Pore pressure prediction using geophysical methods in carbonate reservoirs: Current status, challenges and way ahead

Wang, Zizhen; Wang, Ruihe

doi:10.1016/j.jngse.2015.09.032

Cited by 41 publications

(13 citation statements)

References 58 publications

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“…Hence, the RFT data of those wells was used to validate the pore pressure prediction. (3) and Miller methods equation (5), pore pressure for the study was successfully predicted for 6 wells. As a result, the Eaton DT method with Eaton exponent 3 and Miller method with velocity exponent 3 have able to predict reasonably consistent pore pressure that matches the measured RFT data at well locations.…”

Section: A Pore Pressure Prediction Methodsmentioning

confidence: 79%

“…Overpressure has been one of the major global geological challenges in the drilling operations in the oil and gas industry, while predrill pore pressure remained as the basic input data for proper selection of casing optimization and specifying a reliable of a drilling fluid density [4]. Thus, implied that validated pore pressure (over or under pressure) prediction is crucial for the oil and gas industry due to its decisive impact on the safety and cost of drilling for petroleum exploration and production [5]. Hence, precise pore pressure prediction is required to minimize the risks of drilling incidents such as blowouts, kicks, instability of wellbore, whole washouts, loss of drilling fluid circulation and protect the pay formation [3].…”

Section: Introductionmentioning

confidence: 99%

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Pore Pressure Prediction using Well-Logging Data in the West Baram Delta, Offshore Sarawak Basin, Malaysia

Asfha*,

Gebretsadik,

Wan Yusoff

2019

IJRTE

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Abstract: Well-predicted pore pressure is vital throughout the lifetime of an oil and gas field starting from exploration to the production stage. Here, we studied a mature field where enhanced oil recovery is of high interest and pore pressure data is crucial. Moreover, the top of the overpressure zone in west Baram Delta starts at different depths. Hence, valid pore pressure prediction prior to drilling is a prerequisite for reducing drilling risks, increasing efficient reservoir modeling and optimizing costs. Petrophysical logs such as gamma-ray, density logs, and sonic transit time were used for pore pressure prediction in the studied field. Density logs were used to predict the overburden pressure, whereas sonic transit time, and gamma-ray logs were utilized to develop observed shale compaction trend line (OSCTL) and to establish a normal compaction trend line (NCTL). Pore pressure was predicted from a locally observed shale compaction trend line of 6 wells using Eaton's and Miller's methods. The predicted pore pressure using Eaton's DT method with Eaton's exponent 3 showed a better matching with the measured pressure acquired from the repeat formation test (RFT). Hence, Eaton's DT method with Eaton exponent 3 could be applied to predict pore pressure for drilling sites in the study area and vicinity fields with similar geological settings.

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Section: A Pore Pressure Prediction Methodsmentioning

confidence: 79%

Section: Introductionmentioning

confidence: 99%

Pore Pressure Prediction using Well-Logging Data in the West Baram Delta, Offshore Sarawak Basin, Malaysia

Asfha*,

Gebretsadik,

Wan Yusoff

2019

IJRTE

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“…Lithology, porosity, and fluid content variations have a significant effect on the accuracy and precision of pore pressure estimation based on P-wave velocity (Obradors-Prats et al, 2016;Wang and Wang, 2015;Oloruntobi et al, 2018). This can be explained by the strong dependence of P-wave velocity with lithological and geomechanical parameters.…”

Section: Discussion and Resultsmentioning

confidence: 99%

“…The conventional workflow starts with editing the acquired data by careful investigation of caliper log to locate the wellbore collapses, and identify outlier data points outside the three standard deviations from the mean (Wang and Wang, 2015). Then, we calibrate the Bowers relationship (equation 1) in offset wells via regression analysis of the calculated effective stress at depths with available P-wave velocity (figure 5A significant.…”

Section: Conventional Bowers and Tau Methodsmentioning

confidence: 99%

“…However, generating a comprehensive velocity model compound of various type of data is not straightforward. For instance, One should keep in mind that above methods heavily depend on the relationship between porosity and pore pressure (Mannon and Young, 2017;Wang and Wang, 2015;Zhao et al, 2014) which may not be a valid assumption in case of complex lithology (Obradors-Prats et al, 2016). Also, the presence of secondary phases (e.g., methane, brine) introduce a major uncertainty and may lead to false interpretation of the velocity data and inaccurate pore pressure estimation (Nour and AlBinHassan, 2013).…”

Section: Introductionmentioning

confidence: 99%

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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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Formation Pressure Estimation Method for High Temperature and High Pressure Wells in Ledong Area of South China Sea

Zhang

Yang

et al. 2021

Nat Resour Res

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Pore pressure prediction using geophysical methods in carbonate reservoirs: Current status, challenges and way ahead

Cited by 41 publications

References 58 publications

Pore Pressure Prediction using Well-Logging Data in the West Baram Delta, Offshore Sarawak Basin, Malaysia

Pore Pressure Prediction using Well-Logging Data in the West Baram Delta, Offshore Sarawak Basin, Malaysia

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

Formation Pressure Estimation Method for High Temperature and High Pressure Wells in Ledong Area of South China Sea

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