Velocity determination for pore-pressure prediction

Chopra, Satinder; Huffman, Alan R.

doi:10.1190/1.2405336

Cited by 62 publications

(30 citation statements)

References 26 publications

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“…They also derived the effective stress from measured pore pressure data and calculated overburden stress (equation 3) based on sonic interval velocities from well log data in the Gulf of Mexico (equation 2). They further demonstrated that the sonic velocity and effective stress have the following power-law relationship: where V p is compressional velocity at a given depth, V 0 is compressional velocity in mudline or unconsolidated saturated surface sediments, σ e is effective stress, A (m 2 .s/kg) and B (dimensionless) are empirical constants calibrated with offset velocity versus effective stress data (Chopra and Huffman, 2006).…”

Section: Datasetmentioning

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: Datasetmentioning

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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“…There are several methods in literature dealing with the pre-drill prediction of abnormal pore pressure from seismic survey data (see Eaton Eaton 1975;Bowers 1995;ENI 1999;Kan et al 1999;Dvorkin et al 1999;Carcione and Helle 2002;Sayers et al 2002;Chopra and Huffman 2006;Sundaram and Jain 2008;Lu et al 2009;Babu and Sircar 2011;Brahma et al 2013). Comparing of the measured physical properties of subsurface formations in the abnormal pressure region with the normally pressurized formation properties is the general approach in all the overpressured prediction methods.…”

Section: Geologic and Structural Setting At Beni-suef Basinmentioning

confidence: 99%

Pre-drill pore pressure prediction using seismic velocities for prospect areas at Beni Suef Oil Field, Western Desert, Egypt

El-Werr

Shebl

El-Rawy

et al. 2017

J Petrol Explor Prod Technol

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Pre-drill prediction of formation pore pressure from surface seismic survey is very important for drilling, production, and reservoir engineering because it affects drilling operations and well-planning processes. If it is not properly evaluated, it can lead to numerous drilling problems such as dangerous well kicks, lost circulation, blowouts, stuck pipe, excessive costs, and borehole instability. Pre-drill pore pressure estimation has been obtained from transform models using seismic interval velocities. However, the accuracy of this estimate of pore pressure is directly related to the reliability of these interval velocities. Bulk density was estimated from seismic interval velocity and transit time. Normal pore pressure gradient is estimated from the slope of a trendline that is generated from logarithm transit times versus depth. Overburden pressure at any depth was calculated from the integration of the average interval bulk densities and thicknesses above that depth. Pore pressure has been obtained from overburden pressure and observed interval velocities using modified Eaton's equation. 154 CDPs were used along 28 seismic lines at Beni Suef basin, Western Desert, Egypt, to accomplish the purpose of this study. Two velocity reversal zones showing abnormally high pore pressure were detected and correlated to Abu Roash and Bahariya Formations. Moreover, pore pressure gradient maps were established for these two zones to predict the possible horizontal fluid flow (migration paths) for the proposal of prospects with lower pressures and less drilling risks.Finally, it is possible to calculate and recommend the required heavier mud weight to drill.

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“…Obecnie najlepszym przestrzennym źró-dłem pola prędkości są dane sejsmiczne. W przypadku sejsmiki 2D posiadamy dwuwymiarowy rozkład pola pręd-kości (co przy dużej liczbie profili daje przybliżony obraz trójwymiarowy), a w przypadku danych 3D otrzymujemy (6) przestrzenny rozkład pola prędkości. Najlepsze rezultaty w określeniu rozkładu ciśnień porowych daje zastosowanie prędkości z inwersji sejsmicznej i inwersji tomograficznej na danych sejsmicznych przed składaniem.…”

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“…Prędkości te w praktyce analizowane są z dużym krokiem poziomym, a ich rozdzielczość pionowa uwarunkowana jest tzw. centrami energetycznymi, które obrazują najlepsze dopasowanie refleksów tras sejsmicznych w procesie analiz prędkości [6,10]. Nie zawsze tak jest.…”

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Prognozowanie ciśnień porowych w ośrodku geologicznym z wykorzystaniem modułu Seismic Pore Pressure Modeling firmy WesternGeco

Wilk

2016

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Prognozowanie ciśnień porowych w ośrodku geologicznym z wykorzystaniem modułu Seismic Pore Pressure Modeling firmy WesternGeco Do obliczenia gradientu ciśnień porowych wykorzystano moduł Seismic Pore Pressure Modeling firmy WesternGeco, który został udostępniony autorowi na okres jednego miesiąca. Atutem wspomnianego modułu jest jego interaktywność oraz fakt, iż pracuje on w systemie Petrel firmy Schlumberger, będącym jednym z podstawowych narzędzi interpretacyjnych używanych przez polskie jednostki przemysłowe. Do obliczeń wykorzystano rzeczywiste pomiary ze zdjęcia sejsmicznego 3D z północnego obszaru Polski. Uzyskano zadowalające wyniki potwierdzające użyteczność tego narzędzia w pracach poszukiwawczych.Słowa kluczowe: ciśnienie porowe, gradient ciśnień porowych, naprężenie efektywne, model kompakcji, profilowanie akustyczne, gęstość, prędkość.Predicting pore pressure in the geological medium, based on the module "Seismic Pore Pressure Modeling" of WesternGeco Pore pressure gradient calculations were based on the module "Pore Pressure Seismic Modeling" of WesternGeco, which was made available to the author for a period of one month. The advantage of this module is its interactivity, and the fact that it works in the Schlumberger Petrel system, which is one of the basic tools of interpretation used by our native oil industry. For calculations, actual measurements of the 3D seismic survey of the northern Polish territory were used. Satisfactory results confirming the usefulness of this tool in exploration work was achieved.

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Velocity determination for pore-pressure prediction

Cited by 62 publications

References 26 publications

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

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

Pre-drill pore pressure prediction using seismic velocities for prospect areas at Beni Suef Oil Field, Western Desert, Egypt

Prognozowanie ciśnień porowych w ośrodku geologicznym z wykorzystaniem modułu Seismic Pore Pressure Modeling firmy WesternGeco

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