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
- 255 publications found
Ovanger, Oscar; Eidsvik, Jo; Hauge, Ragnar og Skauvold, Jacob. (2025).
Categorical spatial data: A Bayesian journey through uncertainty quantification, generative modeling, and image comparison.
NVA
Doktorgradsavhandling
Scotti, Agustin Arguello; Eide, Christian Haug; Aarnes, Ingrid; Skauvold, Jacob; Hauge, Ragnar og Howell, John Anthony. (2025).
Facies modelling of shoreface subsurface reservoirs with the GEOPARD workflow and comparison to industry standard methods. Norsk Geologisk Forening
NVA
Vitenskapelig foredrag
Ovanger, Oscar; Lee, Daesoo; Eidsvik, Jo; Hauge, Ragnar; Skauvold, Jacob og Aune, Erlend. (2025).
A Statistical Study of Latent Diffusion Models for Geological Facies Modeling.
Vis sammendrag
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 og Hauge, Ragnar. (2025).
Latent diffusion model for conditional reservoir facies generation.
Vis sammendrag
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 og Abrahamsen, Petter. (2025).
GIG annual meeting 2025 - Summary of 2024 and planned work for 2025.
NVA
Rapport
Fjeldstad, Torstein Mæland; Solberg, Eilif og Hauge, Ragnar. (2024).
Simultaneous time shift and LFC inversion in PCube+.
NVA
Rapport
Scotti, Agustin Arguello; Eide, Christian Haug; Aarnes, Ingrid; Hauge, Ragnar; Skauvold, Jacob og 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 og Abrahamsen, Petter. (2024).
PCube User Manual Version 10.5.
NVA
Rapport
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 og Abrahamsen, Petter. (2024).
GIG annual meeting 2024 - summary of 2023 and planned work for 2024.
NVA
Rapport
Ovanger, Oscar; Eidsvik, Jo; Lee, Daesoo; Hauge, Ragnar; Skauvold, Jacob og Aune, Erlend. (2024).
A statistical study of latent diffusion models for geological modeling.
NVA
Vitenskapelig foredrag
Kjønsberg, Heidi; Semin-Sanchis, Charlotte Juliette og Hauge, Ragnar. (2024).
4D MAP estimate of elastic parameters and reservoir properties.
NVA
Rapport
Kjønsberg, Heidi; Hauge, Ragnar; Nilsen, Carl-Inge Colombo; Ndingwan, Abel Onana og Kolbjørnsen, Odd. (2024).
Bayesian seismic 4D inversion for lithology and fluid prediction.
Vis sammendrag
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 og Vevle, Markus Lund. (2024).
A Tree Representation of Pluri-Gaussian Truncation Rules.
Vis sammendrag
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 og Vazquez, Ariel Almendral. (2024).
Hierarkisk modell for naturtyper til bruk i naturregnskap.
NVA
Rapport
Semin-Sanchis, Charlotte Juliette; Hauge, Ragnar og Nilsen, Carl-Inge Colombo. (2024).
PCube+: 4D inversion of base lithology-fluid classes and vintages properties.
NVA
Rapport
Hauge, Ragnar; Semin-Sanchis, Charlotte Juliette og Kjønsberg, Heidi. (2024).
Joint 4D inversion of fluid saturation and lithology. Geostats
NVA
Vitenskapelig foredrag
Fjellvoll, Bjørn; Hauge, Ragnar; Kjønsberg, Heidi og Semin-Sanchis, Charlotte Juliette. (2024).
Testing the multimodal outside window approximation on two datasets using PCube+.
NVA
Rapport
Aarnes, Ingrid; Skauvold, Jacob; Hauge, Ragnar; Vazquez, Ariel Almendral; Lilleborge, Marie og Næss, Solveig. (2024).
GEOPARD 1.0 user manual.
Lilleborge, Marie; Hauge, Ragnar; Fjellvoll, Bjørn og Abrahamsen, Petter. (2024).
Using Pattern Counts to Quantify the Difference Between a Pair of Three-Dimensional Realizations.
Vis sammendrag
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 og Aarnes, Ingrid. (2024).
Addressing Configuration Uncertainty in Well Conditioning for a Rule-Based Model.
Vis sammendrag
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 og Hauge, Ragnar. (2023).
Noise model setup QC in PCube+.
NVA
Rapport
Hauge, Ragnar og Fjeldstad, Torstein Mæland. (2023).
Simultaneous inversion for LFCs, time shift and elastic parameters.
NVA
Rapport
Aker, Eyvind; Kjønsberg, Heidi og Hauge, Ragnar. (2023).
A transparent time shift noise model.
NVA
Rapport
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 og Abrahamsen, Petter. (2023).
PCube reference manual.
NVA
Rapport
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 og Abrahamsen, Petter. (2023).
PCube User Manual version 10.0.
NVA
Rapport
Semin-Sanchis, Charlotte Juliette og Hauge, Ragnar. (2023).
PCube+ likelihood approximation outside the window: Generalization of Reduced Gaussian mixture.
NVA
Rapport
Ovanger, Oscar; Lee, Daesoo; Eidsvik, Jo; Skauvold, Jacob og Hauge, Ragnar. (2023).
Conditional Facies Sampling using Denoising Diffusion Probabilistic Models. IAMG
NVA
Vitenskapelig foredrag
Aarnes, Ingrid; Scotti, Agustin Arguello; Skauvold, Jacob; Hauge, Ragnar og 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
Vitenskapelig foredrag
Vis sammendrag
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 og 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 og Howell, John. (2023).
Next generation reservoir modelling algortithms - Shallow marine environments. Norwegian Petroleum Society (NPF)
NVA
Vitenskapelig foredrag
Scotti, Agustin Arguello; Aarnes, Ingrid; Eide, Christian Haug; Skauvold, Jacob og Hauge, Ragnar. (2023).
Modeling Shoreface Geometries of the Kenilworth Member, Blackhawk Formation, with the Geopard Algorithm. Society for Sedimentary Geology
NVA
Vitenskapelig foredrag
Lee, Daesoo; Ovanger, Oscar; Eidsvik, Jo; Aune, Erlend; Skauvold, Jacob og Hauge, Ragnar. (2023).
Latent Diffusion Model for Conditional Reservoir Facies Generation.
NVA
Vitenskapelig artikkel
Vis sammendrag
https://arxiv.org/pdf/2311.01968.pdf
Fjeldstad, Torstein Mæland; Lilleborge, Marie; Nilsen, Carl-Inge Colombo; Sanchis, Charlotte Juliette og Hauge, Ragnar. (2022).
Seismic Tiles with application to 4D.
NVA
Rapport
Sanchis, Charlotte Juliette og Hauge, Ragnar. (2022).
PCube+ likelihood approximation outside the window: Reduced Gaussian mixture.
NVA
Rapport
Scotti, Agustin Arguello; Aarnes, Ingrid; Eide, Christian Haug; Skauvold, Jacob og Hauge, Ragnar. (2022).
Testing a rule-based approach for reservoir modelling of shoreface successions: the GEOPARD algorithm. British Sedimentological Research Group
NVA
Vitenskapelig foredrag
Kjønsberg, Heidi; Hauge, Ragnar og Ndingwan, Abel Onana. (2022).
Time-lapse Bayesian AVO inversion applied to the Edvard Grieg field in the North Sea.
Vis sammendrag
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 og Abrahamsen, Petter. (2022).
GIG annual meeting 2022 - Summary of 2021 and planned work for 2022.
NVA
Rapport
Vis sammendrag
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 og Salberg, Arnt Børre. (2022).
Deep learning in seismic interpretation,
Development and experiments 2021-2022.
NVA
Rapport
Aarnes, Ingrid og Hauge, Ragnar. (2022).
Modelling fluvial environments. Winthershall Dea
NVA
Faglig foredrag
Ovanger, Oscar; Eidsvik, Jo; Skauvold, Jacob; Hauge, Ragnar og Aarnes, Ingrid. (2022).
A rule-based reservoir stacking model with effective well conditioning. IAMG
NVA
Faglig foredrag
Aarnes, Ingrid; Hauge, Ragnar; Scotti, Agustin Arguello og Skauvold, Jacob. (2022).
The Geopard project. Norges Geologiske Forening
NVA
Vitenskapelig foredrag
Kjønsberg, Heidi; Fjeldstad, Torstein Mæland og Hauge, Ragnar. (2022).
Estimate Reservoir Properties.
NVA
Rapport
Sektnan, Audun; Vazquez, Ariel Almendral; Hauge, Ragnar; Aarnes, Ingrid; Skauvold, Jacob og Vevle, Markus Lund. (2022).
A Tree Representation of Plurigaussian Truncation Rules.
Vis sammendrag
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 og Hauge, Ragnar. (2022).
Defining the basic rules that describe long-term shoreface dynamics: A process-mimicking approach for reservoir modelling. European Geosciences Union
NVA
Vitenskapelig foredrag
Hauge, Ragnar; Sanchis, Charlotte Juliette og Olsen, Eliane Huygens. (2021).
PCube+ window likelihood approximation.
NVA
Rapport
Lilleborge, Marie; Fjellvoll, Bjørn; Hauge, Ragnar og Abrahamsen, Petter. (2021).
Assessing Multiple-Point Statistics by use of pattern counts to compare images.
NVA
Rapport
Kjønsberg, Heidi; Hauge, Ragnar; Fjeldstad, Torstein Mæland og Nilsen, Carl-Inge Colombo. (2021).
Edvard Grieg 4D inversion results.
NVA
Rapport
Nilsen, Carl-Inge Colombo; Hauge, Ragnar og Kjønsberg, Heidi. (2021).
Improved Monitor Continuity in PCube+ 4D.
NVA
Rapport
Røe, Per; Aker, Eyvind; Barker, Daniel Martin L; Fjeldstad, Torstein Mæland; Hauge, Ragnar; Kjønsberg, Heidi; Nilsen, Carl-Inge Colombo; Sanchis, Charlotte Juliette og Abrahamsen, Petter. (2021).
GIG annual meeting 2021 - Summary of 2020 and planned work for 2021.
NVA
Rapport
Vis sammendrag
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 og Sanchis, Charlotte Juliette. (2021).
PCube User Manual Version 9.0.
NVA
Rapport
Fjeldstad, Torstein Mæland; Avseth, Per Åge; Omre, Henning; Røe, Per og Hauge, Ragnar. (2021).
Spatial Bayesian lithology/fluid class inversion. SEG
NVA
Vitenskapelig foredrag
Sanchis, Charlotte Juliette og Hauge, Ragnar. (2021).
Focused inversion likelihood analysis.
NVA
Rapport
Sanchis, Charlotte Juliette og Hauge, Ragnar. (2021).
PCube+: Likelihood approximation outside the window and range spanning.
NVA
Rapport
Røe, Per; Aker, Eyvind; Barker, Daniel Martin L; Hauge, Ragnar; Kjønsberg, Heidi; Nesvold, Erik; Nilsen, Carl-Inge Colombo; Rummelhoff, Ivar og Sanchis, Charlotte Juliette. (2020).
PCube+ User Manual
Version 8.0.
NVA
Rapport
Kjønsberg, Heidi; Haug, Sissel Grude; Hauge, Ragnar og Nilsen, Carl-Inge Colombo. (2020).
Time lapse AVO inversion for the Heidrun field in the Norwegian Sea. SEG
NVA
Vitenskapelig foredrag
Kjønsberg, Heidi; Nilsen, Carl-Inge Colombo og Hauge, Ragnar. (2020).
4D synthetic tests.
NVA
Rapport
Kolbjørnsen, Odd; Buland, Arild; Hauge, Ragnar; Røe, Per; Ndingwan, Abel Onana og Aker, Eyvind. (2020).
Bayesian seismic inversion for stratigraphic horizon, lithology, and fluid prediction.
Vis sammendrag
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 og Hauge, Ragnar. (2020).
Modelling Well Cost - A simple model of well cost based on Rushmore data.
NVA
Rapport
Røe, Per; Hauge, Ragnar; Aker, Eyvind; Barker, Daniel Martin L; Nilsen, Carl-Inge Colombo; Sanchis, Charlotte Juliette; Kjønsberg, Heidi; Abrahamsen, Petter og Nesvold, Erik. (2020).
GIG annual meeting 2020
Summary of 2019 and planned work for 2020.
NVA
Rapport
Eikvil, Line; Waldeland, Anders U.; Barker, Daniel Martin L; Holden, Marit; Hauge, Ragnar og Salberg, Arnt Børre. (2020).
Deep learning in seismic interpretations - Development and experiments 2020.
NVA
Rapport
Fjeldstad, Torstein Mæland; Røe, Per og Hauge, Ragnar. (2020).
Lithology/fluid class prediction based on a lateral model.
NVA
Rapport
Skauvold, Jacob og Hauge, Ragnar. (2020).
Consequences of changes in grid resolution for process model based variogram estimation.
NVA
Rapport
Kvernelv, Vegard Berg; Hauge, Ragnar og Skauvold, Jacob. (2020).
Geomodel Parameter Estimation from Process Model Analogues.
NVA
Rapport
Sanchis, Charlotte Juliette og Hauge, Ragnar. (2020).
PCube+: Likelihood sensitivity analysis and the prediction of unknown distribution.
NVA
Rapport
Sanchis, Charlotte Juliette og Hauge, Ragnar. (2020).
Scaling of unknown distribution in PCube+ and Focused inversion.
NVA
Rapport
Røe, Per; Kolbjørnsen, Odd og Hauge, Ragnar. (2019).
Comparison of one- and two-step seismic inversion for Lithology and Fluid prediction. Sharp Reflections
NVA
Faglig foredrag
Fjellvoll, Bjørn; Hauge, Ragnar; Vazquez, Ariel Almendral; Abrahamsen, Petter og Skauvold, Jacob. (2019).
Applied Geostatistics and Geomodelling 2019. Equinor
NVA
Faglig foredrag
Røe, Per; Hauge, Ragnar; Aker, Eyvind; Barker, Daniel Martin L; Nilsen, Carl-Inge Colombo; Sanchis, Charlotte Juliette; Kjønsberg, Heidi og Abrahamsen, Petter. (2019).
GIG annual meeting 2019 - Summary of 2018 and planned work for 2019.
NVA
Rapport
Eikvil, Line; Waldeland, Anders U.; Holden, Marit; Salberg, Arnt Børre; Hauge, Ragnar og Barker, Daniel Martin L. (2019).
Deep learning in seismic interpretation.
NVA
Rapport
Aarnes, Ingrid; Vegt, Helena van der; Hauge, Ragnar; Fjellvoll, Bjørn og Nordahl, Kjetil. (2019).
Utilizing sedimentary process-based
models as training images for
multipoint facies simulations.
Vis sammendrag
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 og Kjønsberg, Heidi. (2019).
Expecting the unexpected: The influence of elastic parameter variance on Bayesian facies inversion. EAGE
NVA
poster
Sanchis, Charlotte Juliette; Røe, Per og Hauge, Ragnar. (2019).
PCube+ and focused inversion testing.
NVA
Rapport
Eikvil, Line; Holden, Marit; Hauge, Ragnar og Kvernelv, Vegard Berg. (2019).
Estimation of rock cuttings size from images.
NVA
Rapport
Aker, Eyvind; Røe, Per; Hauge, Ragnar og Abrahamsen, Petter. (2019).
PCube+ principles. Sharp Reflections
NVA
Faglig foredrag
Aker, Eyvind; Røe, Per; Hauge, Ragnar og Abrahamsen, Petter. (2019).
PCube+ principles. Sharp Reflections
NVA
Faglig foredrag
Vevle, Markus Lund; Aarnes, Ingrid; Ledsaak, Karina; Hauge, Ragnar og Skorstad, Arne. (2018).
Facies modelling of a real-life fluvial system using a modern object-based algorithm. EAGE
NVA
Vitenskapelig foredrag
Sanchis, Charlotte Juliette; Hauge, Ragnar og Røe, Per. (2018).
Direct inversion for horizon location.
NVA
Rapport
Sanchis, Charlotte Juliette og Hauge, Ragnar. (2018).
PCube+: Focused inversion testing.
NVA
Rapport
Goodwin, Håvard; Aarnes, Ingrid og Hauge, Ragnar. (2018).
Deep marine modelling with RMS on Karoo.
NVA
Rapport
Lilleborge, Marie; Hofvind, Solveig; Sebuødegård, Sofie og Hauge, Ragnar. (2018).
Optimizing performance of BreastScreen Norway using value of information in graphical models.
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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 og 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
Rapport
Hauge, Ragnar; Vigsnes, Maria; Fjellvoll, Bjørn; Vevle, Markus Lund og 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 og Hauge, Ragnar. (2017).
Applying Truncated Gaussian Fields to
describe geology. Norsk Statistisk Forening
NVA
Vitenskapelig foredrag
Zdanowicz, Hanna Marta; Vigsnes, Maria; Fjellvoll, Bjørn og Hauge, Ragnar. (2017).
Prototype Turbidite Modelling.
NVA
Rapport
Aarnes, Ingrid; Fjellvoll, Bjørn og Hauge, Ragnar. (2017).
Utilizing process-based models in facies modelling workflows.
NVA
Rapport
Røe, Per; Hauge, Ragnar; Aker, Eyvind; Abrahamsen, Petter; Hauge, Vera Louise og Sanchis, Charlotte Juliette. (2017).
GIG annual meeting 2018
Summary of 2017 planned work for 2018.
NVA
Rapport
Sanchis, Charlotte Juliette og Hauge, Ragnar. (2017).
Direct inversion for horizon location.
NVA
Rapport
Lilleborge, Marie; Hauge, Ragnar; Eidsvik, Jo og Huseby, Arne. (2017).
Efficient Information Gathering in Discrete Bayesian Networks.
Aker, Eyvind; Røe, Per; Kjøsnes, Øyvind; Hauge, Ragnar; Dahle, Pål; Ahmadi, Gholam Reza og Sandstad, Odd Arne. (2017).
Probabilistic prediction of lithology-fluid-classes from seismic - A North Sea case study. NTNU
NVA
Vitenskapelig foredrag
Olsen, Håvard Goodwin og Hauge, Ragnar. (2016).
Panther Tongue Modelling with RMS – Part II.
NVA
Rapport
Aarnes, Ingrid og Hauge, Ragnar. (2016).
Truncated Gaussian Simulation -
Comparison of methodologies.
NVA
Rapport
Hauge, Vera Louise; Røe, Per; Aker, Eyvind; Sanchis, Charlotte Juliette og Hauge, Ragnar. (2016).
PCube User Manual
Version PCube+ 6.0.
NVA
Rapport
Kolbjørnsen, Odd; Buland, Arild; Hauge, Ragnar; Røe, Per; Jullum, Martin; Metcalfe, Richard William og Skjæveland, Øyvind. (2016).
Bayesian AVO inversion to rock properties using a local neighborhood in a spatial prior model.
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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 og Røe, Per. (2016).
PCube inversion study in the greater Alvheim area.
NVA
Rapport
Fjellvoll, Bjørn; Abrahamsen, Petter; Hauge, Ragnar og Vazquez, Ariel Almendral. (2016).
Geostatistics course. Statoil
NVA
Faglig foredrag
Skjæveland, Øyvind M.; Metcalfe, Richard William; Kolbjørnsen, Odd; Røe, Per og Hauge, Ragnar. (2016).
Pcube+ - high-resolution horizon update by prestack inversion, a Statfjord case history. NPF
NVA
Vitenskapelig foredrag