’Connor, Stephen; Swarbrick, Richard E.

doi:10.3997/2214-4609.201413638

Cited by 4 publications

(1 citation statement)

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“…Blowouts, kicks, borehole washouts, wellbore breakout and stuck pipe (Oughton et al, 2015) are just some issues that may occur while encountering unexpected fluid pressure anomalies during drilling. To reduce the associated risks of drilling a robust mud plan and casing design is required as a part of every drilling operation (Nguyen et al, 2015;Wild et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Enhanced velocity based pore pressure prediction using lithofacies clustering: A case study from a reservoir with complex lithology in Dezful Embayment, SW Iran

Arabameri¹,

soleymani²,

Tokhmechi³

2018

Preprint

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Velocity based pore pressure prediction methods are widely accepted as a routine technique in the petroleum industry. Despite recent improvements, still, literature suffer from inconsistencies and uncertainties mostly arise from velocity anomalies due to complex lithostratigraphic setting or presence of various formation fluids. The primary goal of this paper is to improve the accuracy and reliability of the conventional Bowers and Tau methods in a reservoir with complex lithology. Our proposed workflow aims to improve the accuracy of the estimations by clustering the input data based on specific petrophysical characteristics. We show since each major zones at the offset test wells have a distinct compaction trend, empirical constants in Bowers and Tau methods can be calibrated for each cluster rather than the whole stratigraphic column. The clustering task was done by statistical analyses of a suite of well logs and validated with core derived lithologies. To find the best clustering algorithm, we applied and compared five techniques namely, K-means, basic sequential algorithmic scheme, single, and complete linkage hierarchical. We found that the self-organizing map (SOM) method provides the best results by maximizing lithology likelihood within each cluster and improve the overall accuracy of the Bowers and Tau methods. This research also aims to provide a systematic comparison of the mentioned clustering algorithms based on their ability in distinguishing various lithofacies. We also try to minimize the user interference in the process of clustering multiple lithofacies and improve the reproducibility of the results and demonstrate the capability of the proposed method through a case study in a reservoir in the Southwest of Iran. Satisfactory results of this study offer a safe ground for implementation of the proposed method in other sedimentary basins.

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Section: Introductionmentioning

confidence: 99%