Assistant Research Director SAND
Ragnar Hauge
- Department Statistical analysis of natural resource data
- Mobile phone +47 473 18 690
- Phone number +47 22 85 26 65
- E-mail hauge@nr.stage.dekodes.no
Projects
Publications
- 256 publications found
Aarnes, Ingrid; Scotti, Agustin Arguello; Hauge, Ragnar; Skauvold, Jacob; Eide, Christian Haug and Howell, John Anthony. (2026).
Representing shoreface reservoirs with a rule-based facies model: The GEOPARD algorithm.
Show summary
We introduce a new rule-based algorithm called GEOPARD, which models shoreface deposits by following geological principles. In this algorithm, the outcomes of geological processes are represented as rules that are integrated into the core of a standard geostatistical modeling framework. The GEOPARD builds on the stochastic object-based facies modeling technique and incorporates a Bayesian framework for conditioning to data and reducing uncertainty. This paper outlines the geological prior model of the GEOPARD algorithm, which generates facies geometries and controls object placement using geological rules. Specifically, the algorithm builds up a parasequence by stacking a succession of prograding shoreface bedsets, bounded by small-scale hiatus, until a final point of maximum shoreline advance. Model parametrization closely follows the geological conceptual model. The rules are implemented as a series of fully automated modeling steps, mapping the wide variety of facies geometries typically associated with shallow-marine deposits. The functionality of GEOPARD is demonstrated through a series of scenarios, including the reproduction of features observed in an outcrop analogue and benchmarking against the truncated Gaussian simulation method. Key modeled features include sand-body thickness, lateral extent of facies, overall parasequence geometry, and the spacing and dip of bedset bounding surfaces.
Ovanger, Oscar; Eidsvik, Jo; Hauge, Ragnar and Skauvold, Jacob. (2025).
Categorical spatial data: A Bayesian journey through uncertainty quantification, generative modeling, and image comparison.
NVA
Doctoral dissertation
Scotti, Agustin Arguello; Eide, Christian Haug; Aarnes, Ingrid; Skauvold, Jacob; Hauge, Ragnar and Howell, John Anthony. (2025).
Facies modelling of shoreface subsurface reservoirs with the GEOPARD workflow and comparison to industry standard methods. Norsk Geologisk Forening
NVA
Scientific lecture
Ovanger, Oscar; Lee, Daesoo; Eidsvik, Jo; Hauge, Ragnar; Skauvold, Jacob and Aune, Erlend. (2025).
A Statistical Study of Latent Diffusion Models for Geological Facies Modeling.
Show summary
There has been much interest recently in implicit artificial intelligence (AI)-based approaches for geostatistical facies modeling. New generative machine learning constructions such as latent diffusion models (LDMs) appear to be competitive with traditional geostatistical approaches for facies characterization. Going beyond visual inspection of predictions, this study examines properties of the statistical distribution of samples generated by an LDM trained to generate facies models. The study uses a traditional truncated Gaussian random field (TGRF) model as a reference data-generating process and as the ground truth for benchmarking the LDM results. The distributions of realizations drawn from the LDM and TGRF models are compared using metrics including bias, variance, higher-order statistics, transiograms and Jensen–Shannon divergence for both marginal and joint (volume) distributions. Comparisons are made with and without conditioning on facies observations in wells for both stationary and nonstationary TGRF models with different covariance functions. The observed distributional differences are modest, and LDMs are regarded as a very promising approach here. Even so, some systematic artifacts are observed, such as underrepresentation of variability by the LDM. Moreover, the performance of the LDM is found to be sensitive to the training data.
Lee, Daesoo; Ovanger, Oscar; Eidsvik, Jo; Aune, Erlend; Skauvold, Jacob and Hauge, Ragnar. (2025).
Latent diffusion model for conditional reservoir facies generation.
Show summary
Creating accurate and geologically realistic reservoir facies based on limited measurements is crucial for field development and reservoir management, especially in the oil and gas sector. Traditional two-point geostatistics, while foundational, often struggle to capture complex geological patterns. Multi-point statistics offers more flexibility, but comes with its own challenges related to pattern configurations and storage limits. With the rise of Generative Adversarial Networks (GANs) and their success in various fields, there has been a shift towards using them for facies generation. However, recent advances in the computer vision domain have shown the superiority of diffusion models over GANs. Motivated by this, a novel Latent Diffusion Model is proposed, which is specifically designed for conditional generation of reservoir facies. The proposed model produces high-fidelity facies realizations that rigorously preserve conditioning data. It significantly outperforms a GAN-based alternative. Our implementation on GitHub: github.com/ML4ITS/Latent-Diffusion-Model-for-Conditional-Reservoir-Facies-Generation
Kjønsberg, Heidi; Barker, Daniel Martin L; Fjeldstad, Torstein Mæland; Fjellvoll, Bjørn; Hauge, Ragnar; Nilsen, Carl-Inge Colombo; Røe, Per; Sanchis, Charlotte Juliette; Solberg, Eilif and Abrahamsen, Petter. (2025).
GIG annual meeting 2025 - Summary of 2024 and planned work for 2025.
NVA
Report
Fjeldstad, Torstein Mæland; Solberg, Eilif and Hauge, Ragnar. (2024).
Simultaneous time shift and LFC inversion in PCube+.
NVA
Report
Scotti, Agustin Arguello; Eide, Christian Haug; Aarnes, Ingrid; Hauge, Ragnar; Skauvold, Jacob and Howell, John Anthony. (2024).
Modelling intra-parasequence reservoir heterogeneity with a process-mimicking algorithm: a case study from the Kenilworth Member, Blackhawk Formation.
Barker, Daniel Martin L; Fjeldstad, Torstein Mæland; Fjellvoll, Bjørn; Hauge, Ragnar; Kjønsberg, Heidi; Nilsen, Carl-Inge Colombo; Røe, Per; Semin-Sanchis, Charlotte Juliette; Solberg, Eilif and Abrahamsen, Petter. (2024).
PCube User Manual Version 10.5.
NVA
Report
Aker, Eyvind; Barker, Daniel Martin L; Fjeldstad, Torstein Mæland; Fjellvoll, Bjørn; Hauge, Ragnar; Kjønsberg, Heidi; Nilsen, Carl-Inge Colombo; Røe, Per; Semin-Sanchis, Charlotte Juliette and Abrahamsen, Petter. (2024).
GIG annual meeting 2024 - summary of 2023 and planned work for 2024.
NVA
Report
Ovanger, Oscar; Eidsvik, Jo; Lee, Daesoo; Hauge, Ragnar; Skauvold, Jacob and Aune, Erlend. (2024).
A statistical study of latent diffusion models for geological modeling.
NVA
Scientific lecture
Kjønsberg, Heidi; Semin-Sanchis, Charlotte Juliette and Hauge, Ragnar. (2024).
4D MAP estimate of elastic parameters and reservoir properties.
NVA
Report
Kjønsberg, Heidi; Hauge, Ragnar; Nilsen, Carl-Inge Colombo; Ndingwan, Abel Onana and Kolbjørnsen, Odd. (2024).
Bayesian seismic 4D inversion for lithology and fluid prediction.
Show summary
Seismic data acquired at different times over the same area can provide insight into changes in an oil/gas reservoir. Probabilities for pore fluid will typically change, whereas the lithology remains stable over time. This implies significant correlations across the vintages. We develop a methodology for the Bayesian prediction of joint probabilities for discrete lithology-fluid classes (LFCs) for two vintages, simultaneously considering the seismic amplitude-variation-with-offset data of both vintages. By taking into account the cross-vintage correlations of elastic and seismic properties, the simultaneous inversion ensures that the individual results of both vintages, as well as their differences, are consistent and constrained by the seismic data of both vintages. The method relies on prior geologic knowledge of stratigraphic layering, the possible lithologies and fluids within each layer, and the possible cross-vintage changes in lithology and pore fluid. Multiple LFCs can be used to represent different strengths of dynamic cross-vintage changes. We test the algorithm on a synthetic data set and data from the Edvard Grieg field in the central North Sea. Synthetic results demonstrate that the algorithm is able to use dual-vintage data together with a prior model specifying their correlations to calculate joint LFC posterior probabilities for both vintages with a lower degree of uncertainty than independent single-vintage inversions. The Edvard Grieg results indicate that the underlying model is sufficiently general to explain 4D variations in seismic data using a reasonably simple prior model of 4D LFC changes.
Sektnan, Audun; Vazquez, Ariel Almendral; Hauge, Ragnar; Aarnes, Ingrid; Skauvold, Jacob and Vevle, Markus Lund. (2024).
A Tree Representation of Pluri-Gaussian Truncation Rules.
Show summary
Stochastic facies models based on truncated Gaussian random fields are known for being flexible and well suited to reproduce patterns and features from analogues or conceptual models. In pluri-Gaussian simulation, the number of random fields is theoretically unlimited, which adds flexibility and makes it possible to model a wider range of geological settings. However, the truncation map traditionally used to set up these models quickly becomes unclear when used for higher dimensions. Hence, in practical pluri-Gaussian applications, the number of fields is typically kept as low as two or three. We present a formulation of pluri-Gaussian simulation in which the truncation rule, the function that maps combinations of Gaussian random field values to facies categories, is represented as a particular binary tree. This is used to decouple the fields in the critical Gibbs sampling step of the conditioning process in such a way that we can use multiple lower-dimensional samples instead of a single higher-dimensional sample. The resulting conditioning algorithm scales excellently with the amount of conditioning data and the number of fields. The algorithm accepts a combination of trends and probabilities in the same model setup, which provides additional flexibility in representing varying depositional geometries. We demonstrate the hierarchical pluri-Gaussian simulation with two practical examples. One is based on real data from the Volve oil field in the North Sea. The other combines a large number of synthetic observations with a truncation tree tailored to a more complex geological concept. The choices made when building the truncation tree affect the features of the realizations, especially when it comes to which facies can be in contact and which can overprint each other. This aspect of tree building is discussed in light of the numerical examples given.
Aarnes, Ingrid; Hauge, Ragnar; Trier, Øivind Due; Haug, Ola and Vazquez, Ariel Almendral. (2024).
Hierarkisk modell for naturtyper til bruk i naturregnskap.
NVA
Report
Semin-Sanchis, Charlotte Juliette; Hauge, Ragnar and Nilsen, Carl-Inge Colombo. (2024).
PCube+: 4D inversion of base lithology-fluid classes and vintages properties.
NVA
Report
Hauge, Ragnar; Semin-Sanchis, Charlotte Juliette and Kjønsberg, Heidi. (2024).
Joint 4D inversion of fluid saturation and lithology. Geostats
NVA
Scientific lecture
Fjellvoll, Bjørn; Hauge, Ragnar; Kjønsberg, Heidi and Semin-Sanchis, Charlotte Juliette. (2024).
Testing the multimodal outside window approximation on two datasets using PCube+.
NVA
Report
Aarnes, Ingrid; Skauvold, Jacob; Hauge, Ragnar; Vazquez, Ariel Almendral; Lilleborge, Marie and Næss, Solveig. (2024).
GEOPARD 1.0 user manual.
Lilleborge, Marie; Hauge, Ragnar; Fjellvoll, Bjørn and Abrahamsen, Petter. (2024).
Using Pattern Counts to Quantify the Difference Between a Pair of Three-Dimensional Realizations.
Show summary
When comparing different ways of modeling discrete three-dimensional realizations such as facies, it is useful to have a measure of difference (or similarity) in the geometry of these realizations.We propose a method for evaluating such difference by comparing pattern counts for a small template. Tests on synthetic datasets demonstrate that the proposed difference effectively differentiates between realizations of a Boolean model and those generated using multiple-point statistics with the Boolean realizations as training images. We also observed that multiple-point statistics realizations based on similar training images yield smaller differences to one another compared to those based on training images from dissimilar concepts. This suggests that the proposed difference is a useful tool for comparing discrete three-dimensional realizations.
Ovanger, Oscar; Eidsvik, Jo; Skauvold, Jacob; Hauge, Ragnar and Aarnes, Ingrid. (2024).
Addressing Configuration Uncertainty in Well Conditioning for a Rule-Based Model.
Show summary
Rule-based reservoir models incorporate rules that mimic actual sediment deposition processes for accurate representation of geological patterns of sediment accumulation. Bayesian methods combine rule-based reservoir modelling and well data, with geometry and placement rules as part of the prior and well data accounted for by the likelihood. The focus here is on a shallow marine shoreface geometry of ordered sedimentary packages called bedsets. Shoreline advance and sediment build-up are described through progradation and aggradation parameters linked to individual bedset objects. Conditioning on data from non-vertical wells is studied. The emphasis is on the role of ‘configurations’—the order and arrangement of bedsets as observed within well intersections in establishing the coupling between well observations and modelled objects. A conditioning algorithm is presented that explicitly integrates uncertainty about configurations for observed intersections between the well and the bedset surfaces. As data volumes increase and model complexity grows, the proposed conditioning method eventually becomes computationally infeasible. It has significant potential, however, to support the development of more complex models and conditioning methods by serving as a reference for consistency in conditioning.
Semin-Sanchis, Charlotte Juliette; Nilsen, Carl-Inge Colombo and Hauge, Ragnar. (2023).
Noise model setup QC in PCube+.
NVA
Report
Hauge, Ragnar and Fjeldstad, Torstein Mæland. (2023).
Simultaneous inversion for LFCs, time shift and elastic parameters.
NVA
Report
Aker, Eyvind; Kjønsberg, Heidi and Hauge, Ragnar. (2023).
A transparent time shift noise model.
NVA
Report
Aker, Eyvind; Barker, Daniel Martin L; Fjeldstad, Torstein Mæland; Fjellvoll, Bjørn; Hauge, Ragnar; Kjønsberg, Heidi; Nilsen, Carl-Inge Colombo; Røe, Per; Semin-Sanchis, Charlotte Juliette and Abrahamsen, Petter. (2023).
PCube reference manual.
NVA
Report
Aker, Eyvind; Barker, Daniel Martin L; Fjeldstad, Torstein Mæland; Fjellvoll, Bjørn; Hauge, Ragnar; Kjønsberg, Heidi; Nilsen, Carl-Inge Colombo; Røe, Per; Semin-Sanchis, Charlotte Juliette and Abrahamsen, Petter. (2023).
PCube User Manual version 10.0.
NVA
Report
Semin-Sanchis, Charlotte Juliette and Hauge, Ragnar. (2023).
PCube+ likelihood approximation outside the window: Generalization of Reduced Gaussian mixture.
NVA
Report
Ovanger, Oscar; Lee, Daesoo; Eidsvik, Jo; Skauvold, Jacob and Hauge, Ragnar. (2023).
Conditional Facies Sampling using Denoising Diffusion Probabilistic Models. IAMG
NVA
Scientific lecture
Aarnes, Ingrid; Scotti, Agustin Arguello; Skauvold, Jacob; Hauge, Ragnar and Eide, Christian Haug. (2023).
Modelling shoreface geometries with a new facies-algorithm informed by geological rules and analogue data. SEPM Society for Sedimentary Geology
NVA
Scientific lecture
Show summary
The main aim of the GEOPARD project is to increase the geological realism of the facies models used in reservoir characterization workflow by integrating geological rules at the core of the statistical modelling framework.
Through the model we define bedset boundaries, capture realistic interfingering patterns between the sand-rich shoreface and the more mud-rich offshore transition zone, and control facies belt thickness and shoreline trajectories. Due to the close relationship between the model parameterization and geological processes, analogue data are utilized to inform the model.
Scotti, Agustin Arguello; Eide, Christian Haug; Aarnes, Ingrid; Skauvold, Jacob; Hauge, Ragnar and Howell, John. (2023).
From Concept to Reservoir Modelling: The Record of Tide-dominated, Progradational Shoreline Systems. INTERNATIONAL ASSOCIATION OF SEDIMENTOLOGISTS; CROATIAN GEOLOGICAL SOCIETY
NVA
Poster
Scotti, Agustin Arguello; Aarnes, Ingrid; Skauvold, Jacob; Hauge, Ragnar; Eide, Christian Haug and Howell, John. (2023).
Next generation reservoir modelling algortithms - Shallow marine environments. Norwegian Petroleum Society (NPF)
NVA
Scientific lecture
Scotti, Agustin Arguello; Aarnes, Ingrid; Eide, Christian Haug; Skauvold, Jacob and Hauge, Ragnar. (2023).
Modeling Shoreface Geometries of the Kenilworth Member, Blackhawk Formation, with the Geopard Algorithm. Society for Sedimentary Geology
NVA
Scientific lecture
Lee, Daesoo; Ovanger, Oscar; Eidsvik, Jo; Aune, Erlend; Skauvold, Jacob and Hauge, Ragnar. (2023).
Latent Diffusion Model for Conditional Reservoir Facies Generation.
NVA
Academic article
Show summary
https://arxiv.org/pdf/2311.01968.pdf
Fjeldstad, Torstein Mæland; Lilleborge, Marie; Nilsen, Carl-Inge Colombo; Sanchis, Charlotte Juliette and Hauge, Ragnar. (2022).
Seismic Tiles with application to 4D.
NVA
Report
Sanchis, Charlotte Juliette and Hauge, Ragnar. (2022).
PCube+ likelihood approximation outside the window: Reduced Gaussian mixture.
NVA
Report
Scotti, Agustin Arguello; Aarnes, Ingrid; Eide, Christian Haug; Skauvold, Jacob and Hauge, Ragnar. (2022).
Testing a rule-based approach for reservoir modelling of shoreface successions: the GEOPARD algorithm. British Sedimentological Research Group
NVA
Scientific lecture
Kjønsberg, Heidi; Hauge, Ragnar and Ndingwan, Abel Onana. (2022).
Time-lapse Bayesian AVO inversion applied to the Edvard Grieg field in the North Sea.
Show summary
Seismic data acquired at different times can provide insight into changes in an oil/gas reservoir. We apply time-lapse AVO inversion methodology to the Edvard Grieg field in the North Sea. The methodology we use inverts for discrete lithology-fluid classes (LFCs), where different fluid fillings and/or pressure effects in the monitor are represented by different LFCs. Our focus here is on fluid substitution. We only look at amplitude effects, and use monitor time-lapse data that is time aligned with the base seismic.
Aker, Eyvind; Barker, Daniel Martin L; Fjeldstad, Torstein Mæland; Hauge, Ragnar; Kjønsberg, Heidi; Nilsen, Carl-Inge Colombo; Røe, Per; Sanchis, Charlotte Juliette and Abrahamsen, Petter. (2022).
GIG annual meeting 2022 - Summary of 2021 and planned work for 2022.
NVA
Report
Show summary
The GIG, Geophysical Inversion to Geology, consortium is a research consortium run by Norwegian Computing Center, with the aim of developing new understanding, new methods and software for obtaining reservoir properties from geophysical measurements
This note gives a summary of the work in 2021 and the suggested work plan for 2022. The final work plan for 2022 will be decided on the annual meeting in Jan 2022.
Eikvil, Line; Waldeland, Anders U.; Barker, Daniel Martin L; Holden, Marit; Hauge, Ragnar and Salberg, Arnt Børre. (2022).
Deep learning in seismic interpretation,
Development and experiments 2021-2022.
NVA
Report
Aarnes, Ingrid and Hauge, Ragnar. (2022).
Modelling fluvial environments. Winthershall Dea
NVA
Lecture
Ovanger, Oscar; Eidsvik, Jo; Skauvold, Jacob; Hauge, Ragnar and Aarnes, Ingrid. (2022).
A rule-based reservoir stacking model with effective well conditioning. IAMG
NVA
Lecture
Aarnes, Ingrid; Hauge, Ragnar; Scotti, Agustin Arguello and Skauvold, Jacob. (2022).
The Geopard project. Norges Geologiske Forening
NVA
Scientific lecture
Kjønsberg, Heidi; Fjeldstad, Torstein Mæland and Hauge, Ragnar. (2022).
Estimate Reservoir Properties.
NVA
Report
Sektnan, Audun; Vazquez, Ariel Almendral; Hauge, Ragnar; Aarnes, Ingrid; Skauvold, Jacob and Vevle, Markus Lund. (2022).
A Tree Representation of Plurigaussian Truncation Rules.
Show summary
Truncated Gaussian fields are a common way of modelling facies, where the correlation structure in the Gaussian field defines a spatial correlation structure for the facies. Plurigaussian simulation takes it further by using several underlying Gaussian fields. This allows more flexibility and makes it possible to model a wider range of geological settings, but conditioning can be difficult.
We present a fast and accurate implementation of conditional plurigaussian simulation. Our approach has two key elements. The first is to combine complex truncation rules with input facies probabilities. The truncation rule, which is a function from the Gaussian fields to a facies value, can be represented neatly as a binary truncation tree. This allows for a general representation that includes all the traditional 2D truncation masks. We show how to combine the use of such trees with facies probabilities, even in complicated cases with more than two Gaussian fields.
The second key element is correct conditioning to all facies observations, not just transitions, by treating them as inequality constraints on the Gaussian fields. We perform inequality Kriging by replacing these facies observations by synthetic observations of the underlying Gaussian fields. To generate synthetic observations that agree with the target posterior distribution, we use a Gibbs sampler. Since this is a quite slow algorithm, we take certain measures to make the calculations faster. Synthetic observations are then used in Kriging, improving the conditioning to facies logs from wells. We demonstrate the method with a synthetic case that combines a large number of observations with the use of a truncation tree tailored from a geological concept.
Scotti, Agustin Arguello; Eide, Christian Haug; Aarnes, Ingrid; Skauvold, Jacob and Hauge, Ragnar. (2022).
Defining the basic rules that describe long-term shoreface dynamics: A process-mimicking approach for reservoir modelling. European Geosciences Union
NVA
Scientific lecture
Hauge, Ragnar; Sanchis, Charlotte Juliette and Olsen, Eliane Huygens. (2021).
PCube+ window likelihood approximation.
NVA
Report
Lilleborge, Marie; Fjellvoll, Bjørn; Hauge, Ragnar and Abrahamsen, Petter. (2021).
Assessing Multiple-Point Statistics by use of pattern counts to compare images.
NVA
Report
Kjønsberg, Heidi; Hauge, Ragnar; Fjeldstad, Torstein Mæland and Nilsen, Carl-Inge Colombo. (2021).
Edvard Grieg 4D inversion results.
NVA
Report
Nilsen, Carl-Inge Colombo; Hauge, Ragnar and Kjønsberg, Heidi. (2021).
Improved Monitor Continuity in PCube+ 4D.
NVA
Report
Røe, Per; Aker, Eyvind; Barker, Daniel Martin L; Fjeldstad, Torstein Mæland; Hauge, Ragnar; Kjønsberg, Heidi; Nilsen, Carl-Inge Colombo; Sanchis, Charlotte Juliette and Abrahamsen, Petter. (2021).
GIG annual meeting 2021 - Summary of 2020 and planned work for 2021.
NVA
Report
Show summary
The GIG, Geophysical Inversion to Geology, consortium is a research consortium run by Norwegian Computing Center, with the aim of developing new understanding, new methods and software for obtaining reservoir properties from geophysical measurements
This note gives a status of the GIG consortium after its fourth year of operation, and also gives the suggested work plan for 2021.
Aker, Eyvind; Barker, Daniel Martin L; Fjeldstad, Torstein Mæland; Hauge, Ragnar; Kjønsberg, Heidi; Kvernelv, Vegard Berg; Nilsen, Carl-Inge Colombo; Rummelhoff, Ivar; Røe, Per and Sanchis, Charlotte Juliette. (2021).
PCube User Manual Version 9.0.
NVA
Report
Fjeldstad, Torstein Mæland; Avseth, Per Åge; Omre, Henning; Røe, Per and Hauge, Ragnar. (2021).
Spatial Bayesian lithology/fluid class inversion. SEG
NVA
Scientific lecture
Sanchis, Charlotte Juliette and Hauge, Ragnar. (2021).
Focused inversion likelihood analysis.
NVA
Report
Sanchis, Charlotte Juliette and Hauge, Ragnar. (2021).
PCube+: Likelihood approximation outside the window and range spanning.
NVA
Report
Røe, Per; Aker, Eyvind; Barker, Daniel Martin L; Hauge, Ragnar; Kjønsberg, Heidi; Nesvold, Erik; Nilsen, Carl-Inge Colombo; Rummelhoff, Ivar and Sanchis, Charlotte Juliette. (2020).
PCube+ User Manual
Version 8.0.
NVA
Report
Kjønsberg, Heidi; Haug, Sissel Grude; Hauge, Ragnar and Nilsen, Carl-Inge Colombo. (2020).
Time lapse AVO inversion for the Heidrun field in the Norwegian Sea. SEG
NVA
Scientific lecture
Kjønsberg, Heidi; Nilsen, Carl-Inge Colombo and Hauge, Ragnar. (2020).
4D synthetic tests.
NVA
Report
Kolbjørnsen, Odd; Buland, Arild; Hauge, Ragnar; Røe, Per; Ndingwan, Abel Onana and Aker, Eyvind. (2020).
Bayesian seismic inversion for stratigraphic horizon, lithology, and fluid prediction.
Show summary
We have developed an efficient methodology for Bayesian prediction of lithology and pore fluid, and layer-bounding horizons, in which we include and use spatial geologic prior knowledge such as vertical ordering of stratigraphic layers, possible lithologies and fluids within each stratigraphic layer, and layer thicknesses. The solution includes probabilities for lithologies and fluids and horizons and their associated uncertainties. The computational cost related to the inversion of large-scale, spatially coupled models is a severe challenge. Our approach is to evaluate all possible lithology and fluid configurations within a local neighborhood around each sample point and combine these into a consistent result for the complete trace. We use a one-step nonstationary Markov prior model for lithology and fluid probabilities. This enables prediction of horizon times, which we couple laterally to decrease the uncertainty. We have tested the algorithm on a synthetic case, in which we compare the inverted lithology and fluid probabilities to results from other algorithms. We have also run the algorithm on a real case, in which we find that we can make high-resolution predictions of horizons, even for horizons within tuning distance from each other. The methodology gives accurate predictions and has a performance making it suitable for full-field inversions.
Lilleborge, Marie and Hauge, Ragnar. (2020).
Modelling Well Cost - A simple model of well cost based on Rushmore data.
NVA
Report
Røe, Per; Hauge, Ragnar; Aker, Eyvind; Barker, Daniel Martin L; Nilsen, Carl-Inge Colombo; Sanchis, Charlotte Juliette; Kjønsberg, Heidi; Abrahamsen, Petter and Nesvold, Erik. (2020).
GIG annual meeting 2020
Summary of 2019 and planned work for 2020.
NVA
Report
Eikvil, Line; Waldeland, Anders U.; Barker, Daniel Martin L; Holden, Marit; Hauge, Ragnar and Salberg, Arnt Børre. (2020).
Deep learning in seismic interpretations - Development and experiments 2020.
NVA
Report
Fjeldstad, Torstein Mæland; Røe, Per and Hauge, Ragnar. (2020).
Lithology/fluid class prediction based on a lateral model.
NVA
Report
Skauvold, Jacob and Hauge, Ragnar. (2020).
Consequences of changes in grid resolution for process model based variogram estimation.
NVA
Report
Kvernelv, Vegard Berg; Hauge, Ragnar and Skauvold, Jacob. (2020).
Geomodel Parameter Estimation from Process Model Analogues.
NVA
Report
Sanchis, Charlotte Juliette and Hauge, Ragnar. (2020).
PCube+: Likelihood sensitivity analysis and the prediction of unknown distribution.
NVA
Report
Sanchis, Charlotte Juliette and Hauge, Ragnar. (2020).
Scaling of unknown distribution in PCube+ and Focused inversion.
NVA
Report
Røe, Per; Kolbjørnsen, Odd and Hauge, Ragnar. (2019).
Comparison of one- and two-step seismic inversion for Lithology and Fluid prediction. Sharp Reflections
NVA
Lecture
Fjellvoll, Bjørn; Hauge, Ragnar; Vazquez, Ariel Almendral; Abrahamsen, Petter and Skauvold, Jacob. (2019).
Applied Geostatistics and Geomodelling 2019. Equinor
NVA
Lecture
Røe, Per; Hauge, Ragnar; Aker, Eyvind; Barker, Daniel Martin L; Nilsen, Carl-Inge Colombo; Sanchis, Charlotte Juliette; Kjønsberg, Heidi and Abrahamsen, Petter. (2019).
GIG annual meeting 2019 - Summary of 2018 and planned work for 2019.
NVA
Report
Eikvil, Line; Waldeland, Anders U.; Holden, Marit; Salberg, Arnt Børre; Hauge, Ragnar and Barker, Daniel Martin L. (2019).
Deep learning in seismic interpretation.
NVA
Report
Aarnes, Ingrid; Vegt, Helena van der; Hauge, Ragnar; Fjellvoll, Bjørn and Nordahl, Kjetil. (2019).
Utilizing sedimentary process-based
models as training images for
multipoint facies simulations.
Show summary
Geostatistical facies modeling algorithms are used in reservoir modeling workflows to create geological models which improve the predictive power of the flow simulation models. In heterogeneous reservoirs, it is of key importance to not only apply statistical techniques, but also incorporate prior geological knowledge. Fluvial dominated deltaic deposits can show a high degree of heterogeneity arising from the interaction of stacking of lobate deposits and the continuous erosion and deposition of the distributary channels while building the delta. To simulate these depositional structures, honouring the physical laws of nature, process-based models can be used to generate synthetic deposits. However, such results are driven by physics and therefore cannot be steered to honour exact well data. We address this challenge by integrating physics-driven process-based models with statistical techniques from MPS. Combining these two different methods is MPS relies on discreet geometric patterns. This is addressed by classifying the process-based model results into discreet facies. A major advantage of this integrated technique is the potential to generate multiple MPS training images through simulation of additional process-based model realizations and we also analyze the effect of using one versus multiple process-based models as input. In this work, we show how the best aspects of both process-based models and MPS modeling can be combined to create improved geological models.
Sanchis, Charlotte Juliette; Hauge, Ragnar and Kjønsberg, Heidi. (2019).
Expecting the unexpected: The influence of elastic parameter variance on Bayesian facies inversion. EAGE
NVA
Poster
Sanchis, Charlotte Juliette; Røe, Per and Hauge, Ragnar. (2019).
PCube+ and focused inversion testing.
NVA
Report
Eikvil, Line; Holden, Marit; Hauge, Ragnar and Kvernelv, Vegard Berg. (2019).
Estimation of rock cuttings size from images.
NVA
Report
Aker, Eyvind; Røe, Per; Hauge, Ragnar and Abrahamsen, Petter. (2019).
PCube+ principles. Sharp Reflections
NVA
Lecture
Aker, Eyvind; Røe, Per; Hauge, Ragnar and Abrahamsen, Petter. (2019).
PCube+ principles. Sharp Reflections
NVA
Lecture
Vevle, Markus Lund; Aarnes, Ingrid; Ledsaak, Karina; Hauge, Ragnar and Skorstad, Arne. (2018).
Facies modelling of a real-life fluvial system using a modern object-based algorithm. EAGE
NVA
Scientific lecture
Sanchis, Charlotte Juliette; Hauge, Ragnar and Røe, Per. (2018).
Direct inversion for horizon location.
NVA
Report
Sanchis, Charlotte Juliette and Hauge, Ragnar. (2018).
PCube+: Focused inversion testing.
NVA
Report
Goodwin, Håvard; Aarnes, Ingrid and Hauge, Ragnar. (2018).
Deep marine modelling with RMS on Karoo.
NVA
Report
Lilleborge, Marie; Hofvind, Solveig; Sebuødegård, Sofie and Hauge, Ragnar. (2018).
Optimizing performance of BreastScreen Norway using value of information in graphical models.
Show summary
This study proposes a method to optimize the performance of BreastScreen Norway through a stratified recommendation of tests including independent double or single reading of the screening mammograms and additional imaging with or without core needle biopsy. This is carefully evaluated by a value of information analysis. An estimated graphical probabilistic model describing the relationship between a set of risk factors and the corresponding risk of breast cancer is used for this analysis, together with a Bayesian network modeling screening test results conditional on the true (but unknown) breast cancer status of a woman.
This study contributes towards evaluating a possibility of improving the efficiency of the screening program, where all women aged 50 to 69 are invited every second year, regardless of individual risk factors. Our stratified recommendation of tests is dependent on the probability that an asymptomatic woman has developed breast cancer at the time she is invited to a screening.
Røe, Per; Aker, Eyvind; Rummelhoff, Ivar; Hauge, Ragnar; Kjønsberg, Heidi; Barker, Daniel Martin L and Sanchis, Charlotte Juliette. (2018).
This is a user manual for PCube. PCube is a seismic inversion software that computes lithology and fluid probabilities from seismic AVO data.
NVA
Report
Hauge, Ragnar; Vigsnes, Maria; Fjellvoll, Bjørn; Vevle, Markus Lund and Skorstad, Arne. (2017).
Object-Based Modeling with Dense Well Data.
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Although object models are popular with geologists due to their ability to control the geometries that are produced, they tend to have convergence issues when conditioning on complex well patterns. In this paper, we present a new well conditioning algorithm that utilizes more local data when generating channels. We show that this algorithm performs better than the currently commercially available state-of-the-art object model and thus makes object models viable in modern mature field well settings.
Aarnes, Ingrid and Hauge, Ragnar. (2017).
Applying Truncated Gaussian Fields to
describe geology. Norsk Statistisk Forening
NVA
Scientific lecture
Zdanowicz, Hanna Marta; Vigsnes, Maria; Fjellvoll, Bjørn and Hauge, Ragnar. (2017).
Prototype Turbidite Modelling.
NVA
Report
Aarnes, Ingrid; Fjellvoll, Bjørn and Hauge, Ragnar. (2017).
Utilizing process-based models in facies modelling workflows.
NVA
Report
Røe, Per; Hauge, Ragnar; Aker, Eyvind; Abrahamsen, Petter; Hauge, Vera Louise and Sanchis, Charlotte Juliette. (2017).
GIG annual meeting 2018
Summary of 2017 planned work for 2018.
NVA
Report
Sanchis, Charlotte Juliette and Hauge, Ragnar. (2017).
Direct inversion for horizon location.
NVA
Report
Lilleborge, Marie; Hauge, Ragnar; Eidsvik, Jo and Huseby, Arne. (2017).
Efficient Information Gathering in Discrete Bayesian Networks.
Aker, Eyvind; Røe, Per; Kjøsnes, Øyvind; Hauge, Ragnar; Dahle, Pål; Ahmadi, Gholam Reza and Sandstad, Odd Arne. (2017).
Probabilistic prediction of lithology-fluid-classes from seismic - A North Sea case study. NTNU
NVA
Scientific lecture
Olsen, Håvard Goodwin and Hauge, Ragnar. (2016).
Panther Tongue Modelling with RMS – Part II.
NVA
Report
Aarnes, Ingrid and Hauge, Ragnar. (2016).
Truncated Gaussian Simulation -
Comparison of methodologies.
NVA
Report
Hauge, Vera Louise; Røe, Per; Aker, Eyvind; Sanchis, Charlotte Juliette and Hauge, Ragnar. (2016).
PCube User Manual
Version PCube+ 6.0.
NVA
Report
Kolbjørnsen, Odd; Buland, Arild; Hauge, Ragnar; Røe, Per; Jullum, Martin; Metcalfe, Richard William and Skjæveland, Øyvind. (2016).
Bayesian AVO inversion to rock properties using a local neighborhood in a spatial prior model.
Show summary
The spatial structure of the subsurface is an important factor when interpreting seismic data. The Bayesian methodology is a valuable tool for integrating these spatial relations in the inversion process as it merges the information together and assesses the uncertainty of the model. In the everyday use of the Bayesian methodology, however, the computational cost is a challenge. We describe a new approach that utilizes a local neighborhood to include the spatial constraints and assess the uncertainties in the inversion using fast and parallelizable computations. The approach is applicable for both discrete lithology-fluid prediction and estimation of rock properties, such as porosity and saturation.
Aker, Eyvind; Dahle, Pål; Hauge, Ragnar and Røe, Per. (2016).
PCube inversion study in the greater Alvheim area.
NVA
Report
Fjellvoll, Bjørn; Abrahamsen, Petter; Hauge, Ragnar and Vazquez, Ariel Almendral. (2016).
Geostatistics course. Statoil
NVA
Lecture