Relative permeability and capillary pressure are the key parameters of the multiphase flow in a reservoir. To ensure an accurate determination of these functions in the areas of interest, the core flooding and centrifuge experiments on the relevant core samples need to be interpreted meticulously. In this work, relative permeability and capillary pressure functions are determined synchronously by history matching of multiple experiments simultaneously in order to increase the precision of results based on additional constraints coming from extra measurements. To take into account the underlying physics without making crude assumptions, the Special Core Analysis (SCAL) experiments are chosen to be simulated instead of using well know simplified analytical or semi-analytical solutions. Corresponding numerical models are implemented with MRST (Lie, 2019) library. The history matching approach is based on the adjoint gradient method for the constrained optimization problem. Relative permeability and capillary pressure curves, which are the objectives of history matching, within current implementation can have a variety of representations as Corey, LET, B-Splines and NURBS. For the purpose of analyzing the influence of correlations on the history matching results in this study, the interpretation process with assumed analytical correlations is compared to history matching based on generic NURBS representation of relevant functions.
{"title":"Automated Interpretation Tool for Synchronous History Matching of Multiple Scal Experiments with Advance Nurbs Representations of Relevant Functions","authors":"R. Manasipov, B. Jenei","doi":"10.2118/200559-ms","DOIUrl":"https://doi.org/10.2118/200559-ms","url":null,"abstract":"\u0000 Relative permeability and capillary pressure are the key parameters of the multiphase flow in a reservoir. To ensure an accurate determination of these functions in the areas of interest, the core flooding and centrifuge experiments on the relevant core samples need to be interpreted meticulously. In this work, relative permeability and capillary pressure functions are determined synchronously by history matching of multiple experiments simultaneously in order to increase the precision of results based on additional constraints coming from extra measurements.\u0000 To take into account the underlying physics without making crude assumptions, the Special Core Analysis (SCAL) experiments are chosen to be simulated instead of using well know simplified analytical or semi-analytical solutions. Corresponding numerical models are implemented with MRST (Lie, 2019) library. The history matching approach is based on the adjoint gradient method for the constrained optimization problem. Relative permeability and capillary pressure curves, which are the objectives of history matching, within current implementation can have a variety of representations as Corey, LET, B-Splines and NURBS. For the purpose of analyzing the influence of correlations on the history matching results in this study, the interpretation process with assumed analytical correlations is compared to history matching based on generic NURBS representation of relevant functions.","PeriodicalId":293517,"journal":{"name":"Day 3 Thu, December 03, 2020","volume":"414 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122908727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
E. Ranaee, G. Guédon, L. Moghadasi, F. Inzoli, M. Riva, G. Maddinelli, M. Bartosek, A. Guadagnini
We aim at developing a viable workflow for the characterization of reservoir responses under Water Alternating Gas (WAG) conditions for enhanced oil recovery. We do so through a numerical Monte Carlo (MC) framework and by relying on (i) a classical approach, which is grounded on employing results from laboratory-scale core-flooding experiments or (ii) an approach based on relative permeability curves inferred from pore-scale numerical simulations. In these settings we investigate (i) the way uncertainties associated with the parameters of a reservoir model estimated through these approaches propagate to target modeling goals and (ii) assess (through Global Sensitivity Analyses) the relative importance of the uncertain quantities controlling the reservoir behavior via given model outcomes. We consider uncertainty in (a) porosity and absolute permeability as well as (b) parameters of relative permeability models. Three scenarios are assessed, accounting for spatial distribution of porosity and absolute permeability with differing degrees of complexity and corresponding to (i) homogeneous; (ii) randomly heterogeneous; and (iii) well-connected randomly heterogeneous fields. Spatial realizations of the heterogeneous fields are generated considering Gaussian random fields with a Gaussian kernel variance driving the degree of spatial correlation. The two modeling approaches considered take advantage of two-phase relative permeability curves, which are interpreted via commonly used models with uncertain parameters. Three-phase relative permeabilities are then characterized through a previously developed and tested sigmoid-based oil relative permeability model by taking into account hysteretic behavior of gas relative permeability. All field-scale simulations are performed on a simple reservoir model and are set within the MRST suite. In the case of a homogeneous reservoir, we note that reservoir simulation responses are strongly sensitive to the degree of convexity of the two-phase relative permeability curves. In the case of heterogeneous reservoir settings, results are almost similarly sensitive to porosity, characteristics of the relative permeability model, and the degree of heterogeneity of the reservoir. In the case of well-connected (randomly) heterogeneous fields, the importance of the porosity is stronger than in the heterogeneous setting lacking well connected regions. Characterization of reservoir model attributes relying on pore-scale simulation approaches in the presence of uncertainty can provide a robust term of comparison which can be integrated within a classical reservoir simulation approach relying on relative permeability data stemming from core-flooding experiments. Our results document that uncertainties in the evaluation of (i) reservoir model petrophysical attributes (porosity/permeability) and (ii) relative permeability model parameters can differently influence field-scale simulation outputs, depending
{"title":"Implementation of Three-Phase Black-Oil Reservoir Models Assisted by Micro-Scale Analyses","authors":"E. Ranaee, G. Guédon, L. Moghadasi, F. Inzoli, M. Riva, G. Maddinelli, M. Bartosek, A. Guadagnini","doi":"10.2118/200651-ms","DOIUrl":"https://doi.org/10.2118/200651-ms","url":null,"abstract":"\u0000 \u0000 \u0000 We aim at developing a viable workflow for the characterization of reservoir responses under Water Alternating Gas (WAG) conditions for enhanced oil recovery. We do so through a numerical Monte Carlo (MC) framework and by relying on (i) a classical approach, which is grounded on employing results from laboratory-scale core-flooding experiments or (ii) an approach based on relative permeability curves inferred from pore-scale numerical simulations. In these settings we investigate (i) the way uncertainties associated with the parameters of a reservoir model estimated through these approaches propagate to target modeling goals and (ii) assess (through Global Sensitivity Analyses) the relative importance of the uncertain quantities controlling the reservoir behavior via given model outcomes.\u0000 \u0000 \u0000 \u0000 We consider uncertainty in (a) porosity and absolute permeability as well as (b) parameters of relative permeability models. Three scenarios are assessed, accounting for spatial distribution of porosity and absolute permeability with differing degrees of complexity and corresponding to (i) homogeneous; (ii) randomly heterogeneous; and (iii) well-connected randomly heterogeneous fields. Spatial realizations of the heterogeneous fields are generated considering Gaussian random fields with a Gaussian kernel variance driving the degree of spatial correlation. The two modeling approaches considered take advantage of two-phase relative permeability curves, which are interpreted via commonly used models with uncertain parameters. Three-phase relative permeabilities are then characterized through a previously developed and tested sigmoid-based oil relative permeability model by taking into account hysteretic behavior of gas relative permeability. All field-scale simulations are performed on a simple reservoir model and are set within the MRST suite.\u0000 \u0000 \u0000 \u0000 In the case of a homogeneous reservoir, we note that reservoir simulation responses are strongly sensitive to the degree of convexity of the two-phase relative permeability curves. In the case of heterogeneous reservoir settings, results are almost similarly sensitive to porosity, characteristics of the relative permeability model, and the degree of heterogeneity of the reservoir. In the case of well-connected (randomly) heterogeneous fields, the importance of the porosity is stronger than in the heterogeneous setting lacking well connected regions.\u0000 \u0000 \u0000 \u0000 Characterization of reservoir model attributes relying on pore-scale simulation approaches in the presence of uncertainty can provide a robust term of comparison which can be integrated within a classical reservoir simulation approach relying on relative permeability data stemming from core-flooding experiments. Our results document that uncertainties in the evaluation of (i) reservoir model petrophysical attributes (porosity/permeability) and (ii) relative permeability model parameters can differently influence field-scale simulation outputs, depending","PeriodicalId":293517,"journal":{"name":"Day 3 Thu, December 03, 2020","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133830584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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