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Probabilistic Assessment of Pore Pressure Prediction With Bayesian Geophysical Basin Modeling.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Probabilistic Assessment of Pore Pressure Prediction With Bayesian Geophysical Basin Modeling./
作者:	Josue Sa da Fonseca.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2024,
面頁冊數:	295 p.
附註:	Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
Contained By:	Dissertations Abstracts International85-11B.
標題:	Geophysics. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31255791
ISBN:	9798382230696

Probabilistic Assessment of Pore Pressure Prediction With Bayesian Geophysical Basin Modeling.
Josue Sa da Fonseca.

Probabilistic Assessment of Pore Pressure Prediction With Bayesian Geophysical Basin Modeling. - Ann Arbor : ProQuest Dissertations & Theses, 2024 - 295 p.

Source: Dissertations Abstracts International, Volume: 85-11, Section: B.

Thesis (Ph.D.)--Stanford University, 2024.

Effective decision-making in exploring and developing subsurface resources hinges on accurately estimating geologically plausible and realistic properties. Nevertheless, conventional inverse modeling formulations for property estimation encounter computational challenges arising from high-dimensional problem settings and the substantial geological uncertainty inherent in subsurface estimation problems. Especially for pore pressure forecast, this challenge yields to disregarding the geological processes occurring in the subsurface in the predictions by standardizing the use of empirical models calibrated only on offset wells. This dissertation introduces and investigates methodologies rooted in the Bayesian Geophysical Basin Modeling (BGBM) workflow to assess pore pressure. The aim is to incorporate geological processes into estimations and utilize probabilistic models, facilitating a comprehensive evaluation of pore pressure predictions across exploration and exploitation stages in subsurface resource activities.BGBM methodology is an interdisciplinary workflow that incorporates data, geological expertise, and physical processes through Bayesian inference in sedimentary basin models. Its application culminates in subsurface models and properties that integrate the geo-history of a basin, rock physics relations, well log and drilling data, and seismic information. Monte Carlo basin modeling realizations are performed by sampling from prior probability distributions on facies parameters and basin boundary conditions. After data assimilation, the accepted set of posterior subsurface models yields uncertainty quantification of subsurface properties. This procedure is especially suitable for pore pressure prediction in a predrill stage. However, the high computational cost of seismic data assimilation decreases the practicality of the workflow. Therefore, we introduce and investigate seismic travel times criteria as computationally faster proxies for analyzing the seismic data likelihood when employing BGBM. A real field case from the Gulf of Mexico using a 2D section for pore pressure prediction considering different kinematics criteria is evaluated. We validate and compare the outcomes for predicted pore pressure with mud weight data from a blind well. We demonstrated that the proposed fast proxies make the BGBM methodology efficient and practical.Nevertheless, the computational time for running 3D Monte Carlo forward basin modeling simulations to quantify pore pressure uncertainty remains a bottleneck. In order to tackle the latter challenge, we propose integrating the directness of empirical relations with process-based simulations of Earth models for further 3D extrapolation. The quantified uncertainty of pore pressure estimation from the posterior 2D results obtained after the BGBM workflow application is encoded into standard empirical equations. Eaton's model was selected to evaluate this procedure. Then, the BGBM 2D section of the real case provides the empirical model calibration. Validation at the blind well in the 2D section was remarkably similar to the BGBM result. The calibrated empirical parameters were then carried out to be extrapolated in a 3D test area near the BGBM study. Mud weight measurements of a test well in the 3D area confirm promising pore pressure forecasts.Ideally, the BGBM workflow should be rerun after new data is observed. BGBM robustly incorporates geological processes in pore pressure prediction. However, this workflow requires reasonable computational resources to simulate Earth models conditioned with data in a Monte Carlo fashion. Thus, real-time revised pore pressure information of deep targets with new data of shallow layers acquired during drilling becomes unfeasible for BGBM. Therefore, we suggest combining probabilistic graphical models with BGBM posterior results to create computationally efficient pore pressure uncertainty workflows to rapidly update beliefs when data arrives while drilling. Bayesian Networks (BNs) are proposed to represent subsurface pore pressure per geological compartment (e.g., stratigraphic layers) connected to facies parameters of layers possibly observed at the vertical location of the well perforation. The BNs are trained with the dataset derived from the BGBM posterior results. Finally, computational evaluations analyzing the inferred results of discrete and continuous BNs are demonstrated and discussed, concluding that BNs offer a viable probabilistic model for rapid pore pressure updates during drilling, provided that the BN has been calibrated with the posterior outputs from BGBM results.

ISBN: 9798382230696Subjects--Topical Terms:

535228
Geophysics.
Subjects--Index Terms:

Bayesian Networks

Probabilistic Assessment of Pore Pressure Prediction With Bayesian Geophysical Basin Modeling.
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245 1 0 $a Probabilistic Assessment of Pore Pressure Prediction With Bayesian Geophysical Basin Modeling.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2024
300 $a 295 p.
500 $a Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
500 $a Advisor: Tapan Mukerji.
502 $a Thesis (Ph.D.)--Stanford University, 2024.
520 $a Effective decision-making in exploring and developing subsurface resources hinges on accurately estimating geologically plausible and realistic properties. Nevertheless, conventional inverse modeling formulations for property estimation encounter computational challenges arising from high-dimensional problem settings and the substantial geological uncertainty inherent in subsurface estimation problems. Especially for pore pressure forecast, this challenge yields to disregarding the geological processes occurring in the subsurface in the predictions by standardizing the use of empirical models calibrated only on offset wells. This dissertation introduces and investigates methodologies rooted in the Bayesian Geophysical Basin Modeling (BGBM) workflow to assess pore pressure. The aim is to incorporate geological processes into estimations and utilize probabilistic models, facilitating a comprehensive evaluation of pore pressure predictions across exploration and exploitation stages in subsurface resource activities.BGBM methodology is an interdisciplinary workflow that incorporates data, geological expertise, and physical processes through Bayesian inference in sedimentary basin models. Its application culminates in subsurface models and properties that integrate the geo-history of a basin, rock physics relations, well log and drilling data, and seismic information. Monte Carlo basin modeling realizations are performed by sampling from prior probability distributions on facies parameters and basin boundary conditions. After data assimilation, the accepted set of posterior subsurface models yields uncertainty quantification of subsurface properties. This procedure is especially suitable for pore pressure prediction in a predrill stage. However, the high computational cost of seismic data assimilation decreases the practicality of the workflow. Therefore, we introduce and investigate seismic travel times criteria as computationally faster proxies for analyzing the seismic data likelihood when employing BGBM. A real field case from the Gulf of Mexico using a 2D section for pore pressure prediction considering different kinematics criteria is evaluated. We validate and compare the outcomes for predicted pore pressure with mud weight data from a blind well. We demonstrated that the proposed fast proxies make the BGBM methodology efficient and practical.Nevertheless, the computational time for running 3D Monte Carlo forward basin modeling simulations to quantify pore pressure uncertainty remains a bottleneck. In order to tackle the latter challenge, we propose integrating the directness of empirical relations with process-based simulations of Earth models for further 3D extrapolation. The quantified uncertainty of pore pressure estimation from the posterior 2D results obtained after the BGBM workflow application is encoded into standard empirical equations. Eaton's model was selected to evaluate this procedure. Then, the BGBM 2D section of the real case provides the empirical model calibration. Validation at the blind well in the 2D section was remarkably similar to the BGBM result. The calibrated empirical parameters were then carried out to be extrapolated in a 3D test area near the BGBM study. Mud weight measurements of a test well in the 3D area confirm promising pore pressure forecasts.Ideally, the BGBM workflow should be rerun after new data is observed. BGBM robustly incorporates geological processes in pore pressure prediction. However, this workflow requires reasonable computational resources to simulate Earth models conditioned with data in a Monte Carlo fashion. Thus, real-time revised pore pressure information of deep targets with new data of shallow layers acquired during drilling becomes unfeasible for BGBM. Therefore, we suggest combining probabilistic graphical models with BGBM posterior results to create computationally efficient pore pressure uncertainty workflows to rapidly update beliefs when data arrives while drilling. Bayesian Networks (BNs) are proposed to represent subsurface pore pressure per geological compartment (e.g., stratigraphic layers) connected to facies parameters of layers possibly observed at the vertical location of the well perforation. The BNs are trained with the dataset derived from the BGBM posterior results. Finally, computational evaluations analyzing the inferred results of discrete and continuous BNs are demonstrated and discussed, concluding that BNs offer a viable probabilistic model for rapid pore pressure updates during drilling, provided that the BN has been calibrated with the posterior outputs from BGBM results.
590 $a School code: 0212.
650 4 $a Geophysics. $3 535228
650 4 $a Engineering. $3 586835
650 4 $a Environmental science. $3 677245
653 $a Bayesian Networks
653 $a Computational challenges
653 $a Seismic data likelihood
653 $a Empirical relations
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690 $a 0768
690 $a 0537
690 $a 0373
710 2 $a Stanford University. $3 754827
773 0 $t Dissertations Abstracts International $g 85-11B.
790 $a 0212
791 $a Ph.D.
792 $a 2024
793 $a English
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