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Keywords: history matching
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Journal Articles
Journal: SPE Journal
SPE J. (2021)
Paper Number: SPE-204008-PA
Published: 18 November 2021
...Guohua Gao; Jeroen Vink; Fredrik Saaf; Terence Wells Summary When formulating history matching within the Bayesian framework, we may quantify the uncertainty of model parameters and production forecasts using conditional realizations sampled from the posterior probability density function (PDF...
Journal Articles
Journal: SPE Journal
SPE J. (2021)
Paper Number: SPE-205158-PA
Published: 31 August 2021
... production condensate bitumen mixture steam chamber asphaltene content sa-sagd experiment experimental data experimental study sa-sagd history matching steam-assisted gravity drainage sandpack condensate band heater sagd experiment SAGD is a commercially successful method for bitumen...
Journal Articles
Journal: SPE Journal
SPE J. 26 (04): 1700–1721.
Paper Number: SPE-205340-PA
Published: 11 August 2021
...Kai Zhang; Jinding Zhang; Xiaopeng Ma; Chuanjin Yao; Liming Zhang; Yongfei Yang; Jian Wang; Jun Yao; Hui Zhao Summary Although researchers have applied many methods to history matching, such as Monte Carlo methods, ensemble-based methods, and optimization algorithms, history matching fractured...
Journal Articles
Journal: SPE Journal
SPE J. (2021)
Paper Number: SPE-200772-PA
Published: 22 June 2021
...Z. Wang; J. He; W. J. Milliken; X. -H. Wen Summary Full-physics models in history matching (HM) and optimization can be computationally expensive because these problems usually require hundreds of simulations or more. In a previous study, a physics-baseddata-driven network model was implemented...
Journal Articles
Journal: SPE Journal
SPE J. 26 (03): 1341–1365.
Paper Number: SPE-203847-PA
Published: 16 June 2021
...Yanhui Zhang; Ibrahim Hoteit Summary We propose a feature-oriented ensemble history-matching workflow with a focus on the integration of time-lapse seismic and electromagnetic (EM) data. The developed workflow consists of two main steps. First, the rock cross-properties, such as water saturation...
Journal Articles
Journal: SPE Journal
SPE J. 26 (03): 1515–1534.
Paper Number: SPE-200867-PA
Published: 16 June 2021
... the compositional change of the produced bitumen. After the experiment, the sandpack was excavated, and samples were taken for analysis of solid, water, oil, asphaltene, and sulfur contents. Experimental data (e.g., propagation of a steam chamber and production of oil and water) were history matched using...
Journal Articles
Journal: SPE Journal
SPE J. 26 (02): 1011–1031.
Paper Number: SPE-205029-PA
Published: 14 April 2021
... 2020 21 12 2020 4 12 2020 10 2 2021 14 4 2021 Copyright © 2021 Society of Petroleum Engineers machine learning flow in porous media bayesian inference reservoir characterization reservoir simulation artificial intelligence history matching fluid dynamics...
Journal Articles
Journal: SPE Journal
SPE J. 26 (02): 973–992.
Paper Number: SPE-204221-PA
Published: 14 April 2021
...Ricardo Vasconcellos Soares; Xiaodong Luo; Geir Evensen; Tuhin Bhakta Summary In applications of ensemble-based history matching, it is common to conduct Kalman gain or covariance localization to mitigate spurious correlations and excessive variability reduction resulting from the use of relatively...
Journal Articles
Journal: SPE Journal
SPE J. 26 (02): 993–1010.
Paper Number: SPE-205014-PA
Published: 14 April 2021
...Xiaopeng Ma; Kai Zhang; Liming Zhang; Chuanjin Yao; Jun Yao; Haochen Wang; Wang Jian; Yongfei Yan Summary History matching is a typical inverse problem that adjusts the uncertainty parameters of the reservoir numerical model with limited dynamic response data. In most situations, various parameter...
Journal Articles
Journal: SPE Journal
SPE J. 25 (06): 3317–3331.
Paper Number: SPE-201106-PA
Published: 17 December 2020
... and transport in porous media in the form of a reservoir simulator. In a forward problem (or a predictive run), the reservoir simulator directly maps the uncertainty space of the model parameters to the uncertainty space of the state variables. Conversely, an inverse problem (or history matching) aims...
Journal Articles
Journal: SPE Journal
SPE J. 25 (06): 3300–3316.
Paper Number: SPE-193838-PA
Published: 17 December 2020
... the model discrepancy during history matching. This leads to a joint inverse problem in which both the model parameters and the parameters of a PCA‐based error model are estimated. For the joint inversion within the Bayesian framework, prior distributions have to be defined for all the estimated parameters...
Journal Articles
Journal: SPE Journal
SPE J. 25 (06): 3349–3365.
Paper Number: SPE-202475-PA
Published: 17 December 2020
... of intrinsic model imperfection. Two approaches are developed for the problem solution including the following: tuning the additional error‐related parameters as a complementary stage of a classical historymatching procedure, and updating these parameters simultaneously with the original model parameters...
Journal Articles
Journal: SPE Journal
SPE J. 25 (05): 2729–2748.
Paper Number: SPE-201237-PA
Published: 15 October 2020
... to model the fracture network. Based on this parameterization method, we present a novel historymatching approach using a data‐driven evolutionary algorithm to explore the Bayesian posterior space and decrease the uncertainties of the model parameters. Empirical studies on hypothetical and outcrop‐based...
Journal Articles
Journal: SPE Journal
SPE J. (2020)
Paper Number: SPE-203833-PA
Published: 09 September 2020
... in the reservoir. 7 4 2020 17 7 2020 12 7 2020 9 9 2020 Copyright © 2020 Society of Petroleum Engineers reservoir characterization artificial intelligence reservoir simulation fluid dynamics flow in porous media upstream oil & gas history matching enhanced recovery...
Journal Articles
Journal: SPE Journal
SPE J. 25 (04): 2055–2066.
Paper Number: SPE-191521-PA
Published: 13 August 2020
...Sarath Pavan Ketineni; Subhash Kalla; Shauna Oppert; Travis Billiter Summary Standard historymatching workflows use qualitative 4D seismic observations to assist in reservoir modeling and simulation. However, such workflows lack a robust framework for quantitatively integrating 4D seismic...
Journal Articles
Journal: SPE Journal
SPE J. 25 (04): 2000–2020.
Paper Number: SPE-195253-PA
Published: 13 August 2020
... in single/multicontinuum models with generic grid designs, both in structured and fully unstructured formats, thereby aiding well‐level history matching and high‐resolution updates of modern geologic models. This work presents, for the first time, an application of the GTTI to dual‐permeability models...
Journal Articles
Journal: SPE Journal
SPE J. 25 (04): 1938–1963.
Paper Number: SPE-193925-PA
Published: 13 August 2020
... gradient production monitoring waterflooding enhanced recovery perturbation nonlinear state constraint production control flow in porous media history matching reservoir surveillance fluid dynamics 11th control step For both the assisted‐historymatching and reservoir‐optimization steps...
Journal Articles
Journal: SPE Journal
SPE J. 25 (04): 1895–1915.
Paper Number: SPE-199368-PA
Published: 13 August 2020
... of matrix properties and complex fracture parameters require efficient history matching of well production and pressure response. We propose a novel reservoir model parameterization method to reduce the number of unknowns, regularize the ill‐posed problem, and enhance the efficiency of history matching...
Journal Articles
Journal: SPE Journal
SPE J. 25 (04): 1557–1577.
Paper Number: SPE-180148-PA
Published: 13 August 2020
... diffusivity equation geologic modeling production monitoring pressure transient analysis production control reservoir characterization DTOF equation pressure transient testing hydraulic fracturing geological modeling reservoir surveillance complex reservoir history matching Adjusting...
Journal Articles
Journal: SPE Journal
SPE J. 25 (04): 2119–2142.
Paper Number: SPE-201209-PA
Published: 13 August 2020
... of the associated uncertainties. This study uses the emulation‐based Bayesian historymatching (BHM) uncertainty analysis for the uncertainty reduction of complex models, which is designed to address problems with a high number of both input and output parameters. We detail how to efficiently choose sets of outputs...

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