Application of Quantitative Risk Analysis to Pore Pressure and Fracture Gradient Prediction

Liang, Q.J.

doi:10.2523/77354-ms

Cited by 3 publications

(8 citation statements)

References 5 publications

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“…where M is the number of collected data points; V k is the calculation result of the pit gain, m 3 ; and V * k is the measurement of the pit gain, m 3 .…”

Section: Appendix a The Numerical Simulation Methods Of The Proposed mentioning

confidence: 99%

“…where V i,t and V i,t+Δt are the volumes of the i th bubble at time t and time t + Δt, respectively, m 3 . For the whole annulus, the total change of gas volume is equal to the sum of the expansion volume of the former (1 − N) th bubbles and volume of the (N + 1) th bubble, i.e.,…”

Section: Model Of Gasmentioning

confidence: 99%

“…At 9 : 00 17 August, the outflow mud density was 2.05 g/cm 3 , and the pump stopped with a slight kick. At 9 : 40 18 August, the outflow mud density was 2.12 g/cm 3 , and the pump stopped without a kick. Regarding well 4, 140 m 3 heavy mud (1.79 g/cm 3 ) was injected.…”

Section: Applicationsmentioning

confidence: 99%

“…Currently, formation pore pressure models are commonly based on seismic and logging data, and the uncertainty within these models is influenced primarily by widely used model coefficients and measurement accuracy [1][2][3][4]. Inaccurate prediction and underestimation of formation pore pressure typically lead to pressure underbalance in an open hole section of the well, causing fluid to enter the wellbore.…”

Section: Introductionmentioning

confidence: 99%

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Modelling of Formation Pore Pressure Inversion during Tight Reservoir Drilling

et al. 2021

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Identifying and controlling a kicking well hinge on quickly obtaining reliable and accurate formation pore pressure. In this study, we derive an analytical model for estimating formation pore pressure when a gas kick occurs during tight reservoir drilling. The model considers the variations in gas volume and pressures in the annulus affected by mutual coupling between the wellbore and formation, as well as bubble migration and expansion in the annulus. Additionally, a numerical computation method that reduces the effect of measurement noise using the Hooke-Jeeves algorithm is proposed. The method is capable of estimating pore pressure during the early stage of a kick in real time, is robust to the inherit noise of the measurements, and can be applied in scenarios when a well shut-in process cannot be performed. The simulation results demonstrate that both kick simulation and formation pore pressure inversion can be conducted via the proposed methodology. The errors of the pore pressure estimating results are less than 2.03% compared to the field data of seven wells. The method is tested and validated to be robust to noise and maintain good convergence performance, thereby providing drilling engineers with a simple and quick way to estimate pore pressure during a kick.

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“…where M is the number of collected data points; V k is the calculation result of the pit gain, m 3 ; and V * k is the measurement of the pit gain, m 3 .…”

Section: Appendix a The Numerical Simulation Methods Of The Proposed mentioning

confidence: 99%

Section: Model Of Gasmentioning

confidence: 99%

Section: Applicationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modelling of Formation Pore Pressure Inversion during Tight Reservoir Drilling

et al. 2021

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“…downhole logs, seismic) based on Monte-Carlo methods (e.g. Liang, 2002;Doyen et al, 2003;Malinvero et al, 2004;Moos et al, 2004). The usual approach of pore pressure modeling involves different steps of human interaction.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty Quantification of Real-Time Pore Pressure Models Caused by Normal Compaction Trend Line Setting

Bartetzko

Wessling

Tesch

et al. 2012

IADC/SPE Drilling Conference and Exhibition

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Pore pressure related wellbore instability is a recognized drilling challenge requiring attention as early as possible to mitigate and remediate severe drilling hazards such as kicks. Methods exist to quantify the effects of certain input parameters on pore pressure models, but the effect of the different, partly manual stages in pore pressure modeling on the uncertainty has rarely been addressed in the past. This paper highlights uncertainty sources associated with the basic stages in pore pressure modeling: shale discrimination using the gamma ray log and definition of the normal compaction trend line, either manually or using a regression analysis. Furthermore, filtering of the data prior to trend line definition may also add to some amount of uncertainty. These uncertainties in pore pressure prediction introduced by the modeling stages is demonstrated on an example data set from an offshore well. In particular, we show that applying different shale discrimination approaches can introduce pore pressure variations. Furthermore, a variation in depth intervals over which the trend line is automatically determined by linear regression can add more uncertainty to the pore pressure model. An approach is presented to quantify pore pressure uncertainty by automatically analyzing the variation in normal compaction trend lines. The application of the proposed method aims to constrain and quantify the error associated in modeling pore pressure in real-time, based on surface and subsurface (MWD and LWD) data. Modeling both pore pressure and its uncertainty creates additional value for safer drilling, because rig and remote monitoring personnel are made aware of a dynamic updated pore pressure range in addition to the possible pressure regimes interpreted during pre-drill modeling.

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Projeto ótimo de poços de petróleo baseado em confiabilidade

Tessari¹

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Application of Quantitative Risk Analysis to Pore Pressure and Fracture Gradient Prediction

Cited by 3 publications

References 5 publications

Modelling of Formation Pore Pressure Inversion during Tight Reservoir Drilling

Modelling of Formation Pore Pressure Inversion during Tight Reservoir Drilling

Uncertainty Quantification of Real-Time Pore Pressure Models Caused by Normal Compaction Trend Line Setting

Projeto ótimo de poços de petróleo baseado em confiabilidade

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