Assisted history matching workflow for unconventional reservoirs

dc.contributor.advisorSepehrnoori, Kamy, 1951-
dc.creatorTripoppoom, Sutthaporn
dc.creator.orcid0000-0002-1747-4283
dc.date.accessioned2019-11-12T16:59:44Z
dc.date.available2019-11-12T16:59:44Z
dc.date.created2019-05
dc.date.issued2019-05-13
dc.date.submittedMay 2019
dc.date.updated2019-11-12T16:59:44Z
dc.description.abstractThe information of fractures geometry and reservoir properties can be retrieved from the production data, which is always available at no additional cost. However, in unconventional reservoirs, it is insufficient to obtain only one realization because the non-uniqueness of history matching and subsurface uncertainties cannot be captured. Therefore, the objective of this study is to obtain multiple realizations in shale reservoirs by adopting Assisted History Matching (AHM). We used multiple proxy-based Markov Chain Monte Carlo (MCMC) algorithm and Embedded Discrete Fracture Model (EDFM) to perform AHM. The reason is that MCMC has benefits of quantifying uncertainty without bias or being trapped in any local minima. Also, using MCMC with proxy model unlocks the limitation of an infeasible number of simulations required by a traditional MCMC algorithm. For fractures modeling, EDFM can mimic fractures flow behavior with a higher computational efficiency than a traditional local grid refinement (LGR) method and more accuracy than the continuum approach. We applied the AHM workflow to actual shale gas wells. We found that the algorithm can find multiple history matching solutions and quantify the fractures and reservoir properties posterior distributions. Then, we predicted the production probabilistically. Moreover, we investigated the performance of neural network (NN) and k-nearest neighbors (KNN) as a proxy model in the proxy-based MCMC algorithm. We found that NN performed better in term of accuracy than KNN but NN required twice running time of KNN. Lastly, we studied the effect of enhanced permeability area (EPA) and natural fractures existence on the history matching solutions and production forecast. We concluded that we would over-predict fracture geometries and properties and estimated ultimate recovery (EUR) if we assumed no EPA or no natural fractures even though they actually existed. The degree of over-prediction depends on fractures and reservoir properties, EPA and natural fractures properties, which can only be quantified after performing AHM. The benefits from this study are that we can characterize fractures geometry, reservoir properties, and natural fractures in a probabilistic manner. These multiple realizations can be further used for a probabilistic production forecast, future fracturing design improvement, and infill well placement decision.
dc.description.departmentPetroleum and Geosystems Engineering
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/2152/78361
dc.identifier.urihttp://dx.doi.org/10.26153/tsw/5448
dc.language.isoen
dc.subjectHistory matching
dc.subjectAssisted History Matching
dc.subjectAutomatic history matching
dc.subjectWorkflow
dc.subjectMCMC
dc.subjectProxy
dc.subjectNeural network
dc.subjectK-nearest neighbors
dc.subjectEDFM
dc.subjectFractures
dc.subjectShale
dc.subjectReservoirs
dc.subjectUnconventional reservoirs
dc.subjectNatural fractures
dc.subjectHydraulic fractures
dc.titleAssisted history matching workflow for unconventional reservoirs
dc.typeThesis
dc.type.materialtext
thesis.degree.departmentPetroleum and Geosystems Engineering
thesis.degree.disciplinePetroleum Engineering
thesis.degree.grantorThe University of Texas at Austin
thesis.degree.levelMasters
thesis.degree.nameMaster of Science in Engineering

Access full-text files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
TRIPOPPOOM-THESIS-2019.pdf
Size:
21.52 MB
Format:
Adobe Portable Document Format

License bundle

Now showing 1 - 2 of 2
No Thumbnail Available
Name:
PROQUEST_LICENSE.txt
Size:
4.46 KB
Format:
Plain Text
Description:
No Thumbnail Available
Name:
LICENSE.txt
Size:
1.85 KB
Format:
Plain Text
Description: