Imaging of a fluid injection process using geophysical data - A didactic example

In many subsurface industrial applications, fluids are injected into or withdrawn from a geologic formation. It is of practical interest to quantify precisely where, when, and by how much the injected fluid alters the state of the subsurface. Routine geophysical monitoring of such processes attempts to image the way that geophysical properties, such as seismic velocities or electrical conductivity, change through time and space and to then make qualitative inferences as to where the injected fluid has migrated. The more rigorous formulation of the time-lapse geophysical inverse problem forecasts how the subsurface evolves during the course of a fluid-injection application. Using time-lapse geophysical signals as the data to be matched, the model unknowns to be estimated are the multiphysics forward-modeling parameters controlling the fluid-injection process. Properly reproducing the geophysical signature of the flow process, subsequent simulations can predict the fluid migration and alteration in the subsurface. The dynamic nature of fluid-injection processes renders imaging problems more complex than conventional geophysical imaging for static targets. This work intents to clarify the related hydrogeophysical parameter estimation concepts

Stochastic Inverse Methods to Identify non-Gaussian Model Parameters in Heterogeneous Aquifers

La modelación numérica del flujo de agua subterránea y del transporte de masa se está convirtiendo en un criterio de referencia en la actualidad para la evaluación de recursos hídricos y la protección del medio ambiente. Para que las predicciones de los modelos sean fiables, estos deben de estar lo más próximo a la realidad que sea posible. Esta proximidad se adquiere con los métodos inversos, que persiguen la integración de los parámetros medidos y de los estados del sistema observados en la caracterización del acuífero. Se han propuesto varios métodos para resolver el problema inverso en las últimas décadas que se discuten en la tesis. El punto principal de esta tesis es proponer dos métodos inversos estocásticos para la estimación de los parámetros del modelo, cuando estos no se puede describir con una distribución gausiana, por ejemplo, las conductividades hidráulicas mediante la integración de observaciones del estado del sistema, que, en general, tendrán una relación no lineal con los parámetros, por ejemplo, las alturas piezométricas. El primer método es el filtro de Kalman de conjuntos con transformación normal (NS-EnKF) construido sobre la base del filtro de Kalman de conjuntos estándar (EnKF). El EnKF es muy utilizado como una técnica de asimilación de datos en tiempo real debido a sus ventajas, como son la eficiencia y la capacidad de cómputo para evaluar la incertidumbre del modelo. Sin embargo, se sabe que este filtro sólo trabaja de manera óptima cuándo los parámetros del modelo y las variables de estado siguen distribuciones multigausianas. Para ampliar la aplicación del EnKF a vectores de estado no gausianos, tales como los de los acuíferos en formaciones fluvio-deltaicas, el NSEnKF propone aplicar una transformación gausiana univariada. El vector de estado aumentado formado por los parámetros del modelo y las variables de estado se transforman en variables con una distribución marginal gausiana.Zhou ., H. (2011). Stochastic Inverse Methods to Identify non-Gaussian Model Parameters in Heterogeneous Aquifers [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/12267Palanci

Inverse Methods in Hydrogeology: Evolution and Recent Trends

[EN] Parameter identification is an essential step in constructing a groundwater model. The process of recognizing model parameter values by conditioning on observed data of the state variable is referred to as the inverse problem. A series of inverse methods has been proposed to solve the inverse problem, ranging from trial-and-error manual calibration to the current complex automatic data assimilation algorithms. This paper does not attempt to be another overview paper on inverse models, but rather to analyze and track the evolution of the inverse methods over the last decades, mostly within the realm of hydrogeology, revealing their transformation, motivation and recent trends. Issues confronted by the inverse problem, such as dealing with multiGaussianity and whether or not to preserve the prior statistics are discussed. (C) 2013 Elsevier Ltd. All rights reserved.The authors gratefully acknowledge the financial support by the Spanish Ministry of Science and Innovation through project CGL2011-23295. We would like to thank Dr. Alberto Guadagnini (Politecnico di Milano, Italy) for his comments during the reviewing process, which helped improving the final paper.Zhou, H.; Gómez-Hernández, JJ.; Li, L. (2014). Inverse Methods in Hydrogeology: Evolution and Recent Trends. Advances in Water Resources. 63:22-37. https://doi.org/10.1016/j.advwatres.2013.10.014S22376

Upscaling and Inverse Modeling of Groundwater Flow and Mass Transport in Heterogeneous Aquifers

Dividimos el trabajo en tres bloques: En el primer bloque, se han revisado las técnicas de escalado que utilizan una media simple, el método laplaciano simple, el laplaciano con piel y el escalado con mallado no uniforme y se han evaluado en un ejercicio tridimensional de escalado de la conductividad hidráulica. El campo usado como referencia es una realización condicional a escala fina de la conductividad hidráulica del experimento de macrodispersión realizado en la base de la fuerza aérea estadounidense de Columbus en Misuri (MADE en su acrónimo inglés). El objetivo de esta sección es doble, primero, comparar la efectividad de diferentes técnicas de escalado para producir modelos capaces de reproducir el comportamiento observado del movimiento del penacho de tritio, y segundo, demostrar y analizar las condiciones bajo las cuales el escalado puede proporcionar un modelo a una escala gruesa en el que el flujo y el transporte puedan predecirse con al ecuación de advección-dispersión en condiciones aparentemente no fickianas. En otros casos, se observa que la discrepancia en la predicción del transporte entre las dos escalas persiste, y la ecuación de advección-dispersión no es suficiente para explicar el transporte en la escala gruesa. Por esta razón, se ha desarrollado una metodología para el escalado del transporte en formaciones muy heterogéneas en tres dimensiones. El método propuesto se basa en un escalado de la conductividad hidráulica por el método laplaciano con piel y centrado en los interbloques, seguido de un escalado de los parámetros de transporte que requiere la inclusión de un proceso de transporte con transferencia de masa multitasa para compensar la pérdida de heterogeneidad inherente al cambio de escala. El método propuesto no sólo reproduce el flujo y el transporte en la escala gruesa, sino que reproduce también la incertidumbre asociada con las predicciones según puede observarse analizando la variabilidad del conjunto de curvas de llegada.Li ., L. (2011). Upscaling and Inverse Modeling of Groundwater Flow and Mass Transport in Heterogeneous Aquifers [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/12268Palanci

A pattern-search-based inverse method

Uncertainty in model predictions is caused to a large extent by the uncertainty in model parameters, while the identification of model parameters is demanding because of the inherent heterogeneity of the aquifer. A variety of inverse methods has been proposed for parameter identification. In this paper we present a novel inverse method to constrain the model parameters (hydraulic conductivities) to the observed state data (hydraulic heads). In the method proposed we build a conditioning pattern consisting of simulated model parameters and observed flow data. The unknown parameter values are simulated by pattern searching through an ensemble of realizations rather than optimizing an objective function. The model parameters do not necessarily follow a multi-Gaussian distribution, and the nonlinear relationship between the parameter and the response is captured by the multipoint pattern matching. The algorithm is evaluated in two synthetic bimodal aquifers. The proposed method is able to reproduce the main structure of the reference fields, and the performance of the updated model in predicting flow and transport is improved compared with that of the prior model.The authors gratefully acknowledge the financial support from the Ministry of Science and Innovation, project CGL2011-23295. The first author also acknowledges the scholarship provided by the China Scholarship Council (CSC [2007] 3020). The authors would like to thank Gregoire Mariethoz (University of New South Wales) and Philippe Renard (University of Neuchatel) for their enthusiastic help in answering questions about the direct sampling algorithm. Gregoire Mariethoz and two anonymous reviewers are also thanked for their comments during the reviewing process, which helped improving the final paper.Zhou ., H.; Gómez-Hernández, JJ.; Li ., L. (2012). A pattern-search-based inverse method. Water Resources Research. 48(3):1-17. https://doi.org/10.1029/2011WR011195S117483Alcolea, A., & Renard, P. (2010). Blocking Moving Window algorithm: Conditioning multiple-point simulations to hydrogeological data. Water Resources Research, 46(8). doi:10.1029/2009wr007943Alcolea, A., Carrera, J., & Medina, A. (2006). Pilot points method incorporating prior information for solving the groundwater flow inverse problem. Advances in Water Resources, 29(11), 1678-1689. doi:10.1016/j.advwatres.2005.12.009Arpat, G. B., & Caers, J. (2007). Conditional Simulation with Patterns. Mathematical Geology, 39(2), 177-203. doi:10.1007/s11004-006-9075-3Caers , J. 2002 Geostatistical history matching under training-image based geological model constraintsCaers, J. (2003). Efficient gradual deformation using a streamline-based proxy method. Journal of Petroleum Science and Engineering, 39(1-2), 57-83. doi:10.1016/s0920-4105(03)00040-8Caers, J., & Hoffman, T. (2006). The Probability Perturbation Method: A New Look at Bayesian Inverse Modeling. Mathematical Geology, 38(1), 81-100. doi:10.1007/s11004-005-9005-9Capilla, J. E., & Llopis-Albert, C. (2009). Gradual conditioning of non-Gaussian transmissivity fields to flow and mass transport data: 1. Theory. Journal of Hydrology, 371(1-4), 66-74. doi:10.1016/j.jhydrol.2009.03.015Carrera, J., & Neuman, S. P. (1986). Estimation of Aquifer Parameters Under Transient and Steady State Conditions: 1. Maximum Likelihood Method Incorporating Prior Information. Water Resources Research, 22(2), 199-210. doi:10.1029/wr022i002p00199Chen, Y., & Zhang, D. (2006). Data assimilation for transient flow in geologic formations via ensemble Kalman filter. Advances in Water Resources, 29(8), 1107-1122. doi:10.1016/j.advwatres.2005.09.007Christiansen, L., Binning, P. J., Rosbjerg, D., Andersen, O. B., & Bauer-Gottwein, P. (2011). Using time-lapse gravity for groundwater model calibration: An application to alluvial aquifer storage. Water Resources Research, 47(6). doi:10.1029/2010wr009859De Marsily, G., Delay, F., Gonçalvès, J., Renard, P., Teles, V., & Violette, S. (2005). Dealing with spatial heterogeneity. Hydrogeology Journal, 13(1), 161-183. doi:10.1007/s10040-004-0432-3Deutsch, C. V., & Tran, T. T. (2002). FLUVSIM: a program for object-based stochastic modeling of fluvial depositional systems. Computers & Geosciences, 28(4), 525-535. doi:10.1016/s0098-3004(01)00075-9Dubuisson, M.-P., & Jain, A. K. (s. f.). A modified Hausdorff distance for object matching. Proceedings of 12th International Conference on Pattern Recognition. doi:10.1109/icpr.1994.576361Emsellem, Y., & De Marsily, G. (1971). An Automatic Solution for the Inverse Problem. Water Resources Research, 7(5), 1264-1283. doi:10.1029/wr007i005p01264Evensen, G. (2003). The Ensemble Kalman Filter: theoretical formulation and practical implementation. Ocean Dynamics, 53(4), 343-367. doi:10.1007/s10236-003-0036-9Falivene, O., Cabello, P., Arbués, P., Muñoz, J. A., & Cabrera, L. (2009). A geostatistical algorithm to reproduce lateral gradual facies transitions: Description and implementation. Computers & Geosciences, 35(8), 1642-1651. doi:10.1016/j.cageo.2008.12.003Fernàndez-Garcia, D., Illangasekare, T. H., & Rajaram, H. (2005). Differences in the scale dependence of dispersivity and retardation factors estimated from forced-gradient and uniform flow tracer tests in three-dimensional physically and chemically heterogeneous porous media. Water Resources Research, 41(3). doi:10.1029/2004wr003125Feyen, L., & Caers, J. (2006). Quantifying geological uncertainty for flow and transport modeling in multi-modal heterogeneous formations. Advances in Water Resources, 29(6), 912-929. doi:10.1016/j.advwatres.2005.08.002Fu, J., & Gómez-Hernández, J. J. (2008). A Blocking Markov Chain Monte Carlo Method for Inverse Stochastic Hydrogeological Modeling. Mathematical Geosciences, 41(2), 105-128. doi:10.1007/s11004-008-9206-0Jaime Gómez-Hernánez, J., Sahuquillo, A., & Capilla, J. (1997). Stochastic simulation of transmissivity fields conditional to both transmissivity and piezometric data—I. Theory. Journal of Hydrology, 203(1-4), 162-174. doi:10.1016/s0022-1694(97)00098-xGuardiano, F. B., & Srivastava, R. M. (1993). Multivariate Geostatistics: Beyond Bivariate Moments. Geostatistics Tróia ’92, 133-144. doi:10.1007/978-94-011-1739-5_12Harbaugh , A. W. E. R. Banta M. C. Hill M. G. McDonald 2000 MODFLOW-2000, the U.S. Geological Survey modular ground-water model-User guide to modularization concepts and the ground-water flow process Reston, Va.Hendricks Franssen, H. J., & Kinzelbach, W. (2008). Real-time groundwater flow modeling with the Ensemble Kalman Filter: Joint estimation of states and parameters and the filter inbreeding problem. Water Resources Research, 44(9). doi:10.1029/2007wr006505Franssen, H.-J. H., Gómez-Hernández, J., & Sahuquillo, A. (2003). Coupled inverse modelling of groundwater flow and mass transport and the worth of concentration data. Journal of Hydrology, 281(4), 281-295. doi:10.1016/s0022-1694(03)00191-4Henrion, V., Caumon, G., & Cherpeau, N. (2010). ODSIM: An Object-Distance Simulation Method for Conditioning Complex Natural Structures. Mathematical Geosciences, 42(8), 911-924. doi:10.1007/s11004-010-9299-0Hoeksema, R. J., & Kitanidis, P. K. (1984). An Application of the Geostatistical Approach to the Inverse Problem in Two-Dimensional Groundwater Modeling. Water Resources Research, 20(7), 1003-1020. doi:10.1029/wr020i007p01003Hoeksema, R. J., & Kitanidis, P. K. (1985). Analysis of the Spatial Structure of Properties of Selected Aquifers. Water Resources Research, 21(4), 563-572. doi:10.1029/wr021i004p00563Hu, L. Y. (2000). 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Issues in characterizing heterogeneity and connectivity in non-multiGaussian media. Advances in Water Resources, 31(1), 147-159. doi:10.1016/j.advwatres.2007.07.002Kwa, Franz, M. O., & Scholkopf, B. (2005). Iterative kernel principal component analysis for image modeling. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(9), 1351-1366. doi:10.1109/tpami.2005.181Kitanidis, P. K. (2007). On stochastic inverse modeling. Geophysical Monograph Series, 19-30. doi:10.1029/171gm04Kitanidis, P. K., & Vomvoris, E. G. (1983). A geostatistical approach to the inverse problem in groundwater modeling (steady state) and one-dimensional simulations. Water Resources Research, 19(3), 677-690. doi:10.1029/wr019i003p00677Li, L., Zhou, H., Hendricks Franssen, H. J., & Gómez-Hernández, J. J. (2011). Groundwater flow inverse modeling in non-MultiGaussian media: performance assessment of the normal-score Ensemble Kalman Filter. Hydrology and Earth System Sciences Discussions, 8(4), 6749-6788. doi:10.5194/hessd-8-6749-2011Li, L., Zhou, H., & Gómez-Hernández, J. J. (2011). Transport upscaling using multi-rate mass transfer in three-dimensional highly heterogeneous porous media. Advances in Water Resources, 34(4), 478-489. doi:10.1016/j.advwatres.2011.01.001Li, L., Zhou, H., & Gómez-Hernández, J. J. (2011). A comparative study of three-dimensional hydraulic conductivity upscaling at the macro-dispersion experiment (MADE) site, Columbus Air Force Base, Mississippi (USA). Journal of Hydrology, 404(3-4), 278-293. doi:10.1016/j.jhydrol.2011.05.001Mariethoz, G., Renard, P., & Straubhaar, J. (2010). The Direct Sampling method to perform multiple-point geostatistical simulations. Water Resources Research, 46(11). doi:10.1029/2008wr007621Mariethoz, G., Renard, P., & Caers, J. (2010). Bayesian inverse problem and optimization with iterative spatial resampling. Water Resources Research, 46(11). doi:10.1029/2010wr009274Neuman, S. P. (1973). Calibration of distributed parameter groundwater flow models viewed as a multiple-objective decision process under uncertainty. Water Resources Research, 9(4), 1006-1021. doi:10.1029/wr009i004p01006Oliver, D. S., Cunha, L. B., & Reynolds, A. C. (1997). Markov chain Monte Carlo methods for conditioning a permeability field to pressure data. Mathematical Geology, 29(1), 61-91. doi:10.1007/bf02769620RamaRao, B. S., LaVenue, A. M., De Marsily, G., & Marietta, M. G. (1995). Pilot Point Methodology for Automated Calibration of an Ensemble of conditionally Simulated Transmissivity Fields: 1. Theory and Computational Experiments. Water Resources Research, 31(3), 475-493. doi:10.1029/94wr02258Rubin, Y., Chen, X., Murakami, H., & Hahn, M. (2010). A Bayesian approach for inverse modeling, data assimilation, and conditional simulation of spatial random fields. 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Novel sampling techniques for reservoir history matching optimisation and uncertainty quantification in flow prediction

Modern reservoir management has an increasing focus on accurately predicting the likely range of field recoveries. A variety of assisted history matching techniques has been developed across the research community concerned with this topic. These techniques are based on obtaining multiple models that closely reproduce the historical flow behaviour of a reservoir. The set of resulted history matched models is then used to quantify uncertainty in predicting the future performance of the reservoir and providing economic evaluations for different field development strategies. The key step in this workflow is to employ algorithms that sample the parameter space in an efficient but appropriate manner. The algorithm choice has an impact on how fast a model is obtained and how well the model fits the production data. The sampling techniques that have been developed to date include, among others, gradient based methods, evolutionary algorithms, and ensemble Kalman filter (EnKF). This thesis has investigated and further developed the following sampling and inference techniques: Particle Swarm Optimisation (PSO), Hamiltonian Monte Carlo, and Population Markov Chain Monte Carlo. The inspected techniques have the capability of navigating the parameter space and producing history matched models that can be used to quantify the uncertainty in the forecasts in a faster and more reliable way. The analysis of these techniques, compared with Neighbourhood Algorithm (NA), has shown how the different techniques affect the predicted recovery from petroleum systems and the benefits of the developed methods over the NA. The history matching problem is multi-objective in nature, with the production data possibly consisting of multiple types, coming from different wells, and collected at different times. Multiple objectives can be constructed from these data and explicitly be optimised in the multi-objective scheme. The thesis has extended the PSO to handle multi-objective history matching problems in which a number of possible conflicting objectives must be satisfied simultaneously. The benefits and efficiency of innovative multi-objective particle swarm scheme (MOPSO) are demonstrated for synthetic reservoirs. It is demonstrated that the MOPSO procedure can provide a substantial improvement in finding a diverse set of good fitting models with a fewer number of very costly forward simulations runs than the standard single objective case, depending on how the objectives are constructed. The thesis has also shown how to tackle a large number of unknown parameters through the coupling of high performance global optimisation algorithms, such as PSO, with model reduction techniques such as kernel principal component analysis (PCA), for parameterising spatially correlated random fields. The results of the PSO-PCA coupling applied to a recent SPE benchmark history matching problem have demonstrated that the approach is indeed applicable for practical problems. A comparison of PSO with the EnKF data assimilation method has been carried out and has concluded that both methods have obtained comparable results on the example case. This point reinforces the need for using a range of assisted history matching algorithms for more confidence in predictions

Simultaneous estimation of both geologic and reservoir state variables within an ensemble-based multiple-point statistic framework

“The final publication is available at Springer via http://dx.doi.org/10.1007/s11004-013-9504-z"The first three authors gratefully acknowledge the financial support by US Department of Energy through project DE-FE0004962. The fourth author acknowledges the financial support by Spanish Ministry of Science and Innovation through project CGL2011-23295. The authors also wish to thank the guest editors, Philippe Renard and Gregoire Mariethoz, as well as three anonymous reviewers for their comments, which substantially helped improving the final version of the manuscript.Li, L.; Srinivasan, S.; Zhou, H.; Gómez-Hernández, JJ. (2014). Simultaneous estimation of both geologic and reservoir state variables within an ensemble-based multiple-point statistic framework. Mathematical Geosciences. 46(5):597-623. https://doi.org/10.1007/s11004-013-9504-zS597623465Aanonsen S, Nævdal G, Oliver D, Reynolds A, Valles B (2009) The ensemble Kalman filter in reservoir engineering—a review. SPE J 14(3):393–412Abdollahifard MJ, Faez K (2013) Stochastic simulation of patterns using Bayesian pattern modeling. Comput Geosci 17(1):99–116Alcolea A, Renard P (2010) Blocking moving window algorithm: conditioning multiple-point simulations to hydrogeological data. Water Resour Res 46:W08511Arpat GB (2005) Sequential simulation with patterns. PhD thesis, Stanford UniversityCaers J (2002) Geostatistical history matching under training-image based geological model constraints. In: SPE annual technical conference and exhibition, SPE 77429Caers J (2003) Efficient gradual deformation using a streamline-based proxy method. J Pet Sci Eng 39(1–2):57–83Chen Y, Zhang D (2006) Data assimilation for transient flow in geologic formations via ensemble Kalman filter. Adv Water Resour 29(8):1107–1122Evensen G (2003) The ensemble Kalman filter: theoretical formulation and practical implementation. Ocean Dyn 53(4):343–367Fu J, Gómez-Hernández JJ (2009) A blocking Markov chain Monte Carlo method for inverse stochastic hydrogeological modeling. Math Geosci 41(2):105–128Gómez-Hernández JJ, Sahuquillo A, Capilla JE (1997) Stochastic simulation of transmissivity fields conditional to both transmissivity and piezometric data. 1. Theory. J Hydrol 203(1–4):162–174Gu Y, Oliver D (2006) The ensemble Kalman filter for continuous updating of reservoir simulation models. J Energy Resour Technol 128(1):79–87Guardiano F, Srivastava R (1993) Multivariate geostatistics: beyond bivariate moments. In: Soares A (ed) Geostatistics-Troia. Kluwer Academic, Dordrecht, pp 133–144Hamill T, Whitaker J, Snyder C (2001) Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter. Mon Weather Rev 129:2776–2790Harbaugh AW, Banta ER, Hill MC, McDonald MG (2000) MODFLOW-2000, the US Geological Survey modular ground-water model. US Geological Survey, Branch of Information Services, Reston, VA, Denver, COHe J, Sarma P, Durlofsky LJ (2013) Reduced-order flow modeling and geological parameterization for ensemble-based data assimilation. Comput Geosci 55:54–69Hendricks Franssen H, Kinzelbach W (2008) Real-time groundwater flow modeling with the ensemble Kalman filter: joint estimation of states and parameters and the filter inbreeding problem. Water Resour Res 44(9):W09408Hoffman BT, Caers J (2005) Regional probability perturbations for history matching. J Pet Sci Eng 46(1–2):53–71Hu LY, Chugunova T (2008) Multiple-point geostatistics for modeling subsurface heterogeneity: a comprehensive review. Water Resour Res 44(11):W11413Hu LY, Zhao Y, Liu Y, Scheepens C, Bouchard A (2012) Updating multipoint simulatings using the ensemble Kalman filter. Comput Geosci 51:7–15Huang Y, Srinivasan S (2012) Efficient conditional simulation of spatial patterns using a pattern-growth algorithm. Geostatistics Oslo 2012:209–220Jafarpour B, Khodabakhshi M (2011) A probability conditioning method (PCM) for nonlinear flow data integration into multipoint statistical facies simulation. Math Geosci 43(2):133–164Kashib T, Srinivasan S (2006) A probabilistic approach to integrating dynamic data in reservoir models. J Pet Sci Eng 50(3):241–257Li L, Zhou H, Hendricks Franssen HJ, Gómez-Hernández JJ (2012) Modeling transient groundwater flow by coupling ensemble Kalman filtering and upscaling. Water Resour Res 48(1):W01537Li L, Srinivadan S, Zhou H, Gómez-Hernández J (2013) A pilot point guided pattern matching approach to integrate dynamic data into geological model. Adv Water Resour. doi: 10.1016/j.advwatres.2013.10.008Mariethoz G, Renard P, Caers J (2010a) Bayesian inverse problem and optimization with iterative spatial resampling. Water Resour Res 46(11):W11530Mariethoz G, Renard P, Straubhaar J (2010b) The direct sampling method to perform multiple-point geostatistical simulaitons. Water Resour Res 46:W11536Meerschman E, Pirot G, Mariethoz G, Straubhaar J, Meirvenne M, Renard P (2013) A practical guide to performing multiple-point statistical simulations with the direct sampling algorithm. Comput Geosci 52:307–324Ramarao BS, LaVenue de AM, Marsily G, Marietta MG (1995) Pilot point methodology for automated calibration of an ensemble of conditionally simulated transmissivity fields. 1. Theory and computational experiments. Water Resour Res 31(3):475–493Rezaee H, Mariethoz G, Koneshloo M, Asghari O (2013) Multiple-point geostatistical simulation using the bunch-pasting direct sampling method. Comput Geosci 54:293–308Straubhaar J, Renard P, Mariethoz G, Froidevaux R, Besson O (2011) An improved parallel multiple-point algorithm using a list approach. Math Geosci 43(3):305–328Strebelle S (2002) Conditional simulation of complex geological structures using multiple-point statistics. Math Geol 34(1):1–21Sun AY, Morris AP, Mohanty S (2009) Sequential updating of multimodal hydrogeologic parameter fields using localization and clustering techniques. Water Resour Res 45(7):W07424Tahmasebi P, Hezarkhani A, Sahimi M (2012a) Multiple-point geostatistical modeling based on the cross-correlation functions. Comput Geosci 16(3):779–797Tahmasebi P, Sahimi M, Mariethoz G, Hezarkhani A (2012b) Accelerating geostatistical simulations using graphics processing units (GPU). Comput Geosci 46:51–59Wen X, Chen W (2006) Real-time reservoir model updating using ensemble Kalman filter with confirming option. SPE J 11(4):431–442Zhang T, Switzer P, Journel A (2006) Filter-based classification of training image patterns for spatial simulation. Math Geol 38(1):63–80Zhou H, Gómez-Hernández J, Hendricks Franssen H, Li L (2011) An approach to handling non-Gaussianity of parameters and state variables in ensemble Kalman filtering. Adv Water Resour 34(7):844–864Zhou H, Gómez-Hernández J, Li L (2012) A pattern-search-based inverse method. Water Resour Res 48(3):W0350

Australasian Groundwater Conference: Groundwater in a Changing World

© Copyright is retained by the author/s of each abstract.The Australasian Groundwater Conference (AGC) was held in Brisbane Queensland, 24-27 November 2019. This conference was an epic event filled with informative presentations, entertaining networking events and stunning field trips exploring the sights and sounds that this subtropical dynamic region has to offer. The AGC 2019 featured a stimulating technical program around the theme of “Groundwater in a Changing World” that covered a broad range of applications to resources, infrastructure and environment. The program included stimulating plenary speakers, engaging panel discussions and enticing social events. Over 600 groundwater researchers, industry professionals and policy development specialists from around the region attended this unique event. There were many opportunities on offer for delegates to share their experiences, inform best practice, and identify the steps they can take to bring about lasting improvements to the management of our vital groundwater resources. Our hard working volunteer organisational team wishes to thank sponsors, speakers, delegates, exhibitors and volunteers for making the conference such a huge success

Ultra-fast screening of stress-sensitive (naturally fractured) reservoirs using flow diagnostics

Quantifying the impact of poro-mechanics on reservoir performance is critical to the sustainable management of subsurface reservoirs containing either hydrocarbons, groundwater, geothermal heat, or being targeted for geological storage of fluids (e.g., CO2 or H2). On the other hand, accounting for poro-mechanical effects in full-field reservoir simulation studies and uncertainty quantification workflows in complex reservoir models is challenging, mainly because exploring and capturing the full range of geological and mechanical uncertainties requires a large number of numerical simulations and is hence computationally intensive. Specifically, the integration of poro-mechanical effects in full-field reservoir simulation studies is still limited, mainly because of the high computational cost. Consequently, poro-mechanical effects are often ignored in reservoir engineering workflows, which may result in inadequate reservoir performance forecasts. This thesis hence develops an alternative approach that couples hydrodynamics using existing flow diagnostics simulations for single- and dual-porosity models with poro mechanics to screen the impact of coupled poro-mechanical processes on reservoir performance. Due to the steady-state nature of the calculations and the effective proposed coupling strategy, these calculations remain computationally efficient while providing first-order approximations of the interplay between poro-mechanics and hydrodynamics, as we demonstrate through a series of case studies. This thesis also introduces a new uncertainty quantification workflow using the proposed poro-mechanical informed flow diagnostics and proxy models. These computationally efficient calculations allow us to quickly screen poro-mechanics and assess a broader range of geological, petrophysical, and mechanical uncertainties to rank, compare, and cluster a large ensemble of models to select representative candidates for more detailed full-physics coupled reservoir simulations.James Watt Scholarshi

INTEGRATED APPROACHES FOR SCALABLE AQUIFER NUMERICAL MODELING, INVERSION, AND UPSCALING

Ph.D