Research My research occurs at the intersection of statistical science, computer science and the geological sciences. What is the fundamental research question I want to address? I believe that from a data-scientific point of view, most geological data and modeling questions can be broadly classified as problems that are high-dimensional but have small sample size. Data are often sparse and computer experiments we run are CPU demanding, resulting in some low sample size. Yet the understanding we attempt to develop requires complex physical or geochemical models, analysis of multivariate, spatial problems over potentially large areas, require aggregation of data at various scale (in space and time) and hence are high dimensional problems. How do we formulate such problems? What are fundamental mathematical and computer science methods for analyzing such problems? How can we build predictive models for such problems? How do we integrate the various disciplines involved? Most of machine learning and statistics research currently does not take place in this setting.
In terms of machine learning, I do not prescribe to the narrow set of tools it usually encapsulates (e.g. kernel learning), but look at the wider use of the “machine” to study our type of problems, including modern fields such as computer graphics and computer vision. In understanding, modeling and forecasting in complex geological systems I believe there is a need for general methods for 1) quantifying sensitivities in such systems and their various interactions 2) learning with data acquired in the field that can be diverse in nature and different in scales of observation and 3) quantifying our lack of understanding through probabilistic models, which is essential for risk quantification & decision making.
Specific areas I am interested in:
Oil/Gas. The subsurface characterization, both depositional & structural of reservoir systems from seismic, well and production data for forecasting requires an integrated set of approaches involving both physical and statistical modeling. I work on comprehensive approaches to forecasting recovery of fluids from the subsurface.
Groundwater/hydrology. The characterization of groundwater systems evidently has many analogies with petroleum reservoirs. My research will focuses on the aquifer to basin scale. I am interested in saltwater intrusion problems, quantitative characterization of karst systems and predicting with reactive transport models.
Minerals. The exploration/ exploitation of minerals deposits will be increasing in importance considering the increasing importance of battery technologies. This would also mean that there will be an increased interest in developing geostatistical methods for the purpose of mineral potential mapping as well as ore body evaluation (economic geology). I am interested in the multi-variate & compositional nature of this problem (geochemistry) as well as scaling issues.
Prediction-Focused Subsurface Modeling: Investigating the Need for Accuracy in Flow-Based Inverse ModelingMATHEMATICAL GEOSCIENCESScheidt, C., Renard, P., Caers, J.2015; 47 (2): 173-191
MS-CCSIM: Accelerating pattern-based geostatistical simulation of categorical variables using a multi-scale search in Fourier spaceCOMPUTERS & GEOSCIENCESTahmasebi, P., Sahimi, M., Caers, J.2014; 67: 75-88
Quantifying Asymmetric Parameter Interactions in Sensitivity Analysis: Application to Reservoir ModelingMATHEMATICAL GEOSCIENCESFenwick, D., Scheidt, C., Caers, J.2014; 46 (4): 493-511
Simulation of Earth textures by conditional image quiltingWATER RESOURCES RESEARCHMahmud, K., Mariethoz, G., Caers, J., Tahmasebi, P., Baker, A.2014; 50 (4): 3088-3107
Comparing Training-Image Based Algorithms Using an Analysis of DistanceMATHEMATICAL GEOSCIENCESTan, X., Tahmasebi, P., Caers, J.2014; 46 (2): 149-169
Multiple-point geostatistics: stochastic modeling with training imagesMariethoz, G., Caers, J.Wiley-Blackwell.2014
SGEMS-UQ: An uncertainty quantification toolkit for SGEMSCOMPUTERS & GEOSCIENCESLi, L., Boucher, A., Caers, J.2014; 62: 12-24
(submitted) Uncertainty Quantification in Inverse Problems: Model-Based versus Prediction-Focused InversionMathematical GeosciencesScheidt, C., Renard, P., Caers, J.2014
Training image-based scenario modeling of fractured reservoirs for flow uncertainty quantificationCOMPUTATIONAL GEOSCIENCESJung, A., Fenwick, D. H., Caers, J.2013; 17 (6): 1015-1031
Conditioning Surface-Based Geological Models to Well and Thickness DataMATHEMATICAL GEOSCIENCESBertoncello, A., Sun, T., Li, H., Mariethoz, G., Caers, J.2013; 45 (7): 873-893
History matching and uncertainty quantification of facies models with multiple geological interpretationsCOMPUTATIONAL GEOSCIENCESPark, H., Scheidt, C., Fenwick, D., Boucher, A., Caers, J.2013; 17 (4): 609-621
Image transforms for determining fit-for-purpose complexity of geostatistical models in flow modelingCOMPUTATIONAL GEOSCIENCESAydin, O., Caers, J.2013; 17 (2): 417-429
A special issue on benchmark problems, datasets and methodologies for the computational geosciencesCOMPUTERS & GEOSCIENCESCaers, J.2013; 50: 1-3
Fast multiple point geostatistical simulation using a multi-scale approachIAMG 2013, Madrid, Sept 2-6, 2013Pejman, T., Caers, J.2013
Modeling Spatial and Structural Uncertainty in the Subsurface Computational Challenges in the Geosciences Institute for Mathematics and its Applications, The IMA Volumes in Mathematics and its ApplicationsGerritsen, M., Caers, J.2013; 156: 143-167
Simulation of Earth textures by Conditional Image QuiltingWater Resources ResearchMahmud , K., Tahmasebi, P., Mariethoz, G., Caers, J., Baker, A.2013
Comparing training-image based algorithms using an analysis of distanceMathematical GeosciencesTan, X., Tahmasebi, P., Caers, J.2013
Assessing the probability of training image-based geological scenarios using geophysical dataIAMG 2013 Hermans, T., Caers, J., Nguyen, F.2013
Possibility as a complement to probability in quantifying geological scenario uncertainty: a deep-water reservoir case studyIAMG 2013 Li, L., Caers, J.2013
Updating of uncertainty in fractured reservoirs driven by geological scenarios IAMG 2013 Jung, A., Fenwick, D., Caers, J.2013
Learning Needed Complexity in Structural Modeling Using Procrustes AnalysisIAMG 2013Aydin, O., Caers, J.2013
A distance-based generalized sensitivity analysis for energy resources modelingIAMG 2013Scheidt, C., Fenwick, D., Caers, J.2013
SGEMS-UQ: An Uncertainty Quantification Toolkit for SGEMSComputers & GeosciencesLi, L., Boucher, A., Caers, J.2013
A quantitative comparison of multiple-point algorithms using an analysis of distance methodIAMG 2013Tan, X., Tahmasebi, P., Caers, J.2013
Modeling Geological Scenario Uncertainty from Seismic Data using Pattern SimilarityIAMG 2013Jeong, C., Scheidt, C., Caers, J., Mukerji, T.2013
Use of Tank Experiment Data In Surface-based ModelingIAMG 2013Xu, S., Jung, A., Mukerji, T., Caers, J.2013
Updating uncertainty in the conceptual geological representation of fractured reservoirs using production data75th EAGE Conference & ExhibitionJung, A., Fenwick, D., Caers, J.2013
Training-image based scenario modeling of fractured reservoir for flow uncertainty quantificationComputational GeosciencesJung, A., Fenwick, D., Caers, J.2013
Probability perturbation applied to the use of groundwater flow models in HydroGeoSphere3rd International HydroGeoSphere User ConferenceHermans, T., Scheidt, C., Caers, J., Nguyen, F.2013
Direct Pattern-Based Simulation of Non-stationary Geostatistical ModelsMATHEMATICAL GEOSCIENCESHonarkhah, M., Caers, J.2012; 44 (6): 651-672
Method for Stochastic Inverse Modeling of Fault Geometry and Connectivity Using Flow DataMATHEMATICAL GEOSCIENCESCherpeau, N., Caumon, G., Caers, J., Levy, B.2012; 44 (2): 147-168
Direct non-stationary multiple-point modeling by distance-based pattern simulation9th International Geostatistics CongressHonarkhah, M., Caers, J.2012
History matching under uncertain geological scenario9th International Geostatistics CongressPark, H., Caers, J.2012
Transformation spaces for determining spatial model complexity9th International Geostatistics CongresAydin, O., Caers, J.2012
Data inversion under geological scenario uncertaintySEG Technical Program Caers, J.2012: 1-2
On internal consistency, conditioning and models of uncertainty9th International Geostatistics CongressCaers, J.2012
A Methodology for Establishing a Data Reliability Measure for Value of Spatial Information ProblemsMATHEMATICAL GEOSCIENCESTrainor-Guitton, W. J., Caers, J. K., Mukerji, T.2011; 43 (8): 929-949
A multiscale method for subsurface inverse modeling: Single-phase transient flowADVANCES IN WATER RESOURCESFu, J., Caers, J., Tchelepi, H. A.2011; 34 (8): 967-979
A multi-resolution workflow to generate high-resolution models constrained to dynamic dataCOMPUTATIONAL GEOSCIENCESScheidt, C., Caers, J., Chen, Y., Durlofsky, L. J.2011; 15 (3): 545-563
Geological modelling and history matching of multi-scale flow barriers in channelized reservoirs: methodology and applicationPETROLEUM GEOSCIENCELi, H., Caers, J.2011; 17 (1): 17-34
Modeling Uncertainty in the Earth SciencesCaers, J.Wiley-Blackwell.2011
Topological uncertainties in structural geology and assimilation of dynamic data: parametrization and samplingWater Resources ResearchCherpeau, N., Caumon, G., Caers, J., Levy, B.2011
Distance-based sampling of posterior distributions in spatial inverse problems IAMG 2011Caers, J., Park, K., Scheidt, C.2011
Integration of engineering and geological uncertainty for reservoir performance prediction using a distance-based approachAAPG Memoir on Modeling Geological UncertaintyCaers, J., Scheidt, C.2011: 191–202.
Assessing the impact of fault connectivity uncertainty in reservoir studies using explicit discretizationSPE Reservoir Characterisation and Simulation Conference and ExhibitionCherpeau, N., Caumon, G., Caers, J., Lévy, B.2011
Bayesian inverse problem and optimization with iterative spatial resamplingWATER RESOURCES RESEARCHMariethoz, G., Renard, P., Caers, J.2010; 46
A flow-based pattern recognition algorithm for rapid quantification of geologic uncertaintyCOMPUTATIONAL GEOSCIENCESAlpak, F. O., Barton, M. D., Caers, J.2010; 14 (4): 603-621
Stochastic Simulation of Patterns Using Distance-Based Pattern ModelingHonarkhah, M., Caers, J.SPRINGER HEIDELBERG.2010: 487-517
Special Issue on Computational Methods for the Earth, Energy and Environment-IAMG 2009MATHEMATICAL GEOSCIENCESCaers, J.2010; 42 (5): 453-455
A multiscale adjoint method to compute sensitivity coefficients for flow in heterogeneous porous mediaADVANCES IN WATER RESOURCESFu, J., Tchelepi, H. A., Caers, J.2010; 33 (6): 698-709
Combining geologic-process models and geostatistics for conditional simulation of 3-D subsurface heterogeneityWATER RESOURCES RESEARCHMichael, H. A., Li, H., Boucher, A., Sun, T., Caers, J., Gorelick, S. M.2010; 46
Bootstrap confidence intervals for reservoir model selection techniquesCOMPUTATIONAL GEOSCIENCESScheidt, C., Caers, J.2010; 14 (2): 369-382
Sampling Multiple Non-Gaussian Model Realizations Constrained to Static and Highly Nonlinear Dynamic Data Using distance-based Techniques IAMG 2010 Annual ConferencePark, K., Caers, J.2010
Value of Information Methodology for Dynamic, Spatial Earth ProblemsWater Resources ResearchTrainor-Guitton, W. J., Caers, J. K., Mukerji, T., Knight, R.2010
Modeling Uncertainty of Complex Earth Systems in Metric SpaceHandbook of GeomathematicsCaers, J., Scheidt, C., Park, K.Springer.2010: 865-889
Uncertainty Quantification in Reservoir Performance Using Distances and Kernel Methods-Application to a West Africa Deepwater Turbidite ReservoirSPE JOURNALScheidt, C., Caers, J.2009; 14 (4): 680-692
Representing Spatial Uncertainty Using Distances and KernelsMATHEMATICAL GEOSCIENCESScheidt, C., Caers, J.2009; 41 (4): 397-419
Incorporating 4D seismic data into reservoir models while honoring production and geologic dataThe Leading EdgeCastro, S., Otterlei, C., Meisinget, H., Hoye, T., Gomel, P., Zachariassen, E., Caers, J.2009; 28: 1498-1505
Solving spatial inverse problems using the probability perturbation method: An S-GEMS implementationCOMPUTERS & GEOSCIENCESLi, T., Caers, J.2008; 34 (9): 1127-1141
Identifying discrete geologic structures that produce anomalous hydraulic response: An inverse modeling approachWATER RESOURCES RESEARCHRonayne, M. J., Gorelick, S. M., Caers, J.2008; 44 (8)
A distance-based prior model parameterization for constraining solutions of spatial inverse problemsMATHEMATICAL GEOSCIENCESSuzuki, S., Caers, J.2008; 40 (4): 445-469
Dynamic data integration for structural modeling: model screening approach using a distance-based model parameterizationCOMPUTATIONAL GEOSCIENCESSuzuki, S., Caumon, G., Caers, J.2008; 12 (1): 105-119
Distance-based Representation of Reservoir Uncertainty: the Metric EnKFProceedings of the 11th European Conference on the Mathematics of Oil Recover (ECMOR XI)Caers, J., Park, K.2008: 8p.
Conditioning facies simulations with connectivity data8th International Geostatistical Congress, Santiago, Chile, Dec. 1-5, 2008Renard, P. H., Caers, J.2008
Ensemble Kalman Filtering in Distance-based Kernel SpaceEnKF WorkshopPark, K., Schiedt , C., Caers, J.2008
Assessing the Value of Information of Geophysical Data for Groundwater ManagementAGU Fall Meeting Trainor, W., Caers, J., Mukerji, T., Auken, E., Knight, R.2008
Simultaneous Conditioning of Multiple Non-Gaussian Geostatistical Models to Highly Nonlinear Data Using Distances in Kernel Space8th International Geostatistical CongressPark, K., Schiedt, C., Caers, J.2008
Streamline Assisted History Matching of Naturally Fractured Reservoirs Using the Probability Perturbation Method8th International Geostatistical CongressFadaei, S., Thiele, M., Caers, J.2008
Distance-based random field models and their applications8th International Geostatistical CongressCaers, J.2008
Comparison of Probabilistic and Forward Modeling Workflow Approaches for Integrating 4D Seismic into Reservoir Models: Application to a North Sea Reservoir70th EAGE Conference & Exhibition Castro, S., Caers, J., Meisingset, H., Høye, T., Gomel, P., Zachariassen, E.2008
Hybridization of the probability perturbation method with gradient informationCOMPUTATIONAL GEOSCIENCESJohansen, K., Caers, J., Suzuki, S.2007; 11 (4): 319-331
History matching by jointly perturbing local facies proportions and their spatial distribution: Application to a North Sea reservoirJOURNAL OF PETROLEUM SCIENCE AND ENGINEERINGHoffman, B. T., Caers, J.2007; 57 (3-4): 257-272
History matching of naturally fractured reservoirs using elastic stress simulation and probability perturbation methodSuzuki, S., Daly, C., Caers, J., Mueller, D.SOC PETROLEUM ENG.2007: 118-129
Conditional simulation with patternsMATHEMATICAL GEOLOGYArpat, G. B., Caers, J.2007; 39 (2): 177-203
Comparing the gradual deformation with the probability perturbation method for solving inverse problemsMATHEMATICAL GEOLOGYCaers, J.2007; 39 (1): 27-52
Hierarchical modeling of multi-scale flow barriers in channelized reservoirsPROCEEDINGS OF THE IAMG '07: GEOMATHEMATICS AND GIS ANALYSIS OF RESOURCES, ENVIRONMENT AND HAZARDSLi, H., Caers, J.2007: 381-385
Solving spatial inverse problems using the probability perturbation method: an S-GEMS implementationPROCEEDINGS OF THE IAMG '07: GEOMATHEMATICS AND GIS ANALYSIS OF RESOURCES, ENVIRONMENT AND HAZARDSLi, T., Caers, J.2007: 727-729
A geostatistical approach to integrating data from multiple and diverse sources: An application to the integration of well data, geological information, 3d/4d geophysical and reservoir-dynamics data in a north-sea reservoirSubsurface Hydrology: Data Integration for Properties and ProcessesCaers, J., Castro, S.2007; 171: 61-71
Hybridization of the probability perturbation method with gradient informationEAGE Petroleum Geostatistics conferenceJohansen, K., Caers, J.2007
Modeling, Upscaling and History Matching Thin, Irregularly-Shaped Flow Barriers; A Comprehensive Approach for Predicting Reservoir Connectivity26th Annual GCSSEPM Foundation MeetingStright, L., Caers, J., Li, H., Van der Vlugt, F., Pirmez, C., Barton, M.2007
History matching in low-dimensional connectivity vector spaceEAGE Petroleum Geostatistics conferencePark , K., Caers, J.2007
Multiple-Point Geostatistics and Near-Surface Geophysics for Modeling Heterogeneity in a Coastal AquiferAGU Fall Meeting SupplementTrainor, W. J., Knight, R. J., Caers, J. K.2007
A Workflow for Modeling Multi-scale Flow Barriers in Deep Water Turbidite ReservoirsAAPG Annual meetingHongmei, L., Caers, J.2007
Hierarchic Modeling and History Matching of Multiscale Flow Barriers in Channelized ReservoirsSPE Annual Technical Conference and ExhibitionLi, H., Caers, J.2007
History matching of reservoir structure subject to prior geological and geophysical constraints EAGE Petroleum Geostatistics ConferenceSuzuki, S., Carmon, G., Caers, J.2007
A practical data-integration approach to history matching: Application to a deepwater reservoirHoffman, B. T., Caers, J. K., Wen, X., Strebelle, S.SOC PETROLEUM ENG.2006: 464-479
Quantifying geological uncertainty for flow and transport modeling in multi-modal heterogeneous formationsADVANCES IN WATER RESOURCESFeyen, L., Caers, J.2006; 29 (6): 912-929
Coupled Geological Modeling and History Matching of Fine-Scale Curvilinear Flow BarriersEAGE 10th European Conference on the Mathematics of Oil RecoveryStright, L., Caers, J., Li, H., Van der Vlugt, F., Pirmez, C., , C., Barton, M., M.2006
Improved modeling of 4D seismic response using flow-based downscaling of coarse grid saturationsECMOR X Castro, S., Caers, J., Durlofsky, L.2006
A probabilistic approach to integration of well log, geological information, 3D/4D seismic and production dataECMOR X Castro, S., Caers, J.2006
A Probabilistic Integration of Well Log, Geological Information, 3D/4D Seismic, and Production Data: Application to the Oseberg FieldSPE Annual MeetingCastro, S., Caers, J., Otterlei, C., Høye, T., Andersen, T., Gomel, P.2006
Probabilistic integration of geological information, seismic and production dataThe Leading EdgeCaers, J., Hoffman, B. T., Strebelle, S., Wen, X.2006; 25: 240-244
History Matching with an Uncertain Geological ScenarioSPE Annual Technical Conference and ExhibitionSuzuki, S., Caers, J.2006
The probability perturbation method: a new look at Bayesian inverse modelingMathematical GeologyCaers, J., Hoffman, T.2006; 38: 81-100
Preserving Fine-Scale, Irregularly-Shaped Geological Features in Reservoir Flow Models Using Edge PropertiesAmerican Association of Petroleum Geologists Annual Convention Stright, L., Caers, J.2006
Discrete Space Optimization Method for History Matching under Uncertain Geological Scenario10th European Conference on the Mathematics of Oil Recovery (ECMOR X)Suzuki, S., Caers, J.2006
A parallel, multiscale approach to reservoir modelingCOMPUTATIONAL GEOSCIENCESTureyen, O. I., Caers, J.2005; 9 (2-3): 75-98
Regional probability perturbations for history matchingJOURNAL OF PETROLEUM SCIENCE AND ENGINEERINGHoffman, B. T., Caers, J.2005; 46 (1-2): 53-71
A direct sequential simulation approach to streamline-based history matchingGEOSTATISTICS BANFF 2004, VOLS 1 AND 2Caers, J., Gross, H., Kovscek, A. R.2005; 14: 1077-1086
A combined geostatistical and source model to predict super-permeability from flowmeter data: application to the Ghawar fieldQuantitative Geology and Geostatistics Volume Voelker, J., Caers, J. A.2005; 14: 591-600
A new multiple-grid method for multiple-scale stochastic Simulation with PatternsGIS AND SPATIAL ANALYSIS, VOL 1AND 2Li, H. M., Arpat, B. G., Caers, J.2005: 633-638
History matching under geological control: Application to a North Sea reservoirGEOSTATISTICS BANFF 2004, VOLS 1 AND 2Hoffman, B. T., Caers, J.2005; 14: 1067-1076
Data conditioning with the probability perturbation methodQuantitative Geology and GeostatisticsArpat, B. G., Caers, J. A.edited by Leuangthong, O., Deutsch, C.Springer, Dordrecht.2005: 255-264
Petroleum GeostatisticsCaers, J.Society of Petroleum Engineers.2005
History Matching of Naturally Fractured Reservoirs Using Elastic Stress Simulation and Probability Perturbation MethodSPE ATCE Dallas, TXSuzuki, S., Daly, C., Mueller, D., Caers, J.2005
Reconciling Prior Geologic Information With Production Data Using Streamlines: Application to a Giant Middle-Eastern Oil FieldSPE ATCEFenwick, D., Thiele, M., Agil, M., Hussain, A., Humam, F., Caers, J.2005
A new multiple-grid method for multiple-scale stochastic simulation with patterns2005 Annual conference of the International Association for Mathematical GeologyHongmei, L., Arpat, B. G., Caers, J.2005
Geologically Consistent History Matching of a Deepwater Turbidite ReservoirSPE ATCEHoffman, T. B., Strebelle, S., Wen, X., Caers, J.2005
Flow-based downscaling of saturations for modeling 4D seismic data75th SEG meetingCastro, S., Caers, J.2005
A multiple-scale, pattern-based approach to sequential simulationGEOSTATISTICS BANFF 2004, VOLS 1 AND 2Arpat, G. B., Caers, J.2005; 14: 255-264
Multiple-point geostatistics: a powerful tool to improve groundwater flow and transport predictions in multi-modal formationsGEOSTATISTICS FOR ENVIRONMENTAL APPLICATIONS, PROCEEDINGSFeyen, L., Caers, J.2005: 197-207
Automatic geobody detection from seismic data using minimum message length clusteringCOMPUTERS & GEOSCIENCESXu, Y., Caers, J., Arroyo-Garcia, C.2004; 30 (7): 741-751
History Matching with the Regional Probability Perturbation Method in Applications to a North Sea ReservoirECMOR IXHoffman, B. T., Caers, J.2004
Geostatistical history matching using the regional probability perturbation methodSociety of Petroleum Engineers Annual Conference and Technical ExhibitionHoffman, B. T., Caers, J.2004
Streamline-Based History Matching Using Geostatistical Constraints: Application to a Giant, Mature Carbonate ReservoirSPE ATCEGross, H., Thiele, M. R., Alexa, M., Caers, J. K., Kovscek, A. R.2004
The probability perturbation method: an alternative to traditional Bayesian approaches for solving inverse problemsECMOR IXCaers, J.2004
Assessment of Global Uncertainty for Early Appraisal of Hydrocarbon FieldsSociety of Petroleum Engineers ATCECaumon, G., Strebelle, S. B., Caers, J. K., Journel, A. G.2004
Reservoir Characterization Using Multiple-Scale Geological PatternsECMOR IXArpat, B. G., Caers, J.2004
Stochastic estimation of facies using ground penetrating radar dataMoysey, S., Caers, J., Knight, R., Allen-King, R. M.SPRINGER.2003: 306-318
Modeling of a deepwater turbidite reservoir conditional to seismic data using principal component analysis and multiple-point geostatisticsStrebelle, S., Payrazyan, K., Caers, J.SOC PETROLEUM ENG.2003: 227-235
History matching under training-image-based geological model constraintsSPE JOURNALCaers, J.2003; 8 (3): 218-226
Efficient gradual deformation using a streamline-based proxy methodCaers, J.ELSEVIER SCIENCE BV.2003: 57-83
A method for static-based upgriddingECMOR VII, European Conference on Mathematics of Oil RecoveryYounis, R., Caers, J.2003
Feature-based probabilistic interpretation of geobodies from seismicStochastic Modeling IICaers, J., Arpat, G. B., Arroyo-Garcia, C., Coburn, C. T.American Association of Petroleum Geologist.2003
From pattern recognition to pattern reproduction Developments in Petroleum ScienceCaers, J.Elsevier.2003: 97-115
Combining geological information with seismic and production dataDevelopments in Petroleum ScienceCaers,, J., Srinivasan, S.Elsevier.2003: 499-525
Sequential Simulation under local non-linear constraints: Application to history matchingAnnual conference of the Internation Association for Mathematical GeologyHoffman, B. T., Caers, J.2003
A geostatistical method for characterizing superpermeability from flowmeter data: Application to the Ghawar fieldSociety of Petroleum Engineers Annual Conference and Technical ExhibitionVoelker, J. J., Liu, J., Caers, J.2003
Stochastic integration of seismic and geological scenarios: a submarine channgel sagaThe Leading EdgeCaers, J., Strebelle, S., Payrazyan, K.2003: 192-196
A two level optimization method for integrating production data on non-uniform gridsSPE Annual Conference and Technical ExhibitionTureyen, O. I., Caers, J.2003
History matching under a training image-based geological model constraintsSPE JournalCaers, J.2003: 218-226
The construction of stochastic facies-based models conditioned to ground penetrating radar imagesCALIBRATION AND RELIABILITY IN GROUNDWATER MODELLING: A FEW STEPS CLOSER TO REALITYMoysey, S., Knight, R., Allen-King, R. M., Caers, J.2003: 395-401
G(S)TL: the geostatistical template library in C++COMPUTERS & GEOSCIENCESRemy, N., Shtuka, A., Levy, B., Caers, J.2002; 28 (8): 971-979
A geostatistical approach to streamline-based history matchingSPE JOURNALCaers, J., Krishnan, S., Wang, Y. D., Kovscek, A. R.2002; 7 (3): 250-266
Integrating rock physics, seismic amplitudes, and geological modelsJOURNAL OF PETROLEUM TECHNOLOGYCaers, J., Avseth, P., Mukerji, T.2002; 54 (6): 43-43
Modeling conditional distributions of facies from seismic using neural netsMATHEMATICAL GEOLOGYCaers, J., Ma, X. L.2002; 34 (2): 143-167
Geostatistical history matching under a training image-based geological model constraints SPE Annual Conference and Technical ExhibitionCaers, J.2002
A geostatical approach to history matching flow and pressure data on non-uniform gridsECMOR VIII, European Conference on Mathematics of Oil RecoveryTureyen, I., Caers, J.2002
Feature-based geostatistics: an application to a submarine channel reservoirSPE Annual Conference and Technical ExhibitionAPR, B., Caers, J., Strebelle, S.2002
Modeling of a deepwater turbidite reservoir conditional to seismic data using multiple-point geostatisticsSPE Annual Technical Conference and ExhibitionStrebelle, S., Payrazyan, K., Caers, J.2002
Geostatistical reservoir modelling using statistical pattern recognitionJOURNAL OF PETROLEUM SCIENCE AND ENGINEERINGCaers, J.2001; 29 (3-4): 177-188
Automatic histogram and variogram reproduction in simulated annealing simulationMATHEMATICAL GEOLOGYCaers, J.2001; 33 (2): 167-190
Geostatistical integration of rock physics, seismic amplitudes and geological models in North-Sea turbidite systemsThe Leading EdgeCaers, J., Avseth, P., Mukerji, T.2001; 20: 308-312
GsTL: a geostatistical template library in C++Proceedings of the IAMG Annual Conference of the International Association for Mathematical GeologyRemy, N., Shtuka, A., Levy, B., Caers, J.2001: 971-979
Data integration with multiple-point geostatisticsThird IMA Conference on Modeling Permeable RocksStrebelle, S., Journel, A. G., Caers, J.2001
A fast Markov chain Monte Carlo simulation method for conditioning reservoir models to dynamic data7th European Conference on Mathematics of Oil Recovery, EAGECaers, J., Srinivasan, S.2001
Feature-based calibration of an automated seismic interpretation tool from human expert knowledgeAnnual Meeting, Stanford Center for Reservoir ForecastingArpat, G. B., Caers, J.2001
Calibrating an automated seismic interpretation tools from human expert knowledgeThird IMA Conference on Modeling Permeable RocksCaers, J., Haas, A.2001
Characterization of West-Africa Submarine channel reservoirs: a neural network-based approach to integration of seismic dataSPE Annual Conference and Technical ExhibitionArpat, B. G., Caers, J., Haas, A.2001
Geostatistical quantification of geological information for a fluvial-type North Sea reservoirCaers, J. K., Srinivasan, S., Journel, A. G.SOC PETROLEUM ENG.2000: 457-467
Adding local accuracy to direct sequential simulationMATHEMATICAL GEOLOGYCaers, J.2000; 32 (7): 815-850
Geostatistical modeling of an offshore diamont deposit6th International Geostatistics CongressCaers, J., Rombouts, L.2000
Statistics for modeling heavy tailed distributions in geology: Part II. ApplicationsMATHEMATICAL GEOLOGYCaers, J., Beirlant, J., Maes, M. A.1999; 31 (4): 411-434
Statistics for modeling heavy tailed distributions in geology: Part I. MethodologyMATHEMATICAL GEOLOGYCaers, J., Beirlant, J., Maes, M. A.1999; 31 (4): 391-410
Conditioning reservoir models to dynamic data - A forward modeling perspectiveSPE Annual Conference and Technical ExhibitionSrinivasan, S., Caers, J.1999
Statistics for Modelling Heavy Tailed Distributions in Geology, Part II: ApplicationsMathematical GeologyCaers, J., Beirlant, J., Maes, M. A.1999; 31: 411-434
Geostatistical modeling of offshore diamond deposits6th International Geostatistics CongressCaers, J.1999
Statistics for Modelling Heavy Tailed Distributions in Geology, Part I: MethodologyMathematical GeologyCaers, J., Beirlant, J., Maes, M. A.1999; 31: 390-410
Nonparametric tail estimation using a double bootstrap methodCOMPUTATIONAL STATISTICS & DATA ANALYSISCaers, J., Van Dyck, J.1998; 29 (2): 191-211
Bootstrap confidence intervals for tail indicesCOMPUTATIONAL STATISTICS & DATA ANALYSISCaers, J., Beirlant, J., Vynckier, P.1998; 26 (3): 259-277
Identifying tails, bounds and end-points of random variablesSTRUCTURAL SAFETYCaers, J., Maes, M. A.1998; 20 (1): 1-23
A Neural Network Approach to Stochastic SimulationGOCAD ENSG Conference on 3D Modelling of Natural ObjectsCaers, J., Journel, A. G.1998
Global Valuation of Primary Diamond Deposits27th Symposium on the Application of Computer Methods and Operations Research in the Mineral IndustryCaers, J., Maes, M. A.1998
Stochastic Reservoir Simulation Using Neural Networks Trained on Outcrop DataSPE Technical Exhibition and Annual ConferenceCaers, J., Journel, A. G.1998: 321-337
Tail Estimation of Bounded Random VariablesIFIP Conference on Optimization and Reliability of Structural SystemsMaes, M. A., Caers, J.1998
Assessing the Quality of DiamondsMineral Resources EngineeringCaers, J., Vervoort, A.1997; 5: 155-177
Petrography and X-ray computerized tomography applied as an integral part of a rock mechanics investigation of discontinuitiesTRANSACTIONS OF THE INSTITUTION OF MINING AND METALLURGY SECTION B-APPLIED EARTH SCIENCECaers, J., Swennen, R., Vervoort, A.1997; 106: B38-B45
Non-conditional and conditional simulation of a spatial point processGEOSTATISTICS WOLLONGONG '96, VOLS 1 AND 2Caers, J., Gelders, J., Vervoort, A., Rombouts, L.1997; 8 (1-2): 270-281
Valuation of primary diamond deposits by extreme value statisticsECONOMIC GEOLOGY AND THE BULLETIN OF THE SOCIETY OF ECONOMIC GEOLOGISTSCaers, J., Rombouts, L.1996; 91 (5): 841-854
Extreme value analysis of diamond-size distributionsMATHEMATICAL GEOLOGYCaers, J., Vynckier, P., Beirlant, J., Rombouts, L.1996; 28 (1): 25-43
A numerical maximum likelihood method for estimating the mean of a compound lognormal distribution26TH PROCEEDINGS OF THE APPLICATIONS OF COMPUTERS AND OPERATIONS RESEARCH IN THE MINERAL INDUSTRYCaers, J., Vervoort, A.1996: 27-32