Predictive uncertainty of fluid models
Abstract/Contents
- Abstract
- Fluid models play a critical role in a wide range of applications, such as oil and gas exploration and production, chemical process design and optimization, and CO2 enhanced oil recovery projects. Accurate predictions of phase behavior and thermophysical properties are key to ensuring the safety, performance, and profitability of these operations. However, predictions derived from fluid models inherently possess uncertainties due to model and parametric factors. These uncertainties are especially significant for hydrocarbon reservoir fluids, which exhibit complex phase behavior. This dissertation discusses the sources of uncertainty in reservoir fluid modeling, encompassing both empirical and compositional approaches, and provides a deeper understanding of their impact on fluid and flow model predictions. Initially, we demonstrate the uncertainty in model selection by comparing nine traditional empirical models for solution gas-oil ratios against experimental data. The results reveal that specific fluid compositions under different pressure and temperature conditions are better represented by distinct models. Consequently, we propose a new model that delivers superior overall performance. We also explore the uncertainty in input parameters for compositional fluid models, specifically highlighting how subjective choices of optimization algorithms and initial guesses impact the equation of state (EoS) regression process. As a result, EoS predictions remain uncertain even after tuning the uncertain inputs to a limited set of experimental data points. We present results for two hydrocarbon reservoir fluids, treating five properties of the heaviest carbon fraction as design variables. Although all considered optimization algorithms and initial guesses match experimental data for gas and liquid properties, the resulting EoS parameterizations lead to dramatically different predictions of the fluid's thermophysical behavior in unsampled pressure and temperature regions. Next, we investigate the uncertainty in input parameters for fluid models, introducing a framework to quantify the predictive uncertainty of multiphase pipe-flow models due to correlated random inputs. A case study evaluates the uncertainty in cumulative production for a reservoir with unknown fluid properties during the exploration phase. Global Sensitivity Analysis using Sobol's indices is employed to identify inputs significantly contributing to the model's predictive uncertainty. This framework facilitates improved risk management and informed decision-making within the energy industry. Lastly, we discuss the calibration of inherently uncertain flow models when field data is available. In particular, we propose a heuristic method for optimizing tuning factors applied to calculated pressure and temperature gradients and demonstrate its efficiency through a real case study.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2023; ©2023 |
| Publication date | 2023; 2023 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Fulchignoni de Paiva, Livia |
|---|---|
| Degree supervisor | Tartakovsky, Daniel |
| Thesis advisor | Tartakovsky, Daniel |
| Thesis advisor | Aziz, Khalid, Ph. D. |
| Thesis advisor | Kovscek, Anthony R. (Anthony Robert) |
| Degree committee member | Aziz, Khalid, Ph. D. |
| Degree committee member | Kovscek, Anthony R. (Anthony Robert) |
| Associated with | Stanford Doerr School of Sustainability |
| Associated with | Stanford University, Department of Energy Resources Engineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Lívia Fulchignoni de Paiva. |
|---|---|
| Note | Submitted to the Department of Energy Resources Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2023. |
| Location | https://purl.stanford.edu/ch064fd3336 |
Access conditions
- Copyright
- © 2023 by Livia Fulchignoni de Paiva
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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