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**Author**

- Abubakar, Aria (1)
- Al Jenaibi, Faisal (1)
- Ben Salem, Ryadh (1)
- Betancourt, S. (1)
- Bonet-Cunha, Luciane (1)
- Bughio, Shams Udin (1)
- Chu, Lifu (1)
- Fohring, Jen (1)
- Habashy, Tarek (1)
- Haber, Eldad (1)
- He, Nanqun (1)
- Hoteit, Ibrahim (2)
- Katterbauer, Klemens (1)
- Lacroix, Sebastien (1)
- Liang, Lin (1)
- Liu, Ning (2)
- Meziani, Said (1)
- Monfared, Hashem (1)
- Oldenziel, T. (1)
- Oliver, D.S. (3)
- Oliver, Dean S. (1)
- Redner, R.A. (1)
- Reynolds, A.C. (2)
- Ruthotto, Lars (1)
- Sun, Shuyu (1)
- van Ditzhuijzen, R. (1)
- van Kruijsdijk, C.P.J.W. (1)
- Zhang, Yanhui (1)

**Concept Tag**

- Abubakar (1)
- algorithm (2)
- application (1)
- Archie (1)
- Artificial Intelligence (13)
- assimilation (1)
- Bayesian Inference (3)
- case study (1)
- coefficient (2)
- complex reservoir (1)
- Computational Geoscience (1)
- conductivity (2)
- conductivity field (1)
- covariance (2)
- covariance matrix (1)
- crosswell electromagnetic data (1)
- Crosswell EM data (1)
- data only (1)
- decomposition (1)
- Drillstem Testing (3)
- drillstem/well testing (3)
- dynamic model (1)
- dynamical equation (1)
- east flank (1)
- eigenvalue (1)
- electromagnetic data (2)
- electromagnetic data only (1)
- electromagnetic survey (1)
- EM data (1)
- Energy Economics (1)
- ensemble (1)
- ensemble Kalman filter (2)
- equation (3)
- estimation (1)
- estimation error (1)
- experiment (1)
- fast-track approach (1)
- fast-track modeling approach (1)
- flow field (1)
- flow in porous media (3)
- Fluid Dynamics (3)
- generate approximate conditional realization (3)
- geologic modeling (2)
- geological modeling (2)
- Habashy (1)
- history matching (13)
- history-matching problem (3)
- information (3)
- inversion (2)
- iteration (3)
- jpt uncertainty quantification (3)
- machine learning (13)
- Markov chain (2)
- matrix (2)
- McMC method (1)
- mismatch (2)
- model parameter (3)
- Modeling & Simulation (1)
- Monte Carlo method (1)
- multiobjective-optimization technique (3)
- multiple local gaussian approximation technique (3)
- objective function (2)
- optimization problem (3)
- permeability (9)
- Pilot Point Method (2)
- porosity (8)
- posteriori estimate (2)
- pressure data (4)
- pressure transient analysis (3)
- pressure transient testing (3)
- procedure (3)
- production data (4)
- quantification (3)
- quantify (3)
- realization (7)
- Reservoir Characterization (13)
- Reservoir Description (1)
- reservoir history matching (2)
- reservoir model (2)
- reservoir simulation (13)
- risk and uncertainty assessment (3)
- risk assessment (3)
- risk management (3)
- saturation (4)
- seismic data (2)
- sequence (2)
- SPE (3)
- spe 181611 (3)
- spe annual technical conference (3)
- special publication editor adam wilson (3)
- Standard Deviation (2)
- State Space (1)
- Statfjord Field (1)
- Technology Conference (1)
- uncertainty quantification (3)
- unconditional realization (2)
- Upstream Oil & Gas (13)
- US government (1)
- variogram (2)
- water saturation (2)

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An ensemble-based history-matching framework is proposed to enhance the characterization of petroleum reservoirs through the assimilation of crosswell electromagnetic (EM) data. As one of advanced technologies in reservoir surveillance, crosswell EM tomography can provide a cross-sectional conductivity map and hence saturation profile at an interwell scale by exploiting the sharp contrast in conductivity between hydrocarbons and saline water. Incorporating this new information into reservoir simulation in combination with other available observations is therefore expected to enhance the forecasting capability of reservoir models and to lead to better quantification of uncertainty.

The proposed approach applies ensemble-based data-assimilation methods to build a robust and flexible framework under which various sources of available measurements can be readily integrated. Because the assimilation of crosswell EM data can be implemented in different ways (e.g., components of EM fields or inverted conductivity), a comparative study is conducted. The first approach integrates crosswell EM data in its original form which entails establishing a forward model simulating observed EM responses. In this work, the forward model is based on Archie's law that provides a link between fluid properties and formation conductivity, and Maxwell’s equations that describe how EM fields behave given the spatial distribution of conductivity. Alternatively, formation conductivity can be used for history matching, which is obtained from the original EM data through inversion using an adjoint gradient-based optimization method. Because the inverted conductivity is usually of high dimension and very noisy, an image-oriented distance parameterization utilizing fluid front information is applied aiming to assimilate the conductivity field efficiently and robustly. Numerical experiments for different test cases with increasing complexity are carried out to examine the performance of the proposed integration schemes and potential of crosswell EM data for improving the estimation of relevant model parameters. The results demonstrate the efficiency of the developed history-matching workflow and added value of crosswell EM data in enhancing the characterization of reservoir models and reliability of model forecasts.

application, Artificial Intelligence, assimilation, Computational Geoscience, conductivity, conductivity field, Crosswell EM data, EM data, ensemble, ensemble Kalman filter, history matching, information, initial ensemble, inversion, localization, machine learning, model parameter, optimization problem, production data, Reservoir Characterization, reservoir simulation, Standard Deviation, Upstream Oil & Gas

SPE Disciplines:

- Reservoir Description and Dynamics > Reservoir Simulation > History matching (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management (1.00)

Technology:

- Information Technology > Artificial Intelligence > Machine Learning (1.00)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Optimization (0.66)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Uncertainty (0.46)

It is well-known that oil- and gasfield development is a high-risk venture. Uncertainties originating from geological models (e.g., structure, stratigraphy, channels, and geobodies) are coupled with uncertainties of reservoir models (e.g., distribution of permeability and porosity in the reservoir) and uncertainties of economic parameters (e.g., oil and gas prices and costs associated with drilling and other operations). It is critically important to properly quantify the uncertainty of such parameters and their effect on production forecasts and economic evaluations. Recently, multiobjective-optimization techniques have been developed to maximize expectations of some economic indicators (e.g., net present value) and, at the same time, to minimize associated uncertainty or risk. Because of limited access to the subsurface reservoir (e.g., it is impossible to measure the permeability and porosity at the location of each gridblock of a simulation model), reservoir properties have quite large uncertainties.

Artificial Intelligence, complex reservoir, Energy Economics, generate approximate conditional realization, history matching, history-matching problem, jpt uncertainty quantification, machine learning, multiobjective-optimization technique, multiple local gaussian approximation technique, oil shale, permeability, porosity, quantification, quantify, realization, Reservoir Characterization, reservoir simulation, shale gas, shale oil, SPE, spe 181611, spe annual technical conference, special publication editor adam wilson, uncertainty quantification, unconventional resource economics, Upstream Oil & Gas, US government

Industry:

- Energy > Oil & Gas > Upstream (1.00)
- Government > Regional Government > North America Government > US Government (0.50)

Oilfield Places: North America > United States > Alaska > Prudhoe Bay > North Slope > North Slope Basin > Prudhoe Bay Field (0.99)

SPE Disciplines:

- Reservoir Description and Dynamics > Reservoir Characterization (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management (1.00)
- Data Science & Engineering Analytics > Information Management and Systems (1.00)
- (4 more...)

Technology:

- Information Technology > Artificial Intelligence > Representation & Reasoning (0.53)
- Information Technology > Artificial Intelligence > Machine Learning (0.53)

It is well-known that oil- and gasfield development is a high-risk venture. Uncertainties originating from geological models (e.g., structure, stratigraphy, channels, and geobodies) are coupled with uncertainties of reservoir models (e.g., distribution of permeability and porosity in the reservoir) and uncertainties of economic parameters (e.g., oil and gas prices and costs associated with drilling and other operations). It is critically important to properly quantify the uncertainty of such parameters and their effect on production forecasts and economic evaluations. Recently, multiobjective-optimization techniques have been developed to maximize expectations of some economic indicators (e.g., net present value) and, at the same time, to minimize associated uncertainty or risk. Because of limited access to the subsurface reservoir (e.g., it is impossible to measure the permeability and porosity at the location of each gridblock of a simulation model), reservoir properties have quite large uncertainties.

Artificial Intelligence, generate approximate conditional realization, history matching, history-matching problem, jpt uncertainty quantification, machine learning, multiobjective-optimization technique, multiple local gaussian approximation technique, permeability, porosity, quantification, quantify, realization, Reservoir Characterization, reservoir simulation, risk and uncertainty assessment, risk assessment, risk management, SPE, spe 181611, spe annual technical conference, special publication editor adam wilson, uncertainty quantification, Upstream Oil & Gas

SPE Disciplines:

- Reservoir Description and Dynamics > Reservoir Characterization (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management (1.00)
- Data Science & Engineering Analytics > Information Management and Systems (1.00)
- (2 more...)

Technology:

- Information Technology > Artificial Intelligence > Representation & Reasoning (0.53)
- Information Technology > Artificial Intelligence > Machine Learning (0.53)

It is well-known that oil- and gasfield development is a high-risk venture. Uncertainties originating from geological models (e.g., structure, stratigraphy, channels, and geobodies) are coupled with uncertainties of reservoir models (e.g., distribution of permeability and porosity in the reservoir) and uncertainties of economic parameters (e.g., oil and gas prices and costs associated with drilling and other operations). It is critically important to properly quantify the uncertainty of such parameters and their effect on production forecasts and economic evaluations. Recently, multiobjective-optimization techniques have been developed to maximize expectations of some economic indicators (e.g., net present value) and, at the same time, to minimize associated uncertainty or risk. Because of limited access to the subsurface reservoir (e.g., it is impossible to measure the permeability and porosity at the location of each gridblock of a simulation model), reservoir properties have quite large uncertainties.

Artificial Intelligence, generate approximate conditional realization, history matching, history-matching problem, jpt uncertainty quantification, machine learning, multiobjective-optimization technique, multiple local gaussian approximation technique, permeability, porosity, quantification, quantify, realization, Reservoir Characterization, reservoir simulation, risk and uncertainty assessment, risk assessment, risk management, SPE, spe 181611, spe annual technical conference, special publication editor adam wilson, uncertainty quantification, Upstream Oil & Gas

SPE Disciplines:

- Reservoir Description and Dynamics > Reservoir Characterization (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management (1.00)
- Data Science & Engineering Analytics > Information Management and Systems (1.00)
- (2 more...)

Technology:

- Information Technology > Artificial Intelligence > Representation & Reasoning (0.54)
- Information Technology > Artificial Intelligence > Machine Learning (0.53)

The oil & gas industry has been the backbone of the world's economy in the last century and will continue to be in the decades to come. With increasing demand and conventional reservoirs depleting, new oil industry projects have become more complex and expensive, operating in areas that were previously considered impossible and uneconomical. Therefore, good reservoir management is key for the economical success of complex projects requiring the incorporation of reliable uncertainty estimates for reliable production forecasts and optimizing reservoir exploitation. Reservoir history matching has played here a key role incorporating production, seismic, electromagnetic and logging data for forecasting the development of reservoirs and its depletion. With the advances in the last decade, electromagnetic techniques, such as crosswell electromagnetic tomography, have enabled engineers to more precisely map the reservoirs and understand their evolution. Incorporating the large amount of data efficiently and reducing uncertainty in the forecasts has been one of the key challenges for reservoir management. Computing the conductivity distribution for the field for adjusting parameters in the forecasting process via solving the inverse problem has been a challenge, due to the strong ill-posedness of the inversion problem and the extensive manual calibration required, making it impossible to be included into an efficient reservoir history matching forecasting algorithm. In the presented research, we have developed a novel Finite Difference Time Domain (FDTD) based method for incorporating electromagnetic data directly into the reservoir simulator. Based on an extended Archie relationship, EM simulations are performed for both forecasted and Porosity-Saturation retrieved conductivity parameters being incorporated directly into an update step for the reservoir parameters. This novel direct update method has significant advantages such as that it overcomes the expensive and ill-conditioned inversion process, applicable to arbitrary reservoir geometries and enables efficient integration with real field crosswell EM data.

Archie, Artificial Intelligence, conductivity, electromagnetic data, ensemble Kalman filter, equation, forecast, geophysics, history matching, machine learning, matrix, porosity, production data, Reservoir Characterization, reservoir history matching, reservoir simulation, saturation, Technology Conference, Upstream Oil & Gas

Oilfield Places:

- Asia > Middle East > Saudi Arabia > Eastern Province > Al-Ahsa Governorate > Arabian Basin > Widyan Basin > Ghawar Field > Haradh Field (0.99)
- North America > United States > Texas > West Gulf Coast Tertiary Basin > Mineral Field (0.98)

SPE Disciplines:

- Reservoir Description and Dynamics > Reservoir Simulation > History matching (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Cross-well tomography (1.00)

Technology:

- Information Technology > Artificial Intelligence > Machine Learning (0.47)
- Information Technology > Artificial Intelligence > Representation & Reasoning (0.46)

**SUMMARY**

Reservoir surveillance is becoming increasingly important as the need to maximize field productivity rises. An important problem is determining the subsurface flow in a reservoir. One option is to estimate the flow from a time series of geophysical imaging data. In this paper, we present a novel method that simultaneously reconstructs the distribution the flow field, which is caused by injection and extraction, from borehole seismic tomography data. The method involves discretization of the flow equations, and combining this with the geophysical monitoring experiment to form a multi-variable inverse problem. The minimizers of this optimization problem are the flow field and the initial state of the reservoir. These approximations are then used to march the initial state in time, showing the motion of fluid in the subsurface, and providing the ability to predict the spatial evolution of the flow.

Artificial Intelligence, equation, experiment, flow field, geophysical imaging, history matching, initial saturation, inverse problem, machine learning, optimization problem, reference list, regularization, reservoir, Reservoir Characterization, reservoir history matching, reservoir simulation, saturation, seg houston 2013, slowness, tomography experiment, Upstream Oil & Gas, velocity field

SPE Disciplines:

- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management (1.00)
- Reservoir Description and Dynamics > Reservoir Simulation > History matching (0.88)

Technology:

- Information Technology > Artificial Intelligence > Representation & Reasoning > Optimization (0.35)
- Information Technology > Artificial Intelligence > Machine Learning (0.34)

The oil and gas industry has a long history of model construction relying on the integration on multiple disciplines including seismic, sedimentology, geology and petrophysics. It is undeniable that all steps are required to lead to a good characterization of the studied reservoir and to provide reliable and realistic predictions. However the necessary time to finalize each step often leads to a modeling process of several years. This paper therefore discusses an alternative workflow, or Fast-Track modeling approach, which allows generating a representative and matched reservoir model in a considerably reduced time. This approach consists in:

- Using in-house technology for porosity/permeability prediction and property population.
- Introducing a new capillary pressure design and dynamic rock-typing concept.
- Using automated history matching tools to fine tune the match at the well level.

This workflow ensures achieving a better petrophysical properties consistency between logs, cores and models, a more representative asset volume estimates and also prevents utilization of non-measured parameters such as irreducible water saturation (Swc's) or unproven and extensive permeability multipliers. This altogether contributes in reducing convergences problems as well as providing more consistent dynamic model setup.

This workflow was applied on a complex offshore reservoir consisting of a large gas cap and significant oil rim. The results and achievements are presented within this paper and demonstrate the suitability of this workflow as a short term alternative and shortcut to complex simulation modeling, in waiting of all adequate studies to be completed and integrated in a detailed reservoir model.

Artificial Intelligence, coefficient, dynamic model, fast-track approach, fast-track modeling approach, flow in porous media, Fluid Dynamics, history matching, information, machine learning, Modeling & Simulation, old model, permeability, porosity, Reservoir Characterization, reservoir simulation, response parameter, saturation, saturation model, simulation model, society of petroleum engineers, spe 166004, transition zone, Upstream Oil & Gas, water saturation, workflow

Country:

- Asia > Middle East > UAE (0.48)
- Europe (0.47)

Oilfield Places:

- Europe > United Kingdom > North Sea Basin (0.98)
- Europe > Norway > North Sea Basin (0.98)
- Europe > Netherlands > North Sea Basin (0.98)
- Europe > Denmark > North Sea Basin (0.98)

SPE Disciplines:

- Reservoir Description and Dynamics > Reservoir Simulation > History matching (1.00)
- Reservoir Description and Dynamics > Reservoir Fluid Dynamics > Flow in porous media (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management (1.00)

Technology:

- Information Technology > Modeling & Simulation (1.00)
- Information Technology > Artificial Intelligence > Representation & Reasoning (0.47)
- Information Technology > Artificial Intelligence > Machine Learning (0.47)

Formation permeability is one of the most important parameters for reservoir modeling. There is a high degree of uncertainty associated with the estimation of permeability using the conventional history matching, which adjusts the model to only fit the production data. In this paper, we present an approach to improve the estimation of permeability by history matching time-lapse crosswell electromagnetic and production data simultaneously. A multiphase fluid-flow simulator is used to calculate the timedependent bottomhole pressure at the wells as well as the temporal and spatial distributions of water saturation and salt concentration

in the reservoir. The latter ones are transformed into the formation resistivity using a resistivity-saturation formula. A 3D finitedifference electromagnetic solver is used to simulate the crosswell electromagnetic data. A regularized Gauss-Newton approach is then used to update the permeability in an iterative fashion until achieving a good match between the simulated and the measured data.

In the inversion process, the derivatives of production data with respect to permeability are computed using the gradient simulator method, and the derivatives of electromagnetic data with respect to permeability are computed using the adjoint method and the chain rule.

Abubakar, Artificial Intelligence, crosswell electromagnetic data, data only, electromagnetic data, electromagnetic data only, electromagnetic survey, estimation, flow in porous media, Fluid Dynamics, fraction, Habashy, history matching, inversion, joint inversion, machine learning, permeability, permeability distribution, production data, Reservoir Characterization, reservoir simulation, seismic data, time-lapse crosswell electromagnetic, Upstream Oil & Gas, water saturation, wbhp

Country:

- North America > United States (0.68)
- Europe (0.47)

Oilfield Places: Europe > Norway > North Sea > Central North Sea > Utsira High > Block 25/11 > Grane Field > Heimdal Formation (0.99)

SPE Disciplines:

- Reservoir Description and Dynamics > Reservoir Simulation > History matching (1.00)
- Reservoir Description and Dynamics > Reservoir Fluid Dynamics > Flow in porous media (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management (1.00)

Technology:

- Information Technology > Artificial Intelligence > Representation & Reasoning > Search (0.40)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning > Gradient Descent (0.40)

**Summary**

Uncertainty in future reservoir performance is usually evaluated from the simulated performance of a small number of reservoir models. Unfortunately, most of the methods for generating reservoir models conditional to production data are known to create a distribution of realizations that is only approximately correct. In this paper, we evaluate the ability of the various sampling methods to correctly assess the uncertainty in reservoir predictions by comparing the distribution of realizations with a standard distribution from a Markov chain Monte Carlo method. The ensemble of realizations from five sampling algorithms for a synthetic, 1D, single-phase flow problem were compared in order to establish the best algorithm under controlled conditions. Five thousand realizations were generated from each of the approximate sampling algorithms. The distributions of realizations from the approximate methods were compared to the distributions from the exact methods. In general, the method of randomized maximum likelihood performed better than other approximate methods.

algorithm, Artificial Intelligence, Bayesian Inference, covariance, Drillstem Testing, drillstem/well testing, history matching, iteration, june 2003, likelihood, machine learning, mismatch, model parameter, Monte Carlo method, permeability, Pilot Point Method, porosity, pressure data, pressure transient analysis, pressure transient testing, production data, realization, Reservoir Characterization, reservoir model, reservoir simulation, sequence, unconditional realization, Upstream Oil & Gas

SPE Disciplines:

- Reservoir Description and Dynamics > Reservoir Simulation > History matching (1.00)
- Reservoir Description and Dynamics > Reservoir Simulation > Evaluation of uncertainties (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization (1.00)
- (2 more...)

Technology:

- Information Technology > Artificial Intelligence > Representation & Reasoning > Uncertainty > Bayesian Inference (0.89)
- Information Technology > Artificial Intelligence > Machine Learning > Learning Graphical Models > Directed Networks > Bayesian Learning (0.89)

Abstract

Uncertainty in future reservoir performance is usually evaluated from the simulated performance of a small number of reservoir models. Unfortunately, most of the methods for generating reservoir models conditional to production data are known to create a distribution of realizations that is only approximately correct. The adequacy of the approximations is unknown, although several previous investigations of the approximate algorithms have suggested that the distributions of realizations could be badly misleading. In this paper, we evaluate the ability of the various sampling methods to correctly assess the uncertainty in reservoir predictions by comparing the distribution of realizations with a standard distribution from a Markov chain Monte Carlo method.

This study compares the ensemble of realizations from five sampling algorithms for a synthetic, one-dimensional, single-phase flow problem, in order to establish the best algorithm under controlled conditions. The small test problem was chosen in order that a large enough number of realizations could be generated from each method to ensure the statistical validity of the comparisons. Five thousand realizations were generated from each of the approximate sampling algorithms. The approximate sampling methods evaluated were linearization about the maximum a posteriori model (the square-root of the covariance matrix method), randomized maximum likelihood, and two two pilot point methods with six and nine pilot points locations. Realizations were also generated by a Markov chain Monte Carlo method with local perturbations and an attempt was made to generate realizations from a rejection sampling algorithm. The distributions of realizations from the approximate methods were compared to the distributions from the exact methods. While the approximate sampling methods performed relatively well for evaluating uncertainty in average reservoir porosity and effective steady-state permeability, most failed to adequately assess uncertainty in some other function of the reservoir model such as the distribution of extreme permeability values or the data mismatch. In general, the method of randomized maximum likelihood performed better than other approximate methods.

Introduction

The only practical methods for quantifying uncertainty in reservoir performance require the generation of multiple random reservoir models conditional to available data. By simulating the future production from each realization, an empirical distribution of production characteristics is obtained. The validity of this method for quantifying uncertainty depends strongly on the quality of the distribution of reservoir models generated. Methods for sampling from the a posteriori probability density function (pdf) of reservoir flow models conditioned to production data have been widely reported in the literature. Rigorous methods of sampling from the a posteriori distribution for reservoir properties have been applied by Oliver et al.^{1}; Bonet-Cunha et al.^{2} and Omre et al.^{3} Most other attempts to quantify uncertainty in reservoir performance are based on approximate sampling algorithms. The purpose of this study is to evaluate the distribution of samples from several of these approximate methods. The same assumptions and models were used for all methods in this study, as differences in the model assumptions have made it difficult to draw quantitative conclusions on the reliability of the methods in previous studies^{4;5;6}.

algorithm, Artificial Intelligence, Bayesian Inference, covariance, Drillstem Testing, drillstem/well testing, history matching, iteration, linearization, machine learning, Markov chain, mismatch, model parameter, permeability, Pilot Point Method, porosity, pressure data, realization, Reservoir Characterization, reservoir simulation, risk and uncertainty assessment, risk assessment, risk management, sequence, spe 71624, uncertainty assessment method spe 71624, unconditional realization, Upstream Oil & Gas

SPE Disciplines:

- Reservoir Description and Dynamics > Reservoir Simulation > History matching (1.00)
- Reservoir Description and Dynamics > Reservoir Simulation > Evaluation of uncertainties (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization (1.00)
- (2 more...)

Technology:

- Information Technology > Artificial Intelligence > Representation & Reasoning > Uncertainty > Bayesian Inference (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Learning Graphical Models > Directed Networks > Bayesian Learning (1.00)

Thank you!