Publications and outreach – DIGIRES

• Paper by: Popov, A.A., Sandu, A., Nino-Ruiz, E.D., Evensen, G. (2023)

  A Stochastic Covariance Shrinkage Approach in Ensemble Transform Kalman Filtering

  Tellus A: Dynamic Meteorology and Oceanography

• Paper by: Luo, X. , Cruz, W.C. (2022)

  Data assimilation with soft constraints (DASC) through a generalized iterative ensemble smoother

  Computational Geosciences

• Monograph by: Evensen, G. , Vossepoel, F.C., van Leeuwen, P.J. (2022)

  Data Assimilation Fundamentals: A Unified Formulation of the State and Parameter Estimation Problem

  Springer

• Lecture by: Chalub Cruz, W., Luo, X. , Petvipusit, K.R. (2022)

  Joint History Matching of Production, Tracer, and 4D Seismic Data in a 3D Field-Scale Case Study

  SPE Norway Subsurface Conference

• Lecture by: Luo, X. (2021)

  An ensemble data assimilation workflow for subsurface characterization

  Talk Series in Computer Science and Applications - AML-CS

• Lecture by: Luo, X. , Lorentzen, R.J. , Bhakta, T. (2021)

  Accounting for Model Errors of Rock Physics Models in 4D Seismic History Matching Problems: A Perspective of Machine Learning

  Mechanistic Machine Learning and Digital Twins for Computational Science, Engineering & Technology

• Lecture by: Chang, Y., Evensen, G. (2021)

  The DIGIRES workflow for ensemble-based decision making

  IOR Centre’s Workshop on Production optimization, value of information and decision-making

• Lecture by: Tadjer, M.A.A., Alyaev, S. , Miner, D., Kuvaev, I., Bratvold, R.B. (2021)

  Unlocking the Human Factor: Geosteering Decision Making as a Component of Drilling Operational Efficacy

  The Unconventional Resources Technology Conference

• Paper by: Luo, X. (2021)

  Novel iterative ensemble smoothers derived from a class of generalized cost functions

  Computational Geosciences

• Conf-paper by: Tadjer, M.A.A., Alyaev, S. , Miner, D., Kuvaev, I., Bratvold, R.B. (2021)

  Unlocking the human factor : Geosteering decision making as a component of drilling operational efficacy

  Society of Petroleum Engineers

• Paper by: Evensen, G. (2021)

  Formulating the history matching problem with consistent error statistics

  Computational Geosciences

• Paper by: Stordal, A.S. , Moraes, R.J., Raanes, P.N. , Evensen, G. (2021)

  p-Kernel Stein Variational Gradient Descent for Data Assimilation and History Matching

  Mathematical Geosciences

• Lecture by: Chang, Y., Evensen, G. (2021)

  Demonstration of the Digires ensemble-based reservoir management workflow

  DIGIRES Steering Committee Project Meeting

• Lecture by: Evensen, G. (2021)

  An Iterative Ensemble-Smoother Solution of the HM Problem Formulated with Consistent Error Statistics

  SIAM GS

• Lecture by: Raanes, P.N. (2021)

  DAPPER: Data Assimilation with Python: a Package for Experimental Research

  EnKF workshop

• Lecture by: Luo, X. (2021)

  Novel ensemble data assimilation algorithms derived from a class of generalized cost functions

  International EnKF workshop 2021

• Lecture by: Evensen, G. (2021)

  Introducing ensemble methods for reservoir management

  IOR Norway 2021

• Paper by: Neto, G.M.S., Soares, R., Evensen, G. , Davolio, A., Schiozer, D.J. (2021)

  Subspace Ensemble Randomized Maximum Likelihood with Local Analysis for Time-Lapse-Seismic-Data Assimilation

  SPE Journal

• Lecture by: Evensen, G. (2021)

  A subspace iterative ensemble smoother for solving DA and inverse problems

  ISDA-online

• Lecture by: Evensen, G. (2021)

  Efficient Subspace Implementation of an Iterative Ensemble Smoother for Solving Inverse Problems

  SIAM CSE

• Paper by: Soares, R., Luo, X. , Evensen, G. , Bhakta, T. (2020)

  Handling Big Models and Big Data Sets in History-Matching Problems through an Adaptive Local Analysis Scheme

  SPE Journal

• Lecture by: Evensen, G. (2020)

  Ensemble Kalman Filter for Increased Oil Recovery

  • Oljedirektoratets IOR-pris 2020 webinar
  • Seminar om metoder for studier av forskningseffekter
• Lecture by: Evensen, G. (2020)

  An international initiative of predicting the SARS-Cov-2 pandemic using ensemble data assimilation

  Second International Workshop on Data Assimilation for Decision Making, Colombia

• Lecture by: Luo, X. (2020)

  Novel Ensemble Data Assimilation Algorithms Derived from A Class of Generalized Cost Functions

  ECMOR XVII

• Lecture by: Evensen, G. (2020)

  Consistent Formulation and Error Statistics for Reservoir History Matching

  ECMOR XVII

• Paper by: Soares, R., Luo, X. , Evensen, G. , Bhakta, T. (2020)

  4D seismic history matching: Assessing the use of a dictionary learning based sparse representation method

  Journal of Petroleum Science and Engineering

• Conf-paper by: Luo, X. (2020)

  Novel Ensemble Data Assimilation Algorithms Derived from A Class of Generalized Cost Functions

  European Association of Geoscientists and Engineers (EAGE)

• Conf-paper by: Luo, X. , Lorentzen, R.J. , Bhakta, T. (2020)

  Accounting for Model Errors of Rock Physics Models in 4D Seismic History Matching Problems: A Perspective of Machine Learning

  Society of Petroleum Engineers

• Lecture by: Evensen, G. (2020)

  Implementering og bruk av Ensemble Kalman Filter for bedre reservoarforståelse og økt utvinning

  Offshore Stragegikonferansen

• Lecture by: Alyaev, S. , Daireaux, B., Suter, E.C., Hong, A., Bratvold, R.B., Luo, X. , Fossum, K. (2019)

  A Geosteeering Decision Support System that Balances Recovery and Drilling Risks

  Formation Evaluation and Geosteering Workshop

• Lecture by: Luo, X. , Lorentzen, R.J. , Bhakta, T. (2019)

  An ensemble-based kernel learning approach to account for model errors of rock physics models in 4D seismic history matching: a real field case study

  NORA (The Norwegian Artificial Intelligence Research Consortium) meeting at NORCE

• Paper by: Evensen, G. , Raanes, P.N. , Stordal, A.S. , Hove, J. (2019)

  Efficient Implementation of an Iterative Ensemble Smoother for Data Assimilation and Reservoir History Matching

  Frontiers in Applied Mathematics and Statistics

• Lecture by: Luo, X. , Lorentzen, R.J. , Bhakta, T. (2019)

  An ensemble-based kernel learning approach to account for model errors of rock physics models in 4D seismic history matching: A real field case study

  FORCE symposium: Applied Machine Learning and Advanced Analytics with Oil and Gas Data

• Lecture by: Evensen, G. (2019)

  DIGIRES Ensemble-based decision making for reservoir engineering

  Data Assimilation for Decision making Universidad del Norte - Computer Science Department

• Lecture by: Luo, X. (2019)

  Ensemble-based kernel learning for a class of data assimilation problems with imperfect forward simulators

  Department Colloquium, Department of Mathematics, University of Bergen

• Poster by: Luo, X. , Lorentzen, R.J. , Bhakta, T. (2019)

  Ensemble-based kernel learning for a class of data assimilation problems with imperfect forward simulators

  Petroleum Geostatistics 2019

• Lecture by: Stordal, A.S. , Moraes, R., Raanes, P.N. , Evensen, G. (2019)

  Stein Variational Gradient Descent with Application to Data Assimilation

  Petroleum Geostatistics

• Lecture by: Raanes, P.N. , Stordal, A.S. , Evensen, G. (2019)

  Revising the Method of Ensemble Randomized Maximum Likelihood

  Petroleum Geostatistics 2019

• Lecture by: Evensen, G. (2019)

  Implementation of an iterative ensemble smoother for big-data assimilation and reservoir history matching

  Invited presentation University of Potsdam

• Lecture by: Evensen, G. (2019)

  Course on Data Assimilation

  SUMMER SCHOOL ON DATA ASSIMILATION AND ITS APPLICATIONS IN OCEANOGRAPHY, HYDROLOGY, RISK & SAFETY AND RESERVOIR ENGINEERING

• Lecture by: Jahani, N., Suter, E.C., Daireaux, B., Bratvold, R.B., Hong, A., Luo, X. , Fossum, K. , Alyaev, S. (2019)

  Realtime multi-objective optimization of well trajectory under geological uncertainty

  ICIAM 2019 Congress

• Paper by: Raanes, P.N. , Bocquet, M., Carrassi, A. (2019)

  Adaptive covariance inflation in the ensemble Kalman filter by Gaussian scale mixtures

  Quarterly Journal of the Royal Meteorological Society

• Paper by: Luo, X. (2019)

  Ensemble-based kernel learning for a class of data assimilation problems with imperfect forward simulators

  PLOS ONE

• Paper by: Luo, X. , Bhakta, T. (2019)

  Automatic and adaptive localization for ensemble-based history matching

  Journal of Petroleum Science and Engineering

• Paper by: Alyaev, S. , Suter, E.C., Bratvold, R.B., Hong, A., Luo, X. , Fossum, K. (2019)

  A decision support system for multi-target geosteering

  Journal of Petroleum Science and Engineering

• Conf-paper by: Luo, X. , Lorentzen, R.J. , Bhakta, T. (2019)

  Ensemble-based Kernel Learning to Handle Rock-physics-model Imperfection in Seismic History Matching: A Real Field Case Study

  European Association of Geoscientists and Engineers (EAGE)

• Paper by: Raanes, P.N. , Stordal, A.S. , Evensen, G. (2019)

  Revising the stochastic iterative ensemble smoother

  Nonlinear processes in geophysics

• Paper by: Evensen, G. (2019)

  Accounting for model errors in iterative ensemble smoothers

  Computational Geosciences

• Lecture by: Luo, X. (2019)

  Ensemble-based kernel learning for a class of data assimilation problems with imperfect forward simulators

  The 14th International Enkf Workshop

• Poster by: Soares, R.V., Luo, X. , Evensen, G. (2019)

  Use of K-SVD Algorithm to Sparsely Represent 4D Seismic Data

  14th International EnKF Workshop

• Lecture by: Raanes, P.N. (2019)

  DAPPER -- a brief overview

  modRSW workshop

• Lecture by: Evensen, G. (2019)

  Formulation of iterative ensemble smoothers

  Workshop on Data Assimilation: Methodology and Applications

• Lecture by: Soares, R.V., Luo, X. , Evensen, G. (2019)

  Sparse Representation of 4D Seismic Signal Based on Dictionary Learning

  SPE Norway One Day

• Lecture by: Luo, X. (2019)

  An ensemble based learning framework for history matching with imperfect forward simulators

  The 11th annual meeting of International Society for Porous Media (InterPore 2019).

• Lecture by: Evensen, G. (2019)

  HM theory course - 1st round

  Internal workshop in Equinor

• Lecture by: Raanes, P.N. (2019)

  EnKF -- FAQ

  • Visiting seminar, ENSEEIHT/IRIT, Toulouse
  • modRSW workshop, University of Leeds
  • EnKF workshop, Bergen

  The following questions were treated

  • Why 1/(N-1)?
  • About nonlinearity
    • How does it cause sampling error?
    • How does it cause divergence?
  • Why do we prefer the Kalman gain form?
  • About ensemble linearisations
    • What are they?
    • Why is this rarely discussed?
    • How does it relate to analytic derivatives?
• Lecture by: Evensen, G. (2019)

  Potential of iterative ensemble methods for solving the nonlinear state and parameter-estimation problem

  7th International Symposium on Data Assimilation

Total: 57